Applied Science, Faculty of
Biomedical Engineering, School of
DSpace
UBCV
Stankiewicz, Laura
Doctor of Philosophy - PhD
University of British Columbia
T cells play a critical role in adaptive immunity by recognizing self, versus non-self antigens, effectively protecting against pathogens and damaged or cancerous body cells. The application of T cells in cellular medicine to treat cancer and immunological disorders, as well as in personalized medicine to model disease pathologies, is rapidly expanding due to the potent role of T cells as mediators of the adaptive immune response. Pluripotent stem cell-derived T (PSC-T) cells have the potential to expand access to immunotherapies and disease modeling. However, current in vitro technologies have failed to recapitulate the signaling environments necessary to guide PSC-T cells towards mature helper and cytotoxic T cells in defined, feeder-free culture systems. T cells develop in the thymus, an organ specialized to support stage-specific T cell training via organization into spatially-defined niches. Previously, understanding of human T cell development and thymus niche biology was limited to interpretations from experimental animal models and observational learnings from patient populations with genetic mutations. These interpretations have proven challenging to adapt for in vitro PSC-T cell culture technology development. Here, we employ closed-loop learning to probe human T cell development. We first apply spatial multiomic technologies to create a spatially-defined map of tissue niches guiding T cell development in human postnatal thymus. We then test key insights into thymus niche biology in vitro using our PSC-T cell differentiation platform, demonstrating how our in vivo reference can be applied in vitro to guide T-lineage branching towards mature helper and cytotoxic PSC-T cells. Finally, we use our PSC-T platform to discover novel insights into the contribution of T cell receptor specificity to T-lineage branching. An additional insight from this work is how sex-based differences in thymus regulation and T cell development arise, and the potential for PSC-T platforms to model sex-based differences in T cell development and function. Overall, these data represent a unique resource to investigate how human thymus niche biology guides T cell development, validate niche signaling insights via PSC-T technology development, and demonstrate a novel technology to study and perturb human immune system development.
A pipeline for discovery-based research on human T cell development: integrating spatial omics and PSC-T cell platforms by Laura Stankiewicz BASc, Queenâs University, 2019 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Biomedical Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) December 2024 © Laura Stankiewicz, 2024 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: A pipeline for discovery-based research on human T cell development: Integrating spatial omics and PSC-T cell platforms submitted by Laura Stankiewicz in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Biomedical Engineering Examining Committee: Fabio Rossi, Professor, Biomedical Engineering, University of British Columbia Co-supervisor Peter Zandstra, Professor, Biomedical Engineering, University of British Columbia Co-supervisor Ninan Abraham, Professor, Microbiology and Immunology, University of British Columbia Supervisory Committee Member Elizabeth Rideout, Associate Professor, Cellular & Physiological Sciences, University of British Columbia University Examiner Laura Evgin, Assistant Professor, Medical Genetics, University of British Columbia University Examiner Darrol Irvine, Professor, Biological Engineering and Materials Science & Engineering, Massachusetts Institute of Technology External Examiner Additional Supervisory Committee Members: Megan Levings, Professor, Biomedical Engineering, University of British Columbia Supervisory Committee Member Ali Bashashati, Assistant Professor, Biomedical Engineering, University of British Columbia Supervisory Committee Member iii Abstract T cells play a critical role in adaptive immunity by recognizing self, versus non-self antigens, effectively protecting against pathogens and damaged or cancerous body cells. The application of T cells in cellular medicine to treat cancer and immunological disorders, as well as in personalized medicine to model disease pathologies, is rapidly expanding due to the potent role of T cells as mediators of the adaptive immune response. Pluripotent stem cell-derived T (PSC-T) cells have the potential to expand access to immunotherapies and disease modeling. However, current in vitro technologies have failed to recapitulate the signaling environments necessary to guide PSC-T cells towards mature helper and cytotoxic T cells in defined, feeder-free culture systems. T cells develop in the thymus, an organ specialized to support stage-specific T cell training via organization into spatially-defined niches. Previously, understanding of human T cell development and thymus niche biology was limited to interpretations from experimental animal models and observational learnings from patient populations with genetic mutations. These interpretations have proven challenging to adapt for in vitro PSC-T cell culture technology development. Here, we employ closed-loop learning to probe human T cell development. We first apply spatial multiomic technologies to create a spatially-defined map of tissue niches guiding T cell development in human postnatal thymus. We then test key insights into thymus niche biology in vitro using our PSC-T cell differentiation platform, demonstrating how our in vivo reference can be applied in vitro to guide T-lineage branching towards mature helper and cytotoxic PSC-T cells. Finally, we use our PSC-T platform to discover novel insights into the contribution of T cell receptor specificity to T-lineage branching. An additional insight from this work is how sex-iv based differences in thymus regulation and T cell development arise, and the potential for PSC-T platforms to model sex-based differences in T cell development and function. Overall, these data represent a unique resource to investigate how human thymus niche biology guides T cell development, validate niche signaling insights via PSC-T technology development, and demonstrate a novel technology to study and perturb human immune system development. v Lay Summary T cells play a critical role in our immune system by protecting against viruses, bacteria, and cancer cells. Engineered T cell-based medicines can effectively kill cancer cells, but are prohibitively expensive. When manufactured at scale, stem cell-derived T cells could lower cost and expand access to these therapies. Here, we create a map of T cell development in the human thymus, the organ where T cells are trained, and demonstrate how our map can be used to improve stem cell-derived T cell technologies. Next, we show how stem cell-derived T cells can model human T cell development and identify mechanisms guiding critical developmental stages. Additionally, we discover sex-based differences in T cell and thymus biology that may contribute to sex-based differences in immune responses. This work provides a resource and platform for discovering and testing insights into human immune system development to improve stem cell-derived T cells for therapy. vi Preface This work is covered by UBC Human Research Ethics Certificate #H18-00027. Sections of Chapter 1, Chapter 5, and Appendix A have been published: Stankiewicz, L.N., Rossi, F.M.V., and Zandstra, P.W. (2024). Rebuilding and rebooting immunity with stem cells. Cell Stem Cell 31, 597â616. https://doi.org/10.1016/j.stem.2024.03.012. In this work, Laura Stankiewicz, Fabio Rossi, and Peter Zandstra conceptualized the perspective and wrote the manuscript. A version of Chapter 2 has been published: Stankiewicz, L.N., Salim, K., Flaschner, E.A., Wang, Y.X., Edgar, J.M., Durland, L.J., Lin, B.Z.B., Bingham, G.C., Major, M.C., Jones, R.D., et al. (2024). Sex-biased human thymic architecture guides T cell development through spatially defined niches. Dev. Cell 0. https://doi.org/10.1016/j.devcel.2024.09.011. In this work, Laura Stankiewicz, Peter Zandstra, and Fabio Rossi conceptualized the study. Laura Stankiewicz built the imaging panel and the sequencing cell preparation methodology. Laura Stankiewicz and Kevin Salim performed the sequencing and imaging experiments. Laura Stankiewicz and Emily Flaschner analyzed the data for CellChat figures. Will Wang and Helen Blau assisted Laura Stankiewicz in quantitative analysis of CODEX images. Laura Stankiewicz and Matthew Major analyzed the data for Figure 2.6F. Laura Stankiewicz and Lauren Durland performed the experiments for Figure 2.5H,I. John Edgar performed the experiments for Figure 2.3I,K. Laura Stankiewicz analyzed the data and built the figure. Laura Stankiewicz analyzed the remaining data and built the figures. Laura Stankiewicz, Peter Zandstra, and Fabio Rossi prepared the manuscript, including writing and responding to peer-review. vii A version of Chapter 3 has been submitted for peer review and is available here: Jones, R.D., Salim, K., Stankiewicz, L.N., Edgar, J.M., Leon, L., Gillies, J.K., Murtaza, A., Durland, L.J., Raval, D., Taberna, T.P., et al. (2024). Tunable differentiation of human CD4+ and CD8+ T cells from pluripotent stem cells. Preprint at bioRxiv, https://doi.org/10.1101/2024.10.29.620998 https://doi.org/10.1101/2024.10.29.620998. In this work, observations of Notch ligand distributions and class II+ double positive thymocyte self-selection niches, as well as analyses of sequencing data on earlier iterations of the PSC-T cell system, were incorporated in vitro via screens performed by Ross Jones, Kevin Salim, John Edgar, and Lauren Durland. Yale Michaels generated the PSC-T cells sequenced in Figure 3.1. Ross Jones performed the analysis and built Figures 3.1A and 3.2B-F. Lauren Durland performed the analysis for Figure 3.2G-I and built the figure. Lauren Durland generated the PSC-T cells sequenced in Figure 3.3. Kevin Salim generated the data, performed the analysis, and built Figure 3.4. Laura Stankiewicz analyzed the data and built the remaining figures. To preserve the completeness of the story, text from other authors was included. A version of Chapter 4 has been submitted for peer review. In this work, Laura Stankiewicz, Ross Jones, and Peter Zandstra conceptualized the study. Ross Jones and Lauren Durland performed in vitro culture and prepared cells for TCR sequencing. Laura Stankiewicz and Ross Jones performed the analysis. Laura Stankiewicz, Ross Jones, Matthieu Heitz, and Irena Zheng conceptualized the ERGO cross-reactivity matching analysis. Matthieu Heitz and Irena Zheng performed the modeling and built Figure 4.3E-H. Laura Stankiewicz wrote the manuscript and built the remaining figures. viii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ....................................................................................................................... viii List of Tables ................................................................................................................................xv List of Figures ............................................................................................................................. xvi List of Abbreviations ................................................................................................................. xix Acknowledgements .................................................................................................................. xxiii Dedication ................................................................................................................................. xxiv Chapter 1: Introduction ................................................................................................................1 1.1 The human thymus .......................................................................................................... 2 1.1.1 Human thymus development .................................................................................. 2 1.1.1.1 Embryonic thymus development ........................................................................ 2 1.1.1.2 Postnatal thymus and thymic involution ............................................................. 3 1.1.2 Cell type composition of the human thymus .......................................................... 3 1.1.2.1 Non-hematopoietic cells in the human thymus ................................................... 3 1.1.2.1.1 Epithelial cells ............................................................................................... 3 1.1.2.1.2 Mesenchymal cells ........................................................................................ 5 1.1.2.1.3 Endothelial cells ............................................................................................ 5 1.1.2.2 Non-T hematopoietic cells in the human thymus ............................................... 6 1.1.3 Conventional T cell development in the human thymus ........................................ 6 ix 1.1.3.1 Thymus seeding .................................................................................................. 7 1.1.3.2 Early thymic progenitors and progenitor T cells ................................................ 8 1.1.3.3 Pro-T to DP transition ......................................................................................... 9 1.1.3.4 Positive selection in the cortex ........................................................................... 9 1.1.3.5 Negative selection in the medulla ..................................................................... 10 1.1.4 CD4 versus CD8 T-lineage branching .................................................................. 11 1.2 Chimeric antigen receptor T cell therapy...................................................................... 14 1.2.1 Autologous CAR T strategies ............................................................................... 15 1.2.2 Allogeneic & PSC-derived CAR T strategies ....................................................... 16 1.3 Spatial multiomic technologies to study human biology .............................................. 17 1.4 Sex differences in T cell development .......................................................................... 19 1.5 PSC-T cell differentiation platform .............................................................................. 20 1.5.1 PSC-T cell differentiation for discovery-based research ...................................... 20 1.5.2 Factor screening and optimization of PSC-T cell differentiation ......................... 21 1.6 Summary of Research ................................................................................................... 22 1.6.1 Hypothesis............................................................................................................. 22 1.6.2 Objective ............................................................................................................... 22 1.6.3 Overview ............................................................................................................... 23 Chapter 2: Sex biased thymic architecture guides T cell development through spatially defined niches ...............................................................................................................................25 2.1 Summary ....................................................................................................................... 25 2.2 Introduction ................................................................................................................... 27 2.3 Results ........................................................................................................................... 28 x 2.3.1 Spatial multiomic profiling of human postnatal thymus identifies sex-based differences in T cells and thymus biology ............................................................................ 28 2.3.2 JAG1 skews early thymic progenitor development towards thymic dendritic cells â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦34 2.3.3 Analysis of the subcapsular zone identifies sex-based differences in fibroblast regulation of DP development and thymus growth .............................................................. 38 2.3.4 Human postnatal thymocytes may self-select in the cortex to support positive selection of conventional αβT cells ...................................................................................... 42 2.3.5 Spatial multiomics identifies key mechanisms regulating negative selection niches in the medulla ........................................................................................................................ 46 2.4 Discussion ..................................................................................................................... 51 2.4.1 Limitations of the Study........................................................................................ 58 2.5 Acknowledgements ....................................................................................................... 58 2.6 Methods......................................................................................................................... 60 2.6.1 Materials Availability ........................................................................................... 60 2.6.2 Data and code availability ..................................................................................... 60 2.6.3 Experimental model and study participant details ................................................ 61 2.6.4 Single cell isolation and flow sorting.................................................................... 61 2.6.5 CITE-seq sample preparation ............................................................................... 64 2.6.6 ATAC-seq sample preparation ............................................................................. 64 2.6.7 Notch ligand in vitro screening ............................................................................. 65 2.6.8 Primary human thymocyte culture ........................................................................ 67 2.6.9 Thymocyte chemokine proteomic expression analysis......................................... 68 xi 2.6.10 CITE-seq and ATAC-seq data integration & analysis .......................................... 68 2.6.11 Histology sample preparation ............................................................................... 72 2.6.12 CODEX staining and image acquisition ............................................................... 72 2.6.13 CODEX image analysis ........................................................................................ 72 2.6.14 Confocal Image Analysis ...................................................................................... 73 2.6.15 Immunofluorescence ligand expression analysis .................................................. 74 2.6.16 Quantification and statistical analysis ................................................................... 75 2.7 Supplementary Figures ................................................................................................. 76 Chapter 3: Generation of PSC-CD4 T cells ...............................................................................86 3.1 Summary ....................................................................................................................... 86 3.2 Introduction ................................................................................................................... 86 3.3 Results ........................................................................................................................... 89 3.3.1 Characterization of immature PSC-DPs ............................................................... 89 3.3.2 Tuning Notch and culture cell type composition to produce PSC-CD4 T cells ... 92 3.3.3 Characterization of PSC-CD4 T cells ................................................................... 97 3.4 Discussion ................................................................................................................... 101 3.5 Methods....................................................................................................................... 105 3.5.1 In vitro culture of PSC T cells ............................................................................ 105 3.5.1.1 Human iPSC maintenance .............................................................................. 105 3.5.1.2 T cell progenitor differentiation ...................................................................... 106 3.5.1.3 T cell stimulation ............................................................................................ 106 3.5.1.3.1 DP-HLA class II upregulation .................................................................. 106 3.5.1.3.2 PSC-CD4 T cell sequencing ..................................................................... 107 xii 3.5.2 Flow sorting ........................................................................................................ 107 3.5.3 CITE-seq and scTCR-seq sample preparation analysis ...................................... 108 3.5.4 CITE-seq and scTCR-seq analysis...................................................................... 109 3.5.5 CODEX data analysis ......................................................................................... 110 3.6 Supplemental Figures.................................................................................................. 111 Chapter 4: In vitro human immune system development reveals TCRs are not HLA class specific in the absence of co-receptor binding .........................................................................113 4.1 Summary ..................................................................................................................... 113 4.2 Introduction ................................................................................................................. 113 4.3 Results ......................................................................................................................... 116 4.3.1 PSC-T cells have diverse TCRs .......................................................................... 116 4.3.2 CD4 & CD8 PSC-T cells have divergent TCR repertoires................................. 118 4.3.3 CD4 vs. CD8 phenotype is not dependent on HLA specificity .......................... 120 4.4 Discussion ................................................................................................................... 125 4.5 Limitations & future work .......................................................................................... 129 4.6 Methods....................................................................................................................... 131 4.6.1 In vitro culture of PSC T cells ............................................................................ 131 4.6.1.1 Human iPSC maintenance .............................................................................. 131 4.6.1.2 PSC-T cell differentiation version 1 ............................................................... 131 4.6.1.3 PSC-T cell differentiation version 2 ............................................................... 133 4.6.2 CITE-seq and scTCR-seq sample preparation analysis ...................................... 135 4.6.3 scTCR-seq analysis ............................................................................................. 136 4.6.4 TCR matching to VDJ database .......................................................................... 137 xiii 4.6.5 ERGO modeling.................................................................................................. 137 4.6.6 Quantification and statistical analysis ................................................................. 137 4.7 Supplemental figures .................................................................................................. 138 Chapter 5: Conclusion ...............................................................................................................143 5.1 Summary ..................................................................................................................... 143 5.2 Contribution ................................................................................................................ 146 5.3 Application and Limitations ....................................................................................... 146 5.4 Future work ................................................................................................................. 149 5.4.1 Regenerating the immune system with stem cells .............................................. 149 5.4.1.1 Consequences of applying immune regeneration strategies in the elderly ..... 149 5.4.1.2 Next steps and future perspectives on immune regeneration .......................... 154 5.4.1.3 Modeling HLA mismatching and determining drivers of tolerance ............... 155 5.4.1.4 Modeling patient stratification and the impact of the aged immune system .. 157 5.4.1.5 Conclusions regarding current immune regeneration strategies ..................... 159 5.4.2 Fetal & aged thymus imaging ............................................................................. 160 Bibliography ...............................................................................................................................163 Appendices ..................................................................................................................................222 Appendix A - Ageing and sex differences in the immune system .......................................... 222 A.1 Immune system ageing is a dynamic, progressive phenomenon ............................ 223 A.2 The initiating events â interplay on molecular, cellular, and physiological levels . 227 A.3 Sex differences are prevalent in age-related immune impairment .......................... 229 A.4 Interventional strategies to reboot and rebuild the immune system ....................... 230 A.5 Technologies to reboot the immune system............................................................ 231 xiv A.6 Technologies to rebuild the immune system .......................................................... 243 Appendix B - Chapter 2 supplemental data ............................................................................ 255 B.1 Postnatal thymus metadata ...................................................................................... 255 B.2 CITE-seq antibody panel ........................................................................................ 256 B.3 Supplemental data tables......................................................................................... 267 xv List of Tables Table 4.1: TCR match data of PSC-T and human reference TCRs to VDJ database. ................ 122 Table 4.2: Summary of the probability of at least one TCR overlap for various possible TCR recombination space estimates given a TCR sample size of S = 1000. ...................................... 130 Table 4.3: scTCR-seq human thymocyte reference data used for analysis. ............................... 136 Table A.1: Summary of the current technologies to reboot the immune system by pushing the native immune system back to a productive, healthy state. ........................................................ 232 Table A.2: Summary of the current technologies to rebuild the immune system using artificial primary immune tissues or tissue niches. ................................................................................... 244 Table B.1: Postnatal thymus sample metadata. .......................................................................... 255 Table B.2: TotalSeq-C Human Universal Antibody Panel v1.0 used to phenotype cells via CITE-seq. .............................................................................................................................................. 256 Table B.3: Key marker genes defining cell types identified via CITE-seq. ............................... 267 Table B.4: Comparison of statistically significant male differentially expressed genes to Y chromosome genes. ..................................................................................................................... 268 Table B.5: Comparison of statistically significant female differentially expressed genes to X chromosome genes. ..................................................................................................................... 269 Table B.6: Cytokine concentrations used in differentiations. ..................................................... 270 Table B.7: Antibodies used in Notch Ligand in vitro screening experiments. ........................... 270 Table B.8: Key resources table describing details of reagents and resources used in the human thymus spatial multiomics study. ................................................................................................ 271 xvi List of Figures Figure 1.1: T cell development and selection is guided within the thymus microenvironment by spatiotemporal signals and interactions with supporting stromal, epithelial, and hematopoietic cells. ................................................................................................................................................ 7 Figure 1.2: Overview of the closed-loop experimental approach to probe human T cell development. ................................................................................................................................. 23 Figure 2.1: Graphical abstract depicting the approach to spatial multiomic sequencing of human postnatal thymus tissue. ................................................................................................................ 26 Figure 2.2: Spatial multiomic analysis identifies sex biased characteristics of thymic niches..... 31 Figure 2.3: Thymic progenitors entering via the corticomedullary junction are exposed to a gradient of Notch ligands which influence lineage specification. ................................................ 37 Figure 2.4: Fibroblasts in the subcapsular zone contribute to regulation of thymus biology and T cell progenitor development. ........................................................................................................ 41 Figure 2.5: HLA class I and II interactions may support thymocyte positive selection in the inner cortical zone. ................................................................................................................................. 45 Figure 2.6: Hassallâs corpuscles (HCs) represent scalable organizing centres for negative selection in the neonatal thymic medulla. ..................................................................................... 50 Figure 2.7: The human early postnatal thymus lobule is spatially organized into sex-biased niches to support stage-specific T cell development. ................................................................... 52 Figure 2.8: Spatial multiomic analysis identifies specific cell types and tissue niches within tissue structure (related to Figure 2.2). ................................................................................................... 77 Figure 2.9: ETP specification at the corticomedullary junction is influenced by differential interactions with a gradient of Notch ligands (related to Figure 2.3). .......................................... 79 xvii Figure 2.10: Cortical fibroblasts regulate tissue function through spatial regulation of tissue matrix proteins and cell growth factors (related to Figure 2.4). ................................................... 81 Figure 2.11: The inner cortical zone regulates positive selection and T-lineage specification through sex biased niche interactions (related to Figure 2.5). ...................................................... 83 Figure 2.12: Regulation of negative selection niches are organized by HCs and regulate inflammatory pathway feedback loops (related to Figure 2.6). .................................................... 85 Figure 3.1: CITE-seq characterizes the phenotype and cytokine expression of PSC-DPs and alternative lineage cells in a previous iteration of the PSC-T differentiation platform. ............... 91 Figure 3.2: Optimization of Notch, TCR, and HLA class II signaling supports development of PSC-CD4SPs................................................................................................................................. 95 Figure 3.3: Multiomic characterization of PSC-CD4 T cell transcriptomic, proteomic, and TCR repertoire landscapes. .................................................................................................................... 99 Figure 3.4: PSC-CD4 T cells can polarize into functionally different T helper subsets.. .......... 100 Figure 3.5: Phenotypic characterization of PSC-CD4 T cells (related to Figure 3.3). ............... 112 Figure 4.1: PSC-T cells have diverse, fetal-like TCRs. .............................................................. 117 Figure 4.2: PSC-T cell TCR repertoires diverge during T cell development. ............................ 119 Figure 4.3: CD4 and CD8 PSC-T and human reference TCRs match both known HLA class I and II reactive TCRs. .................................................................................................................. 124 Figure 4.4: PSC-T cell TCRs are developmentally stable and resemble fetal-like alpha and beta TCR chains (related to Figure 4.1).. ........................................................................................... 139 Figure 4.5: PSC-T TCR repertoire divergence recapitulates human fetal and postnatal TCR repertoire divergence (related to Figure 4.2).. ............................................................................ 141 xviii Figure 4.6: T cell TCR match rate decreases during fetal T cell development (related to Figure 4.3). ............................................................................................................................................. 142 Figure 5.1: Experimental outline for uncovering sex differences in PSC-T cell development.. 148 Figure 5.2: The autoimmunity â infection risk spectrum... ........................................................ 153 Figure 5.3: Continued progress of emerging immune regeneration therapies requires improved understanding of how transplanted cells and tissue interact and function with the host immune system, as well as technology development for in vitro modeling of diverse immune system states ............................................................................................................................................ 154 Figure 5.4: Preliminary CODEX imaging analysis of fetal and aged thymus samples. ............. 162 Figure A.1: Thymic output changes and progressively declines with age driving age-related immune impairment in the elderly.. ............................................................................................ 226 Figure A.2: The two approaches to regenerating aged and damaged immune organs include rebooting the patientâs own immune system and rebuilding the patientâs immune system through artificial supplementation............................................................................................................ 231 xix List of Abbreviations aDC activated dendritic cell AGM aorta gonad mesonephros APECED autoimmune polyendocrinopathy candidiasis ectodermal dystrophy ATO artificial thymic organoid ATO artificial thymic organoid bcl binary base call capFib capsular fibroblast CAR chimeric antigen receptor CMJ corticomedullary junction cTEC cortical thymus epithelial cell DC dendritic cell DN double negative DOE design of experiments DP double positive EBV Epstein Barr virus EC endothelial cell ECM extracellular matrix xx EHT endothelial to hematopoietic transition EMT epithelial to mesenchymal transition ESC embryonic stem cells ETP early thymic progenitor cell FDA Food and Drug Administration GMP good manufacturing practice GSEA gene set enrichment analysis GSI gamma secretase inhibitor GVHD graft versus host disease HAART highly active anti-retroviral treatment HC Hassallâs corpuscle HIV human immunodeficiency virus HLA human leukocyte antigen HSC haematopoietic stem cell HSPC haematopoietic stem and progenitor cell ILC innate lymphoid cell iPSC induced pluripotent stem cell LCK lymphocyte cell-specific protein-tyrosine kinase xxi LEC lymphatic endothelial cell mFib medullary fibroblast MHC major histocompatibility complex mTEC medullary thymus epithelial cell NICD Notch intracellular domain NK natural killer NSG NOD/SCID/Î³câ/â PBMC peripheral blood mononuclear cell pDC plasmacytoid dendritic cell Pro-T progenitor T cell Pro-Treg progenitor T regulatory cell PSC-T pluripotent stem cell-derived T cell rrTreg recirculating/resident T regulatory cell sjTREC signal joint T cell receptor rearrangement excision circles SP single positive TCR T cell receptor TEC thymus epithelial cell TEPC thymic epithelial progenitor cell xxii TF transcription factor TREC T cell receptor excision circles Treg T regulatory cell TSLP thymus stromal lymphoprotein tTreg thymic T regulatory cell VSMC vascular smooth muscle cell xxiii Acknowledgements To my advisors, Drs. Fabio Rossi and Peter Zandstra, thank you for providing me with the space to pursue my passions, both in the lab and on the water. Fabio, your curiosity about biology is infectious. Thank you for always pushing me to explore the unknown. Peter, your efforts to build and expand a Canadian-based biotech ecosystem inspire me. Thank you for encouraging my interest in the intersection between academic and industry-based research. I would also like to thank my lab mates, especially Dr. Ross Jones, Kevin Salim, and Lauren Durland, for joining me in collaborative science these last few years â science is the most fun with friends. A special thank you to my parents for teaching me how to harness my energy and passion. To Josiah, this would not have happened without your unwavering commitment and support. Lastly, to my teammates and coaches on the UBC rowing team, your dedication, perseverance, and grit motivated me every single day. My rowing and PhD experiences will forever be intertwined â I wouldnât have wished for it any other way. This infamous erg room quote sums up the rower mindset for which I have learned the utmost respect: How to be a Champion You wonder how they do it, and you look to see the knack, You watch the foot in action, or the shoulder, or the back. But when you spot the answer, where the higher glamours lurk, youâll find in moving higher up the laurel covered spire, that most of it is practice and the rest of it is work. - Grantland Rice xxiv Dedication For the girls in the boat1 Chapter 1: Introduction Decades of work on mouse thymus and T cell development have laid the foundational knowledge we use when describing T cell development. Through genetic manipulation and drug research, mouse models enable study of specific signaling pathways and developmental dynamics that are unethical, if not impossible, in human. Historically, explanations of human T cell development were inferred from knowledge generated via in-bred strains of genetically engineered mice and humanized mouse models, often leading to the assumption that these findings universally apply to all lymphopoiesis. This is best exemplified in recent reviews describing transcription factor networks driving T cell commitment1, mechanisms of positive and negative selection2, and CD4 versus CD8 T-lineage commitment3. Specific differences in developmental kinetics of hematopoiesis have been well documented in mouse and human. These discrepancies range from differential expression of immunophenotyping markers, to relative rates of hematopoietic stem cell (HSC) division and apoptosis, to specific differences in the gene regulatory networks governing HSC lineage commitment4,5. These examples demonstrate that although the overall hematopoietic differentiation scheme may be conserved between species, there are many critical differences in the phenotype, function, and regulatory mechanisms of T-lineage differentiation4. Recently, single cell sequencing of human thymus tissue has provided invaluable insights into the heterogeneity of thymic tissue6â8. However, this technique provides a static snapshot of a dynamic developmental process and removes cells from their native location within the tissue. This limits functional interpretation of human T cell transcriptional states to those with relevant mouse models where hypotheses can be tested. Furthermore, lack of knowledge of the cellâs 2 original location within the tissue greatly hinders our knowledge of how thymic heterogeneity is spatially organized within the thymus to support stage-specific T cell differentiation. Gaps in our knowledge of human T cell development arising from interspecies inferences and a lack of knowledge of human thymic architecture and organization of developmental microenvironments have become apparent as we begin to translate in vitro mouse T cell production systems towards the production of human T cells. As expected, the timing and strength of key signals regulating T cell development differ between species and have become apparent, and testable, using our human induced pluripotent stem cell (iPSC)-derived T cell (PSC-T) pipeline. 1.1 The human thymus 1.1.1 Human thymus development 1.1.1.1 Embryonic thymus development The thymus is the primary organ responsible for generating mature, functional T cells in vertebrates. The pharyngeal organs, including the thymus, derive from outpockets of the most anterior region of the foregut and are encased in a neural-crest derived mesenchymal capsule6,9â11. These pharyngeal pouches form sequentially, first appearing during week 6 of human development (Carnegie stage (CS) 16-17), with the third pharyngeal pouch eventually giving rise to the single primordia that will form both the parathyroid glands and the thymus11. By week 7 (CD18-19) the third pharyngeal pouch has undergone patterning into thymus and parathyroid domains driven by FOXN1 and GCM2 expression, respectively. During week 7 to mid-week 8 (CS20-21), the primordia detach from the pharynx, the parathyroids separate from the thymus, and the thymic primordia migrates ventrally. By mid-week 8 (CS 22), the lobes have migrated to 3 their final location in the thoracic cavity, on top of the heart and behind the sternum, and have fused to form a bilobed organ attached to the pericardium11. This early thymic primordium contains undifferentiated epithelial cells surrounded by a mesenchymal capsule, as well as intrathymic cell types such as vascular and lymphoid cells9â11. From week 8 to week 16 distinct medullary and cortical compartments develop, and between week 14 and 16 mature lymphocytes begin to leave the thymus and seed the peripheral immune system6,11. 1.1.1.2 Postnatal thymus and thymic involution T cell production in the thymus is most active during early postnatal development (0-2 years)2. T cell production peaks shortly after birth, remaining high during childhood, but steadily declining with age2. Dramatic changes in thymic tissue composition and T cell output begin post-puberty as thymic tissue is replaced with adipose tissue12,13. This process is known as age-related thymic involution and is marked by significant structural and functional changes in the composition and function of thymic stromal and epithelial cells that support T cell development14,15. For further information on thymic involution, see Appendix A. 1.1.2 Cell type composition of the human thymus 1.1.2.1 Non-hematopoietic cells in the human thymus 1.1.2.1.1 Epithelial cells Thymic epithelial cells (TECs) are divided into two main subtypes, cortical (cTECs) and medullary epithelial cells (mTECs), which pattern the thymic lobules into distinct domains and perform different functions during thymocyte development11 (Figure 1.1). Studies relying on immunohistochemistry staining and flow cytometry have demonstrated that undifferentiated TECs appear at early week 7 marked by co-expression of cytokeratins 5 (K5) and 8 (K8), after 4 the initiation of FOXN1 expression10,11. TEC differentiation begins around mid-week 8, marked by a loss of K5 expression from cells at the edge of the thymic lobe, higher expression of the pan-epithelial marker EPCAM by mTECs, and ubiquitous TEC expression of the functional maturation marker, major histocompatibility complex (MHC) class II (human leukocyte antigen (HLA)-DR/DP/DQ in human)11. Further differentiation of specialized mTECs is observed by week 13, marked by the upregulation of the autoimmune regulator (AIRE), with increasing numbers of AIRE+ mTECs observed by week 1711. Terminal differentiation of TECs contributes to the formation of Hassallâs corpuscles (HCs), highly keratinized structures found in the inner medulla of late fetal thymus which increase in number throughout postnatal development16. HCs produce thymus stromal lymphopoietin (TSLP), an IL-7-like cytokine known to activate dendritic cells to promote upregulation of HLA class II and the costimulatory molecules CD80 and CD8617,18, contributing to T regulatory cell (Treg) development19. Defining the heterogeneity among TEC subtypes has been the focus of several recent thymus single-cell RNA sequencing (scRNA-seq) studies6,7,20â22. Park et al. (2020) observed changing ratios of cTECs and mTECs during human embryonic development, as well as several subsets of mimetic mTECs exhibiting tuft-like, neural (NEUROD1+), and myoid (MYOD1+) transcriptional profiles6. Bautista et al. (2021) further expanded this description of heterogeneity by enriching their scRNA-seq dataset for CD45- cells and defined nine subsets of human TECs appearing dynamically throughout fetal, postnatal, and adult development7. More recently, Givony et al. (2023) performed multiomic transcriptional and chromatin landscape analysis of developing mouse mimetic TECs, describing possible functions in maintaining homeostasis of thymus-resident populations and induction of central tolerance. However, these studies included limited 5 data to relate the marker genes of these subsets to their spatial distribution within the thymus. Understanding the location and organizational structure of the tissue microenvironments created by these cells would provide a necessary link to dissect how different signaling environments are tailored to support specific stages of thymocyte development and lineage branch points. 1.1.2.1.2 Mesenchymal cells Neural crest-derived mesenchymal cells form the thymic capsule and trabeculae, the structures which separate individual lobules, by mid-week 711. These cells also generate intrathymic pericytes which seed throughout the epithelial regions by mid-week 8 (Figure 1.1). Thymic pericytes have been shown via scRNA-seq to express a gene encoding inhibin, a subunit of Activin A, which promotes TEC differentiation, whereas the activin antagonist follistatin, which promotes thymic epithelial progenitor cell maintenance and inhibits differentiation, was found primarily in adult thymic mesenchymal cells7. Thymic mesenchymal cells have been shown to interact directly with thymocytes, generating self-antigens for presentation23, as well as play key roles in the maintenance of thymus structure and degeneration during thymus involution24. These findings point towards neural crest-derived mesenchymal cells as key drivers TEC differentiation, maintenance, and function25,26. 1.1.2.1.3 Endothelial cells Vascular progenitor cells (CD34hiCD31+) are found at the edge of the thymus primordium by week 7 and within thymic lobes by mid-week 8, with blood vessels forming by mid-week 911. Venous, arterial, and lymphatic endothelial cells have been identified via scRNA-seq and exhibit dynamic population changes throughout life (Figure 1.1)7. 6 1.1.2.2 Non-T hematopoietic cells in the human thymus Immunohistochemistry studies have identified the earliest CD45+ hematopoietic cells in the perithymic space by week 711. However, recent scRNA-seq of week 7 and 8 embryonic thymuses demonstrate that the lymphoid compartment already contains NK cells, γδT cells, and innate lymphoid cells6,27. Furthermore, differentiating conventional T cells were found at the double negative (DN) stage at week 7 samples and mature single positive (SP) T cells were found by week 126. Other immune cells were also present, with an abundance of macrophages during early gestation and an increasing population of dendritic cells (DCs) throughout prenatal development (Figure 1.1)6. Specifically, conventional DCs dominate at week 12, whereas plasmacytoid DCs increase in frequency with age6,11. Finally, B cells are found within the medulla and act as antigen presenting cells to assist with negative selection (Figure 1.1)28. 1.1.3 Conventional T cell development in the human thymus The process of T cell development and migration described below has largely been deciphered using genetically modified and humanized mouse models. When possible, human data from patients with autoimmune diseases, such as autoimmune polyendocrinopathy candidiasis ectodermal dystrophy (APECED), genetic lymphopenias, athymia, and other genetic alterations affecting the adaptive immune system have provided important comparisons of the discrepancies between mouse and human T cell development4,5. To further uncover important differences in thymocyte development, we require multiomic, spatially resolved data generated with human samples. 7 1.1.3.1 Thymus seeding Bone marrow-derived lymphoid-primed haematopoietic stem and progenitor cells (HSPCs) home to the thymus via the blood stream and enter via upregulation of L-selectin, among other adhesion ligands, and subsequent transmigration29. In mouse, thymic-homing progenitors are initially found at the cortical-medullary junction (CMJ). The relative abundance of venules at the CMJ and the relatively small number of progenitors recruited to the thymus points towards either specialized recruitment structures, or specific recruitment signals, that define specific sites where cells can enter (Figure 1.1). The role of integrins and selectins, such as fibronectin, VCAM1, ICAM1, and CD62P, and chemokines, such as CCR9 and CCL25, on thymus homing have been studied in mouse using blocking antibody strategies or genetically modified mice29. However, these strategies cannot be applied to study human T cell homing, leaving questions regarding similarity between mouse and human processes open to investigation using new technologies. Figure 1.1: T cell development and selection is guided within the thymus microenvironment by spatiotemporal signals and interactions with supporting stromal, epithelial, and hematopoietic cells. 8 Hematopoietic stem cells home from the bone marrow to the thymus and enter at the cortico-medullary junction as early thymic progenitors (ETP). These cells migrate towards the outer edge of the capsule where they receive Notch signals, among other signals, from cortical thymic epithelial cells (cTECs) and fibroblasts to initiate the T cell developmental transcription factor program and become double negative (DN) thymocytes. At the outer edge of the cortex thymocytes rearrange their T cell receptor (TCR) β-chain, upregulate CD4 and CD8 surface coreceptors to become double positive (DP) thymocytes, and undergo rapid proliferation. Immature DP thymocytes with successfully rearranged β chains then rearrange their TCRα chain and begin migrating back towards the medulla as mature DP thymocytes where they receive positive selection signals from cTECs to test their TCR. As DP thymocytes reach the medulla they downregulate either CD4 or CD8 to become immature single positive (SP) CD4 or CD8 thymocytes where they then receive negative selection signals from medullary thymic epithelial cells (mTECs), mimetic thymic epithelial cells (mimetic TECs), dendritic cells, fibroblasts, and B cells. SP thymocytes which pass negative selection upregulate mature naïve T cell markers and egress to the periphery as mature T cells. 1.1.3.2 Early thymic progenitors and progenitor T cells CD34+ early thymic progenitor cells (ETPs), also referred to as CD4-CD8- double negative (DN) cells, enter at the CMJ and undergo aberrant proliferation (Figure 1.1). As these cells begin to migrate towards the outer cortex, they require Notch signaling to upregulate T-lineage commitment gene networks and downregulate alternative lineage gene networks, becoming progenitor T (pro-T) cells. Four Notch ligands â DLL1, DLL4, JAG1, and JAG2 â are expressed in the thymus. Their spatial distribution has been investigated previously, however, this analysis was completed with traditional immunohistochemistry and did not robustly identify which cell types express these ligands30. Regardless, induction of T-lineage commitment, marked by 9 upregulation of CD7 and downregulation of CD44, require physical interactions with Notch ligands that are enabled by adjacent adhesion ligands such as ICAM1 and VCAM131. 1.1.3.3 Pro-T to DP transition As cells progress from T-lineage committed DN cells to CD4+CD8+ double positive thymocytes (DPs), they migrate from the CMJ towards the outer cortex (Figure 1.1). In mouse, this chemotaxis is driven initially by CXCR4 expression on thymocytes and diffuse expression of CXCL12 by VCAM+ cTECs, followed by thymocyte expression of CCR9 and cTEC expression of CCL2529. During this migration, cells undergo several changes to their surface phenotype and transcriptional gene networks. First, they upregulate RAG1, RAG2, and IL-7R and rearrange the β chain of their T cell receptor (TCR) via semi-random V(D)J recombination6. Alternatively, cells can rearrange their γ and δ chains at this stage and diverge to become TCRγδ cells32. In conventional TCRαβ development the rearranged TCRβ chain forms a complex with the pre-TCRα chain and CD3 to create the pre-TCR32. This enables the first stage of thymocyte selection, where cells with a successfully rearranged TCRβ chain increase in proliferative activity and generate the bulk of thymus cellularity. CD4 and CD8 TCR coreceptor proteins are also upregulated at this stage, generating an intermediate stage of CD4+ cells known at CD4 immature single positive (CD4ISP) cells, followed by CD4+CD8+ DPs. DPs with a successfully rearranged TCRβ chain will then undergo TCRα recombination to generate a final, unique TCRαβ chain6,29. 1.1.3.4 Positive selection in the cortex DPs then begin their semirandom migration back towards the medulla (Figure 1.1), during which they continue positive selection by searching for cTECs expressing HLA class I or II molecules 10 (i.e. HLA-A,B,C and HLA-DR,DP,DQ). Thymocytes which express a TCR complex capable of interacting with HLA molecules receive pro-survival signals and continue through development (positive selection), whereas cells whose TCR cannot recognize HLA molecules are forced to further rearrange their TCRα chain or commit apoptosis (death by neglect)32. Cells that successfully undergo positive selection downregulate either CD4 or CD8 to become single positive cells (SPs) and begin directed migration towards the medulla (refer to Chapter 1.1.4 for further details on CD4 versus CD8 T-lineage bifurcation). This migration is thought to be guided by upregulation of CCR7 on SP thymocytes and medullary expression of CCL19 and CCL2129. 1.1.3.5 Negative selection in the medulla Once cells arrive at the outer medulla, they encounter an environment specialized for negative selection. This includes an increased number of antigen-presenting hematopoietic cells, such as B cells, dendritic cells (DCs), and macrophages, as well as specialized mTECs expressing AIRE, a transcription factor which drives ectopic expression of tissue-specific antigens expressed by host cells in the periphery to tolerize SPs against common (self) determinants6 (Figure 1.1). Medullary DCs present a broad set of non-thymic tissue-specific peptides, which appear to be generated by the AIRE+ mTECs and supplied to DCs through cross-presentation33. Here, SPs which can interact with self-antigens receive survival signals, such as IL-7, whereas cells which interact too strongly with self-antigens are removed from the pool via apoptosis (negative selection). As SPs mature they move towards the inner medulla to complete tolerance induction29. Tregs diverge at this stage, a process which is thought to be dependent on the strength of signaling they receive when interacting with self-antigens, in addition to signaling via IL-734,35. 11 The specific vessels that support export to the periphery remain undefined; however, it is known that mature SPs express genes controlling thymocyte egress, such as the transcription factor Krupple-like factor (KLF2) which drives expression of sphingosine-1-phosphate receptor 1 (S1PR1) and CD62L36. Expression of S1PR1 enables cells ready for export to migrate towards the S1P gradient, formed in part by pericytes and DCs surrounding blood vessels of the CMJ, and undergo reverse transendothelial migration into the bloodstream36. 1.1.4 CD4 versus CD8 T-lineage branching Determination of helper CD4 or cytotoxic CD8 T cell lineage fate is a critical stage in T cell development. The lineage decision is thought to be guided by interactions between the TCR-coreceptor complex and either HLA class I or II antigens, with CD4 T cells interacting with class II antigens and CD8 T cells interacting with class I antigens32,37. Several models have been proposed to explain the CD4 versus CD8 lineage choice categorized as either âstochasticâ or âinstructiveâ models depending on whether termination of the coreceptor is random or instructed38. In the stochastic selection model the cells randomly downregulate either CD4 or CD8 coreceptor proteins and then audition their TCR complex with the remaining coreceptor protein to receive positive selection signals39â43. If the TCR-coreceptor complex continues to receive positive selection signals, the cell is rescued and continues development as either a class I-restricted CD8 T cell or a class II-restricted CD4 T cell. If positive selection signals cease, indicating a TCR-coreceptor mismatch, the cell is not rescued and undergoes death by neglect. This theory, which requires stochastic downregulation of a coreceptor, is highly inefficient with the potential for 50% of TCRs to be lost because they were present on a cell with the wrong coreceptor. 12 Transgenic mice designed to test this theory have not replicated rescue of close to 50% of TCRs, and experiments measuring efficiency of repertoire selection of thymocytes with transgenic TCRs have demonstrated that repertoire selection can approach 90% efficiency44. Additionally, this model requires cells with mismatched TCR-coreceptor complexes to be short-lived. However, observations in mice have shown that SP thymocytes with mismatched TCR-coreceptor complexes are not short-lived and can complete maturation to the medulla and emigration into the periphery45,46. Thus, the core principles of the stochastic model fail to match current experimental data. Instructive signaling models are based on the role of TCR signaling strength or duration in determining cell fate, postulating that the types of TCR signals received from class I or class II interactions are sufficiently distinct to specify termination of a mismatched coreceptor. Differences in the strength of TCR signaling are rooted in the observation that the CD4 cytosolic tail binds more intracellular lymphocyte cell-specific protein-tyrosine kinase (LCK) than CD8, generating stronger signals47â49. However, when the TCR signaling intensity was directly modulated by altering the number of immunoreceptor tyrosine-based activation motifs (ITAMs) contained within each TCR signaling complex, differentiation towards CD4 and CD8 was still restricted by MHC specificity50,51. TCR signaling duration has also been proposed to play a role in differentiating instructive CD4 versus CD8 TCR signals. In this model, long duration signals direct cells towards the CD4 lineage and short signals direct cells towards the CD8 lineage52. Support for this model was originally based on observations that DPs first downregulate surface CD8 protein, which would disrupt TCR signaling of class I-restricted cells, causing shorter duration signals53. More recently 13 these observations have been shown to occur due to downregulation of CD8 gene expression, not internalization of CD8 surface receptor, experimentally contradicting this support54,55. Regardless, the concept that TCR signal duration is a major determinant of T-lineage choice remains a central feature of current models, such as the kinetic signaling model. Briefly, the kinetic signaling model postulates that regardless of their TCR, positively selecting TCR signals induce DPs to terminate CD8 gene expression and audition their TCR for class II-restricted positive selection signals. If the cells continue to receive positive selection signals they will migrate to the medulla and continue maturation towards mature CD4SPs. If positive selection signals cease, the cell will downregulate CD4, upregulate CD8, and audition the CD8-TCR complex for class I-restricted signals. If the cell receives positive selection signals it will migrate to the medulla and continue CD8SP maturation. Otherwise, it will commit apoptosis due to death by neglect38,56. This model also involves crosstalk between TCR and cytokine receptor signaling, where cytokine receptors function as a sensor of TCR signaling duration. Persistent signaling impairs IL-7R signal transduction, driving CD4SP differentiation, whereas disrupted TCR signaling leads to IL-7R signaling, coreceptor reversal, and CD8SP differentiation56. Furthermore, several transcription factors and nuclear factors regulate CD4 versus CD8 lineage choice. T-helper-inducing POZ/Kruppel-like factor (ThPOK) is a zinc-finger protein encoded by the Zbtb7b gene shown to be singularly important for CD4 lineage choice57,58. ThPOK maintains CD4 expression by preventing factors such as runt-related transcription factor 3 (RUNX3) from silencing the CD4 gene59, and reducing CD8 expression by downregulating the CD8 enhancer activity of E8160. Alternatively, RUNX3 is a member of the runt-domain family of transcription factors which binds the CD4 silencer element to silence CD4 expression59 and binds to the E81 14 CD8 enhancer element to re-initiate CD8 transcription after failed TCR-CD4 positive selection61. RUNX3 has also been shown to bind a sequence in the Zbtb7b gene to extinguish ThPOK expression62, further promoting CD8 lineage fate. In summary, the current model of CD4 versus CD8 T-lineage choice postulates that kinetic regulation of coreceptors enables sequential audition of the TCR for class II, then class I signals, generating TCR signals with distinct durations. TCR signaling integrates with cytokine signaling and transcription factor regulation to determine CD4 versus CD8 T-lineage choice and direct differentiation toward mature helper or cytotoxic T cells. 1.2 Chimeric antigen receptor T cell therapy Chimeric antigen receptor (CAR) T cell therapy has revolutionized treatment of otherwise treatment-refractory blood cancers such as leukemia and lymphoma. For example, adult B-acute lymphoblastic leukaemia patients post allograft have a median survival of ~7 months. Following CAR T therapy >80% of relapsed and refractory patients achieved a complete remission, resulting in 50-86% of patients being alive one year later63. Based on these unprecedented results, the U.S. Food and Drug Administration (FDA) has approved the use of autologous T cells engineered to express a CAR specific for the CD19 B lymphocyte molecule to treat refractory pre-B cell lymphoblastic leukemia and diffuse large B cell lymphoma64. This success has also motivated research investigating the use of CAR T cell therapy in other types of cancer, including solid tumour cancers such as lung65, liver66, and pancreatic cancers67, as well as other immunological disorders, such as lupus68. Currently, the U.S. National Library of Medicine lists over 900 ongoing T cell therapy clinical trials69. 15 These treatments are increasingly being approved for use in Canada. For example, as of May 2021 Health Canada has given conditional approval for the use of ABECMATM (idecabtagene vicleucel) as the first immunotherapy to treat adults with multiple myeloma70. To support this growth, organizations such as BioCanRx have partnered with clinical, research, and manufacturing partners across Canada to proactively build a network of good manufacturing practice (GMP) facilities and enable Canadian CAR T clinical trials71. 1.2.1 Autologous CAR T strategies Current approved therapies use an autologous CAR T cell strategy, where a patientâs own T cells are genetically engineered to target cancer cells. Briefly, the patientâs white blood cells are separated from blood samples via leukapheresis. The cells are transported to a GMP facility where they are genetically engineered via transduction to express a synthetic receptor specific to a target antigen. These T cells are then expanded in culture by several million-fold and transfused back into the patient where they target cancer cells64. By using a patientâs own cells as the source material there is a reduced risk of systemic immunological reactions, bio-incompatibility, and disease transmission. However, this highly personalized approach is prohibitively expensive72, with an estimated cost per patient for FDA approved therapies of over $500,000 USD73. In addition, this process is time-intensive, requiring approximately three weeks for processing, relies on high-quality patient cells, which may have been damaged in previous chemotherapy treatments, and can produce highly variable products due to initial differences in patientâs T cells74. 16 1.2.2 Allogeneic & PSC-derived CAR T strategies Limitations of autologous CAR T cells have motivated the use of off-the-shelf allogenic T cells. In one strategy, iPSCs are differentiated to mature PSC-T cells that can be cryopreserved and available immediately. This would enable further standardization of the CAR T product, allow multiple cell modifications to increase specificity, and potentially decrease manufacturing cost74. The Zandstra lab has previously engineered a platform to differentiate iPSCs to pro-T cells that could serve as a cell source for allogeneic CAR T cell therapies75. By performing screens of Notch ligands and adhesion ligands, as well as through media optimization, Shukla et al. (2017) demonstrated the ability to produce human CD5+CD7+ pro-T cells from cord blood HSPCs using a fully defined, 2D engineered niche75. This platform has been further optimized for cord blood76, as well as extended to iPSC-derived cells77. However, the previous protocol for iPSC-derived T cells failed to reproducibly yield mature CD4+ T cells. This limited potential for use in cell therapies as clinical trials have demonstrated a link between the ratio of CD4+ T cells and increased persistence of CAR T cells in patients74,78. Moreover, the skewed production of mature CD8+ T cells by our labâs and otherâs feeder-free protocols suggests alternative cell-cell interactions may contribute to positive selection27,79,80. In mouse, DPs actively downregulate MHC class I and II expression81,82, preventing thymocytes from positively selecting off each other and forcing cells to migrate towards cTECs to receive survival signals. If also true in human, this would infer that DPs in a feeder-free culture system would not receive adequate positive selection signals and would commit apoptosis due to neglect rather than commit to the CD8+ T lineage. This highlights our lack of knowledge of the human 17 thymus and indicates that the identified cell signaling pathways are insufficient to explain the impact of positive and negative selection on T-lineage branching in vitro. 1.3 Spatial multiomic technologies to study human biology The nature of studies on human tissue provides a static timepoint and does not allow for temporal or genetic studies as in mouse research. To maximize information gained from an individual tissue, spatial multiomic technologies can be applied, integrating information across levels of cellular and niche-level regulation. For example, scRNA-seq captures transcriptome-wide messenger RNA (mRNA) expression data. When combined with Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq), which uses barcoded antibodies to sequence and quantify targeted surface proteins, one can explicitly link the cellâs surface proteome and transcriptome83. Furthermore, computational integration of CITE-seq with Assay for Transposase Accessible Chromatin with sequencing (ATAC-seq), which captures chromatin accessibility data, can map genome-wide chromatin accessibility and epigenetic states of cells throughout development84. Together, integration of CITE-seq and ATAC-seq generates a multiomic resource capturing transcriptomic, proteomic, and epigenomic data from the desired tissue. These data can be further supplemented with single cell TCR sequencing (scTCR-seq) which performs paired sequencing of the alpha and beta TCR sequences to characterize the TCR repertoire. Although single cell sequencing approaches permit deep phenotypic characterization of physiologically relevant subpopulations, they require dissociation of the sample to individual cells prior to sequencing. This loss of positional information is a major limiting factor when investigating the environmental and cell-cell interactions orchestrating the dynamic migratory 18 path and extensive training regime of T cells in the thymus. To probe this positional information, combined sequencing and imaging approaches using technologies such as the Akoya CO-detection by inDEXing (CODEX) spatial proteomics platform, which enables deep phenotypic characterization of intact thymic tissue sections at cellular resolution85, can map sequenced cells to relevant tissue niches. CODEX imaging requires a custom immunofluorescence antibody panel with antibodies conjugated to short (ie. 10-18 nucleotides) single stranded DNA sequences via maleimide-thiol chemistry85,86. The tissue is stained with the full antibody panel, which typically ranges from 20-50 antibodies, and then iteratively stained with fluorescent reporters conjugated to complimentary single stranded nucleotides. In each imaging cycle the tissue is stained with three fluorescent reporters and a nuclear stain. After images are acquired, the tissue is washed to remove the reporters and then stained with the next set of reporters. This cycle is repeated until all antibodies have been imaged. Images from each cycle are aligned using the nuclear stain channel to account for imaging drift, segmented to identify individual cells, and computationally processed to assign surface marker expression values to individual cells. The resulting dataset can be analyzed via similar methods to scRNA-seq data, where cells are clustered based on surface expression phenotype. However, each cell has a known spatial coordinate, enabling spatial analyses such as quantification of niche composition and cell-cell interaction frequencies85,86. Finally, the CITE-seq antibody panel can be designed to overlap with the same developmental surface markers targeted via CODEX to enable high-confidence cell type matching between sequencing datasets and spatial CODEX data86. 19 1.4 Sex differences in T cell development Sex differences have been demonstrated post-puberty in immune system reactivity and differential ageing kinetics87â91. In 2001, comparison of signal joint T cell receptor rearrangement excision circles (sjTRECs) in peripheral blood, a marker used to estimate thymic function via the number of recent thymic emigrants, established that females contained significantly higher levels of sjTRECs from age 20 onwards87. This demonstrated that females have increased thymic output of naïve T cells throughout middle and later life, which would support greater TCR diversity and ability to respond to new pathogen challenges with age. These data were further supported by a recent study on peripheral blood from 172 healthy adults which characterized sex differences in the age-related kinetics of declining naïve T cells and increasing monocyte and cytotoxic cell functions. Two spikes in the rate of these detrimental changes were detected, with the second spike occurring earlier and with a greater magnitude in males91. Additionally, after age 65 males had higher innate and pro-inflammatory activity, as well as lower adaptive activity91. A separate study demonstrated higher T cell activation signals in young females and again documented sex-specific differences in the composition of immune cells and levels of inflammatory factors with age90. These changes in the peripheral immune system provide an accessible, if delayed, method of assessing declines in thymus contribution to immunity, and generally mirror sex differences in thymus activity and involution throughout life. In the context of the thymus, sex hormone regulation of thymic involution has long sparked a large body of evidence documenting sex differences in thymus biology, involution, and T cell generation92,93. Decreased production of recent thymic emigrants and smaller overall thymus size in older males versus females have also been reported87,94. With respect to cell and niche level 20 changes, some studies describe decreased numbers of AIRE+ mTECs with age, potentially predisposing females who maintain greater thymic function later in life to autoimmune disease95, as well as less interlobular fat in young female thymus94, suggesting that differences in thymic involution kinetics begin pre-puberty. However, the impact of transcript-level sex differences in thymic cell types on thymus and immune function, as well as the impact of sex differences pre-puberty, have not been investigated. For further information on the impact of sex differences in ageing, as well as prospective technologies to address immune system decline, see Appendix A 1.5 PSC-T cell differentiation platform 1.5.1 PSC-T cell differentiation for discovery-based research PSC-T cells present a novel technology to harness for discovery-based research. PSC-T cells have been proposed as a novel source of antigen-specific TCRs. For example, diverse PSC-T cells can be screened for reactivity to specific tumour, viral, or autoimmune antigens96. Alternatively, antigens of interest could be added in culture during positive selection to enrich PSC-T cells with specific reactivities. PSC-T cells also have the potential to improve PSC-derived organoid and organ-on-a-chip modeling of disease pathologies. Stem cell-derived organoids enable disease modeling in a physiologically relevant microenvironment and can enable development of personalized therapies and patient-specific drug response modeling97â100. Complex organoid and organ-on-a-chip technologies have been developed to model complex disorders, such as neurological diseases101,102, inflammatory diseases103â105, and monogenic disorders106â108, as well as to elucidate underlying mechanisms driving disease pathologies109,110. However, these models lack immune cells, key drivers of many disease pathologies, which limits mechanistic disease modeling and prohibits modeling of adaptive immune responses in vitro. 21 Furthermore, the defined, feeder-free PSC-T differentiation platform enables defined genetic and chemical perturbations during human T cell development, a system not previously amenable to experimentation. By screening small molecules76, environmental and niche characteristics77, and gene expression111 this platform can be harnessed to study critical stages in human immune cell development, such as T-lineage branch points (ex. T-lineage commitment vs DC development, conventional vs innate T cell development, or CD4 vs CD8 T-lineage commitment) and TCR repertoire development. Importantly, defined PSC-T systems can be harnessed to screen for differences in developmental kinetics and cell function between sexes and with age, contributing valuable knowledge to our mechanistic understanding of differential immune responses across patient populations. 1.5.2 Factor screening and optimization of PSC-T cell differentiation Specific factors, such as adhesion ligands and cytokines, identified with spatial multiomic human thymus data to be implicated in T-lineage branch decisions can be tested at specific developmental stages in the PSC-T cell differentiation platform using design of experiments (DOE) strategies. DOE is a tool that enables use of a minimum number of experimental runs where all experimental parameters are studied and varied simultaneously112. The conventional DOE scheme is to first design screens, such as via definitive screening designs (DSDs), to determine significant main effects followed by response surface models (RSMs) to justify the design space. DSDs offer several desirable properties, including the identification of main effects, quadratic effects, and two-factor interactions, an orthogonal model, and a minimum number of runs112. Response surface experiments typically involve a small number of continuous factors previously identified as active, where the goal is to create a predictive model of the 22 relationship between the factors and the response. This model can then be used to find optimal process settings113. For example, DSDs can be employed to test various Notch, adhesion, and soluble ligands identified with the spatial multiomic database over a wide range of concentrations at each stage of the differentiation. Ligands exhibiting advantageous effects can then be included in further optimization using an RSM. 1.6 Summary of Research 1.6.1 Hypothesis The overall hypothesis for this research is that integration of spatial multiomic data will provide a predictive platform to identify how thymic developmental niches are spatially organized to provide unique signaling environments for human T cell development. Identified niche-specific signaling factors can be implemented in a defined, feeder-free PSC-T cell differentiation platform to confirm stage-specific effects on T cell development and guide T-lineage branching towards CD4 T cells. Furthermore, this technology can be used as a discovery-based platform to study and probe human T cell development, specifically the contribution of TCR signaling to CD4 versus CD8 T-lineage commitment. 1.6.2 Objective We aim to create a spatial multiomic map of T cell migration and development in the human thymus. Using our resource we will identify stage-specific pathways directing T-lineage branch points and test these hypotheses in our defined, feeder-free PSC-T cell differentiation platform to improve yield of mature PSC-CD4 T cells. We will use this platform to uncover mechanisms guiding CD4 versus CD8 T-lineage branching and demonstrate its potential as a discovery-based research tool for human immune system development. 23 1.6.3 Overview This research was motivated by the need for human-specific data on T cell development to inform improvements to the PSC-T technology to produce mature PSC-CD4 and CD8 T cells for therapy and disease modeling. In Chapter 2 we hypothesized that improved characterization of thymic architecture via spatially resolved transcriptomic and proteomic data would allow us to uncover how developmental niches are organized to support the spatiotemporal signaling directing human T cell development. We generated an age-matched spatial multiomic resource, identifying sex differences in early postnatal T cell and thymus development (Figure 1.2). Using this resource, we demonstrate a possible role for JAG ligands in directing thymic-like dendritic cell development, identify important functions of a novel population of cortical fibroblasts, and characterize the medullary niches surrounding Hassallâs corpuscles. Together, these data represent a unique resource to investigate how sex-based differences in thymus regulation and T cell development arise, providing an essential resource to understand the mechanisms underlying immune function and dysfunction in males and females. Figure 1.2: Overview of the closed-loop experimental approach to probe human T cell development. First, we applied spatial multiomic sequencing to characterize thymic niches and identify factors to screen in vitro to produce PSC-CD4 T cells. Spatial multiomic sequencing of age-matched early postnatal thymus samples 24 identified sex differences at the transcriptomic, cell, and niche levels. Insights into CD4 generation in vivo, namely dynamic Notch signaling regulation and thymocyte HLA class II expression, were screened in vitro to produce mature PSC-CD4 T cells. Generation of PSC-CD4 and CD8 T cells was applied to model human TCR repertoire formation in vitro to gain new insights into kinetic regulation of the TCR coreceptor on HLA specificity. Next, in Chapter 3 we applied learnings from our resource, such as the spatial regulation of Notch signaling and the potential for T cell self-selection, as well as comparisons of in vitro T cells to our human reference dataset, to our feeder-free PSC-T cell differentiation platform to enable production of mature PSC-CD4 and CD8 T cells (Figure 1.2). Finally, we demonstrate the potential of the PSC-T technology in discovery-based research in Chapter 4. Here, we study human TCR repertoire formation, investigating the contribution of TCR specificity to HLA class I or II in determining CD4 or CD8 T-lineage fate (Figure 1.2). We extend insights from our PSC-T cell system to in vivo biology by performing our sequencing analysis in parallel to human thymus and peripheral blood mononuclear cells (PBMCs). We find that TCRs exist on a spectrum of HLA class I/II specificity and kinetic regulation of TCR coreceptor proteins during T cell development determines HLA specificity and functional fate of T cells. Overall, we demonstrate a novel technology to study and perturb human immune system development. 25 Chapter 2: Sex biased thymic architecture guides T cell development through spatially defined niches 2.1 Summary Within the thymus, regulation of the cellular crosstalk directing T cell development depends on spatial interactions within specialized niches. To create a spatially defined map of tissue niches guiding human postnatal T cell development we employed the multidimensional imaging platform CO-detection by indEXing (CODEX), as well as CITE-seq and ATAC-seq. We generated age-matched 4â5-month-old human postnatal thymus datasets for male and female donors, identifying significant sex differences in both T cell and thymus biology. We demonstrate a possible role for JAG ligands in directing thymic-like dendritic cell development, identify important functions of a population of ECM- fibroblasts, and characterize the medullary niches surrounding Hassallâs corpuscles. Together, these data represent an age-matched spatial multiomic resource to investigate how sex-based differences in thymus regulation and T cell development arise, providing an essential resource to understand the mechanisms underlying immune function and dysfunction in males and females (Figure 2.1). 26 Figure 2.1: Graphical abstract depicting the approach to spatial multiomic sequencing of human postnatal thymus tissue. Niche analysis of imaging data was used to screen cell-cell interactions in sequencing data. Niche analysis uncovered novel sex biases in thymus and T cell development in early postnatal development. 27 2.2 Introduction The thymus is the primary organ responsible for the generation and selection of mature, functional, and self-tolerant T cells114. Effective T cell development is a critical component of our immune systemâs ability to accurately, and exclusively, identify and kill foreign entities such as pathogens. During early postnatal T cell development â the period in life when T cell development is most active115 â thymic seeding progenitors migrate to the thymus and mature into thymocytes. Thymic architecture is highly organized to provide spatially-defined, stage-specific signaling cues to migrating thymocytes which guide development towards functional mature T cells29,116â118. Recent single cell sequencing resources demonstrating the diversity of human thymus tissue are incongruous with our current framework of thymus structure and organization6â8,20,24,119â126 which describe a general migratory path thymocytes take through the cortex and medulla during conventional αβT cell development. Spatial transcriptomic sequencing of human thymus has demonstrated a deeper granularity of thymic niches and their evolution during fetal development to support different waves of non-conventional T cells27,126. However, our understanding of how human postnatal thymus niches support conventional and non-conventional T cell development, T-lineage branching, and alternative lineage development remains limited29,116,118. T cells generated at this stage of postnatal human development will become the foundation of our immune system, patrolling the body for decades127. Thus, insights into early postnatal thymus niche biology are crucial to understand how our adaptive immune system is built and how perturbations in postnatal T cell development may emerge as immune dysfunction later in life. 28 To create a spatially defined map of tissue niches guiding human postnatal T and alternative lineage cell development, we employed multi-dimensional spatial proteomic imaging using CO-detection by inDEXing (CODEX)86,128, single cell transcriptomic-proteomic profiling using Cellular Indexing of Transcriptomes and Epitopes (CITE-seq)83, and single cell Assay for Transposase Accessible Chromatin (ATAC-seq)84. Given the emerging recognition of sex differences in thymus gene expression and function87,89,92,94,95,129, we collected and analysed samples from male and female donors. Our analysis identifies significant sex differences during early postnatal development that affect T cell and thymus biology through common and cell type-specific mechanisms. Additionally, we highlight key cell types contributing to thymic involution that exhibit sex-based differences in thymic growth and early transition towards adipogenesis. These data suggest that kinetic differences in thymic involution are present between sexes and, importantly, that mechanisms driving thymic involution begin early in life. Altogether, these data represent a powerful age-matched spatial multiomic resource to investigate how sex-based differences in thymus biology and T cell development arise, and how they contribute to sex differences in diseases caused by immune dysfunction. 2.3 Results 2.3.1 Spatial multiomic profiling of human postnatal thymus identifies sex-based differences in T cells and thymus biology We performed single cell CITE-seq, ATAC-seq, and CODEX imaging on 4-33 month human postnatal thymuses, including six (3 female, 3 male) 4-5 month old age-matched samples (Table B.1), representing the stage of human development where naïve T cell production is highest. Each donor sample was split and processed simultaneously for CODEX imaging and sequencing 29 (Figure 2.2A). We included a comprehensive 137 antibody panel (Table B.2), allowing us to compare epigenomic, transcriptomic, and proteomic expression kinetics across developing thymocytes, and enabling direct comparison of cells identified via phenotypic expression in CODEX with cells captured via CITE-seq. Prior to sequencing we enriched CD45- non-hematopoietic cells and CD25+CD8- regulatory T (Treg) cells to ensure coverage of low abundance cell types. After quality control and computational merging of individually sequenced patient datasets, we obtained a total of 74,334 cells with CITE-seq, including 19,434 non-T lineage cells, and captured 25,717 nuclei with ATAC-seq. Importantly, cell proximity in CODEX tissue niches was used to screen predicted receptor-ligand interactions. CITE-seq cells were clustered based on transcriptional expression and annotated based on marker gene and surface protein expression (Figure 2.8A; Table B.3)6,7. ATAC-seq clusters were computationally labeled using CITE-seq reference cluster labels, which identified 34 ATAC-seq cluster transfer labels for dataset integration (Figure 2.2B, Figure 2.8B). We captured 54,900 thymocytes spanning development from early thymic progenitors (ETPs) to mature single positive (SP) T cells, immature innate cells, innate-like cells, and Tregs. We identified three Treg populations expressing canonical lineage markers, namely Treg progenitors (Pro-Tregs), thymic Tregs (tTregs), and recirculating/resident Tregs (rrTregs)35. We also identified antigen presenting cells, including B cells, mast cells, monocytes, and six populations of dendritic cells (DCs)130. In addition to the activated dendritic cells (aDCs), plasmacytoid dendritic (pDCs), DC1, and DC2/3 populations described by Park et al.6, we found proliferating populations of pDCs and DC1. We also captured 7093 epithelial cells, including cortical epithelial cells (cTECs), medullary epithelial cells (mTECs), activated mTECs, and mimetic TECs. 30 31 Figure 2.2: Spatial multiomic analysis identifies sex biased characteristics of thymic niches: A) Experimental setup for spatial multiomic analysis of thymic niches. CODEX and sequencing data from postnatal samples were integrated to analyze cell types and niche composition. Proximity-based niches guided analysis of integrated sequencing data. B) UMAP plot displaying cell types identified via CITE-seq/ATAC-seq. C) Representative CODEX visualization of cell clusters within thymic lobule. D) Heat map depicting proximity-based niche composition probabilities captured via CODEX. Positive values depict cell types found in the niche. E) Histogram of top 25 GSEA pathways upregulated in male and female thymic cells separated by cell type (padjust<0.05). F) Bar plot of cell counts normalized to total number of cells in the respective lobule per cell type identified via CODEX; * p<0.05, ** p<0.01, *** p<0.001. Importantly, we captured 7721 mesenchymal cells which contribute to negative selection and thymic involution24â26,126,131. Subclustering identified important mesenchymal cell types including two populations of endothelial cells (ECs) defined by differential expression of Notch ligands JAG1, JAG2, DLL1, and DLL4 (ECs, ECs (Notch)). Additionally, we identified lymphatic endothelial cells (LECs), pericytes, vascular smooth muscle cells (VSMCs), and five distinct fibroblast cell types, including DPP4+ capsular fibroblasts (DPP4+ capFibs), capsule fibroblasts (capFibs), medullary fibroblasts (mFibs), KRT+ fibroblasts (KRT+ Fibs), and proliferating fibroblasts (Fibs (P)). We imaged of each tissue sample with a custom 48 antibody CODEX panel to study the architecture and function of niches guiding thymocyte development, aiming to define the niche characteristics guiding T-lineage branch points. Stage specific thymocyte phenotyping markers (CD62L, CCR7, CD1A, CD5, CD7, CD4, CD8, CD3, CD45RO, CD45RA, FOXP3, SATB1) identified CD3+ double positive cells (DPs) undergoing T-lineage commitment towards CD4 or CD8 T cells. Phenotyping markers for non-T lineage hematopoietic cells (CD19, CD11c, 32 CD11b, CD68), epithelial cells (EPCAM, KRT5/8), mural cells (MCAM, SMA), endothelial cells (CD31), and fibroblasts (PDGFRA) identified the remaining major cell types defining thymic niche architecture. Finally, we included functional markers to define patterns of antigen presentation (CD86), human leukocyte antigen (HLA) class I and II expression (HLA-ABC, HLA-DR,DP,DQ), adhesion ligands (ICAM, VCAM), Notch ligands (DLL1, DLL4, JAG1, JAG2), T cell activation (PD-1), self-tolerance (PD-L1), proliferation (Ki67), and enzymatic regulation (15-PDGH). In sum, our CODEX panel enabled investigation of spatially-regulated mechanisms directing human T cell development. Using neural-network driven cell segmentation and Leiden-based clustering86 we identified individual cells within thymic tissue for each sample (Figure 2.8C). We annotated cell types based on tissue location and phenotypic expression compared to CITE-seq clusters (Figure 2.2C), performed proximity-based neighbourhood clustering to identify niches86, and annotated niches based on location and cell type composition (Figure 2.2D; Figure 2.8D). This analysis quantified proximity-based cell-cell interactions (Figure 2.8E) and served as a platform to interrogate spatially-defined thymic niche biology via integrated sequencing-imaging analysis. Because of known sex differences in thymus and T cell gene expression89, we compared our age-matched male and female samples separately. In line with prior reports of sex-biased gene expression on autosomes132â135, only 2% of male differentially expressed genes (DEGs) were found on the Y chromosome and 0.3% of female DEGs were found on the X chromosome (Table B.4 & Table B.5). Gene set enrichment analysis (GSEA) on male versus female cells for each cell type identified pathways commonly upregulated in either sex (Figure 2.2E). Pathways differentially regulated across hematopoietic, epithelial, and stromal cells represent cell-intrinsic 33 sex-based differences. Female cells have higher gene expression of transcription, energy regulation, and antigen presentation. Male cells, in contrast, have increased gene expression of proinflammatory signaling, amino acid metabolism, and GPCR signaling. The top differentially expressed energy regulation and metabolism pathways were similarly sex-biased in human kidney136, suggesting multiple organs show consistent sex-biased enrichment of pathways linked to metabolism and energy production. Our data aligns with sex-biased trends identified in human induced pluripotent stem cell (iPSC) lines137 and other human organs138, indicating these pathways often differ between male and female cells across various cell types. In contrast, some pathways showed cell type-specific sex-biased enrichment. Female T and hematopoietic cells showed enrichment of interferon signaling, and female fibroblast and perivascular cells were enriched in ECM-centric pathways (Figure 2.2E). Our dataset also identified differential sex-specific pathway enrichment between cell types. Gene expression indicated higher cytokine signaling in T cells and hematopoietic cells in females, and in epithelial and mesenchymal cells in males (Figure 2.2E). These data show significant gene expression differences in male and female thymic cells. To demonstrate sex differences at the proteomic level, we identified genes with a log fold change greater than 1 that contributed to increased chemokine signaling in male T cells. CXCR4, an important chemokine receptor in thymocyte migration and development, was expressed in a higher frequency of male progenitor T (pro-T) cells, which we confirmed via flow cytometry (3 male, 3 female; p = 0.03; Figure 2.8F). As higher levels of cytokine and interferon signaling have been previously shown to influence thymus and T cell biology76,139, our data suggests male and female T cells develop in different signaling environments and may respond differently to cytokine stimuli. 34 Next, we quantified cell type abundance within male and female tissues, demonstrating differences in cortical and medullary cell distributions between sexes. When normalized to the total number of cells per lobe, female thymus lobes contained significantly more DPs (p = 0.011) and cTECs (p = 0.0023). In males, we found significantly more SPs (p = 4.2 x 10-4), CD3+ DPs (p = 9.9 x 10-4), activated mTECs (p = 0.0014), and VSMCs (p = 2.4 x 10-6) (Figure 2.2F). Given that thymus lobules with more DPs and cTECs would have a greater proportion of cells undergoing positive selection and lobules with more medullary cells would have more cells undergoing negative selection, these data suggest that sex differences in cell type abundance may influence the resources directed towards specific stages of thymocyte selection. Alternatively, these results may suggest that male and female thymuses are developmentally asynchronous, with males exhibiting faster growth and involution kinetics, resulting in decreased cortical to medullary ratios even in early neonatal stages. We focused further analyses on sequential developmental niches, including analysis of sex differences in cell types and niches at each stage. 2.3.2 JAG1 skews early thymic progenitor development towards thymic dendritic cells We first analysed the cortico-medullary junction (CMJ) where cells home to the thymus (Figure 2.3A). This region recruits and supports ETPs119 and is composed of endothelial cells, VSMCs and pericytes expressing the Notch ligand JAG1 (Figure 2.3B,C). CITE-seq demonstrated that the cell adhesion molecule used by ETPs to enter the thymus, CD62L, is quickly downregulated upon CMJ entrance through the vasculature (Figure 2.9A). However, recently immigrated CD62L+ double negative cells are frequently located in the subcapsular zone (Figure 2.9B), suggesting that ETPs enter the thymus and rapidly migrate to a subcapsular niche where DLL4, a more potent Notch ligand, is highly expressed on fibroblasts and subcapsular epithelial cells 35 (Figure 2.3D, Figure 2.9C). However, the concentrated presence of JAG1 at their entry point indicates that ETPs are first exposed to this Notch ligand. CellChat140 pathway analysis showed that JAG1-NOTCH1 interactions between endothelial and perivascular cells are enriched with ETPs (Figure 2.3E-G), while JAG1-NOTCH2 and JAG1-NOTCH3 interactions are enriched with DC1, DC1 (P), DC2/3, and aDCs (Figure 2.3E-G). These data suggest that JAG1 could induce commitment towards other hematopoietic lineages, such as pDCs, conventional dendritic cells (cDCs), or macrophages, which are known to develop within the thymus119. As JAG ligands induce weaker Notch induction141â144, we hypothesized that early contact with ETPs could maintain T-lineage potential while cells migrate towards DLL4 in the subcapsular niche. We first analysed the ability of the four thymic Notch ligands to induce T-lineage commitment or alternative lineage development from cord-blood derived CD34+ hematopoietic stem and progenitor cells (HSPCs) in a defined, feeder-free culture system76 (Figure 2.3H). We included titrated concentrations of granulocyte-macrophage colony-stimulating factor (GM-CSF), which is produced by mast cells at the CMJ, to support DC development145. We found that only DLL1 and DLL4 ligands induce T-lineage commitment, whereas JAG ligands or no ligand controls supported myeloid cell development and did not induce T-lineage commitment (Figure 2.9D). Specifically, JAG ligands with GM-CSF skewed CD68+ DC development towards CD14- DC1 cells, while no ligand controls skewed CD68+ DC development towards CD14+ DC2/3 cells (Figure 2.3I, Figure 2.9E). 36 37 Figure 2.3: Thymic progenitors entering via the corticomedullary junction are exposed to a gradient of Notch ligands which influence lineage specification: A) Graphic of ETP entry niche at corticomedullary junction. B) Representative image of an arteriole niche at the corticomedullary junction; T087. VSMCs (white) compose arteriole walls and surround ECs (red) which, along with mesenchymal cells (green), express JAG1 (magenta). CCR7+ (cyan) ETPs interact directly with JAG1 ligands upon entry. C) Gene expression of chemokines and Notch ligands; T087. D) Localization of DLL4 (magenta) to the subcapsular zone of the cortex; T085. E-G) CellChat results of Notch interactions. H) Notch ligand screening experimental design. CD34+ hematopoietic stem and progenitor cells (HSPCs) were cultured on one Notch ligand (JAG1, JAG2, DLL1, or DLL4) or no ligands (NL) for 14 days in media containing 0, 2, or 20 ng/mL of GM-CSF. I) Bar plot of GM-CSF effect on the CD14-/CD14+ cell ratio at day 14. J) Notch ligand transfer experimental design. CD34+ HSPCs were cultured on one Notch ligand or no ligands and transferred to DLL4 on day 3, 5 or 7. K) Bar plot of the frequency of CD5+CD7+ pro-T cells at day 14. All in vitro results are mean Â± standard deviation from N=3 independent umbilical cord blood (UCB) donors. Next, to test our hypothesis that Notch signals via JAG1 ligands could act as a bridge towards later DLL4 interactions, we analysed cells grown on JAG1 for 3, 5, or 7 days prior to DLL4 transfer (Figure 2.3J). We found that cells cultured on JAG ligands or no ligands for 3 days maintained reduced T-lineage commitment compared to DLL1 or DLL4 cells (pJAG1 = 0.033; pJAG2 = 0.017), whereas cells cultured on JAG ligands for longer than 3 days lost T-lineage potential (Figure 2K), indicating that JAG ligands could not support T-lineage potential. We next analysed the contribution of different Notch ligands to the development of male and female ETPs (Figure 2.9F,G). Our data suggest that JAG ligand interactions are more abundant and diverse in females with JAG1-NOTCH1 interactions enriched in female ETPs and DLL4 interactions enriched in male ETPs. 38 Together, these data suggest that timely migration from the CMJ to DLL4 ligands at the subcapsular zone is critical for T-lineage commitment and exposure to JAG ligands at the CMJ can guide alternative lineage development towards thymic-derived dendritic cells. Our data further demonstrate previously unrecognized sex-biased regulation by Notch ligands. 2.3.3 Analysis of the subcapsular zone identifies sex-based differences in fibroblast regulation of DP development and thymus growth From the CMJ, ETPs migrate to the subcapsular zone via a CCL25-CCR9 chemokine gradient established by cTECs and directed to pro-T, DP (P), and DP2 (Q), but not DP1 (Q) cells (Figure 2.4A; Figure 2.10A). The subcapsular niche consists of JAG1+ VCAM1+ DCs, cTECs, capsular fibroblasts, DPP4+ capsular fibroblasts, and proliferating fibroblasts which secrete and maintain spatially regulated ECM ligands to support sequential thymocyte development (Figure 2.4B,C; Figure 2.10B,C). GSEA identified that DPP4+ capsule fibroblasts were enriched in the HSP90 chaperone cycle for steroid hormone receptors (padjusted = 0.0065; 18/52 pathway genes significantly upregulated), suggesting an enhanced response to steroid hormones, and supporting their role in sex hormone-based thymic involution24. In contrast, capFibs were enriched for genes related to cytokine (IL33, padjusted = 1.50x10-6; IL34, padjusted = 3.56x10-7) and chemokine signaling (CCL2, padjusted = 5.10x10-40; CXCL3, padjusted = 0.020; CXCL12, padjusted = 1.78x10-8; CXCL14, padjusted = 3.63x10-15), functions previously attributed to TECs. Furthermore, CellChat identified cortical fibroblasts as major contributors to IGF signaling through predicted signaling to cTECs, which are found in close proximity in the cortex (Figure 2.10D), via IGF2-IGF1R and IGF1-IGF1R axes, and to 39 ETPs and β-selection cells, which were found under the capsule (Figure 2.9B), via an IGF2-IGF2R axis (Figure 2.4D-F). We next explored the role of proliferating fibroblasts. GSEA comparisons between capFibs and Fibs (P) showed marked differences in signal transduction pathways. CapFibs resembled traditional fibroblasts which upregulate tyrosine kinase, angiogenesis, and ECM regulation and deposition pathways, whereas Fib (P) upregulate WNT signaling and cell sensing pathways, including genes involved in transient receptor potential (TRP) channels in the stimuli sensing channels pathway and taste receptors (TASRs) (Figure 2.4G). Interestingly, CODEX images identified ECM- PDGFRa+ fibroblasts lacking EDA-FN expression, indicating that Fibs (P) are not involved in fibrotic matrix deposition unlike capFibs (Figure 2.4H; Figure 2.10B). Fibs (P) form a network of PDGFRa+ cells throughout the cortex which does not overlap with the cTEC network, yet maintains cell-cell contact in specific niches, and often localized near cortical capillaries (Figure 2.10D). We found sex-specific differences in VEGFA signaling within ECM- fibroblast (Fib (P)) and other mesenchymal cells. Although all thymic fibroblasts produce the angiogenesis growth factor VEGFA, male fibroblasts express more than female cells (Fibs (P): padjusted = 0.0306; DPP4+ capFibs: padjusted = 0.0318; mFibs: padjusted = 1.85x10-6) (Figure 2.4I). Given that postnatal male thymuses are larger that female thymuses in humans and primates94 (Figure 2.10E), male 40 fibroblasts may provide increased VEGFA to support angiogenesis and rapid thymic growth observed during postnatal development146. 41 Figure 2.4: Fibroblasts in the subcapsular zone contribute to regulation of thymus biology and T cell progenitor development: A) Graphic depicting the subcapsular zone. B) CODEX image depicting spatial localization of extracellular matrix ECM proteins; T097. C) Dot plot of fibroblast ECM gene expression. D-F) CellChat cell-cell interaction plots of IGF1/2 interactions. G) GSEA dot plot of differentially upregulated pathways between capFibs and Fibs (P). Positive normalized enrichment scores (NES) scores = capFibs, negative NES scores = Fibs (P). H) CODEX image depicting a representative PGDRA+ ECM- cell within the cortex. Arrows = PDGFRA+ ECM- cells; T097. I) Violin plot depicting differential expression of FGF7 by Fib (P) vs. other fibroblast populations, and upregulation by male vs. female Fibs (P); *** p<0.001. J) GSEA dot plot of differentially upregulated pathways in male vs. female DPP4+ capFibs. Positive NES score = Male DPP4+ capFibs, negative NES score = female DPP4+ capFibs. Additionally, male mFibs have higher expression of FGF7 (padjusted = 0.0154), which regulates thymus size147. CellChat predicts that male Fibs (P) are enriched in FGF10 compared to females, which supports cTEC proliferation and vascular growth147,148, and only male VSMCs express FGF18 (Figure 2.10F-H). These sex biases in FGF gene expression may contribute to the larger size of early postnatal male thymuses by stimulating epithelial and endothelial cell growth and proliferation. Comparison of DEGs between male and female mesenchymal cells found increased expression of adipogenesis, cytokine, and GPCR signaling pathways in DPP4+ capFibs (Figure 2.4J). We also found increased expression of APOD, a gene associated with androgen, estrogen, progesterone, and glucocorticoid signaling149,150, across male fibroblast populations (Fibs (P): padjusted = 2.18x10-26, mFibs: padjusted = 8.45x10-32) (Figure 2.10I). Given the association of hormone signaling with thymic involution93,95,146, these findings suggest early initiation of thymic involution in postnatal males. 42 In sum, we identified three roles for fibroblasts within the subcapsular niche: maintaining tissue structure and organization via ECM and chemokine signaling, directly regulating cTEC maintenance and expansion, and potentially coordinating T cell development directly through growth factors and cell-cell interactions. 2.3.4 Human postnatal thymocytes may self-select in the cortex to support positive selection of conventional αβT cells Upon exiting the subcapsular zone, DPs migrate into the inner cortex towards the medulla where they receive positive selection signals that guide T-lineage branching towards CD4 or CD8 SP cells (Figure 2.5A). For DPs to transition towards the CD4 lineage, cells must receive TCR stimulation through HLA class II interactions, yet previous mouse studies have shown transcriptional downregulation of HLA class I and II in DPs81,82. Low transcriptional expression is hypothesized to prevent thymocyte-thymocyte self-selection during positive selection, necessitating DP interactions with cTECs to receive positive selection signals. Analogous to mouse literature, quiescent human DPs do not express HLA class II transcripts and have closed CIITA promoters (Figure 2.5B,C). Despite the lack of class II mRNA, thymocytes express low levels of HLA class II protein throughout development (Figure 2.5B). Additionally, in contrast to mouse data, we observe constitutive class I mRNA expression which increased as cells transitioned towards SPs (Figure 2.5D). This is consistent with ATAC-seq data demonstrating that the B2M promoter is open throughout thymocyte development (Figure 2.5E). We confirmed HLA expression via flow cytometry, and found that approximately 25% of DPs express both class I and II, and over 65% of DPs are class I+ (Figure 2.11A). Thus, thymocyte self-selection within the cortex could support positive selection. In support of this notion, 43 CODEX enabled us to identify locations within the cortex devoid of epithelial, fibroblast, endothelial, or dendritic cells, but packed with DPs expressing class II+ molecules concentrated at cell junctions (Figure 2.5F). We confirmed the absence of spindle-like cTEC projections in this niche via confocal imaging (Figure 2.5G). Additionally, we quantified cell-cell interactions and identified a niche (Positive selection niche 1) consisting of class II+ DPs and CD3+ DPs, and a niche (Self-selection niche) containing mainly class II+ DPs (Figure 2.2D). Finally, we sorted thymocytes to isolate immature DPs (CD4+CD8+CD3-TCR-) and mature DPs (CD4+CD8+CD3+TCR+) from three donors and cultured them for 7 days in a feeder-free assay. In the absence of epithelial cells both immature and mature DPs upregulate HLA class II proteins (Figure 2.5H), and immature DPs continue to mature along their developmental pathway, as indicated by increased percentage of CD27+ DPs in culture after 7 days (Figure 2.5I). Next, we identified a niche which directs T-lineage commitment towards CD4 or CD8SPs. We performed differential gene expression analysis on clusters representing this lineage branch point to identify markers for our CODEX panel (Figure 2.11B). We found SATB1 expression increased as DPs transitioned towards SPs (Figure 2.11C), and compared to CD8SP transition cells, CD4SP transition cells had higher expression of this master transcription factor151 (Figure 2.11D,E). Imaging analysis confirmed increased SATB1 expression coincides with CD3 upregulation, consistent with a role in late DP development and lineage branching (Figure 2.5J)6. Neighbourhood analysis identified a niche enriched for mature CD3+ DPs in the inner cortex, suggesting that there either exists a niche specifically for late DP development and CD4 lineage transition, or that cells are pre-disposed to CD4 lineage development through their TCR and migrate as clonal populations after proliferation at the outer cortex. 44 45 Figure 2.5: HLA class I and II interactions may support thymocyte positive selection in the inner cortical zone: A) Graphic depicting the inner cortical zone. B) Violin plots of HLA class II RNA and protein on developing thymocytes and cTECs. C) Chromatin accessibility of the CIITA promoter in DPs. D) Violin plots of HLA class I RNA and protein on developing thymocytes and cTECs. E) Chromatin accessibility of the B2M promoter in DPs. F) CODEX image of class II+ (red) DPs (here shown as CD4+ (green)) interacting in the cortex. Arrows = class II at the junction of DPs; T087. G) Maximum projection confocal image showing lack of epithelial cells (Cytokeratin 5/8+; green) within specific niches of the cortex where class II+ (blue) DPs (CD4+; red) reside. Arrows = class II+ DPs; T096. H) Percentage of HLA class II+ DPs after 7 days of in vitro feeder free culture for immature CD3- DPs, mature CD3+ DPs, and CD8SPs (control) (N=3). I) Percentage of CD27+ cells at day 0 and day 7 in feeder free culture for immature and mature DPs (N=3). J) CODEX image depicting the inner cortical niche specialized for DP-SP transition, identified by SATB1+CD3+ DPs (CD4+). Arrows mark SATB1+CD3+ DPs (CD4+); T087. Error bars = mean +/- SEM. Two-way ANOVA, *p < 0.05, **p < 0.01, ***p < 0.005. We compared cortical niche organization between sexes and found differences in niche organization supporting conventional T cell development, self-selection, and cross presentation. Females showed increased neighbourhood interactions between the cortical DC niche containing JAG1+ VCAM+ DCs and the mature DP niche containing CD3+ DPs, the positive selection niche 1 containing class II+ DPs and CD3+ DPs and the positive selection niche 3 containing DCs and DPs (Figure 2.11F), as well as increased cell-cell interactions between cTECs and class II+ DPs (Figure 2.11G,H). Conversely, males had increased cell-cell interactions between cTECs and CD3+ DPs (Figure 2.11G,H). These data suggest that the proportionally larger female cortex could increase cross presentation from DCs and cTECs to class II+ DPs, possibly facilitating greater use of self-selection as an alternative mechanism for positive selection. 46 Taken together, spatial multiomic analysis of the inner cortex identified cortical niches supporting specific stages of DP development, including three positive selection niches, a specialized niche for self-selection, and a mature DP niche thymocytes migrate through prior to entering the medulla. 2.3.5 Spatial multiomics identifies key mechanisms regulating negative selection niches in the medulla Mature DPs enter the medulla, an environment specialized for negative selection, and transition towards CD4 or CD8 lineages (Figure 2.6A). Within the medulla, cells specialized for negative selection localize around keratinized structures called Hassallâs corpuscles (HCs)19. HCs appear during late prenatal development and are abundant in human postnatal thymuses, but rare in mice152. Here, we demonstrate that HCs can be divided into three major components: an external epithelial border of highly keratinized cells, an inner border of cells expressing prostaglandin-degrading enzyme 15-PGDH (HPGD), and a central PDGFRa+ mass (Figure 2.6B). HCs produce thymic stromal lymphopoietin (TSLP)19, an analog of IL-7, which activates DCs to increase expression of class II and co-stimulatory molecules CD80 and CD86. Importantly, subclustering stromal populations identified a population of KRT+ fibroblasts resembling cells undergoing epithelial-to-mesenchymal transition (EMT)153 (Figure 2.12A,B). CITE-seq identified TSLP and 15-PGDH mRNA expression in KRT+ Fibs, mFibs, mTECs, activated mTECs, and aDCs (Figure 2.6C), implicating these cell types as potential contributors to the function of HCs. Finally, given the inner layer of 15-PGDH+ cells we explored the role of prostaglandin signaling regulation within the medulla. We found that DC1 cells express high levels of PGE2, whereas DC2/3 cells and monocytes express the PTGER2 and PTGER4 receptors, and aDCs express the PTGER3 47 receptor (Figure 2.6C), suggesting prostaglandin signaling is a major regulator of DC activity near HCs. CODEX imaging suggests HCs act as sub-medullary organizational centers to segregate the inner medulla into specialized niches for negative selection. CD86+ APCs, a subset of which express the co-stimulatory ligand CD40, localize near HCs and in direct contact with CD45RA+ mature SPs (Figure 2.6D; Figure 2.12C). In addition, approximately 30% of medullary area is composed of CD19+ B cells28 which cluster into niches surrounding HCs (Figure 2.12D). These B cells are found in close contact with â and are often enveloped within â mTECs, potentially facilitating cross presentation with epithelial cells (Figure 2.6E). These results suggest thymic B cells may comprise an important source of antigen presentation for negative selection28,154. We quantified medullary neighbourhoods and identified six niches, including an mTEC maturation niche, a cross-presentation niche, and four niches specialized for negative selection which vary in relative location to HCs or the CMJ, as well as their composition of APCs, epithelial, and T cells (Figure 2.2D; Figure 2.8D). Negative selection niches surrounding HCs play a key role in conventional T cell and thymic Treg development19. We enriched CD25+ cells for sequencing and found a population of CD25hi pro-Tregs expressing canonical Treg markers CTLA-4, TNFRSF1B (TNFR2) and TNFRSF4 (OX40), positive/negative selection markers (ITM2A, RANBP1, NCL, NME1, MIF, ATP5G1), Treg developmental long non-coding RNA (MIR155HG)155â158, and other markers described in mice (Figure 2.12E). Whereas pro-Tregs expressed high levels of pro-apoptotic gene BCL2L11, mature tTreg subsets expressed the anti-apoptotic gene BCL2. Gene network reconstruction via 48 SCENIC159 identified transcription factor networks activated during pro-Treg to tTreg transition (Figure 2.6F). The thymus also contains mature, highly activated Tregs, labeled as rrTregs, believed to have recirculated from the periphery160,161. rrTregs lack expression of CCR7 or thymic egress markers (KLF2, S1PR1), but express IL1R2 (Figure 2.12F), which sequesters the inflammatory cytokine IL-1ï¢ to reduce local concentrations162. CODEX imaging identified tTregs and rrTregs dispersed throughout the medulla, with rrTregs primarily adjacent to CD68+ DCs (Figure 2.6G). CellChat supported the potential of rrTregs to sequester inflammatory cytokines through interactions with DC2/3 via an IL-1ï¢ - IL-1R2 axis (Figure 2.12G). rrTregs also exhibited a tissue resident Treg phenotype (BATFhigh CCR8+) associated with wound healing and tissue regeneration function163, and expressed remodeling and tissue repair-related genes such as matrix metalloproteinase enzymes (MMP25 and ADAM19) (Figure 2.12H). Overall, these findings illustrate Treg diversity in the thymus with their developmental trajectories and functions yet to be elucidated. Comparisons of male and female rrTregs showed that male rrTregs had higher expression of IL4 & IL13, HSF1, and IL1 signaling pathways (Figure 2.6H), suggesting rrTreg-mediated regulation of IL1R2-mediated anti-inflammatory feedback checkpoints is a more prominent mechanism in male tTreg development in early postnatal thymus. Notably, male activated mTECs have higher expression of CD40 and TNF inflammatory pathways than females, possibly resulting in higher rrTreg activity (Figure 2.12I). 49 50 Figure 2.6: Hassallâs corpuscles (HCs) represent scalable organizing centres for negative selection in the neonatal thymic medulla: A) Graphic depicting the thymic medulla. B) CODEX images detailing three components of HCs. Cytokeratin 5/8 (white) creates an external border around an inner 15-PGDH+ (green) cell layer. PDGFRa (red) marks an inner core; T087. C) Dot plot depicting medullary stromal, epithelial and hematopoietic cell gene expression contributing to functional properties of HCs. D) Representative CODEX images showing immune cells surrounding HCs. CD45RA (magenta) marks mature naïve SP cells, CD86 (red) marks APCs which express CD40 (green), and CD68 (blue) marks DCs. Asterisk marks HCs; T087. E) Representative CODEX image showing B cells (CD45RA+, red; CD19+, blue) enveloped within mTECs (EPCAM+, white). Arrows = enveloped B cells; T087. F) Dot plot of SCENIC gene regulatory transcription factor results detailing transition from pro-Tregs to tTregs, and phenotype of rrTregs vs. tTregs. G) Representative CODEX image showing rrTregs (CD4+, cyan; ICOS+, magenta) and tTregs (CD4+, cyan; ICOS-) located throughout the medulla and surrounded by CD68+ DCs (blue). White arrows = ICOS+ rrTregs. Yellow arrows = ICOS- tTregs. Red arrows = CD68+ DCs; T098. H) GSEA dot plot showing differential expression of regulatory pathways between male and female rrTregs. Positive NES scores = male rrTregs, negative NES scores = female rrTregs. I) GSEA dot plot showing differential expression of regulatory pathways between male and female tTregs. Positive NES score = male tTregs, negative NES score = female tTregs. Finally, as Tregs have been shown in mouse to contribute to thymic involution through JAG1164, we explored sex-based differences in tTreg gene expression. GSEA showed male rrTregs and tTregs have higher expression of adipogenesis pathway (Figure 2.6H,I). Given the presence of cells undergoing EMT, our data underlies the aggressive timeline of thymic involution and suggests that sex-based differences in thymus functional decline begins early in life. Our detailed examination of the medulla identifies several niches specialized for negative selection, cross-presentation, and mTEC maturation around HCs, and demonstrate sex-biases in inflammatory pathways and thymic involution kinetics within these niches. 51 2.4 Discussion We performed spatial multiomics to construct a tissue atlas of niches guiding T cell development in early human postnatal thymus. These datasets characterize how key developmental niches drive lineage branch decisions, identify a possible mechanism for conventional αβT cell development through self-selection, and suggest additional functions for mesenchymal cell types governing thymus biology. Furthermore, we discovered several sex-specific differences in thymus cell and niche biology. As T cell development is a dynamic migratory process, knowledge of cell position in combination with proteomic, transcriptomic, and epigenomic sequencing data provides an invaluable resource to predict niche-specific signaling cues directing T cell development, and mechanisms responsible for maintaining tissue structure and directing thymic involution. We describe an approach to sequencing analysis using multidimensional imaging to establish benchmarks for the location, ligand expression, and composition of key niches in T cell development. This enables us to analyze cell-cell interactions guided by niche composition, identifying physiologically relevant ligand-receptor interactions based on cell proximity within the tissue. Ultimately, this approach maps epigenomic, transcriptomic, and proteomic data to distinct tissue niches at single cell resolution. 52 Figure 2.7: The human early postnatal thymus lobule is spatially organized into sex-biased niches to support stage-specific T cell development: A) ETPs migrate into the thymus lobule at the CMJ (niche 1). ETPs differentiate into pro-T cells if they interact with DLL1 or DLL4, DC1s if they interact with JAG, or DC2/3s if they do not interact with Notch. ETP-JAG interactions are predicted to be greater in the female. B) In the subcapsular zone (niche 2) capFibs interact directly with cTECs and DPs through secreted growth factors. DPP4+ capFibs and ECM- Fibs also secrete signaling molecules, with higher expression observed in male cells. Higher FGF7 and VEGFA expression by male ECM- Fibs causes increased proliferation of cTECs and ECs respectively. C) In the inner cortex (niche 3) DPs may self-select off other DPs that express HLA class II. These interactions, as well as potential cross presentation interactions with DPs, are enriched in the early postnatal female thymus. D) In the inner medulla (niche 4) HCs act as organizing centers for medullary B cells, DC2/3, and DC1 cells. HCs play a role in prostaglandin signaling regulation, where DC1 cells produce prostaglandin (PTGES2) which acts on DC2/3 cells through the receptor PTGER4, and HCs express the 53 prostaglandin degrading enzyme HPGD to decrease local concentrations of PTGES2. HLAhigh activated mTECs in this niche have increased expression of inflammatory molecules CD40 and TNF. To counteract this increased inflammatory environment, male rrTregs express higher levels of IL1R2 to sequester the inflammatory cytokine IL1β produced by DC2/3 cells, inhibiting the costimulatory properties of DC2/3 cells. Furthermore, we included equal numbers of male and female age-matched thymus samples, enabling comparison between sexes across platform modalities. Our analysis of sex-matched human early postnatal thymus demonstrates the highly plastic nature of thymus lobule organization and resource dedication. Each niche responds to sex-biased developmental kinetics, supporting robust T cell development to ultimately produces functional immune systems in different manners (Figure 2.7). The findings herein describe only a subset of the data and we encourage the community to capitalize on this resource to provide insight into sex differences and targeted niche-specific inquiries. In our analysis of Notch ligands we complemented our in silico approach with in vitro analysis. Our analysis suggests that JAG1 at the CMJ cannot support T-lineage commitment as cells migrate towards the subcapsular zone, but instead skew alternative lineage development towards a CD14- DC1 subset (Figure 2.7). CD14 expression on DCs is linked with increased inflammatory cytokine production165, suggesting that JAG ligands promote non-inflammatory DC phenotypes. These results highlight the importance of precise Notch signaling strength and timing in the thymus and emphasize the need for strict spatial control of different Notch ligands within thymic niches. Our observation of high JAG1 expression in the medulla and decreased DLL4 expression on cTECs outside the subcapsular zone aligns with previous studies on human postnatal thymus166. Additionally, our sequencing data identifies two distinct populations of 54 endothelial cells with high expression of JAG2 and DLL4. Future work should characterize the roles of these endothelial cell types in directing different populations of cells upon entry and exit. In the subcapsular zone we characterize important roles of specialized fibroblasts. DPP4+ capFibs, described in mouse as cells with progenitor and anti-fibrotic potential23,167â170, are observed as a fibroblast subset responsive to changes in systemic hormone levels. Since thymic function and involution are regulated by sex hormone levels93,171â173, DPP4+ capFibs likely control these processes and are potential targets for addressing age-related thymic involution174. Previously, only medullary fibroblasts were linked to thymocyte development and selection in the medulla23. We demonstrate that capFibs may directly support thymocyte development in the cortex by producing growth factors like IGF2 (Figure 2.7). Blocking IGF2 signaling arrests thymocytes at the double negative stage175 and our data identify capFibs as the IGF2 source, suggesting capFibs as and additional cell source of cytokines and growth factors for in vitro developmental systems. Finally, we demonstrate that ECM profiles of thymic fibroblasts are tightly regulated based on spatial localization. Future work should characterize how tissue stiffness changes as thymocytes migrate through developmental thymic niches to improve biomaterial strategies for in vitro T cell development176. Furthermore, we identify a population of ECM- cortical fibroblasts which are enriched in cell sensing pathways, such as TASRs and TRP channels. Interestingly, TASRs regulate cell responses to local soluble substances, such as glucose, modulating release of hormones and other signaling molecules177. Similarly, TRP channels play roles in cell sensing, such as pheromone signaling, nociception, temperature sensation, and osmoregulation178. Given the proximity of these cells to vasculature in the cortex, Fibs (P) may play a critical role as regulatory cells by 55 sensing environmental changes and modulating thymus size (Figure 2.7). Their lack of ECM production and network-like structure resembles fibroblast reticular cells (FRCs) in the lymph node, which rapidly proliferate and remodel the cortex during infection179. Our data are generated from early postnatal thymus samples, an age with active T cell development, suggesting these fibroblasts expand the thymic cortex similarly to FRCs during infection, signaling through FGF and IGF to stromal and epithelial cells to orchestrate remodeling. While the dogma in thymocyte positive selection suggests that DPs downregulate class II RNA to prevent self-selection and force interactions with cTECs81,82, several studies suggest that T-lineage cells can select off each other to support CD4 T cell development27,180â182. Here, we describe an inner cortical niche where class II+ DPs reside that may support positive selection via DP-DP self-selection (Figure 2.7). We show that immature DPs cultured without epithelial cells upregulate HLA class II and continue to mature and receive positive selection signals. Additionally, upregulated SATB1 expression identifies mature DPs in an inner cortical niche and the CD4 branch of their progeny, suggesting it may determine early lineage specificity. Future work should investigate critical features of this niche and SATB1âs role in thymocyte development. Within the medulla we identified a niche adjacent to HCs specialized for negative selection and highlighted the role of rrTregs in modulating the medullary inflammatory environment (Figure 2.7). The abundance of HCs in human but not mouse, and their proximity to negative selection niches, suggests these structures evolved to provide niche level organization within the larger human medulla or to regulate negative selection more stringently in longer-lived species. 56 Comparing male and female tissue showed sex differences in both T cell and thymus biology. Studies on post-pubertal males and females show that sex hormones differentially regulate thymic involution between sexes87,92â95,129,146,172,174, and that androgen blockers increase FOXN1 expression, thymic involution, and increased rejuvenation95,129,146,172,174. Additionally, older males produce fewer recent thymic emigrants and have smaller thymuses compared to females 87,94. Some studies describe decreased numbers of AIRE+ mTECs with age and in females183, potentially predisposing females who maintain greater thymic function later in life to autoimmune disease95. These studies also observe less interlobular fat in young female thymus94, suggesting differences in thymic involution kinetics begin pre-puberty. However, current literature has not addressed transcript-level sex differences underlying functional differences in thymic and immune function. Our analysis uncovers that female thymic cells upregulate energy regulation, transcription, and antigen-presentation pathways whereas male cells increase proinflammatory signaling, amino acid metabolism, and GPCR signaling. These cell metabolic differences align with transcript-level sex differences in other organs136â138 and highlight the need for sex-based cell culture optimization in in vitro T cell culture systems. In addition to changes common to other organs135,136, we identify thymus-specific differences affecting key processes in thymocyte development and training. Females have a larger proportion of cortical cells per lobule aligning with lower thymic involution rates and a larger cortex/medulla ratio92,94,146. ETPs have enriched interactions with JAG1 as they migrate away from the CMJ suggesting increased JAG1 interactions could skew ETP lineage commitment towards less inflammatory DC phenotypes (Figure 2.7). In the female cortex, we observe increased cTEC and class II+ DP interactions and increased interactions between cortical DC and 57 positive selection niches, suggesting thymocyte self-selection may play a larger role during positive selection (Figure 2.7). Conversely, the female medulla shows decreased inflammatory pathway activation and less medullary cells. These data suggest females prioritize generating a larger repertoire of DPs over deleting autoreactive cells through negative selection, potentially contributing to sex differences in autoimmune disease prevalence in females184. In males, we observe enriched DLL4 interactions with ETPs, which aligns with previous data demonstrating that androgen levels positively correlate with DLL4 on cTECs95. The male cortex shows increased interactions with mature CD3+ DPs and cTECs, suggesting male thymocytes may have lower proliferation rates post β-selection, allowing sufficient space for positive selection. In the medulla, male activated mTECs exhibit increased inflammatory pathway markers and male Tregs exhibit higher inflammatory modulation and activate thymic involution pathways164. Upregulation of inflammatory modulation by male rrTregs may regulate the higher proinflammatory signaling in male cells (Figure 2.7). Interestingly, post-pubertal males have more Tregs and less CD4 T cells than females, possibly due to a more inflammatory medullary environment skewing CD4 development towards the Treg lineage89. We further explore sex differences in thymus size control mechanisms. Among fibroblast populations we find significant differences in expression of growth and angiogenesis factors, such as VEGFA and FGFs, potentially contributing to the size difference in male and female thymuses at this age (Figure 2.7). These data align with and extend known sex differences in growth factor expression, including sex biased expression of growth hormone and IGF-1 in regulating size of different tissues185,186. Importantly, these results indicate sex-specific differences in early thymus structure maintenance and growth, which could skew T cell 58 development. We also establish an early transition towards an adipogenic environment in males. These observations align with findings in model organisms, where young male rats exhibit higher rates of thymic involution146 and early postnatal male primates have a larger interlobular fat area94. Together, these factors define two possible mechanisms contributing to a male-female difference in thymus size and involution kinetics. Future studies should test how sex differences at the transcript, niche, and organ level impact differential T cell production and quality, as well as explore how sex differences in other organs contribute to known differences in immune responses. Defined in vitro and organoid culture systems replicating the thymic microenvironment present powerful platforms to test if the cell type-specific and sex-specific differences identified here lead to increased autoimmune disease incidence among females and increased infection susceptibility in males. Furthermore, given the surprising sex-based differences at this early postnatal stage, future work should examine aged thymus to investigate how cellular level differences in thymic involution kinetics may translate to larger impacts on our immune system later in life. 2.4.1 Limitations of the Study Our analysis of intra-sex variation is limited by access to patient samples, as well as the inability to conduct mechanistic experiments in the context of a whole organism. There is an opportunity for future work to further validate and expand on predicted ligand-receptor interactions. 2.5 Acknowledgements We thank the parents of thymus donors for their essential contribution to this research, as well as the staff at the British Columbia Childrenâs Hospital who made this study possible; special thanks to Drs. Sanjiv Gandhi, Andrew Campbell and Al Aklabi, as well as Allison Jamieson, 59 Melanie Ganshorn, Aliyah Hassan, Lyn Nguyen and Colleen Ring. We thank Dr. Sabine Ivison for logistical support in tissue collection and Dr. Wilder Scott for his assistance integrating multi-modal sequencing data. We acknowledge T. Stach, Y. Chung, S. Yu, and B. Zhao from the BRC-seq for technical support, M. Williams from the BRC Antibody Lab for technical support and custom reagents, and A. Johnson and J. Wong from UBC FACS for technical support. We thank Jen Ma for technical support with graphics. The cartoon figures were made with BioRender. Funding: Funding for this work was provided by Genome BC (SIP031) and Wellcome Leap Human Organs, Physiology, and Engineering (HOPE) program, the Canadian Foundation for Innovation/B.C. Knowledge Development Fund (John Evans Leaderâs Fund 38395 to F.M.V.R), and the Canadian Institutes of Health Research (CIHR) (ICC-17644 to M.K.L). L.N.S was supported by a National Sciences and Engineering Research Council of Canada (NSERC) Canadian Graduate Scholarship â Doctoral (CGS-D) award and received a Michael Smith Foundation travel award to complete this work. K.S was supported by Wellcome Leap Inc (AWD-017859). E.A.F. was supported by an NSERC Undergrad Student Research Award. J.M.E. was supported by an NSERC CGS-D award and Zymeworks-Michael Smith Laboratories Fellowship in Advanced Protein Engineering. M.C.M was funded by a NSERC USRA and CBR summer award. Y.X.W. was supported by CIHR (MFE-152457) and a National Institute of Health (NIH) K99 award (K99 NS120278). G.C.B is supported by the Raven Fellowship. R.D.J. is supported by the Michael Smith Foundation for Health Research (Trainee Award #RT-2021-1946). H.M.B. was supported by funding from the Baxter Foundation, the Li Ka Shing Foundation, and the NIH (R01 AG069858 and R01 AG075436). E.J.R. holds a CIHR Sex and Gender Science Chair in Genetics and is a Michael Smith Foundation for Health Research Scholar. M.K.L. is a Canada Research Chair in Engineered Immune Tolerance and 60 receives a Scientist Salary Award from the BC Childrenâs Hospital Research Institute. F.M.V.R. is supported by CIHR (FDN-159908). P.W.Z. is a Canada Research Chair in Stem Cell Bioengineering. 2.6 Methods 2.6.1 Materials Availability Custom CODEX antibodies are available from the lead contact upon request. Resource and reagent details are listed in the key resources table (Table B.8). 2.6.2 Data and code availability â¢ Deep imaging computational analysis and CODEX images have been deposited on Zenodo and are publicly available as of the date of publication. Due to the size constraints representative images have been uploaded. Accession numbers are listed in the key resources table. Unprocessed or full resolution images are available from the lead contact upon request. â¢ CITE-seq and ATAC-seq data have been deposited at GEO and are publicly available as of the date of publication. Accession numbers are listed in the key resources table. â¢ Code for data analysis have been deposited on GitLab (https://gitlab.com/stemcellbioengineering). â¢ Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request. 61 2.6.3 Experimental model and study participant details Human research was approved by the University of British Columbia Research Ethics Board (H17-01490 and H18-02553). All tissue samples used for this study were obtained with written informed consent from parents/guardians of participants. Thymus tissue was collected during infant cardiac surgery at British Columbia Childrenâs Hospital and processed immediately upon retrieval. Donors were screened for prior to sample collection and excluded from this study if known developmental and genetic disorders were present. The sex and age of the patients was recorded and used for analysis in this study (Table B.1). Tissue was collected in ImmunoCult-XF T cell Expansion Medium (immunoCult-XF; STEMCELL Technologies, Vancouver, BC, Canada) with 1% penicillin/streptomycin (P/S; Thermo Fisher Scientific) and processed in RPMI medium (Thermo Fisher Scientific, Waltham, MA, USA) with 10% fetal bovine serum (FBS). Upon collection the tissue was sectioned into 1cm3 pieces in RPMI + 10% FBS. One piece was used for CD45- cell enrichment, one piece (~3g) was used for CD25+CD8- Treg enrichment using a custom thymic Treg selection kit (STEMCELL Technologies)187, two pieces were used for thymocyte isolation and cryopreservation, and the remaining pieces were processed for CODEX imaging. Remaining CD45+ cells were frozen in 10% FBS in dimethylsulfoxide (DMSO) and kept in liquid nitrogen prior to use for in vitro cell culture experiments. 2.6.4 Single cell isolation and flow sorting The thymic tissue dissociation protocol detailed by Gustafson et al. was used to enrich for CD45- cells24. Briefly, a 1 cm3 tissue section was crushed using a syringe in a small petri dish. Released thymocytes were collected in RPMI + 10% FBS, spun at 500xg for 5 min at 4oC, and kept on ice. The remainder of the thymic tissue was transferred to the lid of a 50 mL conical tube lid and 62 minced using sterile surgical scissors. 8 mL of warm dissociation cocktail (2 mg/mL Stemxyme (Cedarlane Labs, Burlington, ON, Canada), 6.3 U/mL DNaseI (Sigma-Aldrich; Milwaukee, WI, USA), 1.5% BSA (w/v) (Wisent Bioproducts, Saint-Jean-Baptiste, QC, Canada) in RMPI) was added to the 50 mL tube, inverted 5 times to release thymic tissue from the lid, placed on a horizontal rocker, and incubated at 37oC for 30 min. After 30 minutes the supernatant was removed, filtered over a 50 ï­m strainer, and stored on ice (T3). An additional 8 mL of dissociation cocktail was added and the tissue was incubated for an additional 30 mins. After the second incubation the supernatant was removed, filtered, and stored on ice (T2). For the final 30 min incubation 8 mL of dissociation cocktail followed by 2 mL of 0.25% trypsin-EDTA (Thermo Fischer Scientific) was added to the tissue fragments. After the final incubation the remaining cells were collected and filtered over a 70 ï­m strainer. 3 mL of FBS was added to the 50 mL falcon tube containing the final digestion media to break the trypsin reaction (T3). The three 50 mL collection tubes were spun at 500xg for 5 min at 4oC. Cells were resuspended in 40 mL of Hankâs Balanced Salt Solution (HBSS; Thermo Fisher Scientific, Waltham, MA, USA) with 2% FBS (HF), filtered through a 40 ï­m cell strainer, spun at 500xg for 5min at 4oC. Cells were resuspended in 4 mL of red blood cell lysis buffer (Millipore Sigma, Oakville, ON, Canada), incubated for 5 min at RT, diluted to a total volume of 40 mL with HF, and spun at 500xg for 5 min at 4oC. Cells were resuspended in 10 mL of HF and counted. The first incubation (T1) cells were pelleted (500xg, 5min, 4oC) and stored on ice for later use. T2 and T3 cells were then depleted for CD45+ cells using the Miltenyi LD column prep kit (Miltenyi Biotech Inc., Charlestown, MA, USA). Briefly, T2 cells were added to T3 to reach a final cell number of 5x108 cells. Cells were centrifuged at 300xg for 10min at 4oC and 63 resuspended in 80 ï­L of HF buffer per 107 cells plus 5 ï­L of CD45 beads per 107 cells. The suspension was mixed well via gentle pipetting and incubated for 15 min at 4oC. Cells were washed by adding 1mL of HF buffer per 107 cells and centrifuged at 300xg for 10 min at 4oC. During this incubation the LD column was prepared and washed with 2mL of HF buffer. Pelleted cells were resuspended in 2.5 mL of HF and applied to the column. The column was washed with 2x1 mL buffer and all unlabeled flow through cells were collected as the negative fraction. The positive fraction was collected by passing 5 mL HF through the column using the plunger. Cells from each were counted prior to proceeding to antibody staining for cell sorting. Both fractions were resuspended in Fc buffer (HF + 1:1000 human Fc block (BD Biosciences, Mississauga, ON, Canada)) to a final concentration of 0.5 M cells/mL. CD45-PE antibody (Biolegend) was added at 1:200 and the cell suspension was incubated for 30min on ice in the dark. After incubation cells were spun at 500xg for 5min at 4oC, washed with 5 mL of HF, and spun again. Cells were resuspended to a final concentration of 0.2 M cells/mL, stained with zombie UV live/dead stain (1:500; Biolegend), and stored on ice while transferring to the cell sorting facility. The CD45- fraction was sorted using an MOFLO Astrios EQ (Beckman Coulter) or BD Influx instrument into HBSS + 4% FBS until over 500,000 CD45- cells were collected. The CD45+ fraction was then sorted to collect 500,000 live CD45+ cells. These cell suspensions were pelleted, counted, and kept on ice prior to processing for CITE-seq or ATAC-seq. Finally, to enrich Treg cells, we performed a CD25 positive selection on the bulk thymocytes, followed by CD8 depletion. 64 2.6.5 CITE-seq sample preparation The BioLegend TotalSeq-C CITE-seq antibody preparation and 10X Genomics CITE-seq cell staining protocols were followed. Briefly, prior to the end of the CD45- sort one vial of TotalSeq-C Human Universal Antibody Cocktail v1.0 (BioLegend, San Diego, CA, USA) was equilibrated to room temperature for 5min and then spun at 10,000g for 30 seconds. The lyophilized panel was resuspended in 27.5uL of HBSS + 4% FBS, vortexed for 10 seconds, and then incubated at room temperature for 5min. The vial was vortexed again for 10 seconds and then spun at 10,000g for 30 seconds. The entire volume was transferred to a low protein binding PCR tube and then centrifuged at 14,000g for 10min at 4oC. CD45-, CD45+, and CD25+ cell fractions were combined in a 2:2:1 ratio to a total cell count of 250,000 cells in a 12 x 75mm tube. Cells were spun at 500xg for 5 min at 4oC and resuspended directly in 12.5 uL of HBSS + 4% FBS. 12.5 uL of the antibody staining cocktail was added and the cells were incubated for 30min at 4oC. Cells were washed three times with 3.5mL HBSS + 4% FBS and resuspended in a final volume of 55 uL. Cells were counted, stored on ice, and given immediately to the sequencing facility for 10X Genomics 5â library preparation and sequencing. 2.6.6 ATAC-seq sample preparation The 10X Genomics Nuclei Isolation from Mouse Brain Tissue for Single Cell ATAC Sequencing was followed. Briefly, CD45-, CD45+, and CD25+ cell fractions were combined in a 2:2:1 ratio to a total cell count of 250,000 cells in a 1.5mL Eppendorf tube. Cells were spun at 500g for 5min at 4oC. The supernatant was removed and 100uL of chilled 0.1X lysis buffer was added and mixed by gently pipetting 5x. Cell suspension was incubated for 5min on ice. 65 Immediately after 5min 1mL of chilled wash buffer was added and the solution was gently pipet mixed 5x. Nuclei were centrifuged at 500g for 5min at 4oC, the supernatant was removed, and the nuclei were resuspended in diluter nuclei buffer to a final concentration of 5000 nuclei/uL. Nuclei concentration and acceptable morphology was confirmed via hemocytometer cell counting and brightfield microscopy, and a cell lysis percentage over 95% was confirmed using a Countess II FL Automated Cell Counter (Thermo Fisher Scientific). Nuclei were kept on ice and given immediately to the BRC sequencing core for single cell ATAC-seq library prep and sequencing. The generation of single cell indexed libraries was performed by the Biomedical Research Center Next Generation Sequencing Core using the 10X Genomics chromium controller platform and the Chromium Single Cell 5â Library and Gel Bead Kit v1.1 and Chromium Single Cell ATAC Library and Gel Bead Kit v2 reagents. Briefly, around 10,000 cells were targeted per sample and deep sequencing was run on the NextSeq 2000 using the P3-100 cycle kit. After run completion, the Binary base call (bcl) files were converted to fastq format using the illumine bcl2fastq2 software, and data were received for further analysis. 2.6.7 Notch ligand in vitro screening Recombinant human JAG1-Fc, JAG2-Fc, DLL1-Fc (R&D Systems), or DLL4-Fc (Sino Biological) were diluted to 4 µg/ml in PBS along with 1 µg/ml VCAM-1-Fc (R&D Systems). To coat, 50 µl of each solution was added per well of a 96-well flat-bottom plate and incubated overnight at 4°C. Immediately before use, wells were washed once with 50 µl PBS before cells were seeded. 66 Umbilical cord blood was collected from consenting donors at BC Childrenâs Hospital, Vancouver, Canada in accordance with institutional research ethics. Mononuclear cells were isolated by density gradient centrifugation using Lymphoprep (STEMCELL Technologies) and CD34+ HSPCs were enriched using the EasySep Human CD34+ Positive Selection kit (STEMCELL Technologies) according to the manufacturerâs instructions. Isolated cells were cryopreserved in FBS containing 10% DMSO and stored in vapor phase nitrogen prior to use. Cells were thawed in 37°C water bath and 5x volume of 37°C HBSS with 0.5% BSA was added dropwise. Cells were centrifuged for 7 minutes at 250g, the supernatant aspirated, and resuspended in culture medium. Cells were cultured in Iscoveâs Modified Eagle Medium (IMDM) supplemented with 20% serum substitute (BIT 9500; STEMCELL Technologies), 1 µg/ml low density lipoprotein (STEMCELL Technologies), 60 µM ascorbic acid (Sigma), 24 µM 2-mercaptoethanol (Sigma), and 1% penicillin-streptomycin (Invitrogen). For experiments measuring T-lineage potential, cells were cultured in stage 1 cytokine concentrations from day 0-7 and stage 2 from day 7-14 (Table B.). Experiments investigating monocyte/macrophages and dendritic cells used stage 1 cytokines throughout the entire 14 days. All cytokines were from R&D Systems. To begin experiments, 2000 cells per well were seeded on DLL1-Fc or DLL4-Fc, and 4000 cells per well on JAG1-Fc, JAG2-Fc, or wells without any Notch ligand. For experiments measuring T-lineage potential, cells were passaged to plates freshly coated with DLL4-Fc on day 3 or 7. For experiments measuring monocyte/macrophage and dendritic cells, cells were passaged to the same Notch ligand on day 7. 67 Cells were passaged in 100 µl of medium per well and an additional 100 µl was added 3-4 days after passaging, unless the cells were passaged (as on day 3), in which case the medium was completely changed, and the cells were seeded in 100 µl of medium. Gentle pipetting was used to passage cells, followed by rinsing wells with 30-50 µl of PBS + 0.5% BSA + 2 mM EDTA to remove any remaining adherent cells. Cells were passaged, rinsed once with PBS, then stained for viability using Zombie-UV (BioLegend) diluted 1:500 in PBS. Afterwards, cells were incubated for 5 minutes with 1:100 FC block (BD Biosciences) in HBSS with 2% FBS (HF). For analysis of antibody panels containing CD68, cells were fixed and permeabilized using the Cytofix/Cytoperm kit (BD Biosciences) according to the manufacturerâs instructions. Surface and intracellular antibodies were stained together for 45 minutes on ice, then cells were rinsed twice with permeabilization buffer and resuspended in HF for analysis. For the antibody panels containing only surface markers, cells were stained in Brilliant Violet Buffer (BD Biosciences) for 10 minutes on ice, rinsed once with HF, then resuspended in HF for analysis. A CytoFLEX LX cytometer (Beckman Coulter) was used to collect data. Compensation was performed using the cytometer software (CytExpert v2.3) and gating was performed using FlowJo X. 2.6.8 Primary human thymocyte culture Cell sorting was performed using a MoFlo Astrios sorter (Beckman-Coulter) at the UBC Flow Cytometry Facility. The following antibodies were used for cell sorting experiments: CD4-BB515 (clone SK3, 1:100), CD8ð½-PE-Cy7 (clone SIDI8BEE, 1:100), TCRð¼ð½-APC (clone IP26, 1:100). Fresh or frozen thymocytes were FACS-sorted for populations of interest, then plated onto flat-bottom tissue culture plates at 1 million cells/mL in IMDM with GlutaMAX 68 supplemented with 4% B27 minus vitamin A, 0.5% penicillin/streptomycin, 24uM 2-mercaptoethanol, 60ðM ascorbic acid, 100ðg/mL SCF, 100ðg/mL Flt3L, 100ðg/mL IL-3, 50ðg/mL IL-7, 100ðg/mL TNFð¼, 100ðg/mL CXCL12, and 0.3% Immunocult human CD3/CD28/CD2 T cell activator. Media was topped up on day 4 post-seeding, and flow cytometry was performed on day 7 post-seeding. Flow cytometry was performed using a CytoFLEX LX NUV cytometer (Beckman-Coulter). The following antibodies were used for flow cytometry analyses: CD3-APC-Cy7 (clone UCHT1, 1:100), CD4-BV786 (clone RPA-T4, 1:100), CD8ð½-PE-Cy7 (1:100), CD27-BV421 (clone M-T271, 1:100), HLA-DR/DP/DQ-FITC (clone TÃ¼39, 1:100), TCRð¼ð½-BV711 (clone IP26, 1:100). 2.6.9 Thymocyte chemokine proteomic expression analysis Frozen thymocytes were thawed, counted, and stained overnight at 4°C with CD3 APC-Cy7 (1:400), CD4 BV786 (1:600), CD8b PE-Cy7 (1:600), TCRab BV711 (1:400), CD5 BV605 (1:600), CD7 BV421 (1:600), CXCR4 APC (1:400), CCR9 PE-Dazzle 594 (1:400), HLA-ABC PE (1:400), and HLA-DR,DP,DQ FITC (1:1000). Cells were washed twice and resuspended in 100μL of 2% HF with 1:10000 DAPI. A CytoFLEX LX cytometer (Beckman Coulter) was used to collect data. Compensation was performed using the cytometer software (CytExpert v2.3) and gating was performed using FlowJo X. GraphPad Prism 10 was used for statistical analysis. For comparisons between male and female pro-T cells an unpaired t-test was used. 2.6.10 CITE-seq and ATAC-seq data integration & analysis Raw fastq files from all samples were aligned and quantified using CellRanger (CellRanger 6.0.1 for CITE-seq and CellRanger-ATAC 2.0.0 for ATAC-seq). Following the suggested pipeline for quality control in Seurat v 4.3.0 package, CITE-seq data were filtered for dead cells, doublets, 69 and red blood cells by excluding cells with greater than 5% mitochondrial genes and less than 200, but no more than 5000 genes. Prior to GSEA analysis, patient data (age, sex) was added as identifiers and then samples underwent normalization, scaling, integration using anchors, dimensional reduction, and further downstream analysis using the standard Seurat workflow with the Seurat v.4.3.0 package. PCs were visualized using an elbow plot to select dimensionality of the integrated dataset, from which we determine to implement 20 dimensions as our input for the RunUMAP and FindNeighbors clustering parameters (res = 0.9). Cells were clustered and identified based on known marker genes. To further separate cell types based on distribution of key marker genes (ex. ICOS, DPP4, MKI67), Treg cells and fibroblast cells were re-clustered by subsetting the object containing each respective cell type and then re-processing for variable features, re-scaling, and re-running FindNeighbors and FindClusters functions. Cell types were named based on expression of known marker genes and then re-added back to the main Seurat object. Differentially expressed genes for GSEA were determined by applying model-based analysis of single-cell transcriptomics (MAST) test and the clusterProfiler package to the whole gene expression profile of male versus female for each cell type or cell type 1 versus cell type 2 for certain comparisons. The canonical pathways from the curated gene set provided by Molecular Signature Database (MsigDB) were input as the gene list for GSEA. GSEA results were outputted as a table found in supplement or visualized using the dotplot from ggplot package. To determine common differentially expressed pathways from male and female donors, significantly (p>0.05) upregulated pathways for male and female cell types were identified. The frequency of each pathway in male and female donors was quantified to calculate the total 70 number of cell types upregulated in each pathway and to quantify to the frequency for each main cell type classification. The top 25 most frequently upregulated pathways were plotted for male and female using the ggplot function geom_bar. Frequencies for each pathway were broken down into frequency per cell type classification. To calculate the number of sex chromosome associated genes, we extracted a list of X and Y chromosome genes from Ensembl and calculated the percentage of differentially expressed genes (p<0.05) found on each respective sex chromosome for each cell type. We then calculated the mean and median number of sex chromosome-associated differentially expressed genes across cell types for male and female patients. The mean number of genes was selected, since some cell types had few (0-3) differentially expressed genes which skewed the median. For integration with ATAC-seq data the general Seurat processing workflow was followed using SCTransform, which acts as a replacement for the NormalizeData, ScaleData and FindVariableFeatures functions. The data was then dimensionally reduced and filtered with RunPCA. FindNeighbors and RunUMAP were run with 30 dimensions. Cell labels were transferred from previously named cell types used for GSEA. The ATAC-seq dataset was associated with the human genome (hg19) and processed using the Signac package (version 1.8.0). The dataset was filtered for peak region fragments between 750 and 80000, percent reads in peaks larger than 15, a blacklist ratio less than 0.8, a nucleosome signal less than 4 and a Transcription Start Site (TSS) Enrichment Score above 1. All filtering values were determined using Violin Plots and intercept lines. The dataset was then normalized, dimensionally reduced and clustered. To name the ATAC-seq object cell clusters, transfer 71 anchors were identified between the ATAC-seq (query) and CITE-seq (reference) datasets. Labels were transferred to the ATAC-seq object and assigned accordingly. CITE-seq and ATAC-seq datasets were merged and integrated using Seurat. The cell identities were reassigned according to CITE-seq labels, and the combined object was processed using the workflow described above. As the object contains multimodal data, FindMultiModalNeighbors and RunUMAP were used for final dimensional reduction and plotting. The function LinkPeaks was used to link chromatin accessibility peaks to the genes in the object. Coverage plots were generated using the CoveragePlot function outlined in the Signac package (version 1.8.0) to visualize gene expression data in combination with chromatin accessibility. The combined ATAC-seq and CITE-seq dataset was used. The CellChat R package (version 1.6.0) was used to infer intercellular communication within the processed CITE-seq dataset. The CellChat pipeline was followed as in140 and the CellChat database, CellChatDB, in human was used. After processing steps, the cluster labels were re-ordered and plots were generated using built-in functions for all cell-cell interaction plots and ligand-receptor pair contribution plots. The combined ATAC-seq and CITE-seq dataset, as well as separated male and female datasets, were used in this analysis. The pySCENIC pipeline was used to compute regulons159,188. Co-expression modules were inferred with Grnboost2, and hg19 transcription factor motif enrichment data from the Aerts laboratory was used to identify direct transcription factor targets. The area under the recovery curve metric was computed to quantify regulon activity in each cell in the dataset. 72 2.6.11 Histology sample preparation During thymic tissue dissociation incubations fresh-frozen CODEX tissue blocks were prepared. Tissue was rinsed in PBS, dried carefully, and placed in label histology blocks filled with O.C.T (Thermo Fisher Scientific). Blocks were frozen in cold isopentane cooled via liquid nitrogen and placed directly on dry ice once frozen. Tissue blocks were transferred to an airtight container and stored at -80oC until use. 2.6.12 CODEX staining and image acquisition CODEX buffers were described as in Schurch et al. 2020128 and DNA-barcoded antibodies were produced as outlined in Wang et al. 202286. 8μm tissue sections were prepared, stained and imaged as described in Wang et al. 202286 using an Akoya Biosciences Phenocycler (previously named CODEX) microfluidics instrument. For each patient we took a minimum of five 3x3 tiled images (min = 45 images, average = 116 images; Table B.1). Imaging regions were assigned by the same operator to capture the maximum area of intact tissue present per patient and chosen to ensure coverage of all important regions of the thymus lobule (ie. capsule, vasculature, medulla, cortex, interstitial space). Variability in the area imaged per patient was due to size constraints of the thymus lobule section. 2.6.13 CODEX image analysis Multiplex images are first processed using the custom image analysis pipeline Convolutional Registration of Images with Subpixel Precision (CRISP)86. Briefly, CRISP creates a multidimensional image stack by performing tile alignment and 3D-drift compensation on tile z-stack images. Images are then deconvolved and resolution-corrected through point spread function analyses, and tiles are registered and stitched. After stitching background subtraction 73 was performed where blank cycles imaged at the beginning and end cycles were used to subtract autofluorescence for each channel of each cycle. Linear interpolation of imaging time and exposure time was used to estimate the autofluorescence contribution of signal in the blank cycles imaged at the beginning and end cycles and this signal was subtracted from each channel at single pixel resolution. Individual cells are then detected via convolutional neural-network driven cell segmentation86. After cell segmentation the data can be synthesized into a matrix file resembling single cell RNA sequencing data, where individual cells are assigned quantitative expression values of all proteins captured during imaging. Using the custom image analysis pipeline HFcluster86, autofluorescent cells are removed and the remaining cells are clustered based on protein marker expression via Leiden-based clustering. Cell types are labeled based on phenotypic expression of marker proteins as well as localization within the tissue. Finally, we perform neighbourhood analysis to detect and quantify niches within the tissue. Using K-means neighbourhood clustering, we score each cell based on the composition of its 80 nearest neighbours. Cells are then clustered into tissue neighbourhoods based on this composition score and frequency and composition of niches are quantified and compared across treatment groups. When comparing between sexes we calculate the averages across technical replicates per patient, and then calculate inter-sex variation between sexes from these values (N=3 for each sex). The intra-sex variation is included in our statistical tests when calculating p-values between specific cell types or neighbourhoods. 2.6.14 Confocal Image Analysis Human thymus samples were sectioned at 20μm thickness for confocal imaging. Sections were washed once with PBS for 5 minutes and then fixed with 1.6% paraformaldehyde (PFA) in PBS 74 for 5 minutes. Slides were washed twice with PBS (5 minutes/wash) and then blocked for 30 minutes at room temperature with 0.3% Triton X100 and 3% goat serum in PBS. Slides were washed once with PBS and then stained with 50μL of primary antibody solution (Cytokeratin 5/8 (1:100), CD4 (clone OKT4, 1:100), CD8 (1:100) in blocking buffer). Slides were covered with paraffin and incubated overnight at 4°C. Slides were washed three times with PBS (5 minutes/wash) and then stained with 100μL of secondary antibody solution for 2 hours covered from light (goat anti-mouse IgG1 Alexa Flou 555 (1:500), goat anti-mouse IgG2a Alexa Flour 488 (1:500), goat anti-mouse IgG2b Alexa Flour 647 (1:500) in blocking buffer). Slides were washed 3 times with PBS and then stained with 200μL DAPI at 1:500 in PBS for 10 minutes covered from light. Slides were rinsed once with PBS for 5 minutes, mounted with Fluoromount G and dried at room temperature overnight before storing them at 4°C protected from light. Slides were imaged with the Zeiss LSM 900 with Airy scan. 2.6.15 Immunofluorescence ligand expression analysis Signal intensity of ligands was calculated using ImageJ. Briefly, for ECM and notch ligand expression three representative images which captured a full thymus lobule (ie. Capsule, cortex, and full medulla) were chosen. For each image three lines of thickness 10 were drawn from the center of the medulla to the outer edge of the cortex with the center of the line overlaying the corticomedullary junction. Signal intensity was recorded along the length of the line and binned into 20 segments. The average signal intensity was calculated for each bin. Signal intensities from each line were averaged for each ligand and plotted with GraphPad Prism 10. For analysis of CD19 and CD40 expression around HCs a mask was drawn around each HC and iteratively 75 expanded, calculating mean ligand expression intensity in the new mask area ring for each iteration. Signal intensities for each line were plotted with GraphPad Prism 10. 2.6.16 Quantification and statistical analysis Statistical analysis was performed using R on RStudio, and a two-sided P value of <0.05 was considered statistically significant. Statistical details relevant to each figure can be found in the figure legends. Briefly, statistical analyses of scRNA-seq data were performed using Wilcoxon sum-rank tests with multiple test correction. Statistical analyses on image quantifications of sequencing data were analyzed in R on RStudio, assuming normal data distribution. A model-based analysis of single-cell transcriptomics (MAST) test was used for differential gene expression used for GSEA. For flow cytometry analysis and spatial ligand image analysis GraphPad Prism 10 was used. For comparisons between two groups, unpaired t-tests were used, and for comparisons within the same experimental sample, two-sided paired t-tests were utilized. For analyses involving more than two groups, two-way ANOVA was performed. 76 2.7 Supplementary Figures 77 Figure 2.8: Spatial multiomic analysis identifies specific cell types and tissue niches within tissue structure (related to Figure 2.2): A) Dot plot of key marker genes used to identify and align CITE-seq clusters with existing cell atlas datasets. Naming of cell clusters was based on positive and lineage negative expression of transcriptional and proteomic markers. B) ATAC-seq clusters identified by anchor-based label transfer from CITE-seq clusters. 34 out of 37 CITE-seq clusters are identified. C) CODEX neighbourhood visualization within a thymic lobule. D) Heat map of cell expression of phenotyping markers used to cluster cells identified via CODEX imaging. E) Heat map of individual cell-cell interaction frequencies. F) Frequency of CXCR4+ on male and female pro-T cells. Error bars = mean +/- SEM. Paired t-test. 78 79 Figure 2.9: ETP specification at the corticomedullary junction is influenced by differential interactions with a gradient of Notch ligands (related to Figure 2.3): A) CD62L protein and gene expression dynamics during early thymocyte development captured via CITE-seq. B) CD62L+ ETPs localized at the subcapsular zone. Yellow arrows point to CD62L+ cells. The solid white line denotes the capsule and the dashed line denotes the boundary of the subcapsular zone; T087. C) Notch ligand expression profiles across the thymus lobule. Ligand expression was measured from the middle of the medulla to the outer edge of the capsule. The average ligand expression was calculated for each bin and plotted. Three lines were drawn on each of three lobules from the center of the medulla to the edge of the cortex. The average ligand expression was calculated for each bin and plotted. Data taken from T085 (8 month old male). D) Overall frequencies of various hematopoietic cell types which develop on different Notch ligands. Cell frequencies represent live cells in the 0 ng/mL GM-CSF condition. All results shown are mean Â± standard deviation from N=3 independent UCB donors. E) Dot plot of key marker genes used to define DC CITEseq clusters and align in vitro JAG ligand screening results with thymic-DC phenotypes. F) CellChat results of differential expression and utilization of Notch ligands by female and G) male cells. 80 81 Figure 2.10: Cortical fibroblasts regulate tissue function through spatial regulation of tissue matrix proteins and cell growth factors (related to Figure 2.4): A) CellChat results of CCL25-CCR9 chemokine receptor ligand interactions directing migration to the subcapsular zone. B) CODEX immunofluorescence image of different ECM proteins found across the thymic lobule. Image shows strict spatial organization of various collagens and ECM proteins produced by different mesenchymal populations; T099. C) Image quantification of various ECM proteins localized in distinct zones of the thymic lobule. ECM expression was measured from the middle of the medulla to the outer edge of the capsule. Three lines were drawn from the center of the medulla to the outer edge of the cortex for three different lobules. The average value for each ligand was calculated for each bin and plotted. Data from patient T097. D) CODEX immunofluorescence image depicting distribution of fibroblasts (PDGFRa, red) and cTECs (CK5/8, green) in the cortex. Arrows point to fibroblasts in direct contact with cTECs; T099. E) Box and whisker plot depicting the significant difference in normalized thymus weight of male and female donors. Due to limited patient data, thymus weights were normalized to Canadian weight standard for infants at each age103; * p < 0.05. F) CellChat results of differential expression and utilization of FGF signaling molecules by male and female cells. G) CellChat cell-cell interactions plot of FGF10-FGFR1 and H) FGF18-FGFR1 in male thymus. H) Violin plot showing the differential expression od downstream hormone signaling gene APOD in different fibroblast populations; *** padjusted<0.001. 82 83 Figure 2.11: The inner cortical zone regulates positive selection and T-lineage specification through sex biased niche interactions (related to Figure 2.5): A) Flow cytometry data collected on bulk thymocytes (n=2). Lymphocytes were gated on FSC-A vs SSC-A and live cells, and then on CD3+TCRαβ+ co-expression. Cells were then gated on expression of CD4 and CD8a to identify different stages of thymocyte development. Expression of HLA class I and class II molecules on different subsets of T cells are shown. B) UMAP of first patient sequenced via CITE-seq and used to identify lineage branch point genes. C) Plot of differentially expressed genes in CD4 vs CD8 transition cells. D) UMAPs showing overlap in CD4 protein expression and E) SATB1 gene expression. F) Neighbourhood interaction enrichment heatmap comparing female and male enrichment. The heatmap matrix was created by subtracting male enrichments from female enrichments. Red (upregulated) interactions are enriched in female thymus and blue (downregulated) interactions are enriched in male thymus. G) Female cell-cell interaction enrichment heatmap quantifying spatial imaging data. Black boxes highlight increased interactions between Class II+ DPs and cTECs as well as CD3+ DPs and DCs. H) Male cell-cell interaction enrichment heatmap quantifying spatial imaging data. Black boxes highlight increased interactions between Class II+ DPs and cTECs as well as CD3+ DPs and DCs. 84 85 Figure 2.12: Regulation of negative selection niches are organized by HCs and regulate inflammatory pathway feedback loops (related to Figure 2.6): A) UMAP showing CITE-seq clusters identified after subclustering stromal cells. B) Dot plot of differentially upregulated genes in KRT+ Fibs compared to other fibroblast populations. C) Normalized CD40 expression relative to distance from HCs. A mask was drawn around each HC and iteratively expanded, calculating mean CD40 expression intensity in the new mask area ring for each iteration. D) Normalized CD19 expression relative to distance from HCs. A mask was drawn around each HC and iteratively expanded, calculating mean CD19 expression intensity in the new mask area ring for each iteration. E) Dot plot of key marker genes used to identify 3 Treg subsets. F) Dot plot showing differential expression of IL-1 pathway ligands and receptors across hematopoietic and Treg cells captured in early human postnatal thymus via CITE-seq. G) CellChat cell-cell interaction plot indicating an active IL1B-IL1R1 signalling axis between DC2/3s and rrTregs. H) Dot plot of key tissue regeneration and repair genes by rrTregs. I) GSEA dot plot showing differential expression of key regulatory pathways between male and female activated mTECs. Male activated mTECs have positive NES scores while female activated mTECs have negative NES scores. 86 Chapter 3: Generation of PSC-CD4 T cells 3.1 Summary PSC-T cells have the potential to expand access to cell therapies and patient-specific modeling of drug responses and disease pathologies. While cytotoxic CD8 T cells can be generated from human PSCs, producing therapeutically relevant numbers of helper CD4 T cells remains a significant challenge. Here, we demonstrate that under feeder-free, serum-free conditions, we can control the bias between CD4 vs CD8 T cell commitment from PSCs by fine-tuning the amount of Notch signaling, T cell receptor stimulation, and HLA class II interactions received by PSC-DPs. We show that Notch signaling negatively impacts CD4 T cell commitment and that timed removal of Notch signaling allows high-yield generation of clonally diverse and functional PSC-CD4 T cells. We characterize the phenotype of PSC-CD4 T cells via multiomic sequencing, confirming these cells express lineage specific transcription factors and diverse TCRs. Overall, we demonstrate how our human thymus resource can be applied to in vitro technology development, generating a PSC-T cell differentiation protocol which supports development of conventional, mature PSC-CD4 and CD8 T cells for use in immunotherapies and disease modeling. 3.2 Introduction Engineered T cells have an enormous potential to treat cancer, autoimmunity, and infectious disease64,189,190. However, the high cost and logistical complexity of manufacturing and administering autologous (patient-derived) T cells significantly limits access to T cell therapies. There has therefore been strong interest in achieving scalable and cost-effective production of allogeneic (off-the-shelf) T cells74,189. A promising source of allogeneic T cells are human 87 induced pluripotent stem cells (iPSCs), which are capable of indefinite expansion and can be differentiated in vitro to cytotoxic (CD8) T cells191â194. Recently, we and others identified clinically-relevant, fully-defined (feeder- and serum-free) conditions to differentiate iPSCs into CD8 T cells77,79,195,196. However, a long-standing challenge for iPSC-to-T cell manufacturing, especially in feeder-free systems, has been the inability to make substantial numbers of mature helper (CD4) T cells189. As CD4 T cells are key orchestrators of the immune system, their absence from PSC-derived T cell populations limits the likelihood of clinical success. CD4 T cells play an essential role in âhelpingâ CD8 T cells eliminate cancer in the context of both engineered T cell therapies78,197,198 and immunotherapies199,200. Naïve CD4 T cells can polarize into diverse helper T (Th) cell subsets including Th1, Th2, and Th17 cells (type 1, 2, and 3 immunity, respectively); regulatory T cells (Tregs); and Tfh cells (follicular helper T cells). Thus, iPSC-derived allogeneic CD4 (PSC-CD4) T cells have numerous possible uses in treatments for cancer, chronic inflammation, autoimmunity, and transplant rejection189. Diverse PSC-CD4 T cell types could also be implemented in isogenic âimmune-on-boardâ disease and tissue models to aid drug screening and validation201. The choice to differentiate into CD4 vs CD8 T cells has been well-studied and is primarily driven by the duration of T cell receptor (TCR) signaling38,202. In the thymus, T cell progenitors develop into an intermediate cell type that is double-positive (DP) for CD4 and CD8. DPs generate TCRs semi-randomly via V(D)J recombination; these TCRs are then tested against HLA class I and II proteins expressed on other cells. CD8 or CD4 are coreceptors for HLA class I or II, respectively. Insufficient TCR stimulation by either HLA leads to âdeath by neglectâ, 88 while too-high stimulation can lead to apoptosis and thereby negative selection. At intermediate levels of stimulation, T cells are positively selected and commit to either the CD4 or CD8 lineage. Soon after TCR stimulation, DPs transcriptionally downregulate CD8, interrupting stimulation by HLA class I, but not class II proteins202. The difference in resulting TCR signaling duration directs lineage commitment: short signals generate CD8 T cells and long signals generate CD4 T cells38,52,202,203. At the transcriptional level, CD4 and CD8 T cell commitment is directed by the mutually-repressive transcription factors (TFs) ThPOK (ZBTB7B) and RUNX3 (RUNX3), respectively204. One hypothesis for poor CD4 T cell induction from PSCs is that cells in differentiation cultures express limited HLA class II molecules205. However, as most protocols generate CD8 T cells via HLA-independent, non-specific stimulation of TCRs on DPs by anti-CD3 antibodies or complexes thereof, it is unlikely that low HLA Class II expression alone is sufficient to explain poor PSC-CD4 T cell induction. Therefore, we hypothesized that, in addition to low HLA class II, the TCR stimulation regime itself was suboptimal, and that other factors in our differentiation system may further bias towards CD8 T cells by suppressing TCR signaling duration, downregulating ThPOK, and/or upregulating RUNX3. Of particular interest was Notch signaling, which is essential for T cell development but may have dose-dependent effects on TCR signaling and CD8 T cell development206,207. Using our spatial multiomic human thymus resource12, we identified strict spatial regulation of Notch ligands within the thymus lobule, corresponding to high Notch signaling in early T cell development, and low Notch signaling during maturation in the medulla. Additionally, recent results with artificial thymic organoids (ATOs) showed that iPSCs generate fewer T cells but a higher frequency of CD4 T cells when 89 differentiated with MS5 feeder cells expressing the Notch ligand DLL1 (Delta-like ligand 1) rather than the stronger DLL4205. We thus studied the combined effects of Notch and TCR signaling on in vitro differentiation of PSC-DPs to CD4 vs CD8 T cells. Here, we show that optimization of Notch and TCR stimulation, as well as upregulation of HLA class II molecules on PSC-DPs, enables production of clonally diverse and phenotypically mature PSC-CD4 T cells, with yields approaching those of PSC-CD8 T cells. First, we characterize the immature state of PSC-DPs generated prior to protocol optimization and identify new media formulations and niche conditions to screen in vitro. Next, we find that Notch stimulation suppresses differentiation of PSC-DPs into CD4 T cells. By setting specific levels of TCR and Notch ligands, we demonstrate fully-tunable control over the ratio of CD4 vs CD8 T cells, a key parameter associated with clinical efficacy of engineered CAR-T cell therapies208. Additionally, we find isolating PSC-DPs increases HLA class II expression in culture and supports PSC-CD4 T-lineage branching. Overall, our findings reveal key factors controlling PSC-CD4 and CD8 T cell induction, enabling the production of diverse T cells and paving the way for improved off-the-shelf therapies and in vitro models of immune development and function. 3.3 Results 3.3.1 Characterization of immature PSC-DPs We and others have previously shown that stimulation of immature DPs skews development towards innate-like cells, such as CD8αα T cells, natural killer (NK) cells, and γδT cells79. Thus, we compared maturation of PSC-DPs with our human refence DPs to identify media and niche conditions that would support further maturation towards conventional mature T cells. We 90 sequenced PSC-DPs with CITE-seq prior to artificial stimulation and induction towards PSC-CD4 and CD8 T cell lineages (Figure 3.1A,B). At this timepoint, PSC-DPs accounted for half the sequenced cells, while the remaining populations were composed of alternative lineage innate or myeloid cells. PSC-DPs expressed high levels of T-lineage commitment transcription factors, such as TCF7, BCL11B, GATA3, RAG1, and RAG2, and lacked expression of the DP-SP transition transcription factors RUNX3 and ThPOK (Figure 3.1C), indicating these cells were immature PSC-DPs compared to the human reference DPs. Furthermore, we identified low levels of HLA class I and II expression on PSC-DPs and alternative lineage cells in culture, which may limit positive selection of PSC-DPs in feeder-free culture. 91 Figure 3.1: CITE-seq characterizes the phenotype and cytokine expression of PSC-DPs and alternative lineage cells in a previous iteration of the PSC-T differentiation platform. A) Schematic overview of the critical cell stages in the PSC-T cell differentiation. B) UMAP depicting the various cell types present in culture prior to artificial stimulation. Dot plot displaying C) gene expression of T-lineage transcription 92 factors and HLA protein expression, D) gene expression of cytokine and cytokine receptors, and E) gene expression of ILC lineage markers. Next, we investigated the impact of alternative lineage cells in culture and characterized the production of inflammatory cytokines which could be released in culture and contribute to innate skewing of PSC-DPs. We identified high levels of TGFβ1 and TNF expression, as well as low expression of IL4, IL13, and IL10, by alternative lineage cells (Figure 3.1D). Furthermore, we identified three cell types that resemble progenitor innate lymphoid cells (ILCs). Two populations, progenitor ILCs-1 (pILCs-1) and progenitor ILCs-2 (pILCs-2), expressed RORA, GATA3, TCF3, TCF12, TBX21, RFX5, BCL11B, ICOS, and IL18R1 (Figure 3.1E). The third population, progenitor ILC3s (pILC3s), expressed RORC, ID2, CCR6, and RORA (Figure 3.1E). ILCs have gained increasing interest in the immunology community for their potent role as regulators of tissue immunity209â211, and their development from PSCs has not been previously demonstrated. 3.3.2 Tuning Notch and culture cell type composition to produce PSC-CD4 T cells To guide T-lineage branching towards PSC-CD4 and CD8SPs, we applied two critical observations regarding niche characteristics of mature DP and CD4 T cells identified in our spatial multiomic resource. First, we observed strict spatial regulation of Notch ligand distribution within the thymus lobule. Specifically, we observed high levels of JAG1 and JAG2 at the CMJ, which remained high in the medulla and decreased proportionally in the cortex based on distance from the CMJ (Figure 3.2A). JAG ligands induce lower levels of Notch signaling, the function of which we studied in Chapter 2.3.2. In contrast, DLL4, the strongest Notch ligand and the ligand used in vitro to induce and support T-lineage development, is found exclusively in 93 the outer cortex, with high concentration under the capsule (Figure 3.2A). During T cell development, ETPs enter the thymus at the CMJ and migrate to the subcapsular zone, where they encounter high Notch signaling, upregulate T-lineage commitment programs, and rapidly proliferate as DPs. Once DPs have rearranged an αβTCR, they begin migration into the inner cortex where they receive positive selection signals, and then to the medulla to continue maturation and negative selection. As they migrate along this axis, the concentration of DLL4 ligands and Notch signals rapidly decrease (Figure 3.2A), a phenomenon not previously replicated in vitro in engineered thymus niche maturation conditions. Thus, we tuned Notch signaling strength during in vitro PSC-DP to SP induction to control the output ratio of PSC-CD4 and CD8SPs (Figure 3.1A). To do so, we stimulated PSC-DPs with 0.1% anti-CD2/3/28, a level we previously found to enable low-level PSC-CD4SP induction, in the presence of DLL4/VCAM1, then inhibited Notch signaling via the gamma secretase inhibitor (GSI) DAPT. We found a dose-dependent switch in output from CD8SPs to CD4SPs as a function of increasing DAPT (Figure 3.2B), suggesting that Notch signaling suppresses differentiation of PSCs to CD4 T cells in vitro. We next tested whether the CD8-biasing effect of Notch was specific to DLL4. Different Notch ligands selectively interact with different Notch receptors with different affinities143,212,213. We transferred PSC-DP cells to plates freshly-coated with different Notch ligands (DLL4, DLL1, JAG1, JAG2, all ligands + VCAM1, VCAM1-only, or uncoated), then stimulated with 0.1% anti-CD2/3/28. We found increasing CD8 lineage bias for DLL4 > DLL1 > JAG1 = JAG2 > VCAM1-only = uncoated, which correlates with the affinity and activation of Notch1 receptor by each ligand143 (Figure 3.2C, left). GSI inhibition with DAPT restored CD4 bias in all cases (Figure 3.2C, right). Overall, we found that increased 94 Notch signaling during the late maturation phase of the PSC-T differentiation promoted CD8SP development (Figure 3.2D), whereas the absence of Notch signaling promoted CD4SP development (Figure 3.2D) and maintenance of protein expression of the CD4SP master transcription factor ThPOK (Figure 3.2E). Additionally, high Notch signaling conditions (i.e. +DLL4/-DAPT) coincided with lower TCR pathway stimulation, as indicated by decreased CD69 expression (Figure 3.4F). Notably, the high anti-CD2/3/28 conditions (1.25%) gave slightly higher levels of CD69 while reducing the percent TCR expression (Figure 3.4F). This is likely due to the stronger stimulation input causing more TCR ubiquitination and downregulation214,215. Altogether, these results show that optimizing TCR and Notch signaling can unlock production of mature CD4 T cells from PSCs. 95 96 Figure 3.2: Optimization of Notch, TCR, and HLA class II signaling supports development of PSC-CD4SPs. A) Heatmap depicting the strict spatial control of Notch ligands within the thymus lobule. B-C) PSC-derived cells after 3 weeks of DP maturation were seeded into wells coated with 10 μg/mL Notch ligand +2.5 μg/mL VCAM1 and stimulated with 0.1% anti-CD2/3/28, then measured two weeks later. Notch signaling was inhibited with the γ-secretase inhibitor DAPT. n = 3 independent differentiations. Time course plots depicting the percentage of D) PSC-CD4 T cells, E) ThPOK+ PSC-T cells, and F) CD69+ PSC-T cells under various Notch and TCR stimulation conditions. Box and whisker plots showing G) HLA class II upregulation and H) IL-7R downregulation of sorted TCRαβ+ DPs. I) Summary bar plot of PSC-T cell phenotypes 2 weeks post-sort or post-CD3+ cell selection. n = 2 independent differentiations. Next, we investigated the role of the inner cortical niche devoid of TECs, macrophages, DCs, ECs, and mesenchymal cells, but containing DPs expressing HLA class II, which we observed in our spatial multiomic resource (Figure 2.5F,G). By flow cytometry we found that 50% of thymocyte DPs expressed surface protein HLA class II (Figure 2.11A), which was not seen on our PSC-DPs. A critical component of the observed DP-DP self-selection niches was the absence of alternative lineage cells. Thus, we sorted PSC-DPs for mature TCRαβ+ DPs and observed a rapid increase in surface protein HLA class II on sorted PSC-DPs compared to cells isolated via CD3+ cell selection or sorted PSC-CD8 T cells after one week in culture (Figure 3.2G). Sorted PSC-DPs also downregulated IL-7R (Figure 3.2H), a cytokine receptor known to regulate CD8 T-lineage differentiation38. Furthermore, sorting TCRαβ+ DPs increased the proportion of PSC-CD4SPs in culture two weeks post-sort compared to CD3+ cell selection (Figure 3.2I), potentially via increased PSC-DP HLA class II ligand positive selection signals supporting PSC-CD4SP maturation. 97 In sum, we demonstrate the value of our spatial multiomic resource in generating testable hypotheses on human thymus niche biology and T cell development. We implement these findings in vitro to produce PSC-CD4SPs. 3.3.3 Characterization of PSC-CD4 T cells To further characterize the PSC-CD4 T cells, we performed joint CITE-seq and scTCR-seq on a PSC-T cell differentiation that produced an approximately equal ratio of CD4 and CD8 T cells (Figure 3.3A). We captured immature and mature CD4+ T cells, mature CD8+ T cells, quiescent and proliferating DPs, and several populations of innate-like T cells, including ILC3-like cells and three populations of TCRγδ cells (Figure 3.3A). Looking at key gene expression markers, we saw that CD4 surface protein expression increased and CD8 surface protein decreased with CD4 T cell maturation (Figure 3.3B). Mature CD4 T cells expressed maturation markers such as CD27, CD40L [SELL], CCR4, CCR7, IL7RA, S1PR1, and KLF2; expressed CD4 lineage induction-associated genes including LEF1 and TCF-1 [TCF7]204; and lacked expression of CD8 T cell-specific genes such as RUNX3, granzyme [GZMA, GZMK], perforin [PRF1] or CXCR6 (Figure 3.3C-E). Comparing differentially-expressed genes (DEGs) CD4 T cells expressed more signaling proteins such as LIME1, IFITM1, GLIPR2, and CD52, while CD8 T cells expressed more inflammatory genes such as CSF1, GBP5 (IFNγ-induced), IFNγ [IFNG], and P2RX5, and regulatory genes such as CTLA4 and PDCD4 (Figure 3.3E, Figure 3.5A,B). Comparing DEGs in mature vs immature CD4 T cells, the latter expressed more TCF7, NME2, CAPG, and LIME1 (Figure 3.3E, Figure 3.5B), reflecting changes in metabolism, cell mobility, and TCR signaling as the cells mature. 98 99 Figure 3.3: Multiomic characterization of PSC-CD4 T cell transcriptomic, proteomic, and TCR repertoire landscapes. A) UMAP of the T-lineage cells captured via CITE-seq. Feature plot overlaying B) CD4 and CD8 protein expression, C) mature CD4SP gene markers, and D) mature CD8 gene markers on the UMAP. Gene expression matrix displaying E) the quantified scaled expression of CD4SP and CD8SP marker genes. TCRβ (F) and TCRα (G) chain segment usages across PSC-, thymic-, and PBMC-derived populations. Segments are ordered based on genomic position, frequencies of each row sum to 1. Innate cells represent merged ILC3 and γδT cells depicted in the PSC-T cell UMAP. To determine whether the PSC-CD4 T cells expressed a diverse TCR repertoire, we compared TCR V and J segment usages to human reference data from thymocytes6 (Park et al., 2020) and PBMCs216. We found similar patterns of TCR Vα/β and Jα/β segment usages between PSC-CD4 T cells and thymic- and PBMC-derived CD4 T cells (Figure 3.3F,G). The TCRα chain usages in PSC-T cells showed a unique pattern compared to reference cell types, with a Vα pattern most similar to PBMCs but a Jα pattern more similar to fetal DPs (Figure 3.3G). Overall, our PSC-T cells have diverse TCRβ V segment usage, indicating TCR rearrangement in PSC-T cells is not limited and produces T cells with diverse TCRs. Finally, a hallmark feature of CD4 T cells is their ability to respond to a specific cytokine milieu and be polarized into specialized, helper subsets to support and enhance the functionality of other immune cells. To demonstrate the capability of our PSC-CD4 T cells to take on such helper functionalities, we stimulated the PSC-CD4 T cells, in parallel with naïve CD4 T cells isolated from postnatal thymus or from peripheral blood of healthy donors, with anti-CD2/3/28 in the presence of Th1-, Th2-, or Th17-polarizing cytokines for 7-14 days (Figure 3.4A). To confirm PSC-CD4 T cells can secrete detectable levels of cytokines, polarized cells were restimulated with anti-CD2/3/28 for 48 hours, the supernatant was sampled for cytokines, and results were 100 normalized against Th0 samples (Figure 3.4B-D). Under Th1 conditions, PSC-Th1 minimally upregulated IFNγ secretion unlike thymic- and blood-Th1. While blood-Th1 downregulated IL-4 secretion, PSC-Th1 maintained its expression and thymic-Th1 upregulated IL-4 secretion. Under Th2 conditions the IL-4 secretion pattern was unchanged and showed IFNγ downregulation for PSC-, thymic-, and blood-Th2. Under Th17 condition, PSC-Th17 upregulated IL-17A secretion, and downregulated IFNγ and IL-4 secretion relative to PSC-Th1 and -Th2, respectively. Interestingly, blood-Th17 secreted IL-17F at a concentration 100-1000-fold higher relative to PSC- and thymic-Th17. Thus, PSC-Th cells can secrete detectable levels of Th-related cytokines. Overall, these results confirm that PSC-CD4 T cells are capable of polarizing to different states that differentially secrete key cytokines relevant for immune regulation. Figure 3.4: PSC-CD4 T cells can polarize into functionally different T helper subsets. PSC-, thymic-, and blood-CD4+ T cells were polarized in Th1/2/17 conditions for 7-14 days, rested overnight in 10 IU/mL IL-2, and then measured for relevant cytokines (n=4). (A) Schematic of polarization and the relevant Th1/2/Th17 101 transcription factors, chemokine receptors, and cytokines. Rested polarized cells were measured for extracellular cytokine secretion of B) IFNγ, C) IL-4, and D) IL-17A after 48 hours of stimulation by anti-CD2/3/28. Data are relative values normalized to Th0, and then log2-transformed. All data is shown with mean ± SEM. Statistical significance was determined using ordinary one-way ANOVA with p values shown. 3.4 Discussion Here we showed that optimization of TCR, Notch, and HLA class II signaling enables production of functional CD4 T cells from iPSCs. PSC-CD4 T cells express key markers of maturation towards a naïve-like phenotype and express a diverse TCR repertoire. Collectively, our results have significant potential for advancing allogeneic T cell-based therapies. Our data suggest that Notch suppresses the development of PSC-CD4 T cells by suppressing TCR signaling, thereby reducing activation of the CD4 lineage-defining transcription factor ThPOK [ZBTB7B] and enabling the CD8 lineage-defining transcription factor RUNX3 to dominate and bias differentiation towards CD8 T cells. Removal of Notch signaling to improve PSC-CD4 induction has been concurrently described by two other groups at recent conferences: (i) the Kaneko group presented alongside us at ISCT 2024217,218 and (ii) the Mostoslavsky group presented at FOCIS 2024. These concurrent reports independently confirm the observed CD8 lineage biasing of Notch and indicate that Notch removal or inhibition is applicable to diverse differentiation systems. Interestingly, the role of Notch signaling in in vivo DP-to-SP transition is conflicted, with various studies showing CD4-, CD8-, or no bias following manipulation of Notch signaling206,207. Our observation of Notch suppressing TCR stimulation in thymocytes is consistent with one report in NICD-overexpressing mice219, but contrasts with most literature showing Notch enhances TCR stimulation206,220,221. Thus, the effect of Notch on TCR stimulation is likely context- and dose-dependent206,219, indicating that optimal TCR and Notch signaling 102 input levels may vary for different iPSC lines or sources of DPs (e.g. induced from cord blood or collected from the thymus). Previous work producing PSC-T cells in ATOs found that the use of MS5 feeder cells expressing DLL1 rather than the stronger DLL4 resulted in a higher percent and yield of mature CD4 T cells, though at the cost of reduced T cell yield overall205. These results hinted that weaker Notch ligands may be more permissive to in vitro PSC-CD4 T cell induction, though the effect could have been caused by an upstream change in cell development by DLL1 vs DLL4. Our study is the first definitive demonstration that Notch signaling specifically suppresses PSC-CD4 T cell induction. This effect is dose-dependent and builds upon the work by Montel-Hagen et al. (2019) by confirming that Notch ligands with reduced strength (DLL4>DLL1>JAG2>JAG1>no ligand) confer greater CD4-lineage bias. The use of JAG1/2 in ATO systems to improve PSC-CD4 T cell production is likely infeasible due to the poor performance of these ligands at driving early stages of T cell development213. Instead, our DAPT-mediated Notch inhibition data suggests that appropriately timed DAPT administration during ATO culture could unlock greater PSC-CD4 T cell generation in such systems. Our observation that Notch inhibition by DAPT causes a similar improvement in PSC-CD4 T cell induction as Notch ligand removal indicates that the effect of Notch on TCR stimulation and CD4 vs CD8 bias depends on NICD cleavage and likely also nuclear localization, rather than non-canonical cytoplasmic Notch interactions with other signaling pathways222,223. While Notch is known to increase the nuclear retention of NF-κB (one of the downstream TCR pathways) and thereby increase its transcriptional activation224, the observed reduction in TCR stimulation (as reported by CD69) suggests that Notch may either be inducing the expression of TCR pathway 103 suppressors or re-directing TCR pathway transcriptional regulators away from CD4 lineage target genes like ThPOK, perhaps towards earlier-stage T cell developmental genes regulated by Notch. Further studies will be needed to understand these context-dependencies and uncover changes in chromatin accessibility and binding profiles of key transcription factors1 caused by different combinations of Notch and TCR signaling inputs during PSC-DP-to-SP transition. We demonstrated that our PSC-CD4 T cells efficiently upregulated naïve T cell markers (such as CD27, CCR7, CD45RA, CD40L, and IL7RA) and the CD4 lineage-inducing genes including ThPOK, LEF1, and TCF-1 [TCF7]. Importantly, they expressed low levels of cytotoxic genes such as granzyme and perforin. Additionally, our PSC-T cells generated diverse TCR repertoires when differentiating to both CD4 and CD8 T cells. However, there were notable differences in TCR chain usage frequencies among our PSC-T cells compared to both thymocytes and PBMCs. TCR gene rearrangement, especially of the TCRα chain, is known to be biased by differentiation stage6. The TCRβ chain biases in our cells, however, do not appear consistent with stage-specific TCRβ chain biases. Instead, the observed biases may reflect differences in relative TCRβ locus accessibility during TCRβ chain rearrangement in vitro. The most striking difference in PSC-T cell TCRα fragment usages was a scarcity of Vα and Jα segments at the 5â end of the V array and 3â end of the J array, respectively. The in vivo recombination pattern of the TCRα locus is hypothesized to result from DNA looping and serial recombination, whereby Vα and Jα segments closest to the genomic center preferentially recombine in earlier developmental stages (e.g. DN, DP) while further-apart segments recombine as cells mature towards SP stages6. Our results may thus point to an abridged recombination progress in PSC-T cell populations, which 104 may be due to our artificial PSC-DP-to-SP stimulation process triggering positive selection in cells that have spent less time recombining TCRα than the average in vivo thymocyte. Furthermore, we establish the potential of our PSC-T cell differentiation to produce alternative T-lineage cells for therapy and immune response modeling. We show that early in the differentiation alternative lineage cells, such as progenitor ILCs and γδT cells, are present. ILCs have been shown to act as potent sources of cytokines during immune responses and play critical roles as tissue-resident immune cells in regeneration and disease models. Expanding this PSC-T cell technology to encompass generation of alternative immune cell lineages is a critical step toward comprehensive modeling of immune responses, disease pathology progression, and drug efficacy. Finally, we demonstrate use of our spatial multiomic resource for hypothesis prediction and validation in vitro. The predictive power of our resource is complemented by the ability to test specific perturbations in a defined platform. Here, we outline advancements in PSC-T technology via optimization of Notch signaling regimens, as well as DP isolation, to optimize PSC-CD4SP production. However, we ultimately aim to outline a framework for hypothesis testing of developmental mechanisms of human T cell and alternative lineage thymus-derived cells, such as NK cells, DCs, and ILCs, where one can identify novel hypotheses using our spatial multiomic resource and then screen cytokine and niche conditions to validate predictions. Overall, our results are a significant step forward in understanding and controlling PSC-T cell development. The ability to manufacture diverse types of helper T cells opens the door to manufacture therapeutic PSC-derived T cell products for a variety of diseases including cancer, autoimmunity, chronic inflammation, and transplant189,201. In particular, our work will enable 105 production of PSC-CAR T products with defined ratios of CD4 and CD8 T cells, which is expected to improve clinical responses and patient outcomes in the treatment of cancer78,208. More broadly, the growing capability to reproduce the blood system from PSCs will enable modeling and exploration of human immune development in vitro190, production of isogenic cells for immune-competent tissue modeling201, and production of cell therapies comprising multiple cooperative immune cell types225. Limitations of Study A challenge in PSC-T cell production is the variability among batches of CD34+ cells and experimental runs, which both translate to variability in PSC-CD4 T cell production. As Notch is critical for earlier stages of T cell development, the cell population must achieve a high frequency of DPs, a stage at which Notch signaling is less required1, to avoid crashing when Notch signaling is removed during CD4 T cell induction. Continued improvements to culture systems will therefore translate to improved CD4 T cell induction. Finally, it remains to be established whether PSC-CD4 T cells will persist and maintain their phenotypes and function in an in vivo environment. 3.5 Methods 3.5.1 In vitro culture of PSC T cells 3.5.1.1 Human iPSC maintenance iPS-11 human iPSCs were maintained on plates coated with 80-120mg/mL Cultrex (Bio-Techne) in mTeSR+ media (STEMCELL Technologies). iPSCs were passaged using 0.25% Trypsin-EDTA (Thermo Fischer Scientific), and re-seeded in mTeSR+ media supplemented with 10uM Y-27632 (STEMCELL Technologies). 106 3.5.1.2 T cell progenitor differentiation iPS-11 human iPSCs were differentiated to pro-T cells as described previously77. Pro-T cells were then seeded at 2 million cells/mL onto 96-well plates coated with 10ug/mL human recombinant DLL4 (Sino Biological) and 2.5ug/mL mouse recombinant VCAM1 (R&D Systems), in Maturation Media (IMDM +Glutamax (Thermo Fischer) supplemented with 4% B27-vitamin A (Gibco), 0.5% penicillin/streptomycin (Invitrogen), 24uM 2-mercaptoethanol (Sigma-Aldrich), 60uM ascorbic acid (Sigma-Aldrich), 9.8ng/mL SCF (R&D Systems), 8.5uL Flt3L (R&D Systems), 2.6ng/uL IL-3 (R&D Systems), 72ng/uL IL-7 (R&D Systems), 0.04ng/uL TNFa (R&D Systems), and 15.2ng/uL CXCL12 (R&D Systems)). A half media change was performed every 3-4 days. After 2 weeks of culture, media was removed completely and replaced with Maturation media supplemented with 0.3% Immunocult Human CD3/CD28/CD2 T cell activator (STEMCELL Technologies), and a half media change was performed every 3-4 days. After 2 additional weeks, cultures were enriched for CD3+ cells using the EasySep Release Human CD3 positive selection kit according to manufacturerâs instructions (STEMCELL Technologies), or for TCRαβ+CD4+CD8b+ cells using FACS. 3.5.1.3 T cell stimulation 3.5.1.3.1 DP-HLA class II upregulation FACS-enriched cells were seeded at 1 million cells/mL onto uncoated 96-well plates in Maturation Media supplemented with 1.25% Immunocult Human CD3/CD28/CD2 T cell activator (STEMCELL Technologies), and topped up with Maturation Media after 3 days. After 7 days, cells were transferred 1:1 onto 96-well plates coated with 3ug/mL anti-CD3 monoclonal antibody (BioLegend) and 150ug/mL RetroNectin (Takara Bio), in IMDM +Glutamax (Thermo 107 Fischer) supplemented with 4% B27 -vitamin A (Gibco), 0.5% penicillin/streptomycin (Invitrogen), 24uM 2-mercaptoethanol (Sigma-Aldrich), 60uM ascorbic acid (Sigma-Aldrich), 5ng/mL IL-7 (R&D Systems), 5ng/mL IL-15 (R&D Systems), 50ng/mL IL-12 (R&D Systems), 50ng/mL IL-18 (R&D Systems), 20ng/mL IL-21 (R&D Systems), and 10uM Z-VAD-FMK (R&D Systems). The next day, cells were transferred 1:1 onto 96-well plates coated with 150ug/mL RetroNectin. After 3 days, cells were passaged 1:3 onto fresh RetroNectin-coated 96-well plates. Samples were taken for flow cytometry at day 7 and day 14 post-FACS or EasySep enrichment. 3.5.1.3.2 PSC-CD4 T cell sequencing EasySep-enriched cells were seeded at 1 million cells/mL onto uncoated 96-well plates in Maturation Media, and topped up with Maturation Media after 3 days. After 7 days, cells were transferred 1:1 onto 96-well plates coated with 3ug/mL anti-CD3 monoclonal antibody (BioLegend) and 150ug/mL RetroNectin (Takara Bio), in IMDM +Glutamax (Thermo Fischer) supplemented with 4% B27 -vitamin A (Gibco), 0.5% penicillin/streptomycin (Invitrogen), 24uM 2-mercaptoethanol (Sigma-Aldrich), 60uM ascorbic acid (Sigma-Aldrich), 5ng/mL IL-7 (R&D Systems), 5ng/mL IL-15 (R&D Systems), 50ng/mL IL-12 (R&D Systems), 50ng/mL IL-18 (R&D Systems), 20ng/mL IL-21 (R&D Systems), and 10uM Z-VAD-FMK (R&D Systems). The next day, cells were transferred 1:1 onto 96-well plates coated with 150ug/mL RetroNectin. After 3 days, cells were passaged 1:3 onto fresh RetroNectin-coated 96-well plates, and cultured for an additional 4 days. Samples were collected for sequencing at day 14 post-enrichment. 3.5.2 Flow sorting Cells were counted prior to proceeding to antibody staining for cell sorting. 108 Cells were resuspended in Fc buffer (HF + 1:1000 human Fc block (BD Biosciences, Mississauga, ON, Canada)) to a final concentration of 10M cells/mL. CD4-BB515 (BioLegend), CD8b-PE-Cy7 (eBioscience), TCRαβ-APC antibodies (Biolegend) were added at 1:100 and the cell suspension was incubated for 30min on ice in the dark. After incubation cells were spun at 500xg for 5min at 4oC, washed with 5 mL of HF, and spun again. Cells were resuspended to a final concentration of 10M cells/mL and stored on ice while transferring to the cell sorting facility. The TCRαβ+CD4+CD8b+ fraction was sorted using an MOFLO Astrios EQ (Beckman Coulter) instrument into HBSS + 4% FBS. The cell suspension was pelleted, counted, and kept on ice prior to processing for CITE-seq and scTCR-seq. 3.5.3 CITE-seq and scTCR-seq sample preparation analysis The BioLegend TotalSeq-C CITE-seq antibody preparation and 10X Genomics CITE-seq cell staining protocols were followed. Briefly, one vial of TotalSeq-C Human Universal Antibody Cocktail v1.0 (BioLegend, San Diego, CA, USA) was equilibrated to room temperature for 5min and then spun at 10,000g for 30 seconds. The lyophilized panel was resuspended in 27.5uL of HBSS + 4% FBS, vortexed for 10 seconds, and then incubated at room temperature for 5min. The vial was vortexed again for 10 seconds and then spun at 10,000g for 30 seconds. The entire volume was transferred to a low protein binding PCR tube and then centrifuged at 14,000g for 10min at 4oC. 250,000 cells were partitioned into a 12 x 75mm tube. Cells were spun at 500xg for 5 min at 4oC and resuspended directly in 12.5uL of HBSS + 4% FBS. 12.5uL of the antibody staining cocktail was added and the cells were incubated for 30min at 4oC. Cells were washed three times with 3.5mL HBSS + 4% FBS and resuspended in a final volume of 55uL. Cells were counted, stored 109 on ice, and given immediately to the sequencing facility for 10X Genomics 5â library preparation and sequencing. The generation of single cell indexed libraries was performed by the Biomedical Research Center Next Generation Sequencing Core using the 10X Genomics chromium controller platform and the Chromium Single Cell 5â Library and Gel Bead Kit v1.1 and Chromium Single Cell Library and Gel Bead Kit v2 reagents. The sequencing protocol provided by the supplier was followed without modification and run with the NextSeq 2000. After run completion, the Binary base call (bcl) files were converted to fastq format using the Illumina bcl2fastq2 software, and data were received for further analysis. 3.5.4 CITE-seq and scTCR-seq analysis Raw fastq files from all samples were aligned and quantified using CellRanger (CellRanger 6.0.1) and aligned with the human genome reference hg38. Following the suggested pipeline for quality control in Scanpy (v 1.10.2), CITE-seq data were filtered for dead cells, doublets, and red blood cells by excluding cells with greater than 5% mitochondrial genes and less than 500, but no more than 6000 genes. Genes found in less than 3 cells were removed. Samples underwent normalization, scaling, integration using anchors, dimensional reduction, and further downstream analysis using the standard Scanpy workflow. RunUMAP was run using 50 principal components and FindNeighbors clustering parameters (res = 0.6 for PSC-DP dataset, res = 0.5 for PSC-CD4 and PSC-CD8 datasets). Cells were clustered and identified based on known marker genes. Signac (v 0.17.0) was used to analyze scTCR-seq data. TCRBV analysis was compared to bulk TCR sequencing data from human thymus and PBMCs generated by Michaels et al. (2020)77. 110 3.5.5 CODEX data analysis Signal intensity of Notch ligands was calculated using ImageJ. Briefly, three representative images which captured a full thymus lobule (ie. Capsule, cortex, and full medulla) were chosen. For each image three lines of thickness 10 were drawn from the center of the medulla to the outer edge of the cortex with the center of the line overlaying the corticomedullary junction. Signal intensity was recorded along the length of the line and binned into 20 segments. The average signal intensity was calculated for each bin. Signal intensities from each line were averaged for each ligand and plotted with GraphPad Prism 10. 111 3.6 Supplemental Figures 112 Figure 3.5: Phenotypic characterization of PSC-CD4 T cells (related to Figure 3.3). A) Feature plots depicting cell type markers and differentially expressed genes in PSC-CD4 and CD8 T cells. B) Plots of differentially expressed genes between immature and mature PSC-CD4SPs, CD8SPs, and DPs. 113 Chapter 4: In vitro human immune system development reveals TCRs are not HLA class specific in the absence of co-receptor binding 4.1 Summary T cell receptors (TCRs) play a critical role in adaptive immunity by enabling specific and tunable T cell recognition of self, versus non-self, antigens. During T cell development functional TCRs are selected to create diverse, yet mutually exclusive, helper and cytotoxic TCR repertoires. Previously, TCR repertoires were thought to diverge based on TCR specificity to HLA class I or class II antigens. Here, we characterize the TCR repertoire of pluripotent stem cell-derived T cells (PSC-T cells) developed using a standardized, feeder-free protocol. We find PSC-T cells have diverse TCR repertoires and PSC-helper and cytotoxic T cells have mutually exclusive TCR repertoires. However, when PSC-T and primary cell reference TCRs are matched to public TCR databases, the mutually exclusive helper and cytotoxic TCRs match both known HLA class I and class II reactive TCRs. This data indicates TCR selection towards helper and cytotoxic T cell lineages cannot be due solely to inherent TCR specificity towards a specific HLA class. Instead, we propose TCRs exist on a spectrum of HLA class I/II specificity and kinetic regulation of TCR co-receptor proteins during T cell development determines HLA specificity and functional fate of T cells. Overall, we demonstrate a novel technology to study and perturb human immune system development. 4.2 Introduction Pluripotent stem cell (PSC)-derived T cells (PSC-T cells) with diverse T cell receptors (TCRs) present an opportunity to model adaptive immune responses in vitro. We recently described the ability to produce both CD4 and CD8 PSC-T cells in fully defined feeder-free conditions at 114 controlled ratios. This technology allows study of the formation of the human TCR repertoire, as well as the role of TCR function and specificity in directing key stages of T cell development, such as CD4 versus CD8 T cell lineage bifurcation. TCRs play a critical role in adaptive immunity by enabling specific and tunable T cell recognition of self, versus non-self, antigens in the periphery32. TCR rearrangement results in a T cell pool composed of highly diverse antigen recognition sites, with an estimated functional repertoire size of 1010 TCRs in the postnatal thymus226. Peptide recognition, and therefore the majority of TCR specificity, is encoded by the complementary determining region 3 (CDR3) sequence227. To further increase TCR diversity the length of the CDR3, which averages 13 amino acids in fetal thymus, increases to 15 amino acids in postnatal thymus, a process mediated by TdT-dependent addition of nucleotides into the CDR3 region during TCR rearrangement228. Functional TCRs are selected within the thymus microenvironment, an organ specialized to support TCR rearrangement and T cell development. During development, progenitor T cells first rearrange and test functionality of the β-chain in complex with invariant pre-TCRα (Figure 4.1A). If the pre-TCR complex receives positive selection survival signals the cell will rearrange the α-chain and test the full TCR complex on double positive (DP) thymocytes expressing both CD4 and CD8 coreceptor proteins. If the DP thymocyte continues to receive positive selection signals, indicating that the TCR can interact with HLA molecules, it will continue T cell maturation and undergo lineage commitment towards either the helper CD4 single positive (SP) or cytotoxic CD8SP T cell lineage38. The CD4 versus CD8 lineage decision is thought to be guided by TCR-coreceptor complex interactions with either HLA class I or II antigens, with CD4 T cells interacting with class II 115 antigens and CD8 T cells interacting with class I antigens32,37. Under the kinetic signaling model38,53,56,202,229,230, DPs will first downregulate CD8 and test the functionality of the CD4-TCR complex (Figure 4.1A). If the cell continues to receive positive selection signals from interactions with HLA class II molecules the cell will migrate to the thymic medulla and continue CD4SP maturation. In the absence of signal the cell will downregulate CD4 and upregulate CD8 to test CD8-TCR complex interactions with HLA class I molecules. This lineage bifurcation results in TCR repertoires that are largely divergent between CD4 and CD8 T cells within an individual231â235. Repertoire divergence is thought to occur due to the differential ability of TCRs to interact directly with either the HLA I or II molecules based on the V chain-encoded complimentary determining regions 1 (CDR1) and 2 (CDR2)236, as well as a differential ability of the peptide-specific complimentary determining region 3 (CDR3) to interact with HLA class I and II peptidomes, with larger peptides presented on HLA class II and smaller peptides on HLA class I antigens237. The combination of these interactions must generate persistent/long-duration signals for CD4SP development or disrupted/short-duration signals for CD8SP development202. Current PSC-T cell in vitro differentiation platforms which produce mature CD4 and CD8 T cells have lacked TCR repertoire characterization, as well as technology demonstration to uncover fundamental mechanisms guiding human T cell development. Here, we characterize the TCR repertoire of PSC-T cells generated via our published, and recently optimized feeder-free, chemically-defined engineered thymic niche77. We demonstrate that PSC-DPs have diverse fetal-like TCRs that match with high frequency to public TCRs and that TCR diversity is maintained after stimulation and progression towards CD4SP and CD8SP lineages. Furthermore, we 116 demonstrate that, similar to in vivo T cells, PSC-CD4SP and CD8SPs from the same PSC line have mutually exclusive TCR repertoires. Yet, the TCR sequences of both in vitro and in vivo-derived CD4SP and CD8SPs overlap significantly with both known class I and II reactive TCRs in a public database. This suggests although TCR signaling must play a critical role in CD4SP versus CD8SP lineage bifurcation202, selection cannot be due solely to differences in CDR3 interactions with HLA class I and II peptidomes or CDR1/2 specificity for a definitive HLA class. Instead, we hypothesize that most TCRs can interact with both class I and II antigens and that the decision for a promiscuous TCR to choose between CD4SP versus CD8SP lineages may be due to kinetic regulation of CD4 and CD8 coreceptors during T cell development. 4.3 Results 4.3.1 PSC-T cells have diverse TCRs We first characterized how TCR diversity changes throughout in vitro PSC-T cell development (Figure 4.1B), specifically after artificial TCR stimulation and clonal proliferation. We performed single cell TCR sequencing (scTCR-seq) with 137 CITE-seq antibodies on two different PSC-T cell differentiations, including both unstimulated and artificially stimulated conditions via soluble CD3/CD28/CD2 antibody stimulation (Figure 4.1B; Figure 4.4A). Prior to stimulation PSC-DP and CD8SPs have high TCR diversity (ie. DP-v1 and CD8SP-v1), with over 90% of clones consisting of a clone size of 1, similar to proliferating DPs in fetal thymus (Figure 4.1C). Artificial stimulation in vitro results in minor clonal expansion, whereas in vivo SPs do not have high proliferation rates; however, the post-stimulation PSC-CD4 and CD8SPs remain diverse, with over 80% of clones consisting of a clone size of <10 cells (Figure 4.1C). 117 Figure 4.1: PSC-T cells have diverse, fetal-like TCRs. A) Schematic of TCR rearrangement and selection during T cell differentiation in the thymus. 1: β chain rearrangement and selection; 2: α chain rearrangement; 3: Downregulation of CD8. Test CD4-TCR complex interactions with HLA class II; 4: CD4SP negative selection; 5. Downregulation of CD4, upregulation of CD8. Test CD8-TCR complex interactions with HLA class I; 6: CD8SP negative selection; 7: death by neglect; 8: export to the periphery of cells which pass negative selection. B) Schematic of PSC-T cell scTCR-seq dataset creation. PSC-derived pro-T cells were differentiated to PSC-CD4 or CD8SPs in two different PSC-T cell differentiations. Version 1 (v1) included two sequencing timepoints, one prior to artificial stimulation and one after stimulation and expansion in two different media conditions. Version 2 (v2) isolated mature CD3+ PSC-DPs to promote CD4SP development. Additional information on experimental methods can be found in the supplemental text. C) TCR clonal expansion normalized to total number of cells in each cell type cluster in PSC-T and human fetal and postnatal reference datasets. X-axis ticks separate major T cell development stages. PSC-T cell clusters are labeled in bold text. P = proliferating cells, Q = quiescent cells. D) Alpha and beta CDR3 lengths of PSC-T and human reference T cells; Unpaired t-test; ** p<0.01, *** p<0.001, **** p<0.0001. 118 Next, we investigated how the CDR3 diversity of our PSC-T cells compared to fetal and postnatal thymocytes. Both the alpha and beta CDR3 regions of PSC-T cells had an average length of 13 amino acids, suggesting that they resemble fetal TCRs with low TdT activity and lower TCR diversity than postnatal T cells (Figure 4.1D; Figure 4.4B-E). Furthermore, similar to in vivo fetal T cells the CDR3 length did not change during T cell development (Figure 4.4F,G), suggesting that CDR3 length does not significantly influence T-lineage decisions or the probability of positive selection. 4.3.2 CD4 & CD8 PSC-T cells have divergent TCR repertoires Given the lack of stromal cells in our feeder-free platform, we investigated the impact of positive selection off surrounding PSC-T cell HLA molecules on TCR repertoire selection and diversity. We computed the distance between cell clusters based on clonotype overlap for the paired alpha and beta chains, as well as for individual TCR alpha (TCRA) and TCR beta (TCRB) chains. We did not observe overlap of DPs and CD8SP TCRs between the first and second experiments (Figure 4.2A,B; Figure 4.5A). This suggests TCR rearrangement and selection of PSC-T cells in our feeder-free system is not biased towards a limited subset of antigens. Additionally, the scale of our scTCR-seq limits our ability to estimate the true potential for TCR diversity in our system and scale up of the T cell differentiation pipeline would likely continue to increase the TCR diversity attainable via PSC-T cells. 119 Figure 4.2: PSC-T cell TCR repertoires diverge during T cell development. Plot of TCR repertoire overlap between A) DP clonotypes in v1 and v2, B) CD8 clonotypes in v1 and v2, and C) CD4 and CD8SPs in PSC-T (blue) and human reference (red) datasets. For each TCR repertoire overlap plot TCR clones found in only one group intersect the x or y axis. Heatmap of D) paired TCRA and TCRB chain, E) TCRA chain, and F) TCRB chain repertoire overlap. We were particularly interested if our in vitro system would recapitulate the repertoire divergence between CD4 and CD8SPs observed in mature human thymocytes and PBMCs234. To our knowledge, our dataset represents the first time that both PSC-CD4SP and CD8SP TCRs generated in the same differentiation have been sequenced, enabling us to probe this question. Comparable to human thymocytes, TCR repertoire overlap decreases along developmental trajectories and with lineage branching in PSC-T cells (Figure 4.2C-F). For example, we observe decreasing repertoire overlap as cells transition from PSC-DP to immature CD4 thymocytes and 120 from immature CD4 thymocytes to mature CD4SPs (Figure 4.2D-F). Surprisingly, the mature PSC-CD4 and CD8SPs that were selected in our feeder-free platform had divergent TCR repertoires (Figure 4.2C), similar to fetal (Figure 4.5B-D) and postnatal thymocytes (Figure 4.5E-G), and human PBMCs234. 4.3.3 CD4 vs. CD8 phenotype is not dependent on HLA specificity We were intrigued that the PSC-CD4 and CD8SPs still generated divergent TCR repertoires given the cells developed in a feeder-free platform with artificial TCR stimulation. In the thymus, several cell types contribute to positive and negative selection, presenting different subsets of peptides to prune the TCR repertoire238. However, our platform only contains T cells for self-selection and TCR-agnostic stimulation for positive selection. It is intriguing that PSC-CD4 and CD8SP repertoires diverge even in the absence of cell type-specific positive selection. We next asked whether the TCR clones expressed by PSC-CD4 and CD8SPs were specific to class II and class I, respectively. To this end, we performed exact matching of the V, J, and CDR3 amino acid sequence for the alpha and beta chains separately to the VDJ database. The VDJ database is a repository of human TCR sequences with known reactivity to specific HLA class I or II antigens239. Unfortunately, due to the size of our dataset, as well as the limited size of the VDJ dataset, we could not perform exact matches of the combined alpha and beta chains, which have been shown to improve antigen specificity prediction using computational TCR-antigen matching algorithms227,234. However, we did observe divergent repertoires between individual alpha and beta chains of PSC-CD4 and CD8SPs (Figure 4.2E,F), suggesting that most individual chains should confer specificity to class I and II antigens. 121 We found PSC-T cells matched known TCRs in the database at high frequencies (TCRβ match rate = 1.8 +/- 0.2%; TCRα match rate = 12.2 +/- 2%), similar to cord-blood and fetal T cells, but higher than postnatal thymocytes (TCRβ: p = 0.0024; TCRα: p = 0.0010), PBMCs (TCRβ: p = 0.062; TCRα: p = 0.000007), and the previous protocol for PSC-derived T cells by Micheals et al. which may have contained more innate T cells (TCRβ: p = 0.029)77 (Figure 4.3A, B). Additionally, we found TCRA chains were more than 5x more likely to match public TCRA chains than TCRB chains (Figure 4.3C). Of note, we also observed a decreased match rate between the week 13 fetal thymocytes, which matched at higher frequencies to public TCR databases that postnatal thymocytes (TCRα, p = 0.0041; TCRβ, p = 0.0091), and week 17 fetal thymocytes, which matched at similar frequencies to postnatal thymocytes (Figure 4.6). The week 17 fetal thymocytes also had a longer CDR3 region (Figure 4.1D), indicating TdT activity must begin increasing during this early developmental window prior to peaking during early postnatal development. Unexpectedly, PSC-CD4 and CD8SPs matched to both known HLA class I and class II reactive TCRs (Table 4.1). We normalized the count of class I and II matches to the number of class I and II reactive TCRs in the database to account for the bias in known class I reactive TCRs (ie. 56,712 class I TCRs vs. 2621 class II TCRs), and found that the CD4 and CD8 T cells matched with almost equal frequencies (Figure 4.3D). It is important to note that the VDJ database did not contain any internal V, J, CDR3 region chains that matched both known class I and II antigens.122 Table 4.1: TCR match data of PSC-T and human reference TCRs to the VDJ database. All matches to the VDJ database were performed using the exact V, J, and CDR3 amino acid sequences for individual TCRA and TCRB chains. Source Cell Type # of MHCI matches # of MHCII matches % MHCI matches % MHCII matches Ratio MHCI / MHCII matches # of CDR3A tested # of CDR3B tested # of CDR3A matches # of CDR3B matches % CDR3A matches % CDR3B matches CDR3A/ CDR3B match ratio PSC-T cell-v1 DP 241 8 0.43 0.31 1.39 1544 1544 224 29 14.5 1.9 7.7 CD8 379 6 0.67 0.23 2.92 2486 2486 351 39 14.1 1.6 9.0 PSC-T cell-v2 DP 21 0 0.04 0.00 - 277 277 19 2 6.9 0.7 9.5 CD4 69 1 0.12 0.04 3.19 408 408 64 9 15.7 2.2 7.1 CD8 24 1 0.04 0.04 1.11 184 184 22 3 12.0 1.6 7.3 Fetal Thymus DP 36 3 0.06 0.11 0.55 586 586 35 4 6.0 0.7 8.8 CD4 89 7 0.16 0.27 0.59 890 890 88 10 9.9 1.1 8.8 CD8 122 2 0.22 0.08 2.82 829 829 114 14 13.8 1.7 8.1 Postnatal Thymus DP 8 0 0.01 0.00 - 174 174 8 0 4.6 0.0 - CD4 8 0 0.01 0.00 - 263 263 8 1 3.0 0.4 8.0 CD8 11 0 0.02 0.00 - 257 257 10 1 3.9 0.4 10 PBMC CD4 323 14 0.57 0.53 1.07 10565 10565 315 24 3.0 0.2 13 CD8 330 2 0.58 0.08 7.63 4632 4632 286 69 6.2 1.5 4.1 123 Next, we asked whether the promiscuous matching of PSC-CD4 and CD8SP TCRs to known class I and II reactive TCRs was a result of the in vitro platform and artificial TCR stimulation during DP-SP transition. We performed the same matching of V, J, and CDR3 regions for each alpha and beta chain in fetal (n = 3), postnatal (n = 3), and PBMC T cells (n = 13). We found that CD4 and CD8SPs from each of the in vivo conditions also matched with both known class I and class II reactive TCRs (Table 4.1). Overall, we found that both PSC and in vivo-derived CD4 and CD8SPs contained single chain TCRs that matched known class I and II reactive TCRs. Of note, in vivo-derived T cells matched class I and II reactive TCRs with approximately equal frequencies, whereas PSC-derived T cells were more likely to match class I reactive TCRs (Table 4.1; Figure 4.3D). Furthermore, while we found that CD4 PBMCs matched class I and II antigens with equal frequencies, CD8 PBMCs matched 7x more class I versus class II reactive TCRs (Table 4.1; Figure 4.3D). 124 Figure 4.3: CD4 and CD8 PSC-T and human reference TCRs match both known HLA class I and II reactive TCRs. Bar plot of the A) TCRA match rate, B) TCRB match rate, C) CDR3B to CDR3A match ratio, and D) frequency of matches to known HLA class II versus class I reactive TCRs for in vitro PSC-T and human in vivo reference T cells. All matches to the VDJ database were performed using the exact V, J, and CDR3 amino acid sequences for individual TCRA and TCRB chains. Histogram of the predicted ERGO probability of E) known HLA class I reactive TCRs binding HLA class I peptides, F) known HLA class II reactive TCRs binding HLA class I peptides, G) known HLA class I reactive TCRs binding HLA class II peptides, and H) 125 known HLA class II reactive TCRs binding HLA class II peptides. I) Model of TCR class determination based on the kinetic signaling model; * p<0.05, ** p<0.01, **** p<0.0001. To further strengthen our TCR matching data, we analyzed class specificity within the VDJ database TCRs. We generated a list of all known class I matching TCRs and a list of class II matching TCRs, and used ERGO to generate binding probabilities to known class matched or mis-matched peptides. T cell type (CD4 or CD8SP) was obtained from VDJdb metadata, which we refer to as class II and class I TCRs, respectively. Epitopes were classified based on HLA class I or class II-reactivity. Due to limitations in the number of HLA class II epitopes in the database, we performed parameter sweeps to vary the ratio of HLA class I/II epitopes included in the model training data. To predict the class-matched versus class-mismatched TCR-epitope matching probabilities, four test permutations were simulated. The predicted binding probabilities were plotted as histograms, with average probability denoted in the legend. These simulations supported our TCR match data, where mismatch (ie. class I TCRs binding class II peptides or class II TCRs binding class I peptides) probability profiles resembled expected match probabilities (Figure 4.3E-H). Interestingly, similar to our TCR match data, class II TCRs were more likely to bind class I peptides than class I TCRs were to bind class II peptides. 4.4 Discussion Here we demonstrate that our characterized, feeder-free PSC-T cell differentiation platform produces PSC-CD4 and CD8SPs with diverse, fetal-like TCR repertoires that match known functional TCRs with high frequency. These diverse PSC-T cells present a unique opportunity to include adaptive immune components into organoid and organ-on-a-chip technologies, as well as use PSC-T cells to screen for functional TCRs. 126 Furthermore, we demonstrate how this platform enables insights into fundamental questions of human T cell development. These in vivo data, where CD4 and CD8SPs in the human thymus generate divergent repertories, yet their TCRs do not exhibit class specificity, are extremely intriguing. This implies that the CD4 versus CD8 lineage decision canât simply be due to differences in HLA class I and II peptidomes or CDR1/2 region bias towards a specific HLA class. Therefore, how is it possible to have mutually exclusive TCR repertoires in an individual if, in general, TCRs seem to not be class specific across the population? These data require careful examination of how current models of CD4 versus CD8 lineage bifurcation incorporate TCR signaling into lineage branch decisions. Under the kinetic signaling model of CD4SP versus CD8SP lineage branching, DPs first downregulate CD8 and test for TCR class II specificity, then if positive selection signals are absent, they test CD8 and TCR class I specificity (Figure 4.1A). Once CD4 or CD8 coreceptors are chosen, it is the coreceptor that dictates cell function and HLA class specificity202. Therefore, under this framework where most TCRs are not class specific, we would expect most CD4 PBMC T cells to be equally likely to react to both class I and II antigens, whereas CD8 PBMC T cells would be less likely to react with class II antigens, or else they likely would have become CD4 T cells (Figure 4.3I). Our observations support the kinetic model of CD4 versus CD8 lineage bifurcation, but do not suggest that TCR selection relies solely on interactions with different HLA class I and II peptidomes or CDR1/2 interactions with a specific HLA class as previously proposed. Our observations do not impact general tenets of central tolerance in the periphery, where CD4 cells recognize class II antigens and CD8 cells recognize class I antigens, because it is not the 127 TCR that gives specificity of function, it is the coreceptor202. Once a cell has chosen a CD4 or CD8 coreceptor their TCR interaction complex is restricted to specific class interactions by the coreceptor. This has been demonstrated previously using the FlipFlop mouse model where gene loci for CD4 and CD8 are swapped and cell function is reversed, resulting in cytotoxic class II recognizing cells and helper class I recognizing cells202. Furthermore, this framework suggests one possible reason that the thymus produces ~2x more CD4 T cells is that cells with equally likely reactive TCRs differentiate towards CD4 T cells, whereas only TCRs with less class II reactivity will become CD8 T cells. This hypothesis is also supported by the FlipFlop mouse model, where the authors observe a lower frequency of class II recognizing CD8 cells202. However, our data present an intriguing problem, where CD8 PBMC TCRs can be cross reactive to class II antigens in the database, albeit at lower frequencies than class I. The fact that TCR repertoires of CD4 and CD8SPs are divergent within an individual, yet CD8 TCRs from one individual can be found on CD4SPs in another individual, and vice-versa, suggests that there may be person-specific contributions to TCR repertoire selection. One possible mechanism for this cross-reactivity is an unappreciated dependence on HLA subtypes modulating TCR signaling strength. We propose that regions outside of the peptide-recognizing CDR3 region play an important role in this individualized repertoire selection, such as the CDR1 and CDR2 regions, by being differentially likely to interact sufficiently with class I or II molecules depending on which HLA subtype a person expresses. The CDR1 and CDR2 regions are encoded by the V chain and are the regions of the TCR that interact with the HLA molecule directly, but are less studied because they are not directly involved in antigen recognition. Differential CD4 TCR repertoire development between individuals with different HLA alleles 128 has been previously shown based on the CDR3 region, which is partly coded by the V chain240. Furthermore, HLA-dependency was originally proposed as an important mediator of TCR repertoire selection by Lugonova et al. in 2009240. In sum, this hypothesis could account for why TCR repertoires have some differences between individuals, but many of the V chains remain divergent between CD4 and CD8 T cells across the human population. Under this probabilistic model of TCR reactivity to class I and II, intermediate TCRs could differentiate towards either T cell lineage depending on how the CDR1 and CDR2 regions interact with a given personâs HLA subtype protein to produce short versus long signals202. Given the sensitivity of TCR interactions it is possible that these small differences in interaction strength could tip the balance of a successful positive selection signal. However, some CDR1 and CDR2 regions that fall further towards class I or class II recognition would consistently go CD4 or CD8 in people with diverse HLA subtypes (Figure 4.3I), resulting in some constantly differentially expressed V chains as often characterized in the literature231â234. This poses an interesting evolutionary advantage to reduce restrictions on successful TCR-HLA interactions, where developing T cells make as many TCRs as possible that could potentially support both class I or class II recognition depending on the HLA subtype, effectively increasing the available pool to select from for CD4 and CD8SPs. Interpretations of the in vitro PSC-T cell system are limited by the unknown impact of a lack of stromal and mimetic cells241, as well as artificial TCR stimulation on TCR repertoire selection. Additionally, in vivo comparisons to the VDJ TCR database are limited by the scale of known TCRs, and particularly, the lack of known class II reactive TCR data. Size limitations of the database have largely limited our ability to match paired TCRA and TCRB chains, which may 129 limit interpretation of the mismatch TCR data. However, given that in vivo and in vitro T cells matched class I and II reactive TCRs with nearly equal frequencies, TCRA and TCRB chains were individually divergent between CD4 and CD8 T cells, and including paired TCRA and TCRB chains when matching only minimally improves TCR class specificity predictions227, we believe this data suggests that most TCRs are not class-specific in the absence of coreceptor binding. 4.5 Limitations & future work These data are limited by the size of the PSC-T cell scTCR-seq dataset (approximately 103 PSC-TCRs). Given a total space for TCR recombination of size T, and two samples of size S, the probability of no TCR overlap is (1âðð)ð, and the probability of getting at least one overlap is 1 â (1âðð)ð. Table 4.2 summarises the probability of observing at least one overlap for different TCR recombination spaces if indeed there is TCR repertoire overlap in the sampled population. TCR diversity estimates vary considerably in the literature, with early postnatal peripheral TCR diversity estimated at 1010 88,226, whereas theoretical TCR recombination diversity has been estimated at >1021 242. Therefore, given the conservative estimate of a possible TCR repertoire space of 1021, it is not possible to conclude that PSC-CD4 and CD8 TCRs have divergent repertoires. 130 Table 4.2: Summary of the probability of at least one TCR overlap for various possible TCR recombination space estimates given a TCR sample size of S = 1000. TCR recombination space of size T Probability of at least one TCR overlap 107 10% 108 1% 109 0.1% 1010 0.01% 1021 10-13 % Additionally, the overlap of single chain TCRA or TCRB usage between PBMC-CD4 and CD8 T cells has been previously demonstrated via mouse chimeras243. In this study, the authors fix the TCRB chain to reduce the possible TCR diversity space and demonstrate divergent TCRA chain usage between CD4 and CD8 T cells, similar to our observations in human PSC and PBMC-derived TCRs. However, the authors also observe that mice with alternative TCRB chains can contain peripheral TCRA chains that mismatch with CD4 and CD8 TCRs from TCRB-fixed mice. Thus, while the number of sequenced TCRs in this study was small (i.e. <200 TCRs), these results support our observations that single chain TCRs can mismatch between CD4 and CD8 T cells if paired to a different second TCR chain. Therefore, to further demonstrate use of the PSC-T cell platform for hypothesis testing and modeling of human T cell development we propose modeling the impact of HLA allele subtype on TCR repertoire selection. In this experiment, a PSC line should be edited to remove endogenous expression of B2M, CIITA, and TCRB, as well as force expression a single known TCRB chain. Limiting TCRB diversity would decrease the TCR repertoire space, enabling sequencing readout of PSC-TCR diversity via bulk TCRA sequencing on sorted PSC-CD4 and CD8 T cells. Finally, PSC-lines expressing different HLA allele subtypes should be generated 131 and differentiated towards mature PSC-CD4 and CD8 T cells in vitro. We hypothesize that TCRA chain usage between PSC-CD4 and CD8 T cells captured via bulk TCRA sequencing will differ based on HLA subtype, demonstrating the role of HLA subtype in modifying TCR specificity towards HLA class I or class II molecules. This experimental scheme would demonstrate use of the PSC-T cell system to model specific questions surrounding human T cell development, specifically the impact of HLA subtype in CD4 versus CD8 T-lineage bifurcation and shaping of the TCR repertoire. 4.6 Methods 4.6.1 In vitro culture of PSC T cells 4.6.1.1 Human iPSC maintenance iPS-11 human iPSCs were maintained on plates coated with 80-120mg/mL Cultrex (Bio-Techne) in mTeSR+ media (STEMCELL Technologies). iPSCs were passaged using 0.25% Trypsin-EDTA (Thermo Fischer Scientific), and re-seeded in mTeSR+ media supplemented with 10uM Y-27632 (STEMCELL Technologies). 4.6.1.2 PSC-T cell differentiation version 1 For timepoint 1 cells, iPS-11 human iPSCs were differentiated to pro-T cells as described previously77. For timepoint 2 cells, iPSC-pro-T induction was completed using EHT suspension cultures (Thristan Taberna, confidential unpublished data). For PSC-pro-T to DP maturation, wells were prepared by coating with 10 μg/mL hDLL4 (Sino Biological) + 2.5 μg/mL mVCAM1 (R&D Systems) for 2 hours at RT or 37°C, or overnight at 4°C. Cells output from the pro-T induction stage were seeded at 200-400 x 103 cells/100 μL per 96 well in âPSC2â media. PSC2 comprises 9.76 ng/mL SCF (R&D Systems), 4.96 ng/mL Flt3L (R&D Systems), 15.22 ng/mL 132 CXCL12/SDF-1 (R&D Systems), 0.04 ng/mL TNFα (R&D Systems), 2.55 ng/mL IL3 (R&D Systems), and 71.93 ng/mL IL7 (R&D Systems) supplemented into âJAC Ultraâ base media. JAC Ultra comprises IMDM (Thermo Fischer) supplemented with 4% B27 (without Vitamin A, Gibco), 0.5% Pen/Strep (Invitrogen), 24 μM BME (Sigma-Aldrich) and 60 μM Ascorbic Acid (Sigma-Aldrich). Cells were fed by top-up addition of an equal volume of fresh PSC2 media at DP maturation day 3 or 4 (annotated M3 or M4). Thereafter, cells were fed by 50% media exchange with an equal volume of fresh media every 3-4 days. Cells were typically sampled for flow cytometry every ~7 days (e.g. days M7, M14, and M21). For timepoint 1, media was removed completely at M14 and replaced with Maturation media supplemented with 0.1% Immunocult Human CD3/CD28/CD2 T cell activator (STEMCELL Technologies), and a half media change was performed at day M18. At day M21 media was removed completely and replaced with Maturation media supplemented with 0.3% Immunocult Human CD3/CD28/CD2 T cell activator (STEMCELL Technologies), and a half media change was performed at day M25. At day M28, the culture was enriched for CD3+ cells using the EasySep Release Human CD3 positive selection kit (STEMCELL Technologies) according to manufacturerâs instructions. The cell suspension was pelleted, counted, and kept on ice prior to processing for CITE-seq and scTCR-seq. For timepoint 2, media was removed completely at M20 and replaced with Maturation media supplemented with 0.5% Immunocult Human CD3/CD28/CD2 T cell activator (STEMCELL Technologies), and a half media change was performed at day M24. At day M28, the culture was enriched for CD3+ cells using the EasySep Release Human CD3 positive selection kit according to manufacturerâs instructions and seeded 200 x 103/100 μL per 96 well in PSC2 supplemented 133 with 1.25% Immunocult anti-CD2/3/28 complexes in StemSpan maturation supplement + SFEMII Basal Media (STEMCELL Technologies) for SP induction. Cells were fed by top-up addition of an equal volume of fresh media (without stimulating reagents) at day S3. At day S7, cells were transferred to expansion conditions. For PSC-SP expansion, wells were prepared by coating with 3μg/mL anti-CD3 antibody (OKT3; eBioscience) and 150 μg/mL RetroNectin (Takara Bio) for 2 hours at RT or 37°C, or overnight at 4°C. Cells were seeded at 400 x 103/100 μL per 96 well in âPSC4â for 1 day, and then transferred to a plate with wells coated with only 150 μg/mL RetroNectin (Takara Bio) for 2 hours at RT or 37°C in PSC4, prior to collection for scTCR-seq (timepoint 2). Cells were fed by top-up addition of an equal volume of fresh media at day 4 and 7 post-seeding. PSC4 comprises 0.5 ng/mL IL7 (R&D Systems), and 0.5 ng/mL IL15 (R&D Systems) in JAC Ultra. Dead cell removal using Miltenyiâs Dead Cell Removal Kit (Miltenyi Biotech) was performed prior to sequencing. The cell suspension was pelleted, counted, and kept on ice prior to processing for CITE-seq and scTCR-seq. Cell hashing using BioLegend antibodies anti-Human_Hashtag-0251 and antibodies anti-Human_Hashtag-0252 was performed following the protocol provided by the supplier to denote cells from timepoints 1 and 2, respectively. 4.6.1.3 PSC-T cell differentiation version 2 iPS-11 human iPSCs were differentiated to pro-T cells as described previously77. Pro-T cells were then seeded at 2 million cells/mL onto 96-well plates coated with 10ug/mL human recombinant DLL4 (Sino Biological) and 2.5ug/mL mouse recombinant VCAM1 (R&D Systems), in Maturation Media (IMDM + Glutamax (Thermo Fischer) supplemented with 4% B27 without vitamin A (Gibco), 0.5% penicillin/streptomycin (Invitrogen), 24uM 2-134 mercaptoethanol (Sigma-Aldrich), 60uM ascorbic acid (Sigma-Aldrich), 9.8ng/mL SCF (R&D Systems), 8.5uL Flt3L (R&D Systems), 2.6ng/uL IL-3 (R&D Systems), 72ng/uL IL-7 (R&D Systems), 0.04ng/uL TNFa (R&D Systems), and 15.2ng/uL CXCL12 (R&D Systems)). A half media change was performed every 3-4 days. After 2 weeks of culture, media was removed completely and replaced with Maturation media supplemented with 0.3% Immunocult Human CD3/CD28/CD2 T cell activator (STEMCELL Technologies), and a half media change was performed every 3-4 days. After 2 additional weeks, cultures were enriched for CD3+ cells using the EasySep Release Human CD3 positive selection kit according to manufacturerâs instructions. EasySep-enriched cells were seeded at 1 million cells/mL onto uncoated 96-well plates in Maturation Media, and topped up with Maturation Media after 3 days. After 7 days, cells were transferred 1:1 onto 96-well plates coated with 3ug/mL anti-CD3 monoclonal antibody (BioLegend) and 150ug/mL RetroNectin (Takara Bio), in IMDM +Glutamax (Thermo Fischer) supplemented with 4% B27 without vitamin A (Gibco), 0.5% penicillin/streptomycin (Invitrogen), 24uM 2-mercaptoethanol (Sigma-Aldrich), 60uM ascorbic acid (Sigma-Aldrich), 5ng/mL IL-7 (R&D Systems), 5ng/mL IL-15 (R&D Systems), 50ng/mL IL-12 (R&D Systems), 50ng/mL IL-18 (R&D Systems), 20ng/mL IL-21 (R&D Systems), and 10uM Z-VAD-FMK (R&D Systems). The next day, cells were transferred 1:1 onto 96-well plates coated with 150ug/mL RetroNectin. After 3 days, cells were passaged 1:3 onto fresh RetroNectin-coated 96-well plates, and cultured for an additional 4 days. Samples were collected for sequencing at day 14 post-enrichment. 135 4.6.2 CITE-seq and scTCR-seq sample preparation analysis The BioLegend TotalSeq-C CITE-seq antibody preparation and 10X Genomics CITE-seq cell staining protocols were followed. Note that the PSC-T cell differentiation version 1 did not include CITE-seq. Briefly, one vial of TotalSeq-C Human Universal Antibody Cocktail v1.0 (BioLegend, San Diego, CA, USA) was equilibrated to room temperature for 5min and then spun at 10,000g for 30 seconds. The lyophilized panel was resuspended in 27.5uL of HBSS + 4% FBS, vortexed for 10 seconds, and then incubated at room temperature for 5min. The vial was vortexed again for 10 seconds and then spun at 10,000g for 30 seconds. The entire volume was transferred to a low protein binding PCR tube and then centrifuged at 14,000g for 10min at 4oC. Prior to CITE-seq antibody staining, cells from experiment v1 were tagged with six different BioLegend hashing antibodies based on the culture condition used. For both v1 and v2 sequencing preparations, 250,000 total cells were partitioned into a 12 x 75mm tube. Cells were spun at 500xg for 5 min at 4oC and resuspended directly in 12.5uL of HBSS + 4% FBS. 12.5uL of the antibody staining cocktail was added and the cells were incubated for 30min at 4oC. Cells were washed three times with 3.5mL HBSS + 4% FBS and resuspended in a final volume of 55uL. Cells were counted, stored on ice, and given immediately to the sequencing facility for 10X Genomics 5â library preparation and sequencing. The generation of single cell indexed libraries was performed by the Biomedical Research Center Next Generation Sequencing Core using the 10X Genomics chromium controller platform and the Chromium Single Cell 5â Library and Gel Bead Kit v1.1 and Chromium Single Cell Library and Gel Bead Kit v2 reagents. The sequencing protocol provided by the supplier was followed without modification for CITE-seq and scTCR-seq and run with the NextSeq 2000. 136 After run completion, the Binary base call (bcl) files were converted to fastq format using the Illumina bcl2fastq2 software, and data were received for further analysis. 4.6.3 scTCR-seq analysis Raw fastq files from all samples were aligned and quantified using CellRanger (CellRanger 6.0.1) and aligned with the human genome reference hg38. Following the suggested pipeline for quality control in Scanpy (v 1.10.2), CITE-seq data were filtered for dead cells, doublets, and red blood cells by excluding cells with greater than 5% mitochondrial genes and less than 500, but no more than 6000 genes. Genes found in less than 3 cells were removed. Samples underwent normalization, scaling, integration using anchors, dimensional reduction, and further downstream analysis using the standard Scanpy workflow. RunUMAP was run using 50 principal components and FindNeighbors clustering parameters (res = 0.5 for PSC-CD4 and PSC-CD8 datasets). Cells were clustered and identified based on known marker genes. Signac (v 0.17.0) was used to analyze scTCR-seq data. To compare PSC-T cell TCR repertoires to human fetal and postnatal thymocyte repertoires, the following reference datasets were used from Park et al. (2020) (Table 4.3). Cell cluster labels were modified to include dataset reference identifiers and then merged with the in vitro reference datasets. The CDR3 amino acid sequence and V(D)J sequences for the alpha and beta chains were extracted from each cell in the reference. Additionally, TCR data were extracted from the 13 control PBMC samples sequenced by Park et al. (2023)216. Table 4.3: scTCR-seq human thymocyte reference data used for analysis. Category Identifier Reference dataset Fetal 13w F38_TH_45P 137 Fetal 14w F30_TH_45P Fetal 17w F29_TH_45P Postnatal 10m T03_TH_TOT_1 Postnatal 30m T06_TH_TOT_1 4.6.4 TCR matching to VDJ database Exact matches for a given V, J, and CDR3A for TCRA chains or V, D, J, and CDR3B for TCRB chains to the VDJdb database were recorded, including data on reactivity to class I or II antigens. 4.6.5 ERGO modeling T cell type (ie. CD4 or CD8 T cell) was obtained from VDJdb metadata. We refer to TCRs from CD4 and CD8 PBMC T cells to be HLA class II and I TCRs, respectively. VDJdb epitopes were classified based on HLA class I or II reactivity. Parameter sweeps for the ratio of class I/II-reactive TCRs (ratio = 1:1, 2:1, 10:1, full database) were performed by sampling from the HLA class I pool, creating different training datasets. ERGO predictions were performed using the following variables: TRB, peptide, TRA, TRAV, TRAJ, TRBV, TRBJ, and HLA A allele. The predicted binding probabilities were plotted as histograms, with average probabilities denoted in the legend. ERGO predictions were performed for 4 permutations, including HLA class I TCRs binding class I epitopes, HLA class I TCRs binding class II epitopes, HLA class II TCRs binding class I epitopes, and HLA class II TCRs binding class II epitopes. 4.6.6 Quantification and statistical analysis Statistical details relevant to each figure can be found in the figure legends. For TCR matching and CDR3 length analysis GraphPad Prism 10 was used. For comparisons between two groups, unpaired t-tests were used, and for comparisons within the same experimental sample, two-sided paired t-tests were utilized. 138 4.7 Supplemental figures 139 Figure 4.4: PSC-T cell TCRs are developmentally stable and resemble fetal-like alpha and beta TCR chains (related to Figure 4.1). A) UMAP of integrated PSC-T and human fetal and postnatal thymus reference scTCR-seq datasets. B) Dominant and C) extra TCR alpha chain, and D) dominant and E) extra TCR beta chain CDR3 amino acid lengths for in vitro and in vivo human reference cell types. F) In vitro TCR alpha and G) TCR beta chain CDR3 amino acid length across in vitro T cell development stages. Dotted black line intersects CDR3 length of 13 amino acids. Mean CDR3 length Â± standard deviation displayed for each cell type population. 140 141 Figure 4.5: PSC-T TCR repertoire divergence recapitulates human fetal and postnatal TCR repertoire divergence (related to Figure 4.2). Heatmap of A) integrated PSC-T and human reference paired alpha and beta TCR chain repertoire overlap, B) paired fetal TCRA and TCRB chain repertoire overlap, C) fetal TCRA chain repertoire overlap, D) fetal TCRB chain repertoire overlap, E) paired postnatal TCRA and TCRB chain repertoire overlap, F) postnatal TCRA chain repertoire overlap, and G) postnatal TCRB chain repertoire overlap. 142 Figure 4.6: T cell TCR match rate decreases during fetal T cell development (related to Figure 4.3). In early fetal development (week 13) CDR3A and CDR3B amino acid sequences match public TCR databases with higher frequency than postnatal thymocytes. By week 17, fetal TCRs match public database TCRs at similar frequencies to postnatal T cells. Multiple studentâs t-test; ** p<0.01; NS p>0.05. 143 Chapter 5: Conclusion 5.1 Summary T cell development in the thymus is a tightly regulated process which guides migration of developing thymocytes through functionally distinct microenvironments optimized to support stage-specific differentiation and training. Thymus microenvironments contain an unappreciated complexity of cell types and niche level organization to ensure thymocytes are sufficiently trained to recognize self, and non-self, antigens in the periphery. To maintain immune homeostasis, it is essential that supporting stromal and non-T hematopoietic cells produce T cells with sufficiently diverse TCRs that can recognize and respond to nearly any pathogen or disease, while also ensuring self-reactive peripheral T cells are removed via presentation of a diverse peptidome and proper negative selection. The complexity of this regulatory process has been slowly untangled in model organisms; however, these methods often require tuning one gene or signaling pathway at a time, limiting our understanding of how regulation of signaling, cell-cell, and niche-level regulatory interactions combine to orchestrate conventional T cell, innate T cell, and alternative lineage cell development in the thymus. Furthermore, it is becoming increasingly obvious that while immune systems share many homologies between species, the level of control required to tune immune responses requires deeper knowledge of human immune cell development. To bridge these gaps, we began by applying spatial multiomic technologies to create a map of human T cell development in the early postnatal thymus. We focused our spatial atlas on characterizing how key developmental niches drive lineage branch decisions, demonstrating a role for JAG ligands in directing non-inflammatory thymus-like dendritic cell differentiation 144 from ETPs, identifying a possible new mechanism for conventional αβT cell development through self-selection, and suggesting new functions for mesenchymal cell types governing thymus and T cell biology via ECM and cytokine-based signaling. Our approach to sequencing analysis uses multidimensional imaging to establish benchmarks for the location, ligand expression, and composition of key niches in T cell development. This enables us to guide analysis of sequencing-based cell-cell interaction predictions by niche composition and identify physiologically relevant ligand-receptor interactions based on cell proximity within the tissue. This methodology presents a novel approach to studying dynamic developmental processes in human tissues, and demonstrated how spatial atlases can be used for hypothesis generation followed by testing and validation in vitro. An unexpected finding of our research, we discovered several sex differences in early postnatal thymus cell and niche biology affecting critical processes in thymocyte development and training. Given the early timepoint in postnatal development these differences were surprising. Nevertheless, this is the stage in human development where T cell production is highest, and T cells released during this stage have the potential to circulate in the body for decades. Understanding the potential for sex biases in T cell development and training to affect the reactivity of these cells in the periphery remains limited and requires further investigation. Next, we demonstrate a specific application of this resource in guiding development and optimization of a PSC-T cell technology which can produce tunable ratios of PSC-CD4 and CD8SPs. Generation of PSC-CD4 T cells, especially in defined, feeder-free conditions, has been a long-standing goal in the field to enable allogeneic cell therapies and in vitro immune response modeling. Here, we demonstrate how two insights into CD4 development in the human thymus, 145 namely strict spatial downregulation of Notch ligand signaling as cells migrate away from the capsule and the presence of self-selection niches with HLA class II+ DPs in the inner cortex, were applied in vitro to produce PSC-CD4SPs. Multiomic sequencing analysis confirmed these cells express mature CD4SP lineage markers and diverse TCRs. Finally, we demonstrate how our defined, feeder-free PSC-T cell technology can be used to study human T cell development and gain new insights into fundamental questions of adaptive immune system development. Using scTCR-seq on PSC-CD4 and CD8SPs, we investigated how TCR signaling and repertoire formation guides CD4 versus CD8 T-lineage bifurcation. We find that in both PSC and in vivo reference-derived T cells, CD4 and CD8SPs generate divergent repertories, yet their TCRs do not exhibit class specificity. Our observations support the kinetic model of CD4 versus CD8 lineage bifurcation, but suggest that TCR selection does not rely solely on interactions with different HLA class I and II peptidomes or CDR1/2 interactions with a specific HLA class, as previously proposed. We believe this data suggests that most TCRs are not class-specific in the absence of coreceptor binding and propose a probabilistic model of TCR reactivity to HLA class I and II following the tenants of the kinetic signaling model. In this model, intermediate TCRs could differentiate towards either T cell lineage depending on how the CDR1 and CDR2 regions interact with a given personâs HLA subtype protein and integrate with CDR3 interactions to produce short or long TCR signals. In sum, this work exemplifies how the PSC-T cell platform can be used to identify new mechanisms guiding T cell development and relate these learnings to fundamental mechanisms of T cell development in the human thymus. 146 5.2 Contribution This work demonstrates how one can integrate spatial multiomic technologies to study dynamic cell development in human tissues. It demonstrates how spatial atlases can be used to generate hypotheses regarding human developmental processes, and how these hypotheses can be tested and validated in vitro, as well as used for in vitro technology improvement and optimization to generate human PSC-derived immune cells for clinical immunotherapy and disease modeling. Importantly, this work demonstrates how in vitro technologies can be used to study and perturb human T cell development to uncover fundamental mechanisms guiding development of the human immune system. 5.3 Application and Limitations The research described here provides a basis for studies on other tissues which support human cell development, such as bone marrow and fetal liver, to uncover mechanisms and signaling networks guiding differentiation. The methodology herein can be applied to identify niche factors for testing in alternative PSC differentiation systems. As such, this work is of interest to other academic research laboratories developing in vitro PSC-differentiation systems using defined conditions. Additionally, we generate a powerful resource for hypothesis generation regarding human T cell and thymus biology and propose critical research directions to understand how sex biases in early postnatal T cell development impact immune function in adult and aged individuals. Furthermore, the PSC-T cell technology, optimized to produce controllable ratios of diverse PSC-CD4 and CD8 T cells, opens opportunities for clinical immunotherapies, patient drug response modeling, and disease pathology modeling (refer to Chapter 5.4.1). 147 Access to thymus samples was mainly limited to early postnatal samples (refer to Chapter 5.4.2), enabling age-matched analysis of human T cell development, but limiting resource expansion to fetal and aged thymus microenvironments. PSC-T cells resemble fetal T cells and may require signaling environments resembling the fetal, not postnatal, thymus. Additionally, to use these cells for immune response modeling, further work to produce PSC-T cells resembling adult T cells may be required to accurately model disease pathology progression and drug responses244. The dataset size used to analyze sex biases in T cell development was limited to six patients (three male, three female), restricting intra-sex variability analysis. The potential impact of sex biases in early human immune system development prompt further research into how these biases functionally impact T cell development and reactivity in the periphery. PSC-T cell platforms present a powerful system to test such hypotheses, where PSC lines of different sexes can be differentiated to model how sex differences affect immune cell development and therapeutic T cell production. A challenge with studying sex-related differences is gene expression variation caused by autosomal differences both within and between sexes. Therefore, one could use commercially available PSC lines with an isogenic background but different sex chromosome complements: XX, XY, XXY, and X0137. After acquiring and confirming cell genotype, one could differentiate each cell line to T cells using our PSC-T cell platform (Figure 5.1). To understand how sex chromosome complements bias T cell lineage decisions, experimental readouts could measure the percent and yield of on- and off-target cell types of interest, such as DP, CD8, CD4, NK, myeloid, and innate-like T cells, throughout the differentiation (Figure 5.1). RNA-seq and TCR-seq could also be employed to investigate differences in TCR repertoire formation (Figure 5.1). 148 These experiments could reveal baseline sex differences in T cell development from isogenic PSCs and lay the groundwork for larger-scale analyses of how sex interacts with other genetic variables relevant to disease modeling and therapy. These results would also demonstrate that the PSC-T cell platform can be adapted to produce both male and female T cells for immunotherapy. Figure 5.1: Experimental outline for uncovering sex differences in PSC-T cell development. Isogenic PSC lines could be differentiated using the established PSC-T cell protocol, benchmarking cell type composition through the differentiation. Final readouts using RNA-seq and TCR-seq on mature PSC-CD4 and CD8SPs could further characterize sex effects on cell phenotype and TCR repertoire. Furthermore, analysis of TCR specificity matching to public reactivity databases is limited by the size of the VDJdb database, particularly in HLA class II-reactive TCRs, as well as the number of sequenced PSC-T cell and human reference TCRs. From this analysis we proposed TCR cross-reactivity between CD4 and CD8 T cells from different individuals may be explained by an underappreciated dependence on HLA subtypes modulating TCR signaling strength. To further strengthen our hypothesis, we propose additional experiments using the PSC-T cell system to test HLA dependence of CD4 and CD8 TCR repertoire formation. In these experiments one could 149 generate isogenic PSC lines expressing a single HLA class I and class II loci (see for Chapter 4.5 further details). Cells could be differentiated in the PSC-T platform and analyzed via TCR-seq to readout differences in V segment usage between cell lines. In sum, these experiments would provide insights into why TCR repertoires have some differences between individuals, but many of the V chains remain divergent between CD4 and CD8 T cells across the human population. 5.4 Future work 5.4.1 Regenerating the immune system with stem cells Production of PSC-T cells in defined, feeder-free culture system enables various applications of immune cells for therapy. Given the striking degeneration of the adaptive immune system with age and the known importance of continuous production of naïve T cells later in life245, regenerating the immune system with stem cells, including PSC-T cells, has gained traction. The proposed therapeutic approaches (refer to Appendix A) hold enormous promise in improving healthspan; however, careful consideration of the potential consequences of artificially modifying the immune system is required. 5.4.1.1 Consequences of applying immune regeneration strategies in the elderly Given the promise of these emerging technologies and the movement of several strategies towards the clinic to address age-related changes to the immune system, it is important to reflect on the evolutionary factors driving thymus involution. We and others are particularly interested in understanding why thymus involution initiates so early in human development and how the consequences of this phenomenon on human health may be exasperated in the context of ageing and disease, further motivating immune therapies in the elderly. The âWear and Tearâ theory of thymic involution suggests that the breakdown of thymic tissue function is due to intense use 150 early in life, resulting in deterioration of thymic cellular machinery246,247. However, this theory does not account for the fact that the thymus can robustly regenerate after periods of stress. The âDirect Adaptationâ theory hypothesizes that, since the thymus is highly proliferative, thymus function must be decreased quickly to prevent thymomas and decrease the risk of autoimmunity248. Given this logic, the question remains why we donât observe a parallel decline in B cell production â a similarly proliferative tissue â and see an increased risk of autoimmune disease following removal of the thymus245. Finally, the âDisposable Soma Theoryâ argues that organisms must optimize their investment in the maintenance of thymic tissue, where energy is invested early in life to produce a diverse pool of long-lived peripheral T cells, and T cell production is subsequently decreased to divert energy and resources towards the production of progeny and passing of genes to future generations249â251. Essentially, this theory claims that the process of somatic cell ageing has evolved within the natural environment such that an animal will die of a non-age related illness prior to loss of organ function becoming a sufficiently serious threat to life. For further commentary on the evolutionary cost-benefit analysis of continual production of different immune cells, the authors recommend the important commentary by George & Ritter252. When critiquing this breakdown in immune function it is important to consider that thymic involution was optimized and selected for in individuals who were not subject to advances in modern medicine and infrastructure that extend lifespan of the elderly, and did not mature in our globalized society where pathogens can travel rapidly between continents and people live in highly populated cities burdened by pollution and increased probability of disease reinfection253. For example, thymic involution was optimized in individuals living in small hunter-gatherer 151 groups with relatively little outside exposure to other groups or environments. Therefore, within the first few years of life individuals would have been exposed to most pathogens within their environment252. Compared to today, the breadth of new pathogens and, importantly, the likelihood of repeat, immune polarizing exposures these individuals would be exposed to later in life pales in comparison to the number of repeat exposures individuals living in our globalized society are exposed to253. How does this rapid change in our environmental exposures affect how our peripheral T cell pool skews and remodels during our lifespan? These rapid societal changes argue that increasing the production of naïve T cells in the elderly would increase TCR diversity, enabling better vaccination responses and response to new pathogens to potentially counteract this effect of globalization. For these reasons, the Disposable Soma Theory has gained traction and provided the groundwork to argue for continued production of T cells in the elderly. However, critics of these approaches continue to raise the important question of how artificial manipulation of the delicate balance of immunity could result in unintended autoimmune consequences. For example, we still do not have good models to understand how new naïve T cells might react in an aged, inflammatory environment. In this argument, the authors find it helpful to view possible therapeutic perturbations along an autoimmunity â infection risk spectrum (Figure 5.2). If we consider T cell reactivity along this spectrum, with aged patients having decreased immune reactivity, our goal with immune supplementation is to push the under reactive immune response back towards the center of the scale. However, this push needs to be tightly controlled, as the unknown consequences of tipping the scales towards autoimmunity could be equally severe. It is also critical to consider that this scale is likely non-linear and personalized to each individualâs 152 immunological fingerprint which includes axes such as age, sex, TCR clonality, T cell exhaustion ratios, and levels of inflammatory biomarkers. This is not said to discourage progress on countering age-related immune impairment, but instead to argue for concurrent modeling of patient populations and immune system reactivity as regenerative therapies develop. For example, we could imagine binning patients based on biomarkers of inflammaging and age-related immune impairment and using this stratification to determine dose and applicability of different therapies. Our goal here is to pose the question of risk â reward of immune regeneration and spark discussion on how to model the extreme heterogeneity seen in immune responses in the elderly. The benefits of immune regeneration are vast and exciting, but the consequences are highly relevant to long term health. We hope to highlight how emerging technologies can be used to model patient immune systems and therapeutic responses to address these conflicting perspectives in our discussion below. 153 Figure 5.2: The autoimmunity â infection risk spectrum. A healthy immune response requires a regulated response of T cell activation against pathogens and mutated host cells which is contained to limit damage to surrounding tissues. An under reactive response, which has been associated with individuals of male sex and Th2 skewed biomarkers, lacks the ability to clear infections and cancerous tissue, whereas an over reactive response, which has been associated with individuals of female sex and Th1 or Th17 biomarkers, can result in autoimmune or allergic disease. Immune regeneration therapies aim to push the balance of immunity towards a healthy immune response. To safely achieve this, the initial state of the patientâs immune system along this spectrum must be well characterized and the effect of artificially altering the patientâs immune system must be defined. This could be done by collecting patient biomarkers of age-related immune impairment and inflammaging as screening parameters and binning patients accordingly along this spectrum. Patient responses to a given therapy could be modelled for each bin by recreating the inflammatory immune environment with bin-specific culture media and T cell state conditions. 154 5.4.1.2 Next steps and future perspectives on immune regeneration Given the incredible progress the field has made towards rejuvenating the immune compartment, it has become imperative to revisit questions of patient applicability, timing, dose, and control of our artificial manipulations. How do we account for known differences in immune responses â not only accounting for age, but also accounting for sex, race, and environmental stresses? What platforms for drug testing are we currently lacking to empirically answer these questions? Here we discuss the potential of emerging technologies to address these questions via in vitro and in silico modeling (Figure 5.3). Figure 5.3: Continued progress of emerging immune regeneration therapies requires improved understanding of how transplanted cells and tissue interact and function with the host immune system, as well as technology development for in vitro modeling of diverse immune system states. 155 Key insights into drivers of tolerance emerged from thymus transplant studies where new T cells are tolerized to both host and donor HLA profiles. Further progress to unlock the potential of allogeneic, hydrogel-based, and PSC-based thymus tissue transplants requires improved understanding of how different thymus cell types contribute to TCR repertoire formation and selection. Modeling of patient stratification characteristics is required to enable safe and personalized immune regeneration treatment. Ideally, therapies would be screened in conditions representative of the patientâs immune system to predict how the immune system will respond. To achieve this goal, we need to incorporate and characterize diversity in PSC donor cell lines and culture media conditions to generate predictive models of the function of emerging immune therapies in different patient populations. 5.4.1.3 Modeling HLA mismatching and determining drivers of tolerance Surprisingly, thymus transplants do not require HLA-matching to prevent GVHD and preliminary results have even shown an increase in the number of CD4 T cells produced in complete HLA-mismatched grafts versus grafts with partial HLA matching254â256. These results are surprising, as one would assume the TECs contained within the grafts would tolerize T cells to graft HLAs and not to host HLAs, resulting in naïve T cells released to the periphery that are reactive to host tissue. It has been suggested that the observed tolerance in these patients may be due to infiltration of hematopoietic cells into the tissue256, either as mature dendritic cells or via differentiation of ETPs towards alternative lineages such as dendritic cells123,257â262. Stromal cells may also migrate into thymic tissue to form the vasculature or to encapsulate the transplanted tissue. Thymic fibroblasts have been recently shown to play a role in medullary selection and tolerance23, and possibly in the selection of cortical thymocytes as well12. Host epithelial cells could also migrate into the graft, as is seen in lung transplants263,264. However, the presence of 156 these host cells within the engrafted tissue has only been hypothesized and not definitively shown as almost all the patients who survived the first year post-thymus transplantation were alive at the culmination of the clinical study255. Given the importance of thymic tolerance mechanisms such as TEC expression of AIRE and FEZF2, or the presence of mimetic cells in the medulla241, the success of these grafts is still remarkable. DiGeorge syndrome patients who have received these transplants have manifested autoimmune conditions254,255; however, it remains unclear if the increased risk of autoimmune disease is due to the gaps in thymic tolerance created by the lack of TEC tolerance mechanisms, or if it is due to alternative mechanisms associated with DiGeorge syndrome, which generally presents with increased risk of autoimmune conditions265. Regardless, these results raise important questions which need to be addressed if PSC-based allogeneic strategies for thymic transplantation are to be pursued to increase availability of these therapies and expand possible indications. Providing further insight, children who have received thymic transplant grafts thus far were athymic and had <106 cells/L circulating naïve T cells256. This lack of T cells, along with a course of immunosuppressants during the first few months after thymus transplant, has resulted in minimal complications where host immune cells attack the HLA-mismatched graft254â256. These clinical results are encouraging as it is possible that patients who receive PSC-derived thymic grafts may not require lifelong immunosuppression, the effects of which may counter many of the benefits of the enhanced T cell compartment. However, it remains to be seen if patients without complete DiGeorge syndrome will require myeloablative therapy to ablate the 157 host immune system and if the levels of T cells remaining in the patient post-myeloablative therapy would be sufficient to prevent graft rejection complications. Furthermore, the autoimmune complications seen thus far necessitate further investigation of the TCR repertoire changes these grafts produce. PSC-derived thymic organoid models present a unique opportunity to test questions regarding which thymic cell subsets contribute to the development and tolerance of specific TCR clones, and how alterations to the ratios of different thymic cell types can skew the TCR repertoire. Current thymic organoid systems can combine PSC-derived TECs, fibroblasts, hematopoietic cells, and pro-T cells266. One could design an experiment where each cell type in a thymic organoid has a specific HLA identity and incorporate these cells in different cell type combinations. The pro-T cells would develop in environments where they are tolerized to HLAs by specific cell types and the resulting TCR clonal diversity could be read out via single cell TCR sequencing to determine how specific cell types shape TCR repertoire formation. Insights from these in vitro models, which can be performed in higher throughput, controlled experiments, would provide important insights regarding the impact of non-epithelial cells on TCR repertoire selection in the thymus23,267. 5.4.1.4 Modeling patient stratification and the impact of the aged immune system An emerging question in the field is how patient attributes should affect the choice of strategy, timing, and applicability of a therapy. Current research from our labs has emphasized the presence of sex differences in thymus biology and thymic involution kinetics12. We provided evidence of thymus sex differences in 4-5 month old infants, capturing both common sex differences seen in other body tissues that affect cell metabolism and reactivity to stimulation, as well as thymus-specific differences in the training environment of T cells. Furthermore, we 158 found evidence of differential thymic involution kinetics between sexes, with male cells demonstrating increased expression of adipogenesis pathways even at this early developmental stage. This research contributes to a large body of evidence that thymic involution kinetics differ significantly between sexes and, given the strikingly early onset of thymic involution, highlights the important question of when therapeutic strategies should begin. Should tissue-centric thymus rejuvenation strategies begin early in life to prevent thymic involution? Or, should they aim to restore function in middle-aged individuals to extend thymus productivity? We would argue that research on young-to-old blood transfusions demonstrates that early intervention before thymus involution begins could be advantageous, given the known damaging influence of inflammatory environmental factors in aged tissue on progenitor cell function268. However, the timeline for readouts for these preventative therapies would be long and difficult to justify, and the thymus possesses the capacity to regenerate even after involution has begun269,270. Additionally, there is a concern for increased risk of autoimmune disease with increased naïve T cell generation in middle aged individuals. Further research on the impact of countering thymus involution in otherwise healthy adults and clarification of the target patient populations and timing of implementation of emerging thymus rejuvenation technologies is required. It is also prudent to consider the level of precision and control we require for these therapies when we assess therapeutic design strategies. For example, if employing a cell supplementation approach from defined manufacturing systems do we need to have defined ratios of different T cell subsets? In CAR T cell therapy, inclusion of both CD4 and CD8 CAR T cells shape the overall expansion kinetics of therapeutic products and are crucial for maintaining long-term responses271. To precisely engineer the immune system in the context of age, it will be essential 159 to understand the contribution of individual T cell subset ratios in modulating immunity and how in vitro-derived naïve T cells will function in an aged, inflammatory environment. Similarly, if thymus function is rejuvenated under aged inflammatory conditions will the peripheral immune compartment reach a healthy homeostasis? If technologies to reboot and rebuild the immune system are to be successful in preventing and treating age-related diseases, we need to have a clear understanding of how the aged systemic environment will affect cell function, as well as how the function of key cell types involved in TCR repertoire selection, such as AIRE+ and mimetic cells, change with age. Again, we reference the autoimmunity â infection spectrum and argue that binning patients based on health history and biomarker status provides a first-attempt approach at empirically answering these questions. For example, in the case of cell supplementation approaches in vitro human-on-a-chip models could be developed where culture media is tailored with differential cytokine concentrations to reflect the aged inflammatory environment spectrum. By changing the ratios of CD4, CD8, and T regulatory cells included in these models and measuring changes in T cell skewing and function, we can begin to develop in silico models of how cell therapy products can be tailored to different immune systems. 5.4.1.5 Conclusions regarding current immune regeneration strategies Overall, important unanswered questions remain regarding the feasibility and safety of immune regeneration approaches outside the context of HSC transplantation and genetic immune system defects. As strategies discussed herein progress towards the clinic, consideration of the implications in clinical use and design parameters, such as timing of application, interaction with the host immune system, and functional characterization of the target patient population, become 160 increasingly important. We would argue that these parameters should be a primary consideration guiding design of new therapeutic strategies, with knowledge on timing of implementation and target patient population guiding the choice of strategy. 5.4.2 Fetal & aged thymus imaging Thymus niche biology and function is a dynamic process that changes rapidly through an individualâs lifespan. Here, we obtained access to fetal and aged thymus samples and performed CODEX multidimensional imaging to characterize how cell type composition and T cell developmental niches change throughout life. We received three fetal thymus samples from collaborators in Dr. David Mooneyâs laboratory at Harvard, obtained from aborted fetuses at week 11, 13, and 17 in gestation. In week 11 thymuses we observed a small number of CD45+ cells, with infiltrating macrophages (CD206) and several DAPI+ cells not stained by our extensive imaging panel (Figure 5.4A). The tissue contained large regions of fibroblasts (PDGFRα) with endothelial cells (CD31) dispersed throughout, and an external epithelial border (CK5/8). By week 13 we observed an interesting tissue architecture of epithelial islands with DPP4+ centers as well as epithelial islands with PDGFRα+ centers (Figure 5.4B). These islands were interspersed within DAPI+ cells that again, were not stained by our imaging panel. The epithelial islands at this stage stained positive for our cytokeratin 5/8 antibody, but did not express EPCAM, a primarily mTEC marker in late fetal and postnatal thymus9. Week 17 thymus displayed tissue architecture analogous to postnatal thymus (Figure 5.4C), with lobules organized into distinct medullary and cortical regions and developing thymocytes encompassing the full T cell development spectrum (ie. CD1a, CD5, CD7, CD3, CD4, CD8a). The lobules were 161 encased in a thick PDGFRα border and had few Hassallâs corpuscles. We performed preliminary image analysis, classifying cell types according to marker protein expression (Figure 5.4D). 162 Figure 5.4: Preliminary CODEX imaging analysis of fetal and aged thymus samples. Immunofluorescence images of thymus samples at A) week 11, B) week 13, and C) week 17 in human fetal development. D) Cell type identification markers for downstream quantitative image analysis. E) Immunofluorescence images of a thymus sample from a 50-year-old female. Finally, we have received ten aged thymus samples from organ donors. 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Methods 18, 1333â1341. https://doi.org/10.1038/s41592-021-01282-5. 222 Appendices Appendix A - Ageing and sex differences in the immune system Over the last century advances in modern medicine have enabled a rapid increase in lifespan, pushing our bodies far past the boundaries of natural selection pressures. With this demographic shift it has become apparent that our immune system, which circulates between tissues and integrates body systems, is a weak link in the ageing process272â274. The immune system is increasingly characterized as the driver of age-related diseases including autoimmune disease, cardiovascular disease, neurodegenerative disease, increased risk of infection, and cancer275. This phenomenon, known as immunosenescence, was first defined by Roy Walford in 1964 and describes the decline, remodeling, and dysfunction of innate and adaptive immunity with ageing276,277. In an ageing society, where increased immune function in the elderly would improve healthspan and greatly reduce the rising healthcare burden, stem cell-based cell and tissue therapeutic approaches to counteract immunosenescence and age-related immune impairment have gained traction273. At present there exist two schools of thought, those that focus on rejuvenating primary lymph tissue, namely the bone marrow and thymus, and those that focus on rebuilding the immune compartment directly by providing artificial niches for T cell production or injecting in vitro-derived immune cells14. Tissue-based thymus regeneration technologies include stimulating expansion of host thymic tissue, injection of hydrogel-based scaffolds to support T cell generation, transplantation of blood stem cells or progenitor T cells, and transplantation of PSC-derived thymic tissue. Alternatively, direct in vitro generation of immune cells uses emerging engineered thymic niche technologies to produce mature T cells for direct supplementation of the 223 peripheral immune compartment. Across these approaches the theme is clear, our knowledge of stem and progenitor cells and their potential use in counteracting the effects of age-related immune impairment is the key to challenging our current understanding of healthspan. However, there remain important, unanswered questions regarding the various risks associated with modulating the natural evolution of immunity through ageing. Here, we highlight the biological models of immune system ageing and regeneration which have laid the foundational knowledge of immune system manipulation. Furthermore, we will discuss the strengths, limitations, and clinical relevance of emerging technologies that aim to regenerate the ageing immune system. A.1 Immune system ageing is a dynamic, progressive phenomenon It remains unclear what specific molecular or cellular events initiate immune system deterioration and trigger the cascade of organismal ageing. Age-related decline in immunity is in part catalyzed by a systemic increase in proinflammatory markers, termed âinflammagingâ, which is subsequently mirrored by an increase in immunosuppressive activity via generation of Tregs and anergic T cells272,278,279. Age-related immunosuppressive activity leads to age-related diseases, including poor vaccination outcomes, increased risk of infection, and malignancies173,280â282. Similarly, the deleterious impact of decreased immunity has been well-studied in patients who receive lifelong immunosuppression post-organ transplantation. Immunosuppression patients often present with severe complications later in life due to the side effects of suppressed immune responses283, including an increased risk of cancer284, infections285, autoimmune disease286, hypertension287, neurotoxicity288, and metabolic disorders289,290, among 224 others. These patients represent a defined cohort with accelerated loss of immune function compared to individuals exhibiting age-related decreases in immunity291. Age-related defects in adaptive immunity also play a major role in immune impairment-driven disease. Here we focus on age-related defects in T cell immunity as an early driver of immune impairment. For further information on the age-related impact of B cells on immune impairment we refer readers to the following reviews292â295. The progressive decline of adaptive immune function is initiated by thymic involution - an age-related process by which thymus tissue progressively turns to adipose tissue. In healthy thymus, spatiotemporal developmental signals guide development of HSCs homing from the bone marrow to mature T cells that have been carefully selected to remove autoreactive cells (Figure 1.1). Thymic involution catalyzes major changes in the peripheral T cell compartment, resulting in a marked decrease in naïve T cell generation and altered ratios of naïve T cell subsets released into the periphery296,297 (Figure A.1A,B). This phenomenon has been shown to initiate surprisingly early in development, with declines in thymic cellularity and size, as well as adipogenic activity of mesenchymal cells, seen in infants under the age of one12,13. After one year of age, thymus activity, defined by volume of active, non-adipogenic thymus tissue, declines at a rate of 2-3% per year until middle age when thymic involution slows to a rate of 1% per year, leading to significantly reduced activity in individuals 50-65 years of age87,252,298. However, even with significantly reduced T cell output, the aged thymus continues to contribute to immune function, as demonstrated by a recent retrospective study on aged thymectomy patients discussed below. Thymic involution is also accelerated during periods of stress, such as during puberty, pregnancy, or infection79,299â301, which has been shown in clinical studies to be linked to increased biological age302. In sum, 225 thymic involution significantly pre-dates metabolic and inflammatory age-related changes arising in middle aged individuals and acts as an early driver of age-related immune impairment (Figure A.1C). 226 Figure A.1: Thymic output changes and progressively declines with age driving age-related immune impairment in the elderly. A) Immune cell subset production changes throughout life with increased innate-like phenotypes emerging during early fetal development and increased T regulatory cells in aged individuals. B) Thymus activity, defined by thymus size and productivity changes drastically with age. The thymus grows rapidly during fetal development and the first year of life, and then begins to decrease production. The decrease in function accelerates during puberty and continues to decline until thymic function and activity has been significantly reduced, generally occurring between 50-65 years of age. C) Thymic involution acts as an early driver of age-related immune impairment via deceased production and alterations in the output of specific T cell subsets. This results in decreased TCR diversity which, combined with age-related defects in immune cells and increased basal levels of inflammation, leads to decreased immune function and drives age-related disease. The decline in recent thymic emigrants throughout life coupled with recurrent exposure to pathogens significantly remodels TCR diversity in the peripheral T cell compartment (Figure A.1C). During early fetal and postnatal development the thymus is highly active, generating approximately 4x1011 T cells with an average TCR diversity of 1010 TCRs within the first year of life88,226. Exposure to antigens remodels an individualâs TCR profile to reflect their environmental pathogens and confer adaptive immunity to previously encountered pathogens303. These changes allow an organism to rapidly mount an immune response against previously encountered pathogens to clear infections. However, with age the thymus gradually undergoes involution, significantly reducing the output of naïve T cells. The remaining T cells become increasingly reactive towards routinely encountered pathogens. This is best exemplified by exposure to latent viruses, such as Epstein Barr Virus (EBV), which can periodically leave 227 dormancy and repeatedly activate T cells with TCR specificity towards a specific antigen304â307. This results in decreased TCR diversity and a lack of naïve T cells available to respond to new exposures, as well as an exhausted T cell compartment against the latent virus. The dramatic decrease in T cell diversity with age is captured in recent single cell TCR sequencing studies on elderly individuals, which demonstrate dramatic clonal expansion and decreased TCR diversity with age226,303,307. Ultimately, decreased naïve T cell output, along with age-related chronic inflammation and metabolic changes, result in alterations in the peripheral T cell compartment which may limit immune responses and drive age-related disease272 (Figure A.1C). A.2 The initiating events â interplay on molecular, cellular, and physiological levels Thymic involution is catalyzed by several mechanisms that regulate the progressive decline in T cell output and transition towards adipose tissue. For example, fetal thymus development and regulation of the size of the ETP niche have been shown to be in part regulated by ETPs migrating from the fetal liver or bone marrow308. In early fetal development these cells provide necessary cues to support TEC differentiation into cortical and medullary TECs, a step that is necessary to generate the spatially defined architecture of the thymus lobule that supports positive and negative selection of thymocytes. Throughout life ETPs continue to provide essential crosstalk signals with TECs, as exemplified by the ability of young progenitor T cells injected into the bloodstream to migrate to the involuted thymus and regenerate the developmental niche269,270. However, with age there is a decrease in HSC proliferation and release of ETPs from the bone marrow leading to a decrease in progenitor T cell â TEC crosstalk signals176,270,309. 228 The thymus is also responsive to hormonal changes, which have been shown to affect the stromal compartment and initiate changes in thymic architecture146. For example, thymus regeneration has been observed following castration310. The expansion of thymic stromal cells is catalyzed by a lack of androgen signaling, as demonstrated by studies in mouse where androgen blocker treatment stimulated an increase in FOXN1 expression in TECs driving their proliferation, thereby decreasing the rate of thymic involution and increasing the rate of rejuvenation93,95,129,146,172,174. However, the regenerative effect is temporary, with the thymus returning to pre-castration size within 1-2 months311. Similarly, the thymus is responsive to estrogens and progesterone, where it undergoes rapid involution during pregnancy and then rebounds to its original size post-birth312,313. Growth hormones, thymopoietin, glucocorticoids, and other signaling pathways have also been shown to control thymic size, activity, and regeneration, some of which have been tested in clinical trials to increase thymus activity in age-related disease or support immune system regeneration with human immunodeficiency virus (HIV) treatment146,314,315. These hormonal thymic rejuvenation studies have shown promise in increasing T cell generation. However, there have been serious off-target complications due to the pleotropic nature of these hormonal signaling pathways that have limited clinical therapeutic adoption. Furthermore, when discussing strategies for thymus regeneration it is important to address the reversibility of this process and rejuvenation potential of this organ. For example, under natural stressors, such as during puberty, pregnancy, castration, or infection, thymus size and function can rapidly decrease and then regenerate to steady state function once the stressor has been removed or homeostasis restored79,299â301,310,312,313. This seemingly inherent property of the tissue 229 to respond rapidly to the need for progenitor T cell niche availability and then return to steady state function has also been demonstrated using artificial thymus regeneration strategies discussed below269,270,316,317. This regenerative capacity of the thymus argues that even in aged individuals, thymus tissue has the potential to regenerate functional niches to produce naïve T cells. A.3 Sex differences are prevalent in age-related immune impairment More recently, sex differences in immune system reactivity and differential ageing kinetics have come to light87â91. In 2001, comparison of signal joint T cell receptor rearrangement excision circles (sjTRECs) in peripheral blood, a marker used to estimate thymic function via the number of recent thymic emigrants, established that females contained significantly higher levels of sjTRECs from age 20 onwards87. This demonstrated that females have increased thymic output of naïve T cells throughout middle and later life, which would support greater TCR diversity and ability to respond to new pathogen challenges with age. These data were further supported by a recent study on peripheral blood from 172 healthy adults which characterized sex differences in the age-related kinetics of declining naïve T cells and increasing monocyte and cytotoxic cell functions. Two spikes in the rate of these detrimental changes were detected, with the second spike occurring earlier and with a greater magnitude in males91. Additionally, after age 65 males had higher innate and pro-inflammatory activity, as well as lower adaptive activity91. A separate study demonstrated higher T cell activation signals in young females and again documented sex-specific differences in the composition of immune cells and levels of inflammatory factors with age90. These changes in the peripheral immune system provide an accessible, if delayed, method of assessing declines in thymus contribution to immunity, and generally mirror sex differences in thymus activity and involution throughout life. 230 In the context of the thymus, sex hormone regulation of thymic involution has long sparked a large body of evidence documenting sex differences in thymus biology, involution, and T cell generation92,93. Decreased production of recent thymic emigrants and smaller overall thymus size in older males versus females have also been reported87,94. With respect to cell and niche level changes, some studies describe decreased numbers of AIRE+ mTECs with age, potentially predisposing females who maintain greater thymic function later in life to autoimmune disease95, as well as less interlobular fat in young female thymus94, suggesting that differences in thymic involution kinetics begin pre-puberty. Recently, we described how transcript-level sex differences may underlie functional differences in thymic and immune function in humans12. Our analysis of thymic cell gene expression uncovered differential regulation of pathways involved in cell-cell signaling and metabolism, with female-derived thymic cells having significant upregulation of metabolic, translation, and antigen presentation pathways whereas male thymic cells had increased adipogenesis, proinflammatory signaling, and glucocorticoid signaling12. Importantly, these differences were found in 4-5 month old infants, demonstrating that sex differences in immune system development begin pre-puberty and have the potential to differentially affect the process of T cell development and T cell reactivity in the periphery via differential regulation of negative selection in the thymic medulla. We hope to highlight these and other patient population-specific considerations as we discuss the state of therapeutic strategies to date. A.4 Interventional strategies to reboot and rebuild the immune system At present there exists two schools of thought for interventional immune regeneration strategies in preclinical development, those that focus on regenerating aged immune organs to reboot a 231 patientâs immune system and those that focus on rebuilding the immune compartment directly through artificial supplementation of cells, niches, or tissues (Figure A.1). Figure A.2: The two approaches to regenerating aged and damaged immune organs include rebooting the patientâs own immune system and rebuilding the patientâs immune system through artificial supplementation. To reboot the patientâs immune system, current technologies aim to provide cells or drugs that regenerate the proliferative capacity of HSCs in the bone marrow or regenerate the T cell developmental niche in the thymus to improve production of naïve T cells. To rebuild the patientâs immune system, technologies aim to generate artificial primary lymphoid niches that support immune cell development in vivo or in vitro, as well as direct supplementation approaches that produce T cells in vitro for direct transplantation into a patient. A.5 Technologies to reboot the immune system Strategies to reboot the immune system focus on developing drugs and precision medicine technologies that push the native immune system back to a productive, healthy state. Rebooting the immune system via cell transplantation has a long history of support stemming from the insights of parabiosis experiments and successes of bone marrow stem cell transplants, as well as more recent work with chimeric antigen receptor (CAR) T cell therapies268,318,319. Current strategies include HSC or pro-T cell transplants, which rejuvenate the proliferative capacity of 232 the bone marrow and thymus respectively, as well as drug strategies targeting the regenerative capacity of progenitor cells in the bone marrow and thymus (Table A.1). Table A.1: Summary of the current technologies to reboot the immune system by pushing the native immune system back to a productive, healthy state. Technology Description Advantages Disadvantages Reference HSC transplants Allogeneic transplant of bone marrow grafts containing HSCs to reconstitute the hematopoietic system. - Long history of successful clinical data - Can reboot the potential of all hematopoietic lineages (myeloid, megakaryocyte, lymphoid, ect.) - Adult donor cells may have genetic and epigenetic defects which limit therapeutic potential - Limited donor availability - Risk of GVHD - Requires severe conditioning regime pre-therapy 319,320,321 PSC-derived HSPC transplants Transplant of in vitro PSC-derived HSPCs to reconstitute the hematopoietic system. Allogeneic - No donor required - Potential for low-cost, large-scale manufacturing Autologous - Potential for use of donor cells - Unknown engraftment potential - May require severe conditioning regimen pre-therapy - Does not address limitations of aged bone marrow defects 322,323 233 Progenitor T cell transplants Transplant of progenitor T cells to rejuvenate the thymus microenvironment and increase naïve T cell production. - Improved recovery kinetics of T lymphoid compartment, especially in aged individuals - Progenitor T cells temporarily regenerate the thymus - Potential for low-cost, large-scale manufacturing - Temporary thymus reconstitution 269,270 Bone marrow regeneration therapies Drug regime targeting bone marrow HSC activity and niche characteristics. - Potential to reset epigenetic changes limiting proliferation and lymphoid skewing of aged HSCs - Limited clinical research to address defects in aged bone marrow niche or HSCs in vivo 324,325 Hormonal regeneration therapies Drug regime of hormones, such as recombinant growth factor, known to increase thymus activity. - Clinical results demonstrate increase thymus activity and naïve T cell output in HIV-infected adults - Clinical results demonstrate decreased epigenetic vs. chronological age in older men - Mechanism of action is not well characterized - Pleiotropic effects of hormonal drug regimes increase risk of off target consequences 314,315,326 FOXN1-driven therapies Drug regime of small molecules that drive FOXN1 expression in TECs to increase thymus activity. - Well characterized driven of TEC differentiation, proliferation, and function - Increased FOXN1 expression in - Small molecule and epigenetic regulators of FOXN1 function remain elusive 327â329,330,331 234 mouse increases thymus size and production of naïve T cells - FOXN1 may differentially regulate activity and function of different TEC subsets - Delivery must be tissue specific Mesenchymal cell driven therapies Drug regime or cell therapy targeting mesenchymal cells regulating the progression of thymic involution to modulate thymus architecture and activity. - Mesenchymal cells are known regulators of thymic involution, therefore blocking mesenchymal cell-driven involution signaling pathways could prevent, slow, or reverse thymus involution - Cell signaling pathways regulating age-related thymic involution are unclear - Optimal onset of treatment is undefined - Challenging to deliver to specific tissues 24,332,333 HSC transplants In the clinic bone marrow transplants act as life-saving therapies where they can regenerate a full immune system in cancer patients following chemotherapy and radiation319. For in depth discussion on the current knowledge of bone marrow niche regulation of HSCs in health, disease, and ageing, as well as the state of HSC preclinical and clinical work we refer readers to the following reviews319,334â339. Despite the efficacy of HSC grafts, they exhibit important age-related declines, with grafts in aged patients failing significantly faster than those in young patients320,321. Similarly, studies using the cord-blood expansion molecule UM171 have found that the increase in numbers of naïve T cells, recent thymic emigrants, and T cell clonotypes 235 were higher in younger patients (<40 year old)340. Additionally, the remaining HSC clones in elderly individuals often have accumulated a large burden of somatic mutations which can have broad effects on their activity and function, including malignant proliferation, differential skewing towards specific hematopoietic lineages, and inflammatory activity leading to cardiovascular disease341â343. However, the cost, limited donor availability, and clinical risks associated with HSC transplants have limited their extension to age-related disease in elderly patients. Limited donor availability and questions of age-related defects in HSCs have motivated several labs to develop protocols for generating PSC-derived HSPCs with multi-lineage potential. If these cells can be generated at scale, as is being pursued as an alternative to patient-derived bone marrow transplants, they could provide an unlimited source of HSPCs that could reboot the hematopoietic system in patients with age-related defects, especially if transplant barriers and conditioning challenges can be overcome344. In 2001, Kaufman et al. first demonstrated evidence of blood colony-forming cells from human embryonic stem cells (ESCs)345. Since this discovery, the field has pursued protocols to direct development toward definitive wave HSPCs with the capacity to give rise to functional lymphoid cells. In 2014, Sturgeon et al. showed that activating Wnt signaling during differentiation generated CD34+CD43- hematopoietic endothelium cells with T-lineage competence322. Current research has focused on scaling the production of these HSPCs with T lymphoid potential in bioreactor systems323. Protocol modifications to increase yield and engraftment potential have recently been reviewed here189,346â349. These technologies have greatly expanded our ability to generate PSC-derived HSPCs; however, only recently was serial transplantation demonstrated in mouse350 and the long-term engraftment potential of PSC-236 derived HSPCs remains to be demonstrated in humans. Additionally, this approach addresses age-related cell intrinsic defects, such as somatic mutation burden, but does not address age-related bone marrow niche changes which prevent skewing of HSCs towards T-lineage and may continue to bias transplanted HSCs in aged bone marrow towards myeloid phenotypes. Furthermore, given the known propensity of PSC-derived cells to exhibit fetal-like transcriptional programs and functional properties351,352, there is currently an important focus on understanding how functional properties of HSCs produced along the developmental axis of the aorta-gonad mesonephros region of the early embryo, the fetal liver, or the postnatal bone marrow will impact immune competence88,353â355. Improved characterization of HSCs produced along this developmental axis356, including PSC-derived cells produced in systems mimicking these niches77, is required to understand how these cells will function after transplantation to create a competent host immune system. These limitations have encouraged development of alternative strategies to support T cell development in aged patients. Progenitor T cell supplementation to reboot the thymus Bone marrow transplants often also exhibit a significant delay in, or lack of, naïve T cell generation in patients with damaged or non-functional thymic tissue, either due to patient age or the damaging effects of chemotherapy and radiation357â359. The delay or absence of T cells in patients who receive HSC transplants leads to several complications such as opportunistic infections and increased predisposition to relapse360. To address these complications, Reimann et al. generated human pro-T cells from cord blood in a feeder-free culture system containing immobilized Notch ligand delta like 4 (DLL4) and transplanted these cells into NOD/SCID/Î³câ/â (NSG) mice317. The pro-T cells migrated to the thymus and developed into functional, mature T 237 cells, accelerating the timeline for peripheral T cell reconstitution. Similarly, Awong et al. generated cord blood-derived human pro-T cells in vitro using a co-culture platform of OP9 mouse stromal cells expressing the Notch ligand delta like 1 (DLL1) and co-transplanted the pro-T cells with HSCs into NSG mouse recipients316. They also observed accelerated HSC-derived T-lymphopoiesis and identified a potential receptor activator of nuclear factor κb (RANK) ligand-driven mechanism of thymus tissue regeneration. Singh et al. harnessed this pro-T cell â TEC signaling axis to regenerate the functional capacity of aged thymus by engrafting in vitro co-culture generated cord blood-derived human pro-T cells into aged mouse thymuses post-irradiation269. The pro-T cells homed to the thymus and were found to reconstitute the peripheral T cell compartment significantly faster than HSC transplant controls269. Follow up on this work again co-administered HSC transplants with in vitro co-culture generated cord blood-derived human pro-T cells and showed the accelerated reconstitution of CD4 and CD8 T cells in the peripheral T cell compartment of aged mice lowered the risk of opportunistic infections270. As a first clinical target, pro-T cell therapy, or even lymphoid skewed blood progenitors176, holds promise for improving immune recovery following irradiation and chemotherapy in patients. However, the strong response seen with transplanted pro-T cells, including an increased ability to respond to viral infections, suggests that this type of therapy could also be used to boost immune function in elderly patients who do not require an HSC transplant post-radiation. These successes set the stage for use of cell-based immune regeneration strategies beyond cancer therapy and necessitate careful consideration of how different pro-T cell generation technologies would enable treatments for different patient profiles, specifically aged patients who do not require chemotherapy and radiation. 238 Drug strategies for thymus regeneration Current small molecule and targeted therapeutic strategies focus on harnessing mechanisms of thymus regeneration, such as stimulating in vivo production of T-lineage skewed HSCs or blocking mechanisms of thymic involution by targeting thymic epithelial and mesenchymal cell activity. Bone marrow targeted strategies An alternative to introducing pro-T cells into patients is to directly stimulate HSC proliferation and differentiation toward T-lineage skewed thymic seeding progenitors which express thymus homing chemokines and T-lineage specific transcription factors8. However, it remains unclear what specific changes or defects occur in aged HSCs to inherently limit T-lineage potential, or if there are specific changes to the bone marrow environment that limit the proliferative and T-lineage skewing potential of aged HSCs. To identify the specific molecular changes occurring in aged HSCs, Zeng et al. used multi-omic sequencing on a xenograft inflammation-recovery model and found a pool of inflammatory memory HSCs that accumulated with age and clonal hematopoiesis324. These cells exhibited a distinct transcriptomic and epigenomic signature that was enriched for inflammation response and long-term HSC quiescence324. These data require further functional studies to determine how this signature could affect HSC skewing towards myeloid lineages and quiescence versus T-lineages and proliferation, as well as if these cells are amenable to gene editing or epigenetic modification to reverse this inflammatory, dormant signature. To this end, one study used the small molecule UM171 during in vitro cord blood HSC expansion to increase T cell clone 239 numbers and TCR diversity post-transplantation, demonstrating that small molecules could be used ex vivo to modulate the epigenetic state and activity of HSCs in vivo340. Furthermore, a recent study by Montserrat-Vazquez et al. performed imaging of aged bone marrow and found that aged HSCs reside in a distinct niche from young HSCs325. These data suggest that age-related changes to the bone marrow niche, such as the distribution and density of Notch ligands, may limit further HSC proliferation. Currently we lack technologies that directly modulate levels of regulatory ligands and signaling pathways, such as Notch signaling, within the bone marrow niche. Instead, these niche-level changes to HSC regulation have been addressed with artificial bone marrow niches176, as discussed further below. Hormonal regeneration strategies To date, clinical research on direct thymus rejuvenation has primarily focused on artificially regulating specific hormones. For example, the effects of growth hormone on regulating thymic activity were demonstrated in patients with HIV who began taking highly active anti-retroviral treatment (HAART)314. Patients who started receiving HAART treatment were seen to exhibit immune reconstitution with an increase in naïve CD4 T cells361. These results demonstrated that the thymus is still active in these patients and can continue to produce naïve T cells once disease progression has reached an equilibrium. Immune reconstitution of CD4 cells was then augmented via administration of growth hormone, which had been shown previously to result in thymus regrowth with an increase in thymus density, TREC frequency in peripheral blood, and number of total and naïve CD4 and CD8 T cells314,326. In 2015 the TRIIM (Thymus Regeneration, Immunorestoration, and Insulin Mitigation) clinical trial began to investigate the 240 possibility of using recombinant growth hormone to extend these results and prevent or reverse signs of age-related immune impairment in ten healthy 51-65 year old men315. Although this study did report a decrease in epigenetic versus chronological age, growth hormone has additional known pro-ageing effects, such as hypothalamic inflammation, which may program systemic ageing in adults362,363. Regardless, the pleiotropic nature and concern for off-target effects of hormonal approaches have motivated precision medicine approaches to restoring thymus activity. FOXN1-driven regeneration strategies The main approach to rebooting in vivo thymic tissue function focuses on modulating FOXN1 gene expression. FOXN1 is essential for TEC differentiation and proliferation327â329, and premature downregulation of FOXN1 in mice causes a rapid involution-like phenotype330. General overexpression of FOXN1 in TECs has been shown in mouse to increase aged thymus size and thymopoiesis, as well as restore normal thymus architecture364. These results provided a promising target for clinical thymus regeneration. However, the mechanisms controlling transcriptional regulation of FOXN1 remain unclear and an active area of research by academic and industry teams. Some teams are pursuing broad small molecule screens of drugs to find chemical modulators of FOXN1 activity, while other labs are taking a bottom-up approach by harnessing new multiomic technologies to identify epigenetic regulators of FOXN1 activity365. As these strategies progress to the clinic, it is important to note a recent study in mouse which overexpressed FOXN1 in TECs using a K5.Foxn1 transgene and drove FOXN1 expression in Plet1+ progenitor TEC cells331. Unlike other FOXN1 overexpression models, this transgene did not increase thymus size or delay thymic involution but did improve TEC differentiation and was 241 independently sufficient to drive formation of a small thymus in nude mice331. Importantly, these findings demonstrated the context- and dosage-dependent effects of perturbed FOXN1 expression in different TEC subsets. Single cell RNA sequencing studies from various groups have demonstrated surprising heterogeneity in functional TEC subsets6,7,9,120,366,367, including the presence of a thymic epithelial stem cell phenotype which expresses several keratin genes and can differentiate into both medullary and cortical TECs in vivo and in vitro367. The ratios of these diverse TEC subsets are dynamic throughout life, with studies in mouse demonstrating a decrease in mTECs and increase in cTECs and progenitor TECs with age368. As FOXN1-driven strategies to rejuvenate thymus tissue mature, it becomes increasingly important to understand which subset of TECs a strategy aims to target and modulate. Furthermore, as these strategies move towards rejuvenating aged thymus rather than thymus tissue damaged by HSC transplant conditioning regimens, it will be important to determine how the different ratios of TEC subsets, specifically an increase in the number of progenitor TECs in the elderly368, change the functional outcome of FOXN1 overexpression. To address these considerations, we envision several different strategies maturing to the clinic concurrently as the indications for thymic tissue regeneration expand. For example, a small molecule driven general FOXN1 approach might be successful in driving regeneration in young thymus tissue damaged by radiation, whereas a more targeted approach might be effective to drive regeneration in aged thymus. Regardless, expression of FOXN1 in other tissues, such as the skin369, presents an open challenge to ensure delivery of this therapy is tissue specific. 242 Mesenchymal cell-targeted regeneration strategies Finally, the strategies outlined above focus on countering thymic involution by driving TEC proliferation. These strategies do not address the driving factors of thymic involution, thought in part to be orchestrated by mesenchymal cells in the thymus24,332,333. In the postnatal thymus fibroblasts produce important growth factors to regulate TEC proliferation and maintenance, including fibroblast growth factors (FGF2, FGF7, FGF10) and insulin growth factors (IGF1, IGF2)12,332,370. These mesenchymal â TEC interactions play a crucial role in thymus maintenance, as demonstrated by the congenital hypoplasia seen in patients with DiGeorge syndrome where mesenchymal cells fail to support thymic tissue expansion371,372. How these interactions change within the ageing thymus is complex and not well understood. However, targeting mesenchymal cells â the orchestrators of thymic involution â presents an alternative and relatively untapped opportunity to prevent or delay thymic involution. One recent study explored the role of thymic mesenchymal cells (ThyMCs) in thymic function and regeneration, demonstrating the potential of this alternative approach24. In representative mouse models of HSC transplant, ageing, and chronic lymphoid progenitor deficiency they found significant remodeling of the ThyMC compartment, specifically an increase in interstitial fibroblastic Penk+ ThyMCs and a reduction in perivascular T cell supportive Postn+ ThyMCs. The authors then tested if replenishing ThyMCs could enhance thymic regeneration in the context of HSC transplant and ageing, and transplanted Penk+ and Postn+ ThyMCs or Ccl19-overexpressing bone marrow stromal cells in conditioned mouse models, respectively. In both conditions, stromal cell transfer resulted in improved short-term thymic regeneration via early thymic progenitor recruitment, as well as long-term increases in peripheral naïve T cells and 243 improved vaccination responses. These data support ThyMC transplant as an additional strategy for thymus regeneration, and also suggest that functional ThyMC modification via in vivo drug targeting, such as CCL19 overexpression, may pose an alternative, less invasive therapeutic strategy. However, targeting ThyMCs as a therapeutic strategy requires further research in human and in the aged thymus niche to identify the signaling pathways dictating ThyMC activity and function. For example, DPP4+ mesenchymal cells have recently been characterized as having progenitor capacity, placing these cells at the top of the fibroadipogenic cell hierarchy in several tissues. DPP4+ thymic mesenchymal cells have been identified in single cell sequencing studies in mouse and human12,23,24 and have the potential to differentiate towards fibroblast or adipogenic lineages. Without proper control of the complex signaling pathways guiding this lineage decision, as well as further differentiation towards the functional ThyMC subtypes described above, these therapeutic strategies risk irreversibly increasing thymus tissue fibrosis. As these strategies develop it is important to account for key differences in mouse and human thymic involution pathways, namely a decreased volume of adipogenic thymus tissue in mouse373, as well as how emerging therapies may interact differently within an aged, fibrotic thymus environment. A.6 Technologies to rebuild the immune system Strategies to rebuild the immune system focus on technologies that artificially recreate immune tissues, either by creating an artificial niche for in vivo immune system generation or by direct injection of in vitro-derived immune tissue into the patient (Table A.2). 244 Table A.2: Summary of the current technologies to rebuild the immune system using artificial primary immune tissues or tissue niches. Technology Description Advantages Disadvantages Reference Allogeneic thymus transplant Donor thymus tissue is cultured and then implanted into the quadricep muscle in patients with athymia to provide a niche for T cell generation. - Clinical success in patients with complete DiGeorge Syndrome to reconstitute the peripheral T cell compartment and improve survival from infections - Success requires a lack of native T cells in patient - Limited donor availability - Increased risk of autoimmune disease - Risk of GVHD in early treatment regimen - Risk of TECs positively selecting T cells that donât fully match the host HLA 254â256 PSC-derived thymus transplant PSC-derived thymus tissue is implanted to provide a niche for T cell generation. - Increased tissue availability to patients - Success requires a lack of naïve T cells in patient - Risk of GVHD 374â377,378,379 Bone marrow niche hydrogels DLL4-functionalized hydrogel is implanted subcutaneously post-HSC transplantation - No risk of GVHD - Increases kinetics of peripheral T cell compartment regeneration. - Requires functional, proliferative HSCs 176 245 to provide a niche for HSC skewing toward T lineage progenitors and increase of T cell generation kinetics. Thymus niche hydrogels Functionalized hydrogels are seeded with HSPCs and cultured in media to provide a niche for T cell development. - No risk of GVHD - Could be used as an in vivo thymus niche in patients with athymia or involuted thymus. - Recapitulates the mechanical signaling environment of the thymus - Current technologies have low yield of progenitor or mature T cells in vitro - Lack of AIRE+ cells increases risk of autoimmune disease 380,381 Thymus organoids Key thymus cell types are combined and cultured with HSPCs to support T cell development. - Present a technology to test perturbations to T cell and thymus development, or screen patient characteristics - Isogenic PSC organoids could be transplanted to support T cell production in vivo - Risk of GVHD - Some xenogeneic models - Lack of AIRE+ cells increases risk of autoimmune disease 378,205,382,383,266 Defined manufacturing systems for Defined media and culture conditions are optimized to - Reproducible â defined culture media, cell - Risk of GVHD - Unknown TCR 79,75,77,384,196 246 PSC-derived T cells support in vitro T cell generation in 2D and 3D systems. materials, and cell characterization - Scalable â large scale manufacture can decrease cost - Cell product can be optimized for patient and disease (ex. Treg:CD4:CD8 ratio) - System can be used to screen genetic diseases or patient characteristics repertoire selection and compatibility with patient - Inability to produce mature CD4 T cells Allogeneic thymus transplantation The tissue-centric approach to rejuvenating T cell immunity focuses on rebuilding a thymic microenvironment to better support naïve T cell generation. This approach has gained significant traction given the recent FDA approval of thymus transplant studies385. The safety and efficacy of thymus transplants in children with congenital athymia were established in a clinical study that ran from 1993-2020, and the treatment is now established for this rare pediatric disease under the brand name Rethymic254â256. Rethymic uses allogenic thymus tissue that is processed, cultured, and implanted in a single administration, enabling generation of naïve T cells and improving the survival rates of children with congenital athymia. This therapy represents a big step towards the establishment of thymic tissue transplantation as a feasible strategy to rebuild the immune system. These successes suggest that thymus transplants, once established, could also be indicated in thymectomy and aged patients. For example, an intriguing new study demonstrated the continued importance of thymic function in aged individuals245. In this study, researchers compared a large 247 cohort of patients who had undergone thymectomy with a primary control cohort and found that at 5 years post-surgery all-cause mortality and cancer risk was higher in the thymectomy group. Thymectomy patients with no prior risk of infection, cancer, or autoimmunity also had an increased risk of autoimmune disease245. We believe these results could also be extended to demonstrate the importance of continued thymic function in the elderly. Past the age of 65, thymus production is significantly reduced298, catalyzing changes to the peripheral immune compartment consistent with thymectomy. It is likely that the success of thymus transplants in athymic children and literature on the continued importance of the thymus in adults will soon motivate thymus transplant and rejuvenation technology implementation in the elderly. However, the impact of the patientâs residual circulating T cells on the probability of graft rejection is yet to be determined and will likely limit allogeneic transplants to patients who have already undergone radiation, as discussed below. PSC-based strategies for thymus transplantation The efficacy of thymus transplants has motivated PSC-derived thymus organ replacement strategies to increase therapeutic availability to a broader patient population. To this end, several groups are pursuing in vitro generation of PSC-derived thymic tissue which can be implanted within the body to support naïve T cell generation. This field was catalyzed by in vitro protocols which differentiated PSCs to TECs in vitro374â377. These approaches, which were recently reviewed here386, have been expanded in different model systems to push TEC differentiation towards mature, functional subsets of TECs. However, to support T cell development these model systems require formation of three-dimensional (3D) spatially compartmentalized PSC-248 derived thymus tissue resembling cortical and medullary regions, which current technologies fail to recapitulate. Currently, independent cultures of PSC-derived TEC cells resemble progenitor TEC or cortical TECs and likely require T cell and mesenchymal interactions to push these cells towards a medullary TEC phenotype, as seen in fetal thymus development387â390. One approach to this problem is to implant progenitor TECs under the kidney capsule and allow maturation by T cell â TEC interactions in vivo378. In this system, human PSCs are differentiated in 3D alginate capsules towards thymic epithelial progenitor cell (TEPC) lineage and cocultured with cord blood-derived HSPCs or cord blood-derived pro-T cells in decellularized mouse thymic scaffolds378. The in vitro emigrant population of these thymic organoids co-cultured with pro-T cells contained both CD4 and CD8 single positive T cells after one week of culture and organoids cultured with HSPCs generated both mature populations of T cells within two weeks in vitro. When transplanted in vivo, peripheral lymphoid cells were detected within 4 weeks with a prominent population of mature, TCR diverse, functional T cells detectable within 18-24 weeks. Finally, another group has demonstrated generation of an ectopic thymus under the kidney capsule from TECs induced from FOXN1-overexpressing embryonic fibroblasts379. Using FOXN1-reprogrammed embryonic fibroblasts injected directly into the aged murine thymus the authors found substantial regrowth of native aged murine thymus. These animals exhibited increased naïve T cell generation, reduced senescent T cells in the periphery, and reduced T cell mediated inflammation. These grafts provide an effective environment to support T cell development; however, similar to allogeneic thymus transplant the probability of graft rejection due to residual patient T cells is yet to be determined. 249 Hydrogel-based artificial primary lymphoid tissue niches Complications arising from the delay or absence of T cells in patients who receive HSC transplants, as well as the limitations of introducing HLA-mismatched PSC-derived thymus tissue into immune competent individuals, have motivated technology development to create an artificial niche to support T-lineage skewing and pro-T cell development. Bone marrow niche hydrogels Age-related changes to the bone marrow environment may be limiting HSC activity and T-lineage skewing325. One approach to increasing HSC transplant engraftment and seeding of pro-T cells in the thymus is to use biomaterials to recreate the bone marrow niche to support and skew HSCs towards T-lineage. Biomaterial-based scaffolds do present several advantages, particularly for mitigating graft versus host disease (GVHD) in patients, as they rely on the hostâs own cells to seed the graft and facilitate T-lineage skewing. For example, Shah et al. developed an injectable bone marrow-like scaffold that releases bone morphogenic protein (BMP2) to recruit stromal cells and presents the Notch ligand DLL4 to facilitate T-lineage specification176. The graft was administered subcutaneously in mice and successfully increased T cell neogenesis and pro-T cell seeding in the thymus, improving survival post-HSC transplant. It will be interesting to see how effective this approach can be in elderly patients or patients who have undergone radiation and chemotherapy and have a damaged thymus environment. However, successes in pro-T cell supplementation therapies suggest that the pro-T cell â TEC crosstalk may be sufficient to rejuvenate the damaged thymus270. 250 Thymus niche hydrogels Over the past two decades thymic niche hydrogels have also been developed which aim to recapitulate the three-dimensional (3D) thymus signaling environment to support T cell development. For example, a 3D inverted colloidal crystal (ICC) scaffold functionalized with the Notch signaling ligand Delta like 1 (DLL1) was designed to act as a substitute for the structure and function of thymic stroma380. This group hypothesized that the intensity and configuration of cell-cell contacts is an essential component to the function of thymic-like niches. This technology supported maintenance of cord-blood HSCs but did not support differentiation towards CD4+CD8+ double positive T cells and did not allow easy recovery of the cells from the matrix for further characterization. In 2019, Kratzer et al. reported a PEG hydrogel functionalized with the Notch ligand DLL1 and the adhesion-supporting motif arginyl-glycyl-aspartic acid (RGD)381. The authors report the generation of CD4+CD8+ double positive T cells; however, the frequency of these cells generated in the hydrogel was less than 1% and was not significantly higher than plate bound DLL1 controls. Further optimization of these technologies is required to mimic additional key interactions and signaling pathways to support efficient development of progenitor and mature T cells. For example, using multi-factorial chemical signaling screens in vitro T cell manufacturing systems have identified and employed defined culture media conditions containing developmental stage-specific signaling factors to efficiently direct T cell development toward double positive and CD8 T cells75,77. These approaches have defined a set of cytokines and chemokines which direct T cell development in vitro and should be employed in combination with hydrogels to mimic the native signaling environment of the thymus and improve T cell yield. However, the hydrogel may require multiple formulations patterned into medullary and cortical zones with different mechanical and biochemical signaling 251 properties to recapitulate the spatially defined environmental cues of the thymus. Finally, the application of these hydrogels, whether intended as an in vitro T cell production method or in vivo supportive niche for thymocyte development should be clarified early in design, as the requirements for recreation of stromal signals or ability for stromal cells to migrate into and remodel the hydrogel will significantly affect choice of material and functionalization. Thymus Organoids Thymus organoid cultures present an alternative technology for T cell generation, as well as a scalable platform for modeling the contributions of specific thymic cell types towards T cell development and TCR repertoire development. One platform, termed the artificial thymic organoid (ATO) aggregates cord blood or PSC-derived HSPCs with mouse MS5 stromal cells expressing DLL4 into a 3D structure suspended on transwell inserts in medium205,382. This platform is a 3D extension of the traditional OP9-DLL4 T cell generation system391 and supports T cell development within the aggregate structure to produce mature CD4 and CD8 T cells. This system is xenogeneic, which adds challenges to clinical translation of T cells generated within the ATOs. Notwithstanding, the limited scalable nature of ATOs still enables its use in drug or gene mutation studies. For example, ATOs have been used to study T cell development from CD34+ cells of patients carrying intrinsic hematopoietic or thymic defects that cause T cell lymphopenia383. This study demonstrates an important application of organoid technology, where age-related defects in HSCs could be screened to determine their effect on T cell development and potentially predict efficacy of different therapeutic immune regeneration strategies to guide treatment of specific patient populations. 252 In addition to the HSPC-seeded PSC-derived thymic organoids developed by Zeleniak et al. for in vivo transplantation described above378, complex thymic organoid systems have been designed for in vitro T cell production. Here, human PSC-derived thymic cell types such as mesenchymal and hematopoietic progenitor cells are also included in the aggregate to drive terminal differentiation of the TECs266. The combination of thymic cell types drive terminal differentiation of TEPCs in vitro, including expression of autoimmune regulator (AIRE) and tissue restricted antigen presentation, but differentiation of mimetic TECs has yet to be shown. Importantly, these isogenic human PSC-derived organoids support mature T cell development in vitro, although CD4 development is limited. These systems present an opportunity to probe human diversity in T cell development, where patient-derived organoids could be used to model the developmental impact of genetic defects in T cells or stromal cells, or be used to screen potential therapeutic immune regeneration interventions on organoids derived from diverse patient populations. To achieve these goals, we require a better understanding of how each cell type contributes to T cell development, TCR repertoire formation, and thymus growth. Using well defined input cell types and longitudinal characterization of organoid formation, isogenic PSC-derived thymic organoids present a useful model system to begin exploring these questions. For example, the signaling interactions directing TEC maturation, differentiation, and organization into structured thymus architecture during early fetal thymus development remain unclear. Although TEC maturation has been shown in current thymic organoid models378, it has yet to be shown if these models can generate the diversity of TEC subtypes required to recapitulate effective T cell selection in vitro or recapitulate the spatial organization of thymic architecture. If we can recapitulate spatial 253 architecture, this system presents a unique opportunity to probe developmental thymus signaling networks by perturbing cytokine and chemokine signaling factors or blocking specific cell-cell signaling interactions. Fully defined manufacturing systems Although pro-T cell strategies hold promise for boosting the ageing immune system, the cost and scale of this therapy as a preventative or age-related supplementation strategy is currently prohibitive. One approach for decreasing cost is to generate these pro-T cells from PSCs, therefore enabling scalable generation of an off-the-shelf product. For example, in our lab we are interested in producing PSC-derived T cells in a chemically-defined, feeder-free system75,77. This technology uses plate bound DLL4 and the adhesion ligand VCAM in combination with a defined media of growth factors and cytokines to recreate the thymic niches T cells migrate through during development in the thymus. Similarly, other labs have used T cell-derived PSC cells to generate T cells in vitro79,392. This approach bypasses the need for recreating a niche for TCR rearrangement. However, under some conditions these cells tend to express innate cell markers and could not be used to reconstitute a diverse T cell repertoire191,213,393. The lack of xenogeneic cells or byproducts is an inherent advantage of these platforms; however, scaling production of these cells has proven difficult, due to the necessity of mechanical pulling forces to engage the Notch ligand DLL4394. Recently, a method was developed to enable large-scale T cell production in a bioreactor using gelatin-based microgels encapsulating murine HSCs with OP9-DLL4 stromal cells384. This approach is an important step in generating scalable technologies for T cell development but did not demonstrate mature T cell production and could have limited clinical applicability due to its 254 xenogeneic nature. An alternative non-xenogeneic approach uses DLL4-coated microbeads in combination with sequential lymphopoietic cytokines to differentiate cord blood, peripheral blood, and PSCs to CD3+ TCRαβ mature double positive T cells in a bioreactor system196,395. Similar to many feeder-free differentiation systems, progression of PSC-derived thymocytes towards mature single positive T cell stages was limited, in part due to a lack of artificial stimulation of double positive T cells with anti-TCR/CD3 antibodies196. This inability to differentiate PSC-derived cells towards mature CD4 T cells highlights a lack of understanding on how to guide T-lineage branching and recapitulate additional thymic medulla-like maturation signals in feeder-free or 3D bioreactor systems. As these chemically-defined manufacturing technologies mature to enable generation of naïve CD4, CD8, and T regulatory cells they could allow targeted or population-level modeling of how T cell activation and function varies in different inflammatory environments. For example, in vitro functional cell assays recapitulating patient stratification axes such as age, sex, and blood biomarker inflammatory states via differentiation of diverse PSC lines in combination with disease-relevant culture media formulations can be employed to model patient-specific T cell responses. Establishing these in vitro screening models can assist in prediction of dose, effective T cell subset ratios, and patient responses to therapy. 255 Appendix B - Chapter 2 supplemental data B.1 Postnatal thymus metadata Table B.1: Postnatal thymus sample metadata. Patient ID Age (Months) Sex Thymus Weight (g) Number of Images (20X) Area Imaged (mm2) T086 33 M 20.025 10 x 3x3 = 90 22.4 T087 4 F 22.552 10 x 3x3 = 90 22.4 T096 5 M - 5 x 3x3 = 45 11.2 T097 4 F 16.265 8 x 5x5 = 200 45.1 T098 4 M 16.662 10 x 3x3 = 90 22.4 T099 4 F 39 10 x 3x3 = 90 22.4 T100 4 M 42 10 x 3x3 = 90 22.4 Note: A partial sample was removed from patient T096, therefore weight is not included. 256 B.2 CITE-seq antibody panel Table B.2: TotalSeq-C Human Universal Antibody Panel v1.0 used to phenotype cells via CITE-seq. DNA_ID Description Clone Barcode Ensemble ID Gene name C0006 anti-human CD86 IT2.2 GTCTTTGTCAGTGCA ENSG00000114013 CD86 C0007 anti-human CD274 (B7-H1, PD-L1) 29E.2A3 GTTGTCCGACAATAC ENSG00000120217 CD274 C0020 anti-human CD270 (HVEM, TR2) 122 TGATAGAAACAGACC ENSG00000157873 TNFRSF14 C0023 anti-human CD155 (PVR) SKII.4 ATCACATCGTTGCCA ENSG00000073008 PVR C0024 anti-human CD112 (Nectin-2) TX31 AACCTTCCGTCTAAG ENSG00000130202 NECTIN2 C0026 anti-human CD47 CC2C6 GCATTCTGTCACCTA ENSG00000196776 CD47 C0029 anti-human CD48 BJ40 CTACGACGTAGAAGA ENSG00000117091 CD48 C0031 anti-human CD40 5C3 CTCAGATGGAGTATG ENSG00000101017 CD40 C0032 anti-human CD154 24-31 GCTAGATAGATGCAA ENSG00000102245 CD40LG C0033 anti-human CD52 HI186 CTTTGTACGAGCAAA ENSG00000169442 CD52 C0034 anti-human CD3 UCHT1 CTCATTGTAACTCCT ENSG00000167286 CD3D 257 C0046 anti-human CD8 SK1 GCGCAACTTGATGAT ENSG00000153563 CD8A C0047 anti-human CD56 5.1H11 TCCTTTCCTGATAGG ENSG00000149294 NCAM1 C0050 anti-human CD19 HIB19 CTGGGCAATTACTCG ENSG00000177455 CD19 C0052 anti-human CD33 P67.6 TAACTCAGGGCCTAT ENSG00000105383 CD33 C0053 anti-human CD11c S-HCL-3 TACGCCTATAACTTG ENSG00000140678 ITGAX C0058 anti-human HLA-A,B,C W6/32 TATGCGAGGCTTATC ENSG00000206503 HLA-A C0063 anti-human CD45RA HI100 TCAATCCTTCCGCTT ENSG00000081237 PTPRC C0064 anti-human CD123 6H6 CTTCACTCTGTCAGG ENSG00000185291 IL3RA C0066 anti-human CD7 CD7-6B7 TGGATTCCCGGACTT ENSG00000173762 CD7 C0068 anti-human CD105 43A3 ATCGTCGAGAGCTAG ENSG00000106991 ENG C0070 anti-human/mouse CD49f GoH3 TTCCGAGGATGATCT ENSG00000091409 ITGA6 C0071 anti-human CD194 (CCR4) L291H4 AGCTTACCTGCACGA ENSG00000183813 CCR4 C0072 anti-human CD4 RPA-T4 TGTTCCCGCTCAACT ENSG00000010610 CD4 258 C0073 anti-mouse/human CD44 IM7 TGGCTTCAGGTCCTA ENSG00000026508 CD44 C0081 anti-human CD14 M5E2 TCTCAGACCTCCGTA ENSG00000170458 CD14 C0083 anti-human CD16 3G8 AAGTTCACTCTTTGC ENSG00000203747 FCGR3A C0085 anti-human CD25 BC96 TTTGTCCTGTACGCC ENSG00000134460 IL2RA C0087 anti-human CD45RO UCHL1 CTCCGAATCATGTTG ENSG00000081237 PTPRC C0088 anti-human CD279 (PD-1) EH12.2H7 ACAGCGCCGTATTTA ENSG00000188389 PDCD1 C0089 anti-human TIGIT (VSTM3) A15153G TTGCTTACCGCCAGA ENSG00000181847 TIGIT C0090 Mouse IgG1, κ isotype Ctrl MOPC-21 GCCGGACGACATTAA C0091 Mouse IgG2a, κ isotype Ctrl MOPC-173 CTCCTACCTAAACTG C0092 Mouse IgG2b, κ isotype Ctrl MPC-11 ATATGTATCACGCGA C0095 Rat IgG2b, κ Isotype Ctrl RTK4530 GATTCTTGACGACCT C0100 anti-human CD20 2H7 TTCTGGGTCCCTAGA ENSG00000156738 MS4A1 C0101 anti-human CD335 (NKp46) 9E2 ACAATTTGAACAGCG ENSG00000189430 NCR1 259 C0124 anti-human CD31 WM59 ACCTTTATGCCACGG ENSG00000261371 PECAM1 C0134 anti-human CD146 P1H12 CCTTGGATAACATCA ENSG00000076706 MCAM C0136 anti-human IgM MHM-88 TAGCGAGCCCGTATA ENSG00000211899 IGHM C0138 anti-human CD5 UCHT2 CATTAACGGGATGCC ENSG00000110448 CD5 C0140 anti-human CD183 (CXCR3) G025H7 GCGATGGTAGATTAT ENSG00000186810 CXCR3 C0141 anti-human CD195 (CCR5) J418F1 CCAAAGTAAGAGCCA ENSG00000160791 CCR5 C0142 anti-human CD32 FUN-2 GCTTCCGAATTACCG ENSG00000143226 FCGR2A C0143 anti-human CD196 (CCR6) G034E3 GATCCCTTTGTCACT ENSG00000112486 CCR6 C0144 anti-human CD185 (CXCR5) J252D4 AATTCAACCGTCGCC ENSG00000160683 CXCR5 C0145 anti-human CD103 (Integrin αE) Ber-ACT8 GACCTCATTGTGAAT ENSG00000083457 ITGAE C0146 anti-human CD69 FN50 GTCTCTTGGCTTAAA ENSG00000110848 CD69 C0147 anti-human CD62L DREG-56 GTCCCTGCAACTTGA ENSG00000188404 SELL C0149 anti-human CD161 HP-3G10 GTACGCAGTCCTTCT ENSG00000111796 KLRB1 260 C0151 anti-human CD152 (CTLA-4) BNI3 ATGGTTCACGTAATC ENSG00000163599 CTLA4 C0152 anti-human CD223 (LAG-3) 11C3C65 CATTTGTCTGCCGGT ENSG00000089692 LAG3 C0153 anti-human KLRG1 (MAFA) SA231A2 CTTATTTCCTGCCCT ENSG00000139187 KLRG1 C0154 anti-human CD27 O323 GCACTCCTGCATGTA ENSG00000139193 CD27 C0155 anti-human CD107a (LAMP-1) H4A3 CAGCCCACTGCAATA ENSG00000185896 LAMP1 C0156 anti-human CD95 (Fas) DX2 CCAGCTCATTAGAGC ENSG00000026103 FAS C0158 anti-human CD134 (OX40) Ber-ACT35 (ACT35) AACCCACCGTTGTTA ENSG00000186827 TNFRSF4 C0159 anti-human HLA-DR L243 AATAGCGAGCAAGTA ENSG00000204287 HLA-DRA C0160 anti-human CD1c L161 GAGCTACTTCACTCG ENSG00000158481 CD1C C0161 anti-human CD11b ICRF44 GACAAGTGATCTGCA ENSG00000169896 ITGAM C0162 anti-human CD64 10.1 AAGTATGCCCTACGA ENSG00000150337 FCGR1A C0163 anti-human CD141 (Thrombomodulin) M80 GGATAACCGCGCTTT ENSG00000178726 THBD C0164 anti-human CD1d 51.1 TCGAGTCGCTTATCA ENSG00000158473 CD1D 261 C0165 anti-human CD314 (NKG2D) 1D11 CGTGTTTGTTCCTCA ENSG00000213809 KLRK1 C0167 anti-human CD35 E11 ACTTCCGTCGATCTT ENSG00000203710 CR1 C0168 anti-human CD57 Recombinant QA17A04 AACTCCCTATGGAGG ENSG00000109956 B3GAT1 C0170 anti-human CD272 (BTLA) MIH26 GTTATTGGACTAAGG ENSG00000186265 BTLA C0171 anti-human/mouse/rat CD278 (ICOS) C398.4A CGCGCACCCATTAAA ENSG00000163600 ICOS C0174 anti-human CD58 (LFA-3) TS2/9 GTTCCTATGGACGAC ENSG00000116815 CD58 C0176 anti-human CD39 A1 TTACCTGGTATCCGT ENSG00000138185 ENTPD1 C0179 anti-human CX3CR1 K0124E1 AGTATCGTCTCTGGG ENSG00000168329 CX3CR1 C0180 anti-human CD24 ML5 AGATTCCTTCGTGTT ENSG00000272398 CD24 C0181 anti-human CD21 Bu32 AACCTAGTAGTTCGG ENSG00000117322 CR2 C0185 anti-human CD11a TS2/4 TATATCCTTGTGAGC ENSG00000005844 ITGAL C0187 anti-human CD79b (Igβ) CB3-1 ATTCTTCAACCGAAG ENSG00000007312 CD79B C0189 anti-human CD244 (2B4) C1.7 TCGCTTGGATGGTAG ENSG00000122223 CD244 262 C0206 anti-human CD169 (Sialoadhesin, Siglec-1) 7-239 TACTCAGCGTGTTTG ENSG00000088827 SIGLEC1 C0214 anti-human/mouse integrin β7 FIB504 TCCTTGGATGTACCG ENSG00000139626 ITGB7 C0215 anti-human CD268 (BAFF-R) 11C1 CGAAGTCGATCCGTA ENSG00000159958 TNFRSF13C C0216 anti-human CD42b HIP1 TCCTAGTACCGAAGT ENSG00000203618 GP1BB C0217 anti-human CD54 HA58 CTGATAGACTTGAGT ENSG00000090339 ICAM1 C0218 anti-human CD62P (P-Selectin) AK4 CCTTCCGTATCCCTT ENSG00000174175 SELP C0219 anti-human CD119 (IFN-γ R α chain) GIR-208 TGTGTATTCCCTTGT ENSG00000027697 IFNGR1 C0224 anti-human TCR α/β IP26 CGTAACGTAGAGCGA C0236 Rat IgG1, κ isotype Ctrl RTK2071 ATCAGATGCCCTCAT C0238 Rat IgG2a, κ Isotype Ctrl RTK2758 AAGTCAGGTTCGTTT C0241 Armenian Hamster IgG Isotype Ctrl HTK888 CCTGTCATTAAGACT C0246 anti-human CD122 (IL-2Rβ) TU27 TCATTTCCTCCGATT ENSG00000100385 IL2RB C0247 anti-human CD267 (TACI) 1A1 AGTGATGGAGCGAAC ENSG00000240505 TNFRSF13B 263 C0352 anti-human FcεRIα AER-37 (CRA-1) CTCGTTTCCGTATCG ENSG00000179639 FCER1A C0353 anti-human CD41 HIP8 ACGTTGTGGCCTTGT ENSG00000005961 ITGA2B C0355 anti-human CD137 (4-1BB) 4B4-1 CAGTAAGTTCGGGAC ENSG00000049249 TNFRSF9 C0358 anti-human CD163 GHI/61 GCTTCTCCTTCCTTA ENSG00000177575 CD163 C0359 anti-human CD83 HB15e CCACTCATTTCCGGT ENSG00000112149 CD83 C0363 anti-human CD124 (IL-4Rα) G077F6 CCGTCCTGATAGATG ENSG00000077238 IL4R C0364 anti-human CD13 WM15 TTTCAACGCCCTTTC ENSG00000166825 ANPEP C0367 anti-human CD2 TS1/8 TACGATTTGTCAGGG ENSG00000116824 CD2 C0368 anti-human CD226 (DNAM-1) 11A8 TCTCAGTGTTTGTGG ENSG00000150637 CD226 C0369 anti-human CD29 TS2/16 GTATTCCCTCAGTCA ENSG00000150093 ITGB1 C0370 anti-human CD303 (BDCA-2) 201A GAGATGTCCGAATTT ENSG00000198178 CLEC4C C0371 anti-human CD49b P1E6-C5 GCTTTCTTCAGTATG ENSG00000164171 ITGA2 C0373 anti-human CD81 (TAPA-1) 5A6 GTATCCTTCCTTGGC ENSG00000110651 CD81 264 C0384 anti-human IgD IA6-2 CAGTCTCCGTAGAGT ENSG00000211898 IGHD C0385 anti-human CD18 TS1/18 TATTGGGACACTTCT ENSG00000160255 ITGB2 C0386 anti-human CD28 CD28.2 TGAGAACGACCCTAA ENSG00000178562 CD28 C0389 anti-human CD38 HIT2 TGTACCCGCTTGTGA ENSG00000004468 CD38 C0390 anti-human CD127 (IL-7Rα) A019D5 GTGTGTTGTCCTATG ENSG00000168685 IL7R C0391 anti-human CD45 HI30 TGCAATTACCCGGAT ENSG00000081237 PTPRC C0393 anti-human CD22 S-HCL-1 GGGTTGTTGTCTTTG ENSG00000012124 CD22 C0394 anti-human CD71 CY1G4 CCGTGTTCCTCATTA ENSG00000072274 TFRC C0396 anti-human CD26 BA5b GGTGGCTAGATAATG ENSG00000197635 DPP4 C0407 anti-human CD36 5-271 TTCTTTGCCTTGCCA ENSG00000135218 CD36 C0420 anti-human CD158 (KIR2DL1/S1/S3/S5) HP-MA4 TATCAACCAACGCTT ENSG00000125498 KIR2DL1 C0575 anti-human CD49a TS2/7 ACTGATGGACTCAGA ENSG00000213949 ITGA1 C0576 anti-human CD49d 9F10 CCATTCAACTTCCGG ENSG00000115232 ITGA4 265 C0577 anti-human CD73 (Ecto-5'-nucleotidase) AD2 CAGTTCCTCAGTTCG ENSG00000135318 NT5E C0581 anti-human TCR Vα7.2 3C10 TACGAGCAGTATTCA C0582 anti-human TCR Vδ2 B6 TCAGTCAGATGGTAT C0591 anti-human LOX-1 15C4 ACCCTTTACCGAATA ENSG00000173391 OLR1 C0592 anti-human CD158b (KIR2DL2/L3, NKAT2) DX27 GACCCGTAGTTTGAT ENSG00000243772 KIR2DL3 C0599 anti-human CD158e1 (KIR3DL1, NKB1) DX9 GGACGCTTTCCTTGA ENSG00000167633 KIR3DL1 C0830 anti-human CD319 (CRACC) 162.1 AGTATGCCATGTCTT ENSG00000026751 SLAMF7 C0845 anti-human CD99 3B2/TA8 ACCCGTCCCTAAGAA ENSG00000002586 CD99 C0853 anti-human CLEC12A 50C1 CATTAGAGTCTGCCA ENSG00000172322 CLEC12A C0864 anti-human CD352 (NTB-A) NT-7 AGTTTCCACTCAGGC ENSG00000162739 SLAMF6 C0867 anti-human CD94 DX22 CTTTCCGGTCCTACA ENSG00000134539 KLRD1 C0894 anti-human Ig light chain κ MHK-49 AGCTCAGCCAGTATG ENSG00000211592 IGKC C0896 anti-human CD85j (ILT2) GHI/75 CCTTGTGAGGCTATG ENSG00000104972 LILRB1 266 C0897 anti-human CD23 EBVCS-5 TCTGTATAACCGTCT ENSG00000104921 FCER2 C0898 anti-human Ig light chain λ MHL-38 CAGCCAGTAAGTCAC C0902 anti-human CD328 (Siglec-7) 6-434 CTTAGCATTTCACTG ENSG00000168995 SIGLEC7 C0912 anti-human GPR56 CG4 GCCTAGTTTCCGTTT ENSG00000205336 ADGRG1 C0918 anti-human HLA-E 3D12 GAGTCGAGAAATCAT ENSG00000204592 HLA-E C0920 anti-human CD82 ASL-24 TCCCACTTCCGCTTT ENSG00000085117 CD82 C0944 anti-human CD101 (BB27) BB27 CTACTTCCCTGTCAA ENSG00000134256 CD101 C1046 anti-human CD88 (C5aR) S5/1 GCCGCATGAGAAACA ENSG00000197405 C5AR1 C1052 anti-human CD224 KF29 CTGATGAGATGTCAG ENSG00000100031 GGT1 267 B.3 Supplemental data tables Table B.3: Key marker genes defining cell types identified via CITE-seq. Cell Type Key Marker Genes ETPs SELL+, CD7high, CD34+ Pro-T cells TCF7high, RAG1+, RAG2+, BCL11B+, MKI67low B-selection cells PTCRAhigh DP (P) CD4+, CD8+, MKI67+ DP1 (Q) CD4+, CD8+, MKI67+, SATB1high, CD3Ehigh, RAG1high DP2 (Q) CD4+, CD8+, MKI67+, CD7low DP-SP Transition CD4+, CD8+, SATB1high, PTCRA- CD8 SPs CD8+, CD3+ CD4 SPs CD4+, CD3+ Pro-Treg CD4+CD25loFOXP3loICOS-CD39-CD73-CXCR3- tTreg CD4+CD25medFOXP3medICOSloCD39-CD73+CXCR3- rrTreg CD4+CD25hiFOXP3hiICOS+CD39+CD73-CXCR3+ Immature innate-like cells CD8Ahigh, CD3-, EOMES+, PDCD1+ Innate-like cells NCAM1+, CXCR3low, CD7low, CD56low DC1 CLEC9A+, CXCR3+ DC1 (P) CLEC9A+, MKI67+, CXCR3+ DC2/3 CD1c+, ITGAM+, ITGAX+, CD14+, CD68+, CD4+ aDCs CCL19+, LAMP3+, CCR7+ pDCs THBD+, IL3RA+, CD68+, CD4+, CXCR3+ pDCs (P) MKI67+, IL3RA+, CD68+, CD4+, CXCR3+ Mono ITGAM+, ITGAX+, CD14+, CD68low Mast cells KIT+, FCER1A+ B cells CD19+ cTECs PSMB11+, KRT5+, KRT8+, EPCAM- mTECs EPCAM+, KRT5+, KRT8+ Activated mTECs EPCAM+, Class Ihigh, Class IIhigh Mimetic TECs EPCAM+, NEUROD1+, MYOG+, POU4F1+ DPP4+ capFibs DPP4+, PI16+, PDGFRA+ capFibs DPP4-, PI16+, PDGFRA+ mFibs PDGFRA+, CCL19+, Class Ihigh Fibs (P) MKI67+, PDGFRA+ KRT+ Fibs KRT5+, KRT8+, PDGFRA+ VSMCs MCAM+, ACTA2+ 268 Pericytes MCAM+, ACTA2- ECs (Notch) CD31+, LYVE1-, JAG1+, JAG2+, DLL1+, DLL4+ ECs CD31+, LYVE1- LECs CD31+, LYVE1+ Table B.4: Comparison of statistically significant male differentially expressed genes to Y chromosome genes. Cell Type Total gene count Y chromosome genes Percentage of total genes (%) mTECs 1381 10 0.724113 cTECs 3421 5 0.146156 Activated mTECS 1300 8 0.615385 Mimetic TECs 95 4 4.210526 capFibs 794 7 0.881612 DPP4 capfibs 147 3 2.040816 KRT Fibs 11 1 9.090909 mFibs 3536 8 0.226244 Fibs P 970 8 0.824742 VSMC 195 3 1.538462 Pericyte 186 4 2.150538 ECs Notch 62 2 3.225806 ECs 450 6 1.333333 LECs 10 1 10 Mast 0 0 - Mono 0 0 - DC1 61 2 3.278688525 DC1 (P) 1 0 0 DC2/3 91 3 3.296703297 pDCs 0 0 - pDCs (P) 0 0 - aDCs 1238 7 0.56542811 B cells 828 6 0.724638 Innate cells 107 6 5.607477 rrTregs 2887 5 0.17319 tTregs 2015 2 0.099256 Pro-Tregs 16 1 6.25 Immature innate-like cells 0 0 - 269 CD8 cells 6864 8 0.11655 CD4 cells 630 7 1.111111 DP-SP Transition 12869 7 0.054394 DP1 10223 8 0.078255 DP2 11153 9 0.080696 DP (P) 35467 8 0.022556 B-selection 34432 8 0.023234 Pro-T 1444 8 0.554017 ETPs 7617 8 0.105028 Average 1.79% Table B.5: Comparison of statistically significant female differentially expressed genes to X chromosome genes. Cell Type Total gene count X chromosome genes Percentage of total genes (%) mTECs 1596 8 0.501253 cTECs 2221 9 0.405223 Activated mTECS 1005 4 0.39801 Mimetic TECs 74 0 0 capFibs 738 2 0.271003 DPP4 capfibs 49 0 0 KRT Fibs 9 0 0 mFibs 2709 9 0.332226 Fibs P 757 3 0.396301 VSMC 230 0 0 Pericyte 213 0 0 ECs Notch 68 0 0 ECs 392 2 0.510204 LECs 20 0 0 Mast 0 0 - Mono 0 0 - DC1 102 0 0 DC1 (P) 55 0 0 DC2/3 38 1 2.631578947 pDCs 4 0 0 pDCs (P) 0 0 - aDCs 549 1 0.182149362 270 B cells 851 0 0 Innate cells 32 0 0 rrTregs 1623 4 0.246457 tTregs 587 3 0.511073 Pro-Tregs 56 0 0 Immature innate-like cells 35351 83 0.237617 CD8 cells 6076 21 0.345622 CD4 cells 465 1 0.215054 DP-SP Transition 26857 70 0.26064 DP1 17869 48 0.268622 DP2 12670 32 0.252565 DP (P) 30387 85 0.279725 B-selection 29499 74 0.250856 Pro-T 1288 11 0.854037 ETPs 11739 28 0.238521 Average 0.28% Table B.6: Cytokine concentrations used in differentiations. Cytokine Stage 1 concentration (ng/ml) Stage 2 concentration (ng/ml) SCF 23.9 120.5 Flt3L 8.7 8.0 IL-3 5.3 1.2 IL-7 10.0 44.5 TNFï¡ 4.9 0.4 CXCL12 9.7 14.8 Table B.7: Antibodies used in Notch Ligand in vitro screening experiments. Target Fluorochrome Concentration Vendor Catalog # T-lineage CD5 BB515 1:300 eBioscience 25-0059-42 CD7 PE-Cy7 1:200 BD Biosciences 565211 Dendritic cells CD11c PE 1:300 Biolegend 337206 CD14 VioBlue 1:200 Miltenyi Biotec 130-110-582 CD86 PE-Cy7 1:300 Biolegend 374209 HLA-DR FITC 1:300 Biolegend 307603 271 Monocyte/macrophage/mast CD7 BV605 1:300 BD Biosciences 740392 CD11b BV711 1:300 Biolegend 301343 CD14 VioBlue 1:200 Miltenyi Biotec 130-110-582 CD34 APC-Cy7 1:200 Biolegend 343514 CD68 APC 1:300 Biolegend 333809 CD117 BV785 1:200 Biolegend 313237 FCï¥R1ï¡ PE 1:400 eBioscience 12-5899-42 Table B.8: Key resources table describing details of reagents and resources used in the human thymus spatial multiomics study. REAGENT or RESOURCE SOURCE IDENTIFIER Antibodies CD3-APC-Cy7, BioLegend Cat # 300425 CD4-BB515 BD Biosciences Cat # 566912 CD4-BV786 BD Biosciences Cat # 740962 CD8ð½-PE-Cy7 eBiosciences Cat # 25-5273-42 CD27-BV421 BioLegend Cat # 356417 HLA-DR/DP/DQ-FITC BioLegend Cat # 361706 TCRð¼ð½-APC BioLegend Cat # 306717 TCRð¼ð½-BV711 BioLegend Cat # 306739 CD5 BV605 BioLegend Cat # 364020 CD7 BV421 BioLegend Cat # 334122 CXCR4 APC BioLegend Cat # 306509 CCR9 PE-Dazzle 594 BioLegend Cat # 358918 HLA-A,B,C PE BioLegend Cat # 311406 TotalSeq-C human universal antibody cocktail v1.0 BioLegend Cat # 399905 Anti-mouse IgG1 555 Invitrogen Cat # A-21127 Anti-mouse IgG2a 488 Invitrogen Cat # A-21131 Anti-mouse IgG2b 647 Invitrogen Cat # A-21242 Ki67 BioLegend Cat # 151202 SMA eBiosciences Cat # 14-9760-82 AIRE Thermo Fisher Cat # 14-9534-82 CD86 BioLegend Cat # 305402 272 HLA-ABC BioLegend Cat # 311402 CD44 BioLegend Cat # 103002 CD38 BioLegend Cat # 356602 Laminin Thermo Fisher Cat # PA1-16730 CD4 BioLegend Cat # 300502 CD1a BioLegend Cat # 300102 CD5 BioLegend Cat # 300602 CD326 BioLegend Cat # 324202 CD197 BioLegend Cat # 353202 CD8a BioLegend Cat # 301002 CD19 BioLegend Cat # 302202 VCAM BioLegend Cat # 305802 DLL4 Novus Biologicals Cat # NB600-892 DLL1 Abcam Cat # ab10554 CD68 Thermo Fisher Cat # 14-0688-82 JAG2 R&D Systems Cat # AF1726 CD7 BioLegend Cat # 343102 JAG1 R&D Systems Cat # AF1277 Cytokeratin 5/8 Invitrogen Cat # MA1-06314 CD274 Invitrogen Cat # 14-5983-82 HLA-DR,DP,DQ BioLegend Cat # 361702 PDGFRa Novus Bio Cat # NBP2-52528 CD31 BioLegend Cat # 303102 CD45 BioLegend Cat # 304002 CD45RO BioLegend Cat # 304202 IgM BioLegend Cat # 314502 CD279 Invitrogen Cat # MA5-15780 CD3 BioLegend Cat # 300402 CD40 BioLegend Cat # 313002 HELIOS BioLegend Cat # 137202 FOXP3 BioLegend Cat # 320102 ICOS BioLegend Cat # 313502 CD11b BioLegend Cat # 101202 CD45RA BioLegend Cat # 304102 MCAM R&D Systems Cat # AF932 TCRgd BioLegend Cat # 331202 SATB1 Santa Cruz Biotechnology Cat # sc-376096 273 CD62L BioLegend Cat # 304802 CD11c BioLegend Cat # 371502 ICAM BioLegend Cat # 353102 PGDH-15 Santa Cruz Biotechnology Cat # sc-271418 COL1A1 Cell Signaling Cat # 81375SF EDA-Fibronectin Sigma-Aldrich Cat # SAB4200880-100UL Perlecan NovusBio Cat # NBP1-05170 CD4 BioLegend Cat # 317402 Chemicals, Peptides, and Recombinant Proteins IMDM with GlutaMAX Thermo-Fisher Scientific Cat # 31980030 B27 minus vitamin A Gibco Cat # 12587010 2-mercaptoethanol Sigma-Aldrich Cat # M3148 Ascorbic acid Sigma-Aldrich Cat # A8960 Human recombinant SCF R&D Systems Cat # 255-SC Human recombinant Flt3L R&D Systems Cat # 308-FK Human recombinant IL-7 R&D Systems Cat # 207-IL Human recombinant TNFa R&D Systems Cat # 210-TA Human/Rhesus Macaque/Feline recombinant CXCL12 R&D Systems Cat # 350-NS ImmunocultTM Human CD3/CD28/CD2 T cell activator STEMCELL Technologies Cat # 10970 Costar 96-well clear flat bottom polystyrene tissue culture-treated plates VWR Cat # 29442-056 ImmunoCult-XF T cell Expansion Medium STEMCELL Technologies Cat # 10981 Lymphoprep STEMCELL Technologies Cat # 07801 Penicillin/streptomycin Invitrogen Cat # 15140122 274 RPMI medium Life Technologies Cat # 11875119 FBS Thermo Fisher Scientific Cat # 12483020 DMSO Sigma-Aldrich Cat # 67-68-5 Stemxyme Cedarlane Labs Cat # LS004113 DNase I Fisher Scientific Cat # 260913-10MU BSA Wisent Bioproducts Cat # 809-098-EL HBSS Thermo Fisher Scientific Cat # 14175103 Red blood cell lysis buffer Millipore Sigma Cat # 11814389001 Human Fc Block BD Biosciences Cat # 564220 Zombie UV Live/Dead Stain BioLegend Cat # 423108 10X Genomics Nuclei Isolation Kit 10X Genomics Reagents list described by Buenrosto et al.84,396 JAG1-Fc R&D Systems Cat # 1277-JG-050 JAG2-Fc R&D Systems Cat # 1726-JG-050 DLL1-Fc R&D Systems Cat # 10184-DL-050 DLL4-Fc Sino Biological Cat # 10171-H02H-500 VCAM-Fc R&D Systems Cat # 643-VM-200 BIT 9500 STEMCELL Technologies Cat # 09500 Low density lipoprotein STEMCELL Technologies Cat # 02698 2-mercaptoethanol Sigma-Aldrich Cat # M3148-100ML EDTA Thermo Fisher Scientific Cat # 15-575-020 Brilliant Violet Buffer BD Biosciences Cat # 563794 DNA blocking oligos IDT Barcodes published in Schurch et al.128 Maleimide-functionalized oligos IDT Barcodes published in Schurch et al.128 Triton X100 Sigma-Aldrich Cat # 9036-19-5 PFA Cerdarlane Labs Cat # 15714EM DAPI Sigma-Aldrich Cat # MBD0015-5ML 275 Goat serum Millipore Sigma Cat # G9023 Fluoromount G Thermo Fisher Scientific Cat # 00-4958-02 Critical Commercial Assays Treg selection kit STEMCELL Technologies Cat # 18063 Miltenyi LD column prep kit Miltenyi Biotech Inc. Cat # 130-042-901 Chromium Single Cell 5â Library and Gel Bead Kit v1.1 10X Genomics Cat # PN-1000165 Chromium Single Cell ATAC Library and Gel Bead Kit v2 10X Genomics Cat # PN-1000390 EasySep Human CD34+ Positive Selection kit STEMCELL Technologies Cat # 17826 Cytofix/Cytoperm kit BD Biosciences Cat # 554714 Deposited Data CODEX human thymus imaging data This paper Zenodo: 10.5281/zenodo.11585722 CITE-seq human thymus data This paper GEO: GSE264585 Mendeley: DOI: 10.17632/w3z4z4wwv6.1 ATAC-seq human thymus data This paper GEO: GSE264587 Mendeley: DOI: 10.17632/w3z4z4wwv6.1 Software and Algorithms Code This paper GitLab: https://gitlab.com/stemcellbioengineering/ thymus-multiomics DOI: 10.5281/zenodo.13381792 CellRanger (v. 6.0.1) 10X Genomics https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/installation CellRanger-ATAC (v 2.0.0) 10X Genomics https://support.10xgenomics.com/single-cell-276 atac/software/pipelines/latest/installation#header CytExpert v2.3 Beckman Coulter https://www.beckman.pt/flow-cytometry/research-flow-cytometers/cytoflex/software FlowJo X BD Biosciences https://www.flowjo.com/solutions/flowjo Prism 10 GraphPad https://www.graphpad.com/features Seurat (v. 4.3.0) The R-package was published by Hao et al.397 https://satijalab.org/seurat/install.html; RRID:SCR_007322 R (v. 4.2.0) R core https://www.r-project.org/ Bioconductor The software project was published by Huber et al.398 http://www.bioconductor.org/; RRID: SCR_006442 Signac (v. 1.8.0) The software was published by Stuart et al.399 https://github.com/stuart-lab/signac CellChat (v. 1.6.0) The software was published by Jin et al.140 https://github.com/sqjin/CellChat pySCENIC The software was published by Kumar et al.159,188 https://github.com/aertslab/pySCENIC CRISP The software was published by Wang et al.86 https://github.com/will-yx/ HFcluster The software was published by Wang et al.86 https://github.com/will-yx/ Other MoFlo Astrios EQ Sorter Beckman Coulter Cat # B52102 BD Influx BD Biosciences https://www.bd.com/resource.aspx?IDX=17866 Countess II FL Automated Cell Counter Thermo Fisher Scientific https://www.thermofisher.com/ca/en/home/references/newsletters-and-journals/bioprobes-journal-of-cell-biology-applications/bioprobes-277 70/countess-ii-fl-automated-cell-counter.html 10X Genomics Chromium controller platform 10X Genomics https://www.10xgenomics.com/instruments/chromium-controller Zeiss LSM 900 with Airy scan Zeiss https://www.zeiss.com/microscopy/en/products/light-microscopes/confocal-microscopes/lsm-900-with-airyscan-2.html Keyence BZ-X810 Keyence https://www.keyence.com/products/microscope/fluorescence-microscope/bz-x700/models/bz-x810/ Akoya Biosciences Phenocycler Akoya Biosciences https://www.akoyabio.com/phenocycler/ NextSeq 2000 Illumina Cat # 20038897 CytoFLEX LX cytometer Beckman Coulter Cat # C40313
Thesis/Dissertation
eng
Biomedical Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Rossi, Fabio M. V.
Zandstra, Peter
A pipeline for discovery-based research on human T cell development : integrating spatial omics and PSC-T cell platforms
Text