Medicine, Faculty of
DSpace
UBCV
Wong, Alana Rachel
Doctor of Philosophy - PhD
University of British Columbia
T-cell development and lineage commitment are temporally protracted processes in which the interplay between transcription factors and epigenetic modulators orchestrate the sequential exclusion of alternative fates and acquisition of specialized T-cell functions. Alterations during this process can lead to diseases such as T-cell acute lymphoblastic leukemia (T-ALL). While much work has focused on the transcription factors which drive normal development and T-ALL, the underlying epigenetic constraints remain poorly characterized. The central objective of the work presented in this thesis is to gain a greater understanding of the molecular events during early human T-cell differentiation to allow for dissection of particular genetic events that may occur in T-ALL. This was addressed through a detailed transcriptomic characterization of T-ALL within the framework of normal development, and through genetic perturbation and functional characterization of in vitro-differentiating T-cell subsets. The findings revealed; 1) an improved classification of T-ALL which better captures the developmental context in which specific transcription factors operate and which improves upon the identification of clinically relevant disease subgroups, and 2) novel insights into a role of DNMT3A in restricting lineage-specific signal responses in T-cell development and T-ALL. The results of functional assays revealed that DNMT3A loss increased signaling plasticity in T-lineage-restricted populations, as measured by response to cytokines typically affiliated with the myeloid lineage. This was enriched within a population of lineage-restricted T-cells expressing the G-, M-, and/or GM-CSF receptors, and was associated with proliferation and acquisition of a myeloid-like phenotype. Further investigation of the expression of these receptors revealed their prevalence in human T-ALL cell lines and post-natal thymus, thus indicating the biological relevance of CSF receptor expression in human tissues. Stimulation of the CSF receptor-expressing population by adding cognate ligands revealed a proliferative advantage in T-ALL cells, but only upon DNMT3A loss. These results suggest that DNMT3A may act to preclude response to alternative-lineage factors in T-cells, and furthermore point to a mechanism of selective advantage in a subset of T-ALL.
EXPLORING MECHANISMS OF T-CELL ACUTE LYMPHOBLASTIC LEUKEMIA PATHOGENESIS THROUGH MOLECULAR CHARACTERIZATION OF NORMAL T-CELL DEVELOPMENT by Alana Rachel Wong B.Sc. (Honours and Co-op), University of Victoria, 2015 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Interdisciplinary Oncology) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) September 2022 © Alana Rachel Wong, 2022 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Exploring mechanisms of T-cell acute lymphoblastic leukemia pathogenesis through molecular characterization of normal T-cell development submitted by Alana Rachel Wong in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Interdisciplinary Oncology Examining Committee: Andrew Weng, Professor, Pathology & Laboratory Medicine, UBC Supervisor Gregg Morin, Professor, Medical Genetics, UBC Supervisory Committee Member Ninan Abraham, Professor, Department of Microbiology and Immunology, Department of Zoology, UBC University Examiner Fumio Takei, Professor, Pathology & Laboratory Medicine, UBC University Examiner Additional Supervisory Committee Members: Christian Steidl, Professor, Pathology & Laboratory Medicine, UBC Supervisory Committee Member iii Abstract T-cell development and lineage commitment are temporally protracted processes in which the interplay between transcription factors and epigenetic modulators orchestrate the sequential exclusion of alternative fates and acquisition of specialized T-cell functions. Alterations during this process can lead to diseases such as T-cell acute lymphoblastic leukemia (T-ALL). While much work has focused on the transcription factors which drive normal development and T-ALL, the underlying epigenetic constraints remain poorly characterized. The central objective of the work presented in this thesis is to gain a greater understanding of the molecular events during early human T-cell differentiation to allow for dissection of particular genetic events that may occur in T-ALL. This was addressed through a detailed transcriptomic characterization of T-ALL within the framework of normal development, and through genetic perturbation and functional characterization of in vitro-differentiating T-cell subsets. The findings revealed; 1) an improved classification of T-ALL which better captures the developmental context in which specific transcription factors operate and which improves upon the identification of clinically relevant disease subgroups, and 2) novel insights into a role of DNMT3A in restricting lineage-specific signal responses in T-cell development and T-ALL. The results of functional assays revealed that DNMT3A loss increased signaling plasticity in T-lineage-restricted populations, as measured by response to cytokines typically affiliated with the myeloid lineage. This was enriched within a population of lineage-restricted T-cells expressing the G-, M-, and/or GM-CSF receptors, and was associated with proliferation and acquisition of a myeloid-like phenotype. Further investigation of the expression of these receptors revealed their prevalence in human T-ALL cell lines and post-natal thymus, thus indicating the biological relevance of CSF receptor expression in human tissues. Stimulation of the CSF receptor-expressing population by adding cognate ligands revealed a proliferative advantage in T-ALL cells, but only upon DNMT3A loss. These results suggest that DNMT3A may act to preclude response to alternative-lineage factors in T-cells, and furthermore point to a mechanism of selective advantage in a subset of T-ALL. iv Lay Summary The development of T-cells normally occurs in a highly sequential manner, in which genes are turned on or off in response to external signaling cues in order to drive normal T-cell development. Failure to appropriately regulate these genes can result in diseases such as leukemia. To gain a better understanding of how aberrations in development may progress to disease, a comprehensive comparison of normal vs leukemic cells was performed, and the effect of altering genes in normal cells was interrogated using a model of normal T-cell development. The results of these findings revealed that alteration of a specific gene in normal cells can reprogram T-cells. In the context of leukemia, this can result in increased cell growth. The results presented here provide insight into normal cell development and novel mechanisms for disease pathogenesis. v Preface The experiments outlined in this thesis were conceived and designed by Dr. Andrew Weng and me. I designed and performed all experiments, data analysis and generated the figures and tables that are presented in this thesis unless otherwise stated below. A version of Chapter 2 has been published: Wong R, Nguyen A, Wang X, Chong L, Tyshchenko K, Brown SD, Holt RA, Steidl C, Weng AP. Improved resolution of phenotypic subsets in human T-ALL by incorporation of RNA-seq based developmental profiling. Leuk Res. 2021 Nov;110:106712. doi: 10.1016/j.leukres.2021.106712. Epub 2021 Sep 22. PMID: 34583126. I conceived and designed the study together with Dr. Andrew Weng. I performed all bioinformatics analyses and figure generation. RNA-seq alignment was performed by Kateryna Tyshchenko and Lauren Chong under the supervision of Dr. Christian Steidl. Dr. Xuehai Wang provided guidance for data analysis. MiXCR analysis was performed by Andrew Nguyen with assistance from Dr. Scott Brown and under the supervision of Dr. Robert Holt. I wrote the manuscript with Andrew Weng. Chapters 3 and 4 are being prepared for publication. Andrew Weng and I conceived and designed all experiments with assistance from Dr. Samuel Gusscott. I performed all experiments, data analyses, and figure generation. Samuel Gusscott produced the western blot of DNMT3A protein levels, performed cloning of constructs, designed CRISPR guide RNAs, and assisted with sample collection for RNA-seq and whole genome bisulfite sequencing. Andrew Weng and I interpreted the data. The gemBS pipeline for quantifying CpG methylation levels, as well as data quality control, was performed by Reanne Bowlby and Anaaïck Carles. All cell sorting was performed in the Terry Fox Laboratory Flow Core with assistance from Wenbo Xu and Guillermo Simkin. The research presented in this thesis was approved by UBC BC Cancer Research Ethics Board (H20-00710). Primary patient tissues were obtained with informed consent from donors. vi Table of Contents Abstract ........................................................................................................................................................ iii Lay Summary............................................................................................................................................... iv Preface .......................................................................................................................................................... v Table of Contents ........................................................................................................................................ vi List of Tables ............................................................................................................................................... ix List of Figures .............................................................................................................................................. x List of Abbreviations ................................................................................................................................. xii Acknowledgements ................................................................................................................................... xv Dedication .................................................................................................................................................. xvi Chapter 1: Normal and aberrant T-cell development ............................................................................... 1 1.1 Introduction .......................................................................................................................................... 1 1.2 Normal T-cell development .................................................................................................................. 1 1.2.1 Overview of T-cell development in the thymus ............................................................................ 2 1.2.2 The role of thymic epithelial cells in T-cell development ............................................................. 5 1.2.3 Egress from the thymus ............................................................................................................... 9 1.2.4 T-cell receptor rearrangement ................................................................................................... 10 1.2.5 T-lineage commitment ............................................................................................................... 12 1.3 DNA methylation in normal T-cell development ................................................................................ 14 1.3.1 Overview of DNA methylation by DNMT3A, DNMT3B, and DNMT1 ........................................ 14 1.3.1.1 DNA methyltransferase protein structure ............................................................................. 16 1.3.1.2 DNA demethylation ............................................................................................................... 19 1.3.2 DNA methylation in T-cell differentiation and commitment ....................................................... 20 1.4 T-cell acute lymphoblastic leukemia .................................................................................................. 22 1.4.1 A brief overview of T-ALL .......................................................................................................... 22 1.4.2 Genotypic and phenotypic characterization of T-ALL ............................................................... 23 1.4.2.1 Developmental stage-based classification ........................................................................... 24 1.4.2.2 Transcription factor subgroups ............................................................................................. 25 1.4.2.3 Mutations in epigenetic modifiers ......................................................................................... 26 1.4.2.4 DNMT3A mutation and consequences of DNMT3A loss in T-ALL....................................... 28 1.4.3 Current treatment and challenges ............................................................................................. 30 1.5 Thesis objective and chapter summaries .......................................................................................... 32 Chapter 2: Characterization of T-cell acute lymphoblastic leukemia in the context of normal T-cell development ............................................................................................................................................... 34 2.1 Introduction ........................................................................................................................................ 34 2.2 Materials and methods ...................................................................................................................... 36 2.2.1 Public data resources ................................................................................................................ 36 2.2.2 RNA-seq alignment .................................................................................................................... 36 2.2.3 Principal component analysis .................................................................................................... 36 2.2.4 Unsupervised clustering ............................................................................................................ 37 2.2.5 Differential gene expression ...................................................................................................... 37 2.2.6 T-cell receptor analysis .............................................................................................................. 37 2.2.7 Transcription factor subgroup classification .............................................................................. 38 2.2.8 Gene set variation analysis........................................................................................................ 38 2.2.9 Diffusion map ............................................................................................................................. 38 vii 2.2.10 Statistical analysis ..................................................................................................................... 38 2.3 Results ............................................................................................................................................... 39 2.3.1 Creation of a gene signature associated with human intrathymic T-cell development ............. 39 2.3.2 Classification of T-ALL according to gene signatures that correlate with normal T-cell development ........................................................................................................................................... 42 2.3.3 Developmental classification reveals patient subgroups with high minimal residual disease .. 46 2.4 Discussion.......................................................................................................................................... 48 Chapter 3: Normal human T-cell development using a feeder-free in vitro culture system.............. 50 3.1 Introduction ........................................................................................................................................ 50 3.2 Materials and methods ...................................................................................................................... 52 3.2.1 Cells and cell culture .................................................................................................................. 52 3.2.1.1 Primary cells ......................................................................................................................... 52 3.2.1.2 Primary cell culture ............................................................................................................... 53 3.2.2 Limited dilution analysis ............................................................................................................. 53 3.2.3 Flow cytometry and cell sorting ................................................................................................. 54 3.2.3.1 Flow cytometry ...................................................................................................................... 54 3.2.3.2 Cell sorting ............................................................................................................................ 54 3.2.3.3 Flow cytometry data analysis ............................................................................................... 54 3.2.4 RNA-seq .................................................................................................................................... 55 3.2.4.1 RNA-seq sample processing ................................................................................................ 55 3.2.4.2 RNA-seq analysis ................................................................................................................. 55 3.2.4.3 Public data resources ........................................................................................................... 56 3.2.5 TCR analysis.............................................................................................................................. 56 3.2.6 Whole-genome bisulfite sequencing.......................................................................................... 56 3.2.6.1 WGBS sample processing .................................................................................................... 56 3.2.6.2 WGBS sample analysis ........................................................................................................ 57 3.3 Results ............................................................................................................................................... 57 3.3.1 Phenotypes of in vitro CB-derived T-cell populations................................................................ 57 3.3.2 In vitro generated T-cell subsets are transcriptionally similar to human thymic populations .... 61 3.3.3 In vitro generated T-cell subsets exhibit T-cell receptor rearrangement and myeloid-lineage restriction ................................................................................................................................................ 64 3.3.4 Interrogation of DNA methylation patterns and gene expression profiles of T-cell populations throughout differentiation ........................................................................................................................ 71 3.4 Discussion.......................................................................................................................................... 75 Chapter 4: Characterization of the functional consequences of DNMT3A loss in normal development and in T-ALL ........................................................................................................................ 79 4.1 Introduction ........................................................................................................................................ 79 4.2 Materials and methods ...................................................................................................................... 80 4.2.1 Cell culture and conditions......................................................................................................... 80 4.2.1.1 Primary cells ......................................................................................................................... 80 4.2.1.2 Cell lines ............................................................................................................................... 80 4.2.1.3 In vitro development of T-cells .............................................................................................. 81 4.2.2 Cell proliferation assays............................................................................................................. 81 4.2.3 Media transfer experiments ....................................................................................................... 81 4.2.4 Lentiviral vectors and transduction ............................................................................................ 82 4.2.5 CRISPR knockout experiments ................................................................................................. 83 4.2.6 Analysis of CRISPR knockout cells ........................................................................................... 83 4.2.7 Flow cytometry and cell sorting ................................................................................................. 83 viii 4.2.7.1 Intracellular Phospho-flow cytometry.................................................................................... 83 4.2.7.2 Flow cytometry ...................................................................................................................... 84 4.2.7.3 Cell sorting ............................................................................................................................ 84 4.2.7.4 Flow cytometry data analysis ............................................................................................... 84 4.2.8 Whole-cell lysate extraction ....................................................................................................... 85 4.2.9 Western blotting ......................................................................................................................... 85 4.3 Results ............................................................................................................................................... 86 4.3.1 DNMT3A loss enhances the outgrowth of myeloid-like cells .................................................... 86 4.3.1.1 The timing of DNMT3A knockdown influences the frequency of myeloid-like cell growth ... 91 4.3.1.2 Selection for DNMT3A loss occurs in myeloid-like populations ........................................... 92 4.3.2 DNMT3A knockout does not demonstrably alter T-cell differentiation ...................................... 94 4.3.3 Myeloid-like cell potential is retained in the G-CSF, M-CSF and/or GM-CSF receptor expressing population ............................................................................................................................. 97 4.3.4 Knockdown of DNMT3A induces proliferation in response to G-, M-, and GM-CSF in human T-ALL cell lines ......................................................................................................................................... 101 4.4 Discussion........................................................................................................................................ 107 Chapter 5: Conclusions........................................................................................................................... 111 5.1 Incorporation of RNA-seq based developmental profiling improves resolution of phenotypic subsets in human T-ALL ......................................................................................................................................... 111 5.2 Identification of DNMT3A as a regulator of lineage-specific responses in developing human T-cells 113 5.3 Limitations of the work and future directions ................................................................................... 116 5.4 Concluding remarks ......................................................................................................................... 119 References ................................................................................................................................................ 120 Appendices ............................................................................................................................................... 149 Appendix A Cord blood pools accessed .................................................................................................. 149 Appendix B Flow cytometry antibodies .................................................................................................... 150 Appendix C In vitro-derived T-cell subsets and thymus populations isolated for RNA-seq and WGBS . 152 Appendix D sgRNA targeting of the DNMT3A locus ............................................................................... 153 Appendix E shRNA, sgRNA, and primer sequences ............................................................................... 154 Appendix F Immunophenotypes of in vitro-derived T-cell subsets in additional cord pools ................... 155 Appendix G Supplementary figures related to Chapter 2 ........................................................................ 156 ix List of Tables Table 2.1 Sensitivity/specificity of Cluster Teal vs ETP-ALL classifier in identifying day 29 post-induction MRD positive cases. .................................................................................................................................... 48 x List of Figures Figure 1.1 Overview of human T-cell development. ...................................................................................... 2 Figure 1.2 Overview of intrathymic  T-cell migration. ................................................................................ 5 Figure 1.3 Schematic of TCR and TCR rearrangement and assembly of pre-TCR and TCR complexes. ................................................................................................................................................... 12 Figure 1.4 Domain architecture of the DNA methyltransferase proteins. .................................................... 16 Figure 1.5 Model of DNMT3-DNMT3L tetramer formation .......................................................................... 19 Figure 2.1 PCA projection of intrathymic T-cell subsets and T-ALL. ........................................................... 40 Figure 2.2 Cluster analysis of normal thymic T-cells. .................................................................................. 41 Figure 2.3 k-medoid clustering of T-ALL samples. ...................................................................................... 44 Figure 2.4 Developmental-based clustering of patient T-ALLs. .................................................................. 45 Figure 2.5 Developmental ordering and clinical significance of k-medoid clusters. .................................... 46 Figure 3.1 Flow cytometric analysis of T-cell subsets derived in vitro from CD34+ cord blood. ................. 59 Figure 3.2 Comparison of surface marker expression of cord blood-derived T-cell subsets generated in feeder-free or OP9-DL1 culture systems. .................................................................................................... 60 Figure 3.3 Overview of datasets used for the analysis of human T-cell differentiation. .............................. 62 Figure 3.4 Transcriptomic comparison of in vitro generated T-cells and human intrathymic populations. . 63 Figure 3.5 CD44 loss in T-cells correlates with myeloid-lineage exclusion................................................. 67 Figure 3.6 14-parameter UMAP distribution of flow cytometric immunophenotypes of T-lineage restricted cells after transfer into T-cell or myeloid growth conditions. ........................................................................ 68 Figure 3.7 T-lineage restriction in in vitro-derived T-cell populations after 28 days of culture. ................... 69 Figure 3.8 Pathways associated with myeloid differentiation are upregulated in early T-cell subsets. ...... 70 Figure 3.9 Comparison of T-cell receptor rearrangement analysis of in vitro-derived T-cell subsets and human intrathymic populations. ................................................................................................................... 71 Figure 3.10 Whole genome bisulfite sequencing of T-cell subsets. ............................................................ 73 Figure 3.11 Comparison of CpG methylation by WGBS and gene expression by RNA-seq in the in vitro-derived T-cell subsets. ................................................................................................................................. 74 xi Figure 4.1 shRNA-mediated knockdown of DNMT3A in T-cells correlates with increased growth in myeloid culture conditions............................................................................................................................ 88 Figure 4.2 14-parameter UMAP distribution of flow cytometric immunophenotypes of DNTM3A knockdown T-lineage restricted cells after transfer into T-cell or myeloid growth conditions. .................... 89 Figure 4.3 T-lineage restricted cells transferred into myeloid growth conditions adopt an intermediate T-lymphoid/myeloid phenotype. ...................................................................................................................... 90 Figure 4.4 Stage-specific DNMT3A knockdown influences the proportion of T-lineage restricted myeloid-like cells. ....................................................................................................................................................... 92 Figure 4.5 Selection for DNMT3A loss occurs in CRISPR/Cas9 edited populations upon transfer to myeloid conditions ....................................................................................................................................... 94 Figure 4.6 T-cell differentiation in single-cell-derived CRISPR clones. ....................................................... 96 Figure 4.7 Myeloid-like cells are enriched in the G-CSF, M-CSF, and GM-CSF receptor-expressing populations within T-lineage restricted compartment. ................................................................................. 99 Figure 4.8 Analysis of the expression of G-CSF, M-CSF, and GM-CSF receptors by flow cytometry. ... 100 Figure 4.9 Levels of CpG methylation over the CSF3R and CSF1R loci. ................................................. 101 Figure 4.10 G-CSF, M-CSF, and GM-CSF receptor-expressing populations are present in human postnatal thymus and T-ALL cell lines. ...................................................................................................... 104 Figure 4.11 Populations expressing the G-CSF, M-CSF or GM-CSF receptors expand in response to the addition of cognate ligands upon DNMT3A knockdown in T-ALL cells. .................................................... 105 Figure 4.12 STAT5 phosphorylation increases upon stimulation of T-ALL cells with G-CSF, M-CSF, and GM-CSF. .................................................................................................................................................... 106 Figure 4.13 SPI1 is highly expressed in DNMT3A mutated T-ALLs. ........................................................ 107 xii List of Abbreviations 5caC – 5-carboxylcytosine 5mC – 5-methylcytosine ADD – ATRX-DNMT3-DNMT3L AML – Acute myeloid leukemia BAH – bromo-adjacent homology BM – bone marrow CB – cord blood CCL – chemokine ligand CCR – chemokine receptor CD – cluster of differentiation CGI – CpG island CMJ – cortico-medullary junction CRISPR – Clustered regularly interspaced short palindromic repeats CSF – colony-stimulating factor CSFR – colony-stimulating factor receptor cTEC – cortical thymic epithelial cell CXCR – CXC chemokine receptor DC – dendritic cell DE – differentially expressed DLL – Delta-like DMAP1 – DNA methyltransferase 1-associated protein 1 DN – double negative DNMT – DNA methyltransferase DNMT3L – DNA methyltransferase 3-like DP – double positive ETP – early T-cell precursor ETP-ALL – early T-cell precursor acute lymphoblastic leukemia xiii FTOC – fetal thymus organoid culture G-CSF – granulocyte colony stimulating factor G-CSFR – granulocyte colony stimulating factor receptor GFP – green fluorescent protein UHRF1 – Ubiquitin-like, containing PHD and RING finger domains GM-CSF – Granulocyte-macrophage colony-stimulating factor GM-CSFR – Granulocyte-macrophage colony-stimulating factor receptor GSI – gamma secretase inhibitor GSVA – gene set variation analysis IL-7R – interleukin-7 receptor IL7 – interleukin-7 M-CSF – macrophage colony-stimulating factor M-CSFR – macrophage colony-stimulating factor receptor MHC – major histocompatibility complex MRD – minimal residual disease MTase – methyltransferase mTEC – medullary thymic epithelial cell Myeloid cytokines – M-CSF, G-CSF and GM-CSF NK – natural killer PC – principal component PCA – principal component analysis PCNA – Proliferating cell nuclear antigen PNT – postnatal thymus PWWP – proline-tryptophan-tryptophan-proline RFTS – Replication focus targeting sequence RNP – ribonucleoprotein SCF – Stem cell factor shRNA – short hairpin RNA xiv SP – single positive T-ALL – T-cell acute lymphoblastic leukemia TCR – T-cell receptor TEC – thymic epithelial cell TET – ten-eleven translocation TF – transcription factor TRD – target recognition domain TSP – thymus seeding progenitor UMAP – uniform manifold approximation and projection VCAM – Vascular cell adhesion protein 1 xv Acknowledgements Throughout this PhD, I have been privileged to learn from many scientists and trainees. Their support and enthusiasm have helped me grow both personally and academically, and I am grateful to have been introduced to such a thoughtful group of people. First and foremost, my sincere gratitude and appreciation go towards my supervisor, Dr. Andrew Weng, for teaching me to approach research with a careful and critical eye, and for sharing an ever-abundant enthusiasm towards science. I have grown to appreciate your endless analogies, even if they still leave me confused. To my committee members, Dr. Christian Steidl and Dr. Gregg Morin, I thank you for providing an abundance of advice and expertise throughout the years. Your insightful perspectives, optimism and encouragement have been instrumental in shaping this project. I would also like to extend my sincere gratitude towards my dissertation examiners – Drs Ninan Abraham, Fumio Takei, Gregg Morin and Gay Crooks, for their thoughtful comments and probing questions. I would like to thank Weng lab members, past and present – Drs Catherine Jenkins, Michael Nissen, Sam Gusscott, Xuehai Wang, Vincenzo Giambra, and Cath Hoofd, thank you for the insightful discussions, mentorship, and for providing guidance whenever I felt lost in data. Thanks also to Ann Sun, Gabriela Segat, Kate Tyshchenko, Kash Doshi, Prakruti Uday, Dr. Hayedeh Behzad, Dr. Matthew Carr, Dr. Anne-Sophie Hatat and Marissa Foo for the comradery. It has been a pleasure working with all of you, and I will always value the encouragement and thoughtful discussions that we have had over the years. My appreciation also goes towards the support I have received within the department – Drs Wenbo Xu and Guillermo Simkin, thank you for your patience and assistance in the Flow Core, and for the encouraging discussions over hours spent sorting. I am grateful to have received financial assistance over the duration of my PhD. The work presented in this thesis would not have been possible without funding from the Canadian Institutes for Health Research, the Terry Fox Research Institute, and the University of British Columbia. And to my friends and family – thank you for always inspiring me. I am bolstered by your ever-supportive words and love. xvi Dedication To Doris. 1 Chapter 1: Normal and aberrant T-cell development 1.1 Introduction Leukemogenesis can be understood as an interruption of normal differentiation in which the typical developmental cues that facilitate the controlled development of a cell may be superseded. Establishing a blueprint of normal developmental systems can provide integral context for the study of diseases that arise from them. This thesis investigates normal T-cell development to provide a framework within which to understand the pathogenesis of T-cell leukemia. In this introductory chapter, I will first provide a current perspective of normal T-cell development. As many aspects of T-cell differentiation are primarily understood from studies that have focused on development in mouse models, many of these sections will refer to T-cell development as it pertains to murine systems. Nonetheless, references to human T-cell development are included where data are available. I will also discuss T-cell leukemia as a consequence of aberrant T-cell development. Focusing on mutations present in T-cell acute lymphoblastic leukemia, I will highlight the role of DNMT3A in both normal and aberrant human T-cell development. Lastly, I will provide the objectives and major findings of the work discussed in this thesis summarized per chapter. 1.2 Normal T-cell development The focus of this section is  T-cell development, which comprises the majority path that T-cells follow, and which differs from  T-cell development. For the purpose of clarity, all phenotypic T-cell populations described in the following chapters refer to T-cell populations in human development, unless otherwise specified. The naming convention used throughout this text to describe human T-cell populations can be found in Figure 1.1 (adapted from1,2). 2 Figure 1.1 Overview of human T-cell development. Human  T-cell development in the cortex and medulla. Early thymic progenitors (ETP) develop from hematopoietic progenitors which seed the thymus. Exposure to Notch ligand results in T-cell specification and lineage commitment within the double negative (DN) compartment. Expression of an in-frame TCR chain together with an invariant pre-TCR results in -selection and differentiation into immature single positive (ISP) T-cells. Further differentiation results in upregulation of CD8, and together with CD4 expression these cells are classified as double positive (DP) T-cells. Positive selection through interaction with cells within the cortex results in progression to single positive (SP) CD4 or CD8 T-cells. These cells undergo negative selection in the medulla prior to exiting the thymus as mature T-cells. The approximate stages in which rearrangement of the TCR loci (i.e., TRD, TRG, TRB and TRA) occur are shown below. 1.2.1 Overview of T-cell development in the thymus Although the thymus is the primary lymphoid organ responsible for generating mature T-cells, it must be continuously seeded by hematopoietic progenitors which emigrate from the bone marrow (BM) (reviewed in3). The mechanisms that underly which progenitors will exit the BM and mobilize throughout blood to enter the thymus, or the factors within the niche that recruit progenitor migration, are not fully understood4–12. In the context of postnatal thymus in mice, thymic homing of progenitors may be partly controlled through low-affinity interactions between P-selectins expressed on the endothelium of thymic blood vessels and the P-selectin ligand PSGL-1, which is expressed on the surface of a limited subset of BM progenitors13. Also identified to be important from murine studies are the chemokine receptors CCR9, CCR7, and possibly CXCR414–16. The expression of cognate ligands, CCL25, CCL19, CCL21, and CXC12, by the thymic epithelium may guide those progenitors expressing the appropriate receptors to settle into 3 and throughout the thymic niche17,18. The action of integrins, including the 4 integrin ligand VCAM1, and other adhesion molecules, such as CD44, are also implicated in thymus homing, migration, and maintaining continuous T-cell—stromal interaction19,20. The importance of these molecules in migration may be observed by enrichment of ligands near sites of thymic entry19,20, and in the case of VCAM1 due to lymphocyte migration defects upon receptor-ligand blockade21. Relatively few BM progenitors are thought to journey into the thymus per day, but the few progenitors that do enter through vasculature near the cortico-medullary junction22,23, a region within each thymus lobule that lies at the interface of two major cellular zones referred to as the cortex and medulla. After thymic entry by extravasation through blood vessels near the cortico-medullary junction, the progenitor cells, typically referred to as thymus seeding progenitors (TSPs), migrate deeper into the cortex and in doing so begin to progressively differentiate into early T-cell precursors (ETPs) through continuous interaction with stromal cells present in the thymus22,23 (reviewed in24). Both the TSP and ETP populations represent very primitive stages of T-cell development in which multilineage potential is thought to be accessible (reviewed in25,26). Early progenitor exposure to Notch ligands in the thymus, such as DLL1, DLL4, JAGGED1, and JAGGED2 expressed by thymic stroma, initiates and maintains these primitive T-cells along a T-cell developmental path27–33, and this can be measured by the eventual co-expression of CD4 and CD831. Within the thymus, the CD4+CD8+ double positive (DP) population can be found near the outermost region of the cortex, the subcapsular region, which is marked by an abundance of mitotically active cells34,35. Further maturation and eventual thymus egress occurs once cells migrate to and differentiate throughout the medulla, where they eventually downregulate either CD4 or CD8 expression and enter into the periphery as mature CD4+ or CD8+ single positive (SP) T-cells (reviewed in36). Historically, the differentiation of T-cells has been assessed based on the expression of various CD markers which were initially identified in murine subsets; downstream of the TSP and ETP stages, the major stages of T-cell development could be primarily defined according to the expression (or lack thereof) of CD4 and CD8. Immature T-cells, which lack both CD4 and CD8 expression and are termed double negative (DN) T-cells, eventually differentiate into DP T-cells (CD4+CD8+), and finally CD4+ or CD8+ SP T-cells. Although the expression of these markers can provide rough landmarks to estimate the stage of 4 differentiation, further resolution of the DN population may be achieved with the inclusion of additional markers. In murine subsets, this occurs upon the basis of CD44 and CD2537. These markers are not analogous in human T-cell populations, however, and instead subdivision may be achieved through the temporally coordinated expression of markers such as CD1a, CD44, CD34, CD38, CD7, and CD5, which are sequentially gained or lost throughout human T-cell differentiation (reviewed in25). A current understanding of how T-cells passage throughout the thymus, the CD markers that approximate each stage of human T-cell development, and additional features of T-cell development which will be covered in the ensuing text are summarized in Figure 1.1 and Figure 1.2. While the naming conventions to describe these early stages of human T-cell development often vary among different research groups, the convention used in this thesis to define the CD4-CD8- DN T-cell subsets is indicated in Figure 1.1 (adapted from1,2). Importantly, although the majority of the thymic compartment comprises of migrating and developing thymocytes, they are not the only cells which constitute the thymic cellularity; also resident within the thymus are thymic epithelial cells (TECs), as well as mesenchymal cells, endothelial cells, dendritic cells, and B-cell populations, which altogether form the bedrock upon which T-cells develop38. Collectively referred to as the thymic stroma, these cells provide indispensable signals to support T-cell specification, maturation, migration, and selection, and are thus fundamental in T-cell ontogeny (reviewed in39). The focus of the next section will highlight how these cells, and in particular the thymic epithelial compartment, support the ever-evolving developmental requirements of distinct T-cell stages. 5 Figure 1.2 Overview of intrathymic  T-cell migration. Thymic seeding precursors enter through blood vessels (indicated by the red circle) near the cortico-medullary junction (CMJ) and circulate throughout the cortex. Entry into the thymus results in progression from early thymic precursors (ETPs) to various stages of CD4-CD8- double negative (DN) subsets. Further differentiation leads to acquisition of CD4 positivity in the immature single positive (ISP) subset. Cells then progress into double positive (DP) cells, which are characterized by expression of both CD4 and CD8. After reaching the subcapsular zone (SCZ), cells undergo positive selection through interaction with cortical thymic epithelial cells and this results in the generation of CD8 and CD4 single positive (SP) cells. Further selection of the SP subset occurs in the medulla through interactions with medullary epithelial cells and dendritic cells before mature thymocytes exit the thymus. 1.2.2 The role of thymic epithelial cells in T-cell development In early T-cell progenitors, activation of the T-cell program occurs by engagement of the Notch receptor expressed on T-cells by Notch ligands expressed by the thymic stroma30,31,33,40. Canonical activation of Notch signaling is thought to occur through four receptors (NOTCH1-4) and five ligands (DLL1,3,4 and JAGGED1,2) (reviewed in)41. Activation of the Notch receptor results in a series of proteolytic cleavages which release the intracellular portion of the receptor from the plasma membrane, leading to translocation to the nucleus and activation of downstream target genes (e.g., Hes-1, p21, cMyc) through interaction with additional proteins (reviewed in42). Continuous activation of Notch signaling through ongoing interaction with the stromal layer is thought to constrain cells along a T-lineage trajectory by 6 potently excluding the development of alternative lineages (e.g., natural killer (NK) cells, dendritic cells (DCs), and macrophages)9,43,44 (reviewed in45). Depending on the stage of T-cell development, Notch signaling performs various roles in cell proliferation, survival, and enforcement of T-lineage specification27,46,47, and is thus necessary for proper T-cell development and function. Nonetheless, T-cell differentiation likely requires additional stage-specific factors that extend beyond Notch signaling48,49. In fact, various stages of progressive T-cell differentiation can be traced to particular regions of the thymic architecture throughout both the cortex and medulla22,23. This observation possibly suggests that disparate regions of the thymus provide unique conditions that trigger or support distinct stages of development (reviewed in24). The two main regions within the thymus can be broadly organized into the cortex and the medulla (Figure 1.2). These regions are not separated by a formal anatomical structure but rather can be distinguished by functional and spatial heterogeneity35. Functionally, differences between the cortex and medulla are driven by variation in cellularity. The cortex and medulla are also spatially disparate, with the cortex forming the outermost region of each thymus lobule and the medulla comprising the central core (reviewed in50,51). These differences can be visually discerned by histological staining35; the darker-staining cortex is densely packed and resident primarily to developing thymocytes, but also cortical TECs (cTECs), and to a lesser extent mesenchymal cells and endothelial cells (reviewed in50,51). The lighter-staining medulla is relatively less cellular, with a smaller population of maturing T-cells and a stromal compartment populated mostly by medullary TECs (mTECs), though dendritic cells may also be found (reviewed in52). Although the thymic epithelial compartment is divided according to the spatial localization within the thymus (i.e., cortical or medullary), differences among TECs also extend beyond thymic location. These differences, which include variations in secreted cytokine and chemokine milieu, transcription factor expression, and antigen processing machinery, ultimately reflect a shift in the developmental requirements of a T-cell as it migrates throughout the cortex to the medulla, and in doing so begins to lose features of multipotency and gain features that are unique to T-cell function and survival (reviewed in53). The cortical epithelium are among the first cells that thymus seeding progenitors encounter22,23. During early development, the role of cTECs is primarily implicated in T-lineage specification/early activation of the T-cell program and expansion of the thymic immigrant pool (reviewed in18). It is estimated 7 that newly arrived thymic immigrants expand ~1000-fold, perhaps through the action of cytokines secreted by the cTECs and other stromal cells, which at least in mice may include stem cell factor (SCF)54, interleukin-7 (IL-7)55, and Wnt ligand56, although data on the precise cellular source, concentrations, and compendia of these signaling factors is limited (reviewed in24). It is likely that the composition and concentration of signaling factors secreted by cTECs varies even within the cortex; many of these cytokines have pleiotropic effects on T-cell development, and their function in supporting the DN compartment must too evolve with the developmental requirements of migrating T-cells (reviewed in57). Some of these pleiotropic, stage-specific effects include provision of proliferation and survival signals during early development, and contribution to cell adhesion and specialized T-cell function during subsequent developmental stages (reviewed in24). For example, IL-7 secreted by TECs supports early T-cell development by promoting cell expansion and survival49; in the context of immature T-cells, engagement of the IL-7 receptor has been shown to activate STAT5, resulting in upregulation of the pro-survival protein Bcl-258–61. At later stages of development, however, IL-7 may instead act to increase accessibility of the T-cell receptor (TCR) loci62. Increased locus availability, coupled with upregulation of RAG recombinase, initiates the irreversible and T-cell-specific process of TCR rearrangement63. cTECs also play important roles in positive selection of DP thymocytes post-TCR rearrangement, primarily through diversification and positive selection of the TCR repertoire (reviewed in53). This occurs through the expression of a unique subset of peptides, thought to be generated through the action of a unique set of proteolytic enzymes presented by major histocompatibility complexes (MHC)64–66 (reviewed in18). Engagement of peptide-MHC complexes by the TCR results in survival signals among the T-cells expressing receptors that bind with the appropriate affinity, and death by neglect in cells that do not (reviewed in67). At this stage, cells are thought to be programmed for cell death, perhaps due to a decrease in IL-7 signaling as a consequence of IL-7R downregulation at the DP stage, and can only be rescued from apoptosis through the process of positive selection68. By the time thymocytes have begun positive TCR selection, they are phenotypically distinct from when their migration first initiated, and now express e.g., CD4 and CD8. At this point in development, cells have reached the subcapsular zone of the cortex, located at the outermost region of each thymic lobule, and have begun migrating to the medulla for further TCR selection. It is perhaps paradoxical that the same 8 stromal tissue that supported the differentiation of these once-DN cells migrating outwards to the subcapsular region must also support the DP and CD4+/CD8+ SP cells that now traverse back throughout the cortex for further selection and maturation in the medulla (reviewed in36). Although the stromal matrix remains itself unchanged, the developmental requirements of the T-cells now differ; though a main feature of early T-cell development was proliferation induced by e.g. SCF and Notch signaling, DP cells now migrating towards the medulla show a reduction in both proliferation34 and reliance on Notch signaling69 (reviewed in24). The ability of T-cells to shift signaling programs in response to the same environmental conditions may reflect developmental context-specific signaling and the pleiotropic effects of signaling factors (reviewed in24). For the cells to continue their development, however, they must eventually encounter an altogether new environment. In the cortex, migration is thought to be influenced by the presence of CXCL12-producing stromal cells, which act on CXCR4-expressing T-cells17. The paucity of CXCL12-producing cells in the medulla, however, suggests that there are alternative factors that influence migration17. Indeed, the stromal compartment expresses an overlapping and distinct complement of cytokines that influences cell migration at various stages of development (reviewed in53). Migration to the medulla, which is thought to be required for further TCR selection and maturation, may occur through upregulation of CCR7 at the surface of T-cells after they undergo positive selection in the cortex and through secretion of the CCR7 ligands, CCL19 and CCL21, by resident mTECs70. mTECs, through interplay with dendritic cells and other antigen-presenting cells, act to remove any cells bearing TCRs that bind to self-antigen with too high affinity in a process referred to as negative selection (reviewed in67). This is achieved through the unique capacity of mTECs to express nearly all coding genes present in the genome through the activity of the autoimmune regulator (AIRE)71,72. AIRE facilitates ectopic expression of tissue-restricted proteins, thus generating a plethora of self-peptides which are presented by MHC. Ultimately, interaction with mTECs and antigen presenting cells in the medulla results in screening against T-cells bearing TCRs that will be autoreactive upon exit from the thymus (reviewed in73). Overall, it is estimated that only 1-3% of T-cells survive the stringent selection pressures presented by the thymus34,74. Yet, it is estimated that approximately 1-2 million post-selected cells leave the thymus 9 per day in young adult mice75. Thus, while a prominent role of the stromal compartment is to provide a series of unique microenvironments that select for T-cells which express the appropriate TCRs, it must also function to efficiently support the proliferative demands of developing T-cells and their ever-shifting requirements as they enter the thymus, establish the T-cell program, and undergo the highly rigorous process of positive and negative selection, which together serves to prepare cells for their eventual exit from the thymus. 1.2.3 Egress from the thymus Equally important to how cells enter into and develop within the thymus is the process by which cells exit into the periphery, where further maturation and acquisition of specialized T-cell functions occurs. Thymic egress necessitates a shift in signaling requirements and/or signaling source. For example, reliance on thymic signaling factors for survival and proliferation, such as Notch signaling, may be lost or contracted upon thymic egress; where the activation of Notch signaling through continuous interaction between ligand-expressing stromal cells and early developing T-cells is thought to constrain cells to the T-lineage and provide pro-survival cues, the degree/duration of signaling may be reduced at the DP stage, after a cell is T-cell committed24,30,69,76 (reviewed in77). This may be reflected by the level of Notch ligand expression throughout the thymus, which is highest among the cortical epithelial compartment54. Although Notch signaling remains important once cells leave the periphery, its function in this context is thought to shift towards specialized roles in immune response including cytokine production, proliferation, and T-cell activation, and away from roles in T-cell proliferation, survival, and lineage enforcement77,78 (reviewed in77). Nevertheless, reliance on some signaling factors (e.g., IL-7) is maintained even after cells exit the thymus, and may be necessary for mature T-cell survival79,80 (reviewed in81). The source of IL-7 stimulation necessarily shifts from stromal cells in the medulla to cells in the periphery. Although the precise extrathymic cellular source of IL-7 is difficult to determine, studies using IL-7 reporters in mice indicate the presence of IL-7 in peripheral tissues including the lymph nodes, liver, and intestine79,80. 10 1.2.4 T-cell receptor rearrangement Expression of a functional TCR is central to the role of T-cells and is the outcome of successful rearrangement of the variable region of the TRA, TRB, TRG, and TRD genes. Each TCR chain consists of a variable (V), diversity (D), joining (J) and constant (C) gene segment, and the unique recombination of the VDJ segments (in the case of TCR and TCR) or the VJ segments (in the case of TCR and TCR) generates a unique receptor (Figure 1.3, reviewed in82). Productive rearrangement of either TRD and TRG, or TRB, results in the surface expression of either TCR or TCR, respectively. The majority of cells (>90%) adopt a TCR fate, in which an in-frame TRB rearrangement results in expression of the pre-TCR composed of a TCR chain, an invariant pre-TCR chain, and CD3 signaling molecules (Figure 1.3, reviewed in45). Cells with an in-frame TCR rearrangement are selected in a process known as -selection; signaling through a functional pre-TCR complex rescues T-cells from apoptosis and induces proliferation, cessation of further rearrangement of the TCR locus, and maturation by initiating TCR gene rearrangement and CD4/CD8 co-expression (reviewed in45). The -selection checkpoint is reserved for cells that express a pre-TCR; the minority of cells that express a  TCR undergo a distinct maturation and selection path which involves signaling through the  TCR and does not typically result in the generation of DP T-cells (reviewed in83). The mechanisms controlling  and  lineage bifurcation are incompletely understood but may be related to TCR signaling strength84–86. In a manner reflective of the stepwise differentiation of thymocytes, rearrangement of TCR loci occurs in a sequential fashion, with the order of rearrangement correlating with development. Studies from human T-ALL and normal T-cell development suggest that rearrangement occurs in as follows: TRD, TRG, TRB, and TRA, as indicated in Figure 1.1, with the first instance of TRD rearrangements found in the CD34+CD38-CD1a- subset, and detection of complete in-frame TRB rearrangements beginning in the CD4 ISP stage87–89. This sequential rearrangement of the TCR loci may occur due to the stage-specific expression of recombination-activating genes, RAG1 and RAG2 recombinase, which induce double-strand breaks at recombination sequences within the V(D)J region to facilitate TCR rearrangement, but also by stage-specific accessibility of the TCR loci themselves90. 11 At the RNA level, RAG1/2 expression initiates in the earliest T-cell progenitors, reaches maximal expression at the DP CD3- stage, and is eventually extinguished in SP CD4+ or SP CD8+ cells87. Reporter studies in mice suggest that Rag1/2 levels may be characterized by two expression peaks; one coincident with the initiation of TRB rearrangement, and the other occurring immediately prior to TRA rearrangement91,92. Because the order of chain rearrangement is also influenced by the ability of RAG enzymes to access TCR gene segments, unilateral opening of the TCR loci can also control the order in which rearrangement is initiated (reviewed in93). Sequential accessibility of the TCR loci may depend on the action of cytokines such as IL-7; transcription factors such as STAT5, E2A, and HEB; and epigenetic factors such as the chromatin remodeling SWI-SNF complex62,94, although many additional factors are likely involved. TCR rearrangement is integral to proper T-cell function, but it also creates an indelible mark of a cell’s differentiation history; the permanence of rearrangement, which involves irreversible breaking and re-joining of DNA fragments, can thus indicate a certain degree of T-cell development95,96. Under permissive conditions, however, TCR-rearranged cells may yield non-T-lineage cells, such as NK, DC, granulocytic, and monocytic cells97,98. This suggests that T-lineage decisions may be altered even after immutable, lineage-specific DNA rearrangements are initiated. 12 Figure 1.3 Schematic of TCR and TCR rearrangement and assembly of pre-TCR and TCR complexes. A Organization of the TCR loci. Depicted is the genomic organization of the TRB, TRA, TRD and TRG loci containing variable (V), diversity (D), joining (J) and constant (C) regions. Recombination of the V(D)J regions results in a diverse TCR repertoire which is further diversified by addition or deletion of nucleotides at junction sites. The order of processing is listed vertically (from top to bottom) for each gene. B Diagram of the assembled pre-TCR complex at the cell surface containing a rearranged TCR chain and a surrogate pre-TCR chain complexed with CD3 signaling molecules (, , , and ). The pre-TCR complex is important for regulating T-cell development prior to rearrangement of the  chain. C Diagram of the final assembled TCR complex at the cell surface containing a rearranged TCR and TCR chain in complex with CD3 signaling molecules. 1.2.5 T-lineage commitment Although the precise cells capable of seeding the thymus are a subject of continued investigation in both murine and human T-cell development3,6,99,100 (reviewed in99), the cells that do migrate to the thymus have multi-lineage potential, including B, T, NK, myeloid, DC, and erythroid lineages, and thus are not yet 13 bound to the T-cell program – at least when cultured in the appropriate in vitro conditions9,25,43,95,101,102 (reviewed in25). The selective elimination of alternative (non-T) lineage potential is referred to as “commitment”, and is influenced by cell-intrinsic factors (e.g., transcription factors and chromatin accessibility of gene targets), cell-extrinsic factors (e.g., environmental contexts supporting lineage development), or the interplay of both (e.g., a cell’s ability to migrate to new environments and respond to key signals)103 (reviewed in104,105). The process of T-cell commitment is not likely to occur instantaneously; the elimination of alternative lineage potentials is a temporally protracted series of events which occur in an ordered fashion and over multiple rounds of cell division (reviewed in106). Commitment can be measured by probing a cell’s potentials under permissive conditions. In the case of T-lineage commitment, this may be done by extracting thymic subpopulations and placing the cells in conditions that support the lineage in question. Loss of potential may be determined by the inability of a cell to respond to lineage-specific conditions. The complete loss of all non-T lineage potentials indicates full commitment to the T-lineage (reviewed in104,106). When combined with immunophenotypic and transcriptomic profiling, examination of thymic subsets pre- and post-commitment can reveal the approximate developmental stages in which alternative lineage potentials are lost, as well as the transcriptional or epigenetic changes associated with commitment. Initial studies in human thymocyte subsets suggested that committed cells reside within the CD34+CD1a+ compartment43, a population which also demonstrates increased levels of TCR rearrangement87, and that multipotent cells could be found among CD34+CD1a- cells. The CD34+CD1a- compartment was later revealed to be functionally heterogeneous, as subsequent studies indicated that the B, NK, myeloid and erythroid lineage potentials varied with CD7 expression101. Even so, further enrichment of the T-cell committed population could be achieved by selecting the CD7+CD5+CD45dimCD44- population, with commitment and the onset of TCR rearrangements correlating with the loss of CD44dim expression95. This was recently confirmed on a more granular level; in a study by Le et al. (2020)107, single-cell RNA sequencing was paired with in vitro lineage potential assays, resulting in further division of the uncommitted CD44dim subset into CD2+ and CD2- fractions. Gain of CD2 expression coincided with a loss of B, NK, and myeloid potential, with a more prominent loss of B-lineage potential. The authors concluded that B-lineage potential is lost prior to NK and myeloid potential during human thymocyte development107. Collectively, 14 studies such as those highlighted here provide a framework for understanding the phenotypic contexts in which alternative lineage potentials remain accessible under permissive conditions in vitro in human T-cells, and thus allow for further dissection of the transcriptional and epigenetic factors that guide eventual commitment. Commitment to the T-cell lineage can be understood as a series of repressive mechanisms; the choice to become a T-cell is only made once all other options have been excluded (reviewed in106). This is particularly salient from the aforementioned experiments which have demonstrated that alternative (non-T) lineage potentials remain upon removal of cells from the thymus10,32,43,95,97,101,107,108, and might suggest that T-cell restriction is necessarily maintained by the external signaling cues provided by the highly specialized thymic microenvironment (e.g., Notch signaling109). Yet, T-cells which naturally egress from the thymus remain T-lineage restricted, suggesting that while signaling must be sustained through to commitment, it may not be needed thereafter. This set of observations warrants further investigation of the repressive mechanisms which might constrict non-T lineage potential within the thymus. In eukaryotic cells, DNA methylation is among the most well-characterized repressive mechanisms that are stably maintained throughout cell division (reviewed in110). The role of DNA methylation to repress pluripotent genes has been previously identified in the context of embryonic stem cell differentiation111,112 (reviewed in113). The role of DNA methylation in T-lineage specification and repression of stem-associated genes, however, remains unclear. The next section focuses on DNA methylation and highlights a current understanding of its role throughout T-cell development. 1.3 DNA methylation in normal T-cell development 1.3.1 Overview of DNA methylation by DNMT3A, DNMT3B, and DNMT1 Establishment of DNA methylation patterns is critical for normal tissue differentiation and involves the addition of a methyl group to the 5’ carbon of a cytosine base, referred to as 5-methylcytosine (5mC), which typically occurs in the context of CpG dinucleotides in mammals. Methylation is established predominantly through the activity of the de novo methyltransferases, DNA methyltransferase 3A (DNMT3A) and DNMT3B, and must be faithfully maintained throughout cellular replication by DNMT1114–116. Classification of DNMTs into initiation versus maintenance functions generally relates to substrate 15 specificity; DNMT1 displays higher catalytic activity directed towards hemimethylated CpGs115 (reviewed in117), whereas DNMT3A and DNMT3B typically show preferential activity toward unmethylated CpGs118. However, this division of labor is not always clear; in some studies, DNMT3A and DNMT3B showed no preference for unmethylated or hemimethylated DNA119, and in other studies, DNMT1 displayed de novo methylation activity at certain CpG sites120,121. Nonetheless, the coordination of methylation initiation and maintenance creates a system where DNA methylation patterns, once established, are faithfully propagated to downstream progeny. The distribution of CpG dinucleotides throughout the genome can lend clues to the functional role of DNA methylation; although mammalian genomes are globally depleted of CpGs, the CpGs that are present are distributed asymmetrically throughout the genome122–124. Regions of high CpG density tend to concentrate near the transcriptional start sites of genes, especially highly expressed housekeeping genes and developmentally important genes123–127. These regions, termed CpG islands (CGIs) are generally hypomethylated relative to other CpG sites throughout the genome (reviewed in128). There are exceptions, however, and these include imprinted genes and X-chromosome genes, which are not typically expressed at high levels129 (reviewed in130). Generally, there is an inverse correlation between CpG methylation and gene transcription131–138, and this becomes stronger with increasing CpG density (reviewed in128). Collectively, these studies contribute to a growing body of evidence that implicates CpG methylation in gene silencing. Both in vivo and in vitro experiments generally support the role of DNA methylation in transcriptional silencing. Although the mechanism of gene repression is not fully resolved as of yet, two major models have emerged132,135,138 (reviewed in139): First, the addition of a 5-methyl group can prevent some, but not all, transcriptional activators from binding to DNA, perhaps through steric hindrance or by alterations to the local DNA structure140,141. In some instances, however, CpG methylation may recruit transcription factor (TF) binding, or may not have an effect on TF binding at all140,142, suggesting that other regulatory mechanisms may be involved. Second, CpG methylation may inhibit gene transcription by creating binding sites for proteins with a methyl CpG binding domain143, which can lead to recruitment of chromatin remodeling complexes and transcriptional co-repressors that silence transcription139,143–145. In other cases, 16 DNA methylation machinery may be instead influenced by pre-existing histone marks; active histone marks, such as H3K4 methylation, have been shown to preclude CpG methylation146,147. The interplay between CpG methylation by DNMTs, epigenetic regulators, and chromatin structure adds additional layers of transcriptional control which can ultimately result in stable gene repression via heterochromatin formation (reviewed in148). This raises the question of how DNMTs are consistently directed to certain genomic regions, such as imprinted genes, X chromosome genes, and repeated DNA sequences like those found in centromeres, to facilitate methylation. The answer may lie in the partners with which DNMTs interact; importantly, DNMTs participate in larger protein complexes through various interaction and binding domains, and do not alone exert CpG methylation (reviewed in149). 1.3.1.1 DNA methyltransferase protein structure Recruitment to, and methylation of, specific regions of DNA may be facilitated by the various protein domains of DNA methyltransferases. DNMT proteins generally consist of a diverse amino terminus, which differs between DNMT1 and DNMT3A/B, and a relatively conserved carboxyl terminus. The N-terminus has roles in nuclear localization, substrate specificity, activation of enzymatic activity, and protein-protein interactions. The C-terminus is important for cytosine methyltransferase activity and contains the catalytic methyltransferase (MTase) domain which is common to DNMT proteins (reviewed in150) (Figure 1.4). The domain architecture of the DNMT1 and DNMT3 enzymes are discussed below. Figure 1.4 Domain architecture of the DNA methyltransferase proteins. Domains of human DNMT1, DNMT3A, DNMT3B and DNMT3L. 17 DNMT1 Although DNMT1 contains the conserved C-terminal catalytic domain present in all cytosine DNMTs, the N-terminus of DNMT1 is unique, which may relate to its function in maintaining DNA methylation during replication. The N-terminal region of DNMT1 contains several domains which facilitate DNA or protein binding. This includes the DNA methyltransferase-associated protein 1 (DMAP1) domain, which interacts with the transcriptional repressor DMAP1151; the proliferating cell nuclear antigen (PCNA)-interacting domain, which recruits DNMT1 to DNA repair sites152 and to the replication fork during S-phase153; the replication-foci targeting sequence (RFTS) domain, which localizes DNMT1 to replication foci via binding to ubiquitylated histone H3154 and to constitutive heterochromatin throughout S and G2/M phase and participates in active site regulation116,155–157; a CXXC zinc finger domain that binds specifically to DNA containing unmethylated CpG dinucleotides158; and two bromo-adjacent-homology (BAH) domains which also have roles in targeting DNMT1 to replication foci during S-phase159. To explain the observation that DNMT1 preferentially methylates hemimethylated DNA, a model of allosteric regulation of DNMT1 has emerged in which multiple domains at the N-terminus form a regulatory region that inhibits de novo methylation by occluding the active site of DNMT1 unless bound by hemimethylated DNA158,160,161 (reviewed in157). This is achieved through the CXXC domain, RFTS domain and the CXXC-BAH1 linker which joins the CXXC and BAH1 domains (referred to as the autoinhibitory linker158). Binding of unmethylated CpGs by the CXXC domain positions the autoinhibitory linker across the catalytic cleft of DNMT1, thereby excluding DNA from the catalytic site and preventing de novo methylation158. When not bound to DNA, the RFTS domain inserts into the DNA-binding pocket of the catalytic site and consequently blocks access to the active site156,157. The catalytic DNA-binding pocket of DNMT1 becomes accessible upon binding of other proteins, such as UHRF1 (ubiquitin-like, containing PHD and RING finger domain protein 1), which interacts with the RFTS domain and results in its extrusion from the catalytic site162,163. 18 DNMT3 family DNMT3A and DNMT3B DNMT3A and DNMT3B belong to the DNMT3 family, which consists of three paralogues: DNMT3A, DNMT3B, and the catalytically inactive paralogue, DNMT3-like (DNMT3L). DNMT3A and DNMT3B have a similar domain structure; the N-terminal region, which is involved in chromatin targeting and binding, contains an ATRX-DNMT3-DNMT3L (ADD) reader domain and a Pro-Trp-Trp-Pro (PWWP), and the C-terminal region contains an MTase domain. DNMT3L lacks the majority of the N-terminal region, containing only the ADD domain at the N-terminus, and at the C-terminus has an MTase-like domain which lacks motifs integral for catalytic function (reviewed in164). The ADD domain functions in protein targeting by binding to unmodified lysine 4 of H3 tails (H3K4me0)146 and in enzyme autoinhibition. Structural analysis of DNMT3A indicates that the ADD domain binds to two distinct sites, inducing either allostery or autoinhibition depending on interaction, or lack of, with H3K4me0 tails; when not bound to H3 peptide, DNMT3A adopts an autoinhibitory conformation in which the ADD domain occludes the DNA binding cleft at the catalytic site165–167, thus linking DNMT3A with specific H3 chromatin marks. This method of enzyme regulation is also likely to operate in DNMT3B due to conservation of residues that facilitate the autoinhibitory mechanism168. The PWWP domain of DNMT3A and DNMT3B, named for its proline-tryptophan-tryptophan-proline motif, is important for localization to pericentromeric chromatin. This can occur through recognition of, and subsequent binding to, H3K36me3169,170. The PWWP domain is also implicated in targeting enzyme activity to actively transcribed gene bodies171. The N-terminus of DNMT3A and DNMT3B also contains a variable region which is relatively divergent between the two paralogues, and may have roles in regulating protein localization and DNA binding172; at least in the case of DNMT3A, isoforms with a truncated N-terminus region show localization to euchromatin, as opposed to heterochromatin observed in the full length isoform. Isoforms of DNMT3B may also differ in catalytic activity, and in the context of murine HSCs cells, the catalytically inactive splice isoform is most dominant173. The MTase domain performs the enzymatic addition of cytosine and contains a number of key residues which facilitate this process. Some of these residues form two loops, known as the target 19 recognition domain (TRD) and the catalytic loop. Together, the TRD and catalytic loop bind to and enclose the target CpG site174,175. DNMT3L DNMT3L is catalytically inactive due to a lack of several motifs within the MTase domain, including the TRD and catalytic loop175, and does not directly bind DNA174–176. Nonetheless, it plays important structural roles in augmenting the enzymatic activity of DNMT3A and DNMT3B146 and is involved in DNMT3A/B protein stability and processivity177,178. Together with DNMT3A and/or DNMT3B, DNMT3L forms a tetramer in which two DNMT3L-DNMT3A/B heterodimers interact to form a 3L-(3A/B)-(3A/B)-3L complex, which creates a (3A/B)-(3A/B) interface and a (3A/B)-3L interface (Figure 1.5) 150,176,179. This arrangement forms two sites in which CpGs located on opposite DNA stands may bind to DNMT3A/B179, and this interaction is stabilized by DNMT3L174. Figure 1.5 Model of DNMT3-DNMT3L tetramer formation Tetrameric complex formed by A) two DNMT3A-DNMT3L heterodimers and B) two DNMT3B-DNMT3L heterodimers. 3L = DNMT3L; 3A = DNMT3A; 3B = DNMT3B. 1.3.1.2 DNA demethylation The addition of a methyl group to the 5’ carbon of cytosine forms a covalent bond that, once established, is chemically stable180; under physiological conditions, removal requires enzymatic cleavage, or disruption of methylation maintenance. These two major mechanisms of DNA demethylation are referred to as active or passive DNA demethylation, respectively. Active removal of 5mC occurs through the action of the ten eleven translocation (TET) family of enzymes, which include TET1, TET2, and TET3. Together, the TET enzymes catalyze oxidation of 5mC to a series of intermediates, which are ultimately converted to 5-carboxylcytosine (5caC)181. The oxidized 20 intermediates are not major substrates for DNMT1182–184, and are eventually returned to unmodified cytosine through passive dilution (reviewed in185). Alternatively, some oxidized intermediates, including 5caC, may be excised by thymine DNA glycosylase through base excision repair mechanisms186,187. Passive removal of DNA methylation marks occur during successive rounds of replication in the absence of methylation of newly synthesized DNA strands, eventually resulting in dilution of 5mC by division (reviewed in188). Therefore, the relationship between replication and DNA methylation is necessarily linked; to preserve DNA methylation patterns in daughter cells, methylation must be re-established during replication. Altering the equilibrium of cell proliferation and expression/activity levels of maintenance methylation machinery may therefore influence the inheritance of CpG methylation in daughter cells189 (reviewed in190). Since passive DNA demethylation first requires a cell to divide, it is perhaps more suitable for temporally protracted events such as lineage-specification and development, which is typically accompanied by cellular replication, and less appropriate for biological events which require an immediate response185. 1.3.2 DNA methylation in T-cell differentiation and commitment Establishment of de novo DNA methylation has important consequences during blood cell development. The differentiation of HSCs into mature progeny is accompanied by the acquisition of DNA methylation patterns which are distinct among the hematopoietic lineages191,192 and which may be used to distinguish different cell types193. The importance of DNA methylation patterning may be most evident upon targeted deletion of the de novo DNA methyltransferases, which are critical for establishing DNA methylation; in murine hematopoiesis, conditional ablation of Dnmt3a and Dnmt3b in HSCs using an Mx1-Cre driver results in enhanced self-renewal (measured by increased HSC expansion in up to four serial transplantations) and a block in differentiation of all downstream hematopoietic lineages (measured by a reduction in cell output)173. This phenotype is most prominent upon Dnmt3a loss; while deletion of Dnmt3b alone shows only minor effects on differentiation compared to wildtype HSCs173,194, Dnmt3a-null HSCs demonstrate a measurable differentiation defect accompanied by enhanced self-renewal in comparison to wildtype HSCs194. In subsequent studies that focused on the effects of Dnmt3a loss on T-cell differentiation, conditional loss of Dnmt3a in murine HSCs was associated with an accumulation of immature thymocytes 21 at the DN2 stage of murine T-cell development. This phenotype, however, was incompletely penetrant; differentiation arrest was only observed after secondary transplantation and in a minority of recipients (9/24 recipients)195, possibly suggesting that additional events are required for Dnmt3a loss to disrupt T-cell development. Dynamic changes in DNA methylation occur at multiple stages throughout murine and human T-cell development192,196. In particular, earlier stages of differentiation are associated with a more pronounced gain of DNA methylation as opposed to loss, implicating de novo methylation as a possible important regulator of early T-cell differentiation192,196. When focusing on differentially methylated regions during key developmental timepoints throughout T-cell development, including T-cell commitment, -selection, TCR expression, and positive selection, the greatest increase in DNA methylation can be observed during the process of T-lineage commitment196. During the latter development checkpoints, however, DNA demethylation events, as opposed to de novo methylation events, are more common196. Throughout these later stages of development, progressive demethylation may be important for activation of certain genes specific for T-cell function, such as Lck; Lck regulates TCR signaling and is progressively demethylated throughout T-cell differentiation, which is associated with a concomitant increase in gene expression192. In contrast, the increase in de novo DNA methylation observed during T-cell commitment presents a possible role of DNA methylation in myeloid and T-lineage specification; in murine and human hematopoiesis, committed myeloid lineage cells display a lower level of global DNA methylation compared to cells committed to the lymphoid lineage191,192, and treatment of murine T-cell progenitors with the DNMT inhibitor, 5-azacytodine, in the presence of both myeloid- and T-lymphoid-promoting cytokines, results in a modest skew towards the myeloid lineage192. Taken together, this suggests that DNA methylation may play important roles in establishing or maintaining T-lineage commitment. Yet, the role of de novo DNA methylation in T-cell commitment, and the compendia of genes which may be regulated by this process, is not well characterized in human development. When focusing on genes associated with progenitor or alternative lineage programs (i.e., LYL1, HHEX, SPI1, CEBPA, and LMO2), reduced expression throughout T-cell development does not consistently correlate with an increase in promoter methylation, possibly suggesting that additional mechanisms of regulation may be involved in the repression of alternative lineages196. 22 Further insight into how de novo DNA methylation may be operative in the context of T-cell development may be gained from diseases in which methylation machinery is mutated. Frequently, DNTM3A is altered in T-lymphoid and myeloid leukemias, though to a lesser extent DNMT1 and DNMT3B mutations are also observed197 (reviewed in198). The next section discusses aberrant T-cell development, with a primary focus on T-cell acute lymphoblastic leukemia (T-ALL), and briefly highlights the role of DNA methylation machinery as understood in this context. 1.4 T-cell acute lymphoblastic leukemia 1.4.1 A brief overview of T-ALL The processes that govern T-cell development must be tightly regulated to orchestrate the production and differentiation of T-cell progenitors into functionally mature subsets. The expansion of immature T-cells is highly controlled and centered around successful rearrangement of T-cell receptor loci. Given the complexity of this highly regulated process, combined with requisite double stranded DNA breakage and rejoining events, it is perhaps inevitable that errors at any step can interrupt developmental progression and ultimately lead to cellular transformation. T-cell acute lymphoblastic leukemia (T-ALL) is the pathologic designation applied to malignant accumulations of immature T-cells, most often involving thymus, bone marrow, and peripheral blood. Current treatment with high intensity multi-agent chemotherapy achieves an event-free survival rate of approximately 85% over 5 years in the pediatric setting (reviewed in199). There are, however, long term side effects from cytotoxic chemotherapy and radiation, in addition to dismal outcomes for relapsed disease (reviewed in199). In adults, current therapy regimens fare poorly, with disease-free survival rates below 40% (reviewed in200), thus advocating for increased understanding of the molecular underpinnings of T-ALL for improved treatment outcomes. A molecular basis for T-ALL pathogenesis could be first understood from cytogenetic analyses which revealed frequent chromosomal aberrations that altered the expression of a recurrent selection of genes (reviewed in201,202). Most commonly, these include deletions affecting chromosome 9 that result in loss of the CDKN2A locus203–205, which encodes the cell cycle genes p16/INK4A and p14/ARF. An outcome of this deletion is resistance to apoptosis and progression through the cell cycle206,207. Also common in T-ALL are chromosomal translocations that juxtapose transcription factors (TFs) with active T-cell-specific 23 promoters or enhancers, such as TCR regulatory elements208,209, thus resulting in aberrant TF overexpression. Among the TFs frequently translocated and placed under the control of strong T-cell-specific promoters are TAL1, TAL2, LYL1, LMO1/2, TLX1/3, NKX2.1, and NKX2.2, among others (reviewed in210,211). Unlike CDKN2A loss, which is present in the majority of T-ALLs203,205, mutations affecting these TFs appear to be typically mutually exclusive. Also frequent in T-ALL are mutations resulting in constitutive activation of the NOTCH1 receptor212. Although NOTCH1 is not commonly overexpressed as a result of gross chromosomal abnormalities, the first evidence that it may have pathogenetic roles in T-ALL was, however, identified by a rare translocation event which placed a truncated form of NOTCH1 adjacent to the TCRB locus213. The high frequency of NOTCH1 mutation in T-ALL pathogenesis was not apparent until more than a decade later; this came with the discovery of mutations that resulted in ligand-independent NOTCH1 activation and/or increased stability of an active form of NOTCH1 among T-ALL cases212. Since this discovery, NOTCH1 has emerged as a predominant oncogene in T-ALL biology, with mutations affecting the Notch signaling pathway present in more than 60% of T-ALL cases214,215 (reviewed in216). 1.4.2 Genotypic and phenotypic characterization of T-ALL Diagnosis of T-ALL is based on a combination of immunophenotypic and morphological analysis, and to a lesser extent cytogenetic assessment (reviewed in217). Historically, this has resulted in the classification of T-ALL according to patterns of CD marker expression that are reminiscent of distinct T-cell differentiation stages218. The increasing prominence of gene expression profiling and whole-genome sequencing, however, have led to a greater appreciation of the molecular heterogeneity of T-ALL219–224 (reviewed in210,225). Although genome-wide sequencing does not inform T-ALL diagnosis at present, molecular subtyping has emerged as a valuable classifier for greater understanding of disease biology, and has led to the identification of aggressive disease subgroups in T-ALL226. Further integration of methods currently employed for T-ALL characterization may improve understanding, and thus treatment, of disease. Two prevailing methods of T-ALL classification, attained through genetic subtyping and immunophenotypic analysis of blasts, are discussed here, although additional methods of disease characterization will also be highlighted. 24 1.4.2.1 Developmental stage-based classification Immunophenotypic profiling The current standard for T-ALL diagnosis and classification is achieved through immunophenotypic analysis of blasts by multi-channel flow cytometry, which allows for the simultaneous detection of several CD markers. Historically, classification of T-ALL was achieved by examination of markers such as CD3, CD1a, CD7, CD2, and CD8. The pattern of expression of these markers was used to orient leukemias along a normal T-cell developmental schema and thus resulted in classification into pro-T, pre-T, cortical, and mature T-ALL subsets218. Clinically, immunophenotyping presents a useful tool for leukemia subclassification and minimal residual disease (MRD) detection due to its broad accessibility. Moreover, as immunophenotypic subtypes show variable prognosis, immunophenotypic profiling may be of prognostic relevance. For example, the outcome of cortical T-ALL tend to show superior outcomes227,228, while other subgroups are associated with worse outcome. Early T-cell precursor (ETP)-ALL is such an example; although first identified from gene expression profiling, ETP-ALL was initially described by CD1a and CD8 negativity, CD5 dim or negative expression, and expression of at least one myeloid or stem marker such as CD117, CD34, HLA-DR, CD13, CD33, CD11b, CD65226,229, however several alternative proposals for immunophenotypic identification have since emerged229–232. ETP-ALL has been associated with unresponsiveness to standard therapies and poor prognosis in initial investigations226,230, although in recent years improved outcomes have been achieved with more effective therapies233,234. Nonetheless, the utility of immunophenotyping as a prognostic indicator is limited by the number of CD markers that may be measured in parallel and thus precludes comprehensive, multi-dimensional phenotypic analysis of individual cells. This is most salient when considering incongruencies in phenotypic definitions of high-risk subgroups such as ETP-ALL, which may benefit from incorporation of additional defining markers for increased precision in disease classification229–232. Furthermore, the limited resolution of flow cytometry can result in conflation of genetically distinct T-ALL subgroups into broad developmentally based categories, as discussed in further detail in Chapter 2. Incorporation of sequencing technologies to inform disease subgroups, together with comprehensive immunophenotypic profiling, will improve disease classification and thus may expedite the development of targeted therapies. 25 T-cell receptor rearrangement status Treatment efficacy can be monitored by MRD, but this requires highly sensitive and ideally non-invasive methods which can detect low levels of malignant cells. In T-ALL, detection of TCR rearrangements through PCR-based methods may be employed for MRD monitoring, and can achieve high degrees of sensitivity202,235 (reviewed in236). Because TCR rearrangement is thought to occur in a stepwise manner that is intrinsically linked to developmental stage (discussed in detail in Chapter 1.2.4), it may also provide valuable information regarding the stage of arrest in T-ALL cases. Although not currently used in clinical practice to inform stage-based classification of T-ALL, the inclusion of TCR rearrangement status as an additional developmental marker may provide a finer degree of resolution to the current developmental stage-based classification89. 1.4.2.2 Transcription factor subgroups From the growing outputs of gene expression profiling and genome sequencing studies, it has become increasingly evident that T-ALL is a heterogenous disease that can be parsed into distinct molecular subgroups219–224,237. Each molecular subgroup is typically characterized by unique TF overexpression including, but not limited to, HOXA, LMO2/LYL1, TLX1/NKX2.1 (previously described as the proliferative cluster219), TLX3, and TAL/LMO206,221 (reviewed in225,238). ETP-ALL, which lacks a unifying mutation224,226, is an exception to this general observation, but can be distinguished on a molecular level by high levels of expression of progenitor-associated genes226. Overall, however, the gene expression profiles of each T-ALL subgroup are distinct and can be typified by the overexpression of a particular TF, often as a consequence of gross chromosomal aberrations. Integration of phenotypic and genotypic profiling has contributed to the generally accepted notion that molecular TF subgroups may be associated with specific developmental stages. For example, ETP-ALL is often associated with arrest at the ETP stage of T-cell development owing to the unique compendia of markers expressed (i.e., immature T-cell markers in addition to myeloid/progenitor-associated markers) and a gene expression profile reminiscent of murine ETP cells226. The TAL/LMO T-ALL subgroup, at the opposite end of the differentiation spectrum, correlates with arrest at a late cortical stage of development due to expression of mature T-cell markers such as CD4, CD8, and CD3 (reviewed in210), and accordingly 26 demonstrates upregulation of genes important for specialized T-cell function such as RAG and PTCRA (reviewed in239). The rapid rate at which sequencing, and more recently single-cell sequencing, technologies have advanced has led to a greater appreciation of the cellular and molecular contexts in which specific oncogenic TFs may operate, and further studies which integrate high-dimensional analyses of developmental, molecular, and epigenetic profiles will provide valuable mechanistic insight into T-ALL pathogenesis. 1.4.2.3 Mutations in epigenetic modifiers Although genetic profiling has resulted in the classification of subtypes driven by dysregulation of TFs, mutations within epigenetic modulators also frequently occur in T-ALL223,224 (reviewed in240,241). A landmark study focusing on the epigenetic landscape in over 21 different pediatric cancer contexts revealed that epigenetic regulators are among the most frequently mutated in T-ALL242, pointing to the relevance of this protein class in T-ALL pathogenesis. Among the most commonly mutated epigenetic modulators in T-ALL are factors important for regulation of DNA methylation, such as DNMT3A (up to 20%243–245), TET1 (up to 14%241), and IDH1/2 (up to 9%241); polycomb repressive complex 2 (PRC2) members such as EZH2, SUZ12, and EED (up to 25%241,246); the histone H3K27 demethylase UTX (up to 12%223,242); the deubiquitinating enzyme USP7 (~8%242), and PHF6 (~16-38%247), among others (reviewed in240,241). The roles of some of these epigenetic modulators in T-ALL pathogenesis have only recently been investigated and are briefly highlighted here. Mutations in PRC2 components A high prevalence of loss of function mutations in PRC2 members, including EZH2, SUZ12, and EED, are observed in T-ALL224,246,248,249. In murine models, conditional loss of Ezh2 resulted in spontaneous development of T-ALL249, and in subsequent studies, deletion of Ezh2 or Eed resulted in accelerated leukemogenesis250, perhaps supporting a tumor suppressor role of this complex. In normal development, PRC2 establishes transcriptional repression through the deposition of repressive H3K27me3 histone marks (reviewed in251), and in the context of T-ALL, PRC2 and NOTCH1 may have antagonistic roles; studies in 27 T-ALL mouse models and human T-ALL cell lines indicate a reciprocal relationship between NOTCH1 and PRC2 in which the binding of NOTCH1 leads to EZH2 eviction and H3K27me3 loss246. This, in addition to enrichment of known PRC2 targets among Notch1 binding sites, perhaps indicate a co-operative role between inactivation of PRC2 components and oncogenic activation of Notch signaling246. UTX mutations The prevalence of gene mutation among PRC2 components might underscore a critical balance of H3K27 methylation in T-ALL pathogenesis. This may be further understood from investigation of the UTX demethylase, which catalyzes demethylation of H3K27me3. The presence of UTX mutations in T-ALL, predicted to cause loss-of-function252,253, are perhaps contradictory to the tumor suppressive role of PRC2 also reported in this disease context246. However, although the enzymatic activity of UTX and PRC2 components are diametrically opposed, they may exert their activity towards different loci (reviewed in241). The tumor suppressive role of UTX has been demonstrated in mouse models of T-ALL, in which Utx knockdown accelerated leukemia progression252,253. PHF6 mutations Loss-of-function mutations in PHF6 have long been recognized in T-ALL247, but the functional consequences in this disease context have only recently been described and require further investigation. Initial studies in murine models of T-ALL suggest that Phf6 loss may enhance hematopoietic stem cell renewal and may enrich for a leukemia-initiating cell population, but the molecular mechanisms driving these phenotypes have yet to be elucidated254. USP7 mutations USP7 is frequently upregulated in T-ALL255, and its suppression inhibits T-ALL proliferation in in vivo mouse models and human T-ALL cell lines255,256. This may occur by removal of ubiquitination marks and subsequent stabilization of NOTCH1 through the activity of USP7, which functions as a ubiquitin-specific protease255,256. These early studies might advocate for USP7 inhibition in T-ALL treatment. Nonetheless, reports of USP7 mutations in T-ALL223,242, purported to cause protein loss-of-function223, 28 indicate otherwise, and altogether highlight that further investigation of USP7 in T-ALL pathogenesis is needed. 1.4.2.4 DNMT3A mutation and consequences of DNMT3A loss in T-ALL DNMT3A mutations in T-ALL Frequently, mutations affecting DNA methylation machinery are observed in T-ALL (reviewed in240); this most commonly involves the de novo DNA methyltransferase DNMT3A244,257,258, but to a lesser extent mutations are also observed in DNA demethylases such as TET1259 and TET2260. Rarely, mutations in IDH1/2, which are thought to result in inhibition of TET enzyme activity261,262, are reported. In the T-cell lineage, mutations in DNMT3A are present in up to 20% of T-ALL cases244,257,258 and are often enriched in ETP-ALL and immature T-ALL243–245. Mutations in the other de novo methyltransferase, DNMT3B, are rarely reported. The majority of DNMT3A mutations reported in T-ALL are biallelic244,263 and generally occur throughout the gene body244,245,257; this is in contrast to mutations observed in other hematological malignancies, such as acute myeloid leukemias, in which DNMT3A mutations tend to localize to R882264–266. In both disease contexts, however, DNMT3A mutation is thought to result in a loss of protein function198,244,263 (reviewed in198). In a screen of more than 200 disease-associated DNMT3A mutations, 74% were implicated in a loss of protein function, with many variants resulting in reduced protein stability267. Among those mutations reported in T-ALL, approximately 88% decreased methyltransferase activity relative to wildtype DNMT3A267. These results, together with the mutational spectra observed in sequencing studies, implicate DNMT3A as a tumor suppressor in the context of T-ALL. DNTM3A loss in T-ALL Functional evidence that DNMT3A loss can predispose to T-ALL was demonstrated by studies in which deletion of Dnmt3a in the murine HSC compartment resulted in hematological malignancies that affected both the myeloid and T-cell lineages268. Although the majority of mice developed myeloid malignancies, development of T-ALL was also observed and moribund mice had enlarged thymuses, splenomegaly, and an accumulation of DP blasts in the bone marrow268. Progression to T-ALL was 29 associated with acquisition of additional mutations; frequently, activating mutations in Notch1 were observed. This suggests that co-operation between Dnmt3a loss and Notch1 activation may result in progression to T-ALL268. Further understanding of the interplay between DNMT3A and NOTCH1 in T-ALL could be understood from mouse models in which concurrent activation of Notch1 and Dnmt3a deletion in HSCs resulted in rapid T-ALL progression195. The leukemias generated showed upregulation of several genes characteristic of myeloid lineage cells (e.g., Elane, Mpo, Csf2rb and Gfi1b), thus bearing resemblance to ETP-ALL. Dnmt3a knockout was also associated with global hypomethylation in comparison to Dnmt3a wildtype T-ALL and to normal DP T-cell populations, with the most prominent methylation loss observed in exons and enhancers195. These leukemias also showed resistance to apoptosis as a result of downregulation of the orphan nuclear receptor Nr4a1, which in a T-cell context can promote apoptosis through interaction with Bcl-2195. When expression of Nr4a1 was induced using a chemical agonist, apoptosis was restored both in Dnmt3a-knockout murine thymocytes and in a human T-ALL cell line treated with shRNA targeting DNMT3A. The mechanism of Nr4a1 downregulation upon Dnmt3a loss is yet to be elucidated195, but further investigation may lend insight to the role of DNMT3A in normal T-cell development and leukemia progression. Beyond the context of Notch, co-operation between DNMT3A and other oncogenes has been observed in murine leukemias generated by internal tandem duplication of FLT3 (FLT3-ITD)269. FLT3-ITD results in constitutive activation of the FLT3 cytokine receptor270,271, and is commonly reported in hematological malignancies such as AML272. Mutations affecting FLT3 have also been observed in T-ALL273. In the context of Dnmt3a-null BM progenitors, expression of FLT3-ITD resulted in progression to T-ALL. The resulting leukemias resembled ETP-ALL based on CD marker expression and gene expression profile269, and genome-wide DNA methylation analysis revealed global hypomethylation in comparison to DP T-cells or T-ALLs generated by FLT3-ITD alone. Notably, hypomethylation was pronounced in enhancer regions269, consistent with previous reports in the context of murine T-ALL195. Many of the hypomethylated enhancer regions were identified to be putative binding sites for TFs including FLI1, GFI1B, and PU.1, possibly suggesting that DNMT3A acts as a tumor suppressor by regulating enhancer methylation of progenitor-associated genes269. 30 Together, these functional studies emphasize the importance of DNMT3A in T-ALL pathogenesis. However, further studies are required to understand the complex role of DNMT3A during normal T-cell development and in disease progression. For example, upregulation of genes associated with progenitor/myeloid-lineage programs is consistently reported upon Dnmt3a loss195,269, but the mechanism is unclear; this observation might suggest that Dnmt3a knockout induces a differentiation arrest at an ETP stage, or that Dnmt3a knockout results in derepression of genes associated with progenitor/myeloid-lineage programs. Yet, differentiation arrest was inconsistently induced195, and DNA methylation changes do not consistently correlate with gene expression changes173,194,195. This paradox might suggest that there are additional roles of DNMT3A that are yet to be defined in T-cell development and in progression to T-ALL. A better understanding of how mutations may be operative in distinct developmental contexts will certainly lend insight into the molecular underpinnings of T-ALL pathogenesis and may yield better treatment of this disease for improved patient outcomes. 1.4.3 Current treatment and challenges The standard of care for the majority of individuals with T-ALL is multi-agent chemotherapy, in which different treatment regimens are administered in three phases (i.e., induction, consolidation, and maintenance) aimed to prevent development of therapy resistance (reviewed in274). Treatment is often risk-intensified according to treatment response, which may be monitored by MRD assessment. MRD monitoring involves screening for the presence of T-ALL blasts remaining after the induction and consolidation phases. MRD response at the end of consolidation is a major determinant of prognosis in T-ALL, with MRD negativity indicating a generally favorable outcome (reviewed in199). Although strategies which implement risk-based treatment intensification have been met with success, particularly in subtypes associated with poor prognosis such as ETP-ALL (reviewed in199), long-term side effects from cytotoxic chemotherapy, in addition to dismal outcomes for relapsed disease (reviewed in199), advocate for the use of more targeted therapies. Targeted therapy for use in T-ALL treatment is impeded by a lack of druggable targets. Inhibition of Notch signaling is an attractive candidate due to the oncogenic role of NOTCH1 in T-ALL, and a number of clinical trials have investigated NOTCH1 inhibition, primarily with -secretase inhibitors (GSIs). GSIs 31 prevent NOTCH1 activation through inhibition of the -secretase complex, which proteolytically cleaves and activates the NOTCH1 receptor (reviewed in275); in a subset of T-ALL cell lines, treatment with GSI induced G0/G1 cell cycle arrest212. In clinical trials, however, success has been limited due to significant gastrointestinal toxicity276 (reviewed in277). Recent clinical trials aim to combine GSIs with additional agents, such as dexamethasone278, to relieve off-target tissue effects. Alternative approaches to targeting the Notch signaling pathway include monoclonal antibodies directed against Notch279, or by targeting factors downstream of Notch signaling, such as CXCR4 (reviewed in280). Also frequently deregulated in T-ALL are cell cycle components; the majority of T-ALLs exhibit CDKN2A loss and/or CCND3 overexpression, resulting in upregulation of CDK4/6281 (reviewed in282). Thus, inhibition of the cell cycle regulators, CDK4 and CDK6, presents a possible therapeutic opportunity. In T-ALL mouse models and human cell lines, CDK4/6 inhibition induces cell-cycle arrest282,283, and may act synergistically with glucocorticoids and other molecular inhibitors283. The use of CDK4/6 inhibitors is currently under investigation for the treatment of T-ALL, as well as other disease contexts in which CDK4/6 are upregulated (reviewed in284). There is, however, significant myelotoxicity and neutropenia associated with some CDK4/6 inhibitors, such as palbociclib, thus requiring stringent dose administration (reviewed in285). In the absence of a pan-T-ALL target, precise classification of T-ALL subtypes may guide novel, subtype-specific therapeutic strategies. As a goal of targeted treatment is elimination of malignant cells with few adverse effects on normal tissues, it will be important to understand the specific developmental contexts in which various mutations operate. In T-ALL, mutation of certain factors correlates with particular stages of T-cell differentiation, which might suggest co-operation between stage of development and factor mutation, and furthermore may indicate a unique dependency of certain factors on the transcriptional programs that are rooted in development. This perspective would then imply that while mutations may drive disease, the mutations are intrinsically dependent on the developmental contexts in which they operate. Thus, a greater understanding of the complex interplay between various mutations and developmental programs may lend insight into targeting disease in a subtype-specific fashion. 32 1.5 Thesis objective and chapter summaries T-ALL has long been characterized as a malignancy of developmentally arrested T-cells, but this central tenet has yet to be leveraged into improved therapies. The overarching objective of this thesis is to establish a greater understanding of how genetic and molecular events might operate in specific developmental contexts in T-ALL. The work presented in this thesis was motivated by the observation that there is synchrony between specific mutations and the developmental framework within which they are embedded, and that further understanding of disease pathogenesis could be understood by first probing the developmental contexts in which these factors operate. In the studies that will be discussed here, an emphasis was placed on understanding the role of DNMT3A in the context of early T-cell development. This was approached (1) by establishing a comprehensive developmental landscape of T-ALL, using normal intrathymic populations as a baseline for differentiation; (2) through the multi-omic and functional characterization of a chemically-defined in vitro T-cell model to then interrogate the consequence of DNMT3A loss on T-cell development, and (3) by the application of findings derived from DNMT3A loss in normal T-cell development to T-ALL. In Chapter 2, I present analyses that characterize the developmental states of T-ALL through incorporation of RNA-seq data from normal human intrathymic subsets. To this end, I first generated a gene signature of normal T-cell development using publicly available expression data from sorted intrathymic subsets286, and then applied this signature to two publicly available RNA-seq datasets of diagnostic T-ALL samples222,223. The goal of this chapter was to view T-ALL on a more granular level through the framework of normal T-cell development. The results of these analyses reveal that the use of high-dimensional data to classify developmental stage can improve resolution achieved from phenotypic analysis alone, and that this increased dimensionality can enhance sensitivity in detecting a subgroup of T-ALL that has previously been associated with poor prognosis. I also present observations on the coordination between specific transcription factor subgroups and the limited developmental contexts in which they appear. Overall, the results presented in this chapter underscores the value in approaching T-ALL classification with higher-dimensional methodology for improved understanding of the developmental contexts in which mutations may operate. 33 In Chapters 3 and 4, I characterize a chemically-defined model for in vitro T-cell development to then probe the consequence of DNMT3A loss in specific developmental contexts. This was achieved through multi-omic profiling (i.e., RNA-seq and whole-genome bisulfite sequencing) of T-cell subsets collected at various time points throughout differentiation, and through functional assessment of the developmental checkpoints that are characteristic of T-cell differentiation, such as T-cell commitment and T-cell receptor rearrangement. After a baseline of normal in vitro development was established, I then introduced genetic perturbation through CRISPR/Cas9-mediated knockout and shRNA-mediated knockdown experiments. Although DNMT3A loss did not demonstrably affect T-cell differentiation, I observed that knockdown and knockout of DNMT3A in committed T-cell populations was associated with an altered response to cytokines which typically contribute to the proliferation and differentiation of myeloid populations (‘myeloid’ cytokines). This was observed by an outgrowth of cells which appeared myeloid-like by immunophenotypic profiling. When this phenotype was examined in the context of human T-ALL cells, I observed that the addition of these ‘myeloid’ cytokines to culture media resulted in enhanced proliferation upon DNMT3A knockdown. The findings presented in this chapter shed novel insights into a possible role of DNMT3A in constraining lineage-specific signal responses in T-cell development and in T-ALL. 34 Chapter 2: Characterization of T-cell acute lymphoblastic leukemia in the context of normal T-cell development 2.1 Introduction The processes that govern T-cell development must be tightly regulated to orchestrate the production and differentiation of T-cell progenitors into functionally mature subsets. In early T-cell ontogeny, a small number of hematopoietic progenitors emigrate from the bone marrow to the thymus. Exposure to the thymic environment restricts the alternative lineage potential of early T-lineage precursors and eventuates commitment to the T-cell fate43,97. The majority of cells adopt an  T-cell fate, where commitment is punctuated by proliferative expansion of select cells in a process termed -selection, and results in progression to CD4+CD8+ DP T-cells. The expansion of immature T-cells is highly controlled, and the survival of cells is dependent upon successful rearrangement of T-cell receptor loci. Errors throughout this process, which involves double stranded DNA breakage and re-ligation, can trigger gross chromosomal rearrangements that result in aberrant factor expression and ultimately lead to aberrant development and cellular transformation. T-cell acute lymphoblastic leukemia (T-ALL) is the pathologic designation applied to malignant accumulations of immature T-cells. Although current chemotherapy achieves an event-free survival rate of approximately 85% over 5 years199, long term side effects from cytotoxic chemotherapy and radiation, in addition to dismal outcomes for relapsed disease, advocate for the use of more targeted therapies and thus improved disease classification. Previous efforts to characterize T-ALL by immunophenotyping using a few select CD markers and TCR rearrangement status previously led to classification according to developmental stage. However, with the advent of gene expression/RNA profiling, classification by TF signature has become increasingly common. While TF clusters correspond roughly to normal T-cell developmental stages, there are frequent discordant samples that suggest additional genetic features influence the stage of developmental arrest. The relevance of characterization by developmental stage has been highlighted in studies describing an ETP-like subgroup of T-ALL with a distinct immunophenotype (CD1a- CD8- and weak CD5, 35 in addition to expression of myeloid or stem markers including CD117, CD34, HLA-DR, CD13, CD33, CD11b, CD65). ETP-ALL was initially identified based on similarity to a murine ETP genetic signature226, though additional studies have similarly pointed to T-ALLs marked by an early developmental arrest89,206,221,248. In some studies, ETP-ALL has been associated with unresponsiveness to standard induction therapy based on MRD, as well as poor prognosis226,287. These observations suggest a detailed, more highly resolved classification of T-ALL according to stage of differentiation arrest could provide added value in refining diagnostic subclassification and risk stratification. Methods currently employed to infer T-cell developmental stage, such as clinical immunophenotyping or TCR rearrangement status, are relatively limited in dimensionality and thus are particularly sensitive to errors introduced by aberrant expression of individual component markers. Indeed, previous studies have highlighted T-ALL cases with a high degree of transcriptional similarity to the mouse ETP gene signature originally used to describe ETP-ALL, with only a subset of these cases fulfilling the immunophenotypic criteria defining ETP-ALL231,288. For example, Coustan-Smith et al. (2009)226 reported a subgroup of T-ALL cases which were transcriptionally similar to the mouse ETP gene signature, but with higher expression of CD5, and this group has been subsequently classified as near-ETP-ALL223. The outcomes of near-ETP-ALL, however, are not well characterized. Taken together, this points to the need for a higher dimensional, more robust approach to developmental classification of T-ALL. To address this, we sought to derive a comprehensive developmental classification of T-ALL based on NGS-derived gene expression profiling data (i.e., RNA-seq) from purified subsets of normal human thymic T-cell progenitors. By integrating publicly available RNA-seq datasets from normal intrathymic T-cell subsets and diagnostic T-ALL samples, we generated a developmental based classification of human T-ALL samples that closely recapitulates stages of progressive T-cell differentiation. We found that developmentally immature T-ALLs (corresponding to normal CD34+CD7+/-CD1a-CD4-CD8- T-cells) show inferior response to induction therapy and may thus help to identify high-risk patients. These immature T-ALLs share a common transcriptional signature with ETP-ALLs, but many do not fit immunophenotypic criteria for ETP-ALL, suggesting that a molecular signature of immature T-ALLs is more inclusive and captures a greater proportion of high-risk cases than the more restrictive immunophenotypic definition of ETP-ALL. In sum, we present a developmentally based classification of human T-ALL derived from high dimensional RNA-36 seq data that defines clinically relevant disease subsets and exceeds upon existing lower dimensional strategies in clinical use such as flow cytometric immunophenotyping. 2.2 Materials and methods 2.2.1 Public data resources The TARGET RNA-seq dataset as originally published by Liu et al. (2017)223 was obtained from dbGaP under the accession numbers phs000218 and phs000464. The CGAD RNA-seq data as originally published by Chen et al. (2018)222 was obtained from the Chinese Leukemia Genotype-Phenotype Archive under accession number CGAS00000000002. The RNA-seq dataset of human thymic populations as originally published by Casero et al. (2015) was obtained from the National Centre for Biotechnology Information Gene Expression Omnibus (GEO) database under the accession code GSE69239. The microarray dataset of human thymic populations as originally published by Lee et al. (2004)289 was obtained from the GEO database under the accession code GSE1460. 2.2.2 RNA-seq alignment RNA-seq data was quality assessed using FastQC (version 0.11.9). Quality read trimming was performed using Trimmomatic (version 0.40)290. Trimmed reads were aligned to the human reference genome (GRCh38/hg38) using STAR (version 2.7.8a) using default parameters291. To obtain read counts, the featureCounts function in Rsubread (version 2.8.2)292 available in R (version 4.1.1) was used. Genes with <1 read count across all samples were removed for all downstream analysis. To identify differentially expressed genes, the DESeq2 package (version 1.34.0) in R (version 4.1.1) was used293. Differentially expressed genes were identified based on a BH-adjusted p-value < 0.05 and a log2 fold change >1 unless otherwise indicated. 2.2.3 Principal component analysis We performed variance stabilizing transformation using the DESeq2 R Package (version 1.26.0) on protein-coding genes which were filtered to remove genes with low expression. We then performed principal component analysis using the top 500 variable genes. In cases where analysis was limited to 37 specific gene sets, we used the top 500 variable genes pertaining to that gene set. To perform data projection, we performed matrix multiplication of the samples to be projected (TARGET or CGAD cohorts) and the eigenvector matrix determined from the reference dataset (sorted thymic populations). To obtain a list of genes that contribute to variance, we extracted the gene loadings associated with the first two principal components. 2.2.4 Unsupervised clustering We performed k-medoid and k-means clustering using the factoextra R Package (version 1.0.7). The number of optimal clusters (between 2-10 clusters) was determined using the silhouette and gap statistic. Clusterwise stability was determined by bootstrap resampling using the fpc package (version 2.2.7) and the cluster stability was determined by the mean Jaccard coefficient over 100 bootstrap iterations. Hierarchical clustering was performed using the hclust R package (R version 3.6.0) using Ward’s linkage and Euclidean distance. 2.2.5 Differential gene expression Differential expression analysis was performed using the DESeq2 R package (version 1.26.0) on read count data outputted from featureCounts as described above. To determine significantly differentially expressed genes, we used a threshold of a log2 fold change > 1 with FDR < 0.05, unless otherwise specified throughout the text. 2.2.6 T-cell receptor analysis TCR rearrangements were determined from the RNA-Seq data using MiXCR as previously described (Kusakabe et al., 2019)294. We then filtered for unique sequences supported by at least 10 reads and comprising at least 2% of the total abundance per sample. T-ALLs were classified based on the extent of rearrangement observed; that is, a sample showing evidence of TRD, TRG and TRB rearrangement would be classified as TCR rearranged. 38 2.2.7 Transcription factor subgroup classification We used a combination of chromosomal translocation and gene expression to classify patients from the CGAD cohort by transcription factor subgroup. Individuals were assigned to subgroups based on the presence of translocations affecting transcription factors frequently observed in T-ALL, as reported by the authors. We additionally classified individuals using an expression threshold corresponding to the top 10th percentile for each transcription factor. 2.2.8 Gene set variation analysis Gene set variation analysis was performed using the GSVA package (version 1.34.0). Gene sets for each intrathymic subset (referred to as “Thy” throughout) were created by performing differential gene expression between the subset of interest and the remaining subsets (see Figure 2.2A). Enrichment of gene sets was determined using the default settings for GSVA. The enrichment scores for each gene set were visualized using heatmaps. 2.2.9 Diffusion map To plot RNA-seq data from different sources onto the same diffusion map, we first performed rank normalization of the 2,824 genes implicated in T-cell differentiation using the CGAD, TARGET or Crooks datasets. The results were plotted using the destiny R package (version 3.0.1). 2.2.10 Statistical analysis To determine the specificity and sensitivity of T-ALL classification methods (immunophenotypic ETP-ALL classification vs. differentiation signature classification), we performed paired McNemar’s tests. Patients were dichotomized into MRD positive or MRD negative categories using a threshold of >1% to indicate MRD positivity, or <1% to indicate MRD negativity. All statistical analyses were performed using R (version 3.6.0). 39 2.3 Results 2.3.1 Creation of a gene signature associated with human intrathymic T-cell development We first focused our analysis on thymic populations which reflect the developmental window in which T-ALLs are arrested. We accessed publicly available RNA-seq data from human intrathymic subpopulations (Thy1-Thy6), which captures the progressive differentiation of normal T-cells286. Principal component analysis (PCA) of these subsets using the top 500 variable genes placed each subpopulation along an ordered differentiation trajectory, with the highest variance associated with differentiation along the first principal component (PC1) (Figure 2.1A). To pare down our analysis to the T-cell subsets which are most relevant to T-ALL, we projected patient T-ALL samples from two independent RNA-seq datasets accessed online (TARGET and CGAD studies) into PCA space defined by normal thymic subpopulations. We observed that T-ALL samples tend to fall among the immature T-cell subsets (Thy1-4), but not the terminally differentiated SP4+ (Thy5) or SP8+ (Thy6) populations, and thus determined Thy1-4 to be most suitable for our analyses (Figure 2.1B)222,223. We then defined gene expression patterns associated with T-cell differentiation by performing pairwise differential expression analysis among the various thymic subsets as shown in Figure 2.2A. We chose this approach, as opposed to a sequential pairwise DE analysis (ie: Thy1 vs Thy2, Thy2 vs Thy3, Thy3 vs Thy4), in order to capture the major gene expression changes that occur as cells establish and elaborate upon their developmental programs, since a sequential pairwise analysis might exclude relevant gene expression changes. This analysis revealed a set of gene expression signatures altogether comprising 2,824 unique genes. Among the 2,824 T-cell differentiation gene set, we grouped genes which co-varied across the Thy1-4 subsets using k-means clustering, yielding 5 distinct gene expression patterns (absolute log2 fold-change >1, BH-adjusted p-value < 0.05; Figure 2.2B); gene cluster 1-5 (GC1-5). Genes within each cluster showed high correlation (median Pearson correlation = 0.93), even when examined within an independent microarray dataset of sorted human thymic populations (McCune dataset; median Pearson correlation = 0.78), thus validating the composition of the gene expression clusters (Figure 2.2C)289. Among the five gene expression clusters, we noted downregulation of factors important for alternative lineage programs, such as SPI1 (GC2), and upregulation of key regulators associated with T-40 cell commitment and/or development including PTCRA, RAG1/2, and GATA3 (GC4 and GC5) (Figure 2.2C). Figure 2.1 PCA projection of intrathymic T-cell subsets and T-ALL. A PCA of sorted human thymus subsets as published by Casero et al., 2015286. B Patient T-ALL samples from CGAD and TARGET studies (n = 129 and 264, respectively) projected into PCA space defined by normal T-cell subsets (Thy1-6). Black dots indicate the PCA projections of normal sorted thymic populations. T-ALL samples are coloured based on transcription factor (TF) subgroup. 41 Figure 2.2 Cluster analysis of normal thymic T-cells. A Differential gene expression comparisons employed to generate the 2,824 gene signature. The number of unique differentially expressed genes for each comparison is shown in brackets. B k-means gene clustering of 2,824 genes implicated in T-cell development based on differential gene expression analysis. The relative gene expression is shown for each sorted human thymus subset (Thy1-4), with the mean expression for each gene cluster shown as a red line. The number of genes within each gene cluster is shown in brackets. GC = gene cluster. C Intra-cluster gene correlation of the five k-means gene clusters of genes implicated in T-cell development. Intra-cluster correlations were also tested on an independent microarray of sorted thymic populations (McCune dataset) using the k-means gene clusters identified by Casero et al., 2015 (Crooks dataset)286. A high correlation is observed within each of the five k-means gene clusters (GC1-GC6) when compared to the correlation across all genes (“all”). 42 2.3.2 Classification of T-ALL according to gene signatures that correlate with normal T-cell development Having pared down to the subset of genes most informative with respect to thymic T-cell differentiation, we sought to parse the spectra of patient T-ALL samples against this background and, in essence, to perceive T-ALL though the lens of normal differentiation. We thus performed unsupervised k-medoid clustering of the TARGET and CGAD T-ALL datasets with analysis limited to the 2,824 gene set. Based on average silhouette width and gap statistic, we determined the datasets were optimally divided into 6 clusters (Figure 2.3A, Appendix G.1). The transcriptomic profiles of the sample clusters were highly correlated across both the TARGET and CGAD T-ALL datasets, supporting consistency of the cluster assignments (Figure 2.3B,C). In an attempt to order the 6 clusters along a linear progression of intrathymic T-cell development, we looked for correlation with TCR rearrangement status as determined by MiXCR analysis of the RNA-seq reads295. Cluster Teal was notably enriched for samples showing no TCR rearrangements, while Cluster Yellow was most enriched for TRA rearrangements, suggesting that Teal and Yellow represent the extremes of most immature and mature stages, respectively (Figure 2.4, Appendix G.2)87. Further, ETP cases were strongly enriched in Cluster Teal, supporting its assignment as the most immature stage. Based on the next highest abundance of TRA rearranged samples, Clusters Green and Pink would presumably represent the 2nd/3rd most mature clusters, thus leaving Blue and Orange as 4th/5th most mature. Inspection of TF subgroup assignments suggested LMO2/LYL1 cases were least mature, whereas TAL1 cases were most mature. We next attempted to define developmental order from RNA-seq data first by projecting normal T-cells onto the same PCA space as T-ALL cases. This revealed an arc-like trajectory of progressive differentiation states (Figure 2.5A, Appendix G.3). We also applied non-linear dimensionality reduction (Appendix G.4), which revealed a similar trajectory, thus supporting that a developmental-based ordering was possible. We thus proceeded to impute a differentiation-based order based on transcriptional signatures of the normal thymic subsets. We first defined gene sets corresponding to each of the Thy1-4 subsets (Appendix G.5), then used Gene Set Variation Analysis (GSVA)296 to generate Thy1-4 signature scores for each of the samples in TARGET and CGAD datasets. We found that the 6 clusters could be 43 ordered along a developmental continuum based on these GSVA signature scores with Cluster Teal most aligned with Thy1/2, followed by Orange aligned with Thy2, then Blue with Thy3, Green with Thy3/4, and finally Yellow with Thy4 (Figure 2.5B,C, Appendix G.5). Cluster Pink was more difficult to place by this approach as its scoring pattern differed between the two datasets, aligning most with Thy1 in TARGET vs. somewhat ambiguously near Thy4 in CGAD. Incorporation of TCR rearrangement status information from both datasets would however support assignment of Cluster Pink to a more mature stage. This ambiguity in placement of Cluster Pink would suggest that it describes a subset of T-ALLs with more pronounced dysregulation of developmental gene expression programs. 44 Figure 2.3 k-medoid clustering of T-ALL samples. 45 A PCA plot with optimal k=6 clustering for TARGET (left) and CGAD (right) T-ALL datasets. Cluster stability was calculated for each cluster based on 100 bootstrap iterations. B Cluster-wise sample correlations across the TARGET and CGAD RNA-seq datasets. The Pearson correlation between each CGAD and TARGET T-ALL dataset is shown as a boxplot or heatmap and grouped by k-medoid cluster membership (t-test with Holm-Sidak correction for multiple comparisons). ns: not significant; *** p<0.001; **** p<0.0001. Figure 2.4 Developmental-based clustering of patient T-ALLs. PCA plots of the TARGET T-ALL cohort (n=264) showing six k-medoid clusters in light grey with annotations according to transcription factor (TF) subgroup, T-cell receptor rearrangement (TCR) status, and ETP-ALL immunophenotype. ND = not detected. 46 Figure 2.5 Developmental ordering and clinical significance of k-medoid clusters. A Projection of CGAD and TARGET T-ALL cohorts into PCA space defined by normal T-cell subsets (Thy1-4). Samples are colored based on k-medoid cluster membership. The black arrow depicts an inferred developmental trajectory. B Gene set variation analysis (GSVA) was performed on TARGET cohort to determine relative enrichment for each gene set (Thy1-4). C Consensus developmental ordering of the six k-medoid sample clusters based on GSVA, TCR rearrangement and immunophenotype. D Minimal residual disease (MRD) at 29 days post-induction therapy for the TARGET cohort. Horizontal bars indicate median MRD values for each subgroup. *, p<.05; **, p<.01; ***, p<.001; ****, p<.0001 (Dunn’s test with Benjamini-Hochberg multiple test correction). 2.3.3 Developmental classification reveals patient subgroups with high minimal residual disease Finally, we considered whether the 6 differentiation-based clusters held any clinical significance. Based on clinical outcome information available for the TARGET T-ALL dataset (n = 264), we found that the 6 clusters showed no significant association with event-free survival (Appendix G.6); however, we did find that Cluster Teal was associated with significantly higher MRD level at day 29 post-induction therapy 47 (Figure 2.5D). This is perhaps not surprising given the overlap with ETP-ALL which is already known to be associated with increased MRD (Appendix G.6); however, only 53% (17/32) of samples within Cluster Teal were immunophenotypically classified as ETP-ALL (Figure 2.4, Appendix G.6). Interestingly, segregation of Cluster Teal into ETP/near-ETP/non-ETP types revealed non-significant differences in MRD level (Appendix G.6), supporting that Cluster Teal identifies a greater proportion of high-risk patients than does ETP classification. To assess the relative utility of ETP-ALL vs. Cluster Teal designations, we performed a pairwise McNemar’s test which revealed the Cluster Teal designation was significantly more sensitive than the ETP-ALL designation (p = 0.00079), while maintaining an equivalent degree of specificity (Table 2.1). These findings support that the RNA-seq-based differentiation classifier is capable of segregating patient T-ALL samples into clinically relevant subgroups. 48 Table 2.1 Sensitivity/specificity of Cluster Teal vs ETP-ALL classifier in identifying day 29 post-induction MRD positive cases. The sensitivity (top) and specificity (bottom) of identifying day 29 post-induction minimal residual disease positive cases are shown. Statistical p-values are calculated by McNemar's test. MRD positive on day 29 post-induction therapy (sensitivity): Cluster Teal group Sensitivity difference: 14% ETP-ALL group Yes No p = 0.00079 Yes 14 1 15 (16.13%) No 14 64 78 (83.90%) 28 (30.10%) 65 (69.90%) 93 MRD negative on day 29 post-induction therapy (specificity): Cluster Teal group Specificity difference: 0% ETP-ALL group Yes No p = 1.0 Yes 3 1 4 (2.33%) No 1 167 168 (97.70%) 4 (2.33%) 168 (97.70%) 172 2.4 Discussion Our approach to characterize T-ALL according to developmental stage is ostensibly orthogonal to other studies which have emphasized classification based on distinct genetic transcription factor subgroups219,222,223. Indeed, some studies have specifically identified TF-mediated mechanisms of developmental arrest297; however, the analysis presented here emphasizes the clinical relevance of a dedicated approach to developmental-based classification of disease that is not fully captured by TF or CD marker-based clustering. Although genetic/TF subgroup-based classification provides unique insight into transcriptional signatures that likely drive leukemogenesis, it remains difficult to segregate out the operative oncogenic effectors from those genes more generally associated with the prevailing stage of differentiation arrest. For example, among 18 LMO2/LYL1 cases in the TARGET cohort, 17 segregated into either Cluster Teal (13) or Green (4) which reside at opposite ends of PC1 (Figure 2.5C), thus prompting investigation as to whether these TFs activate the same or different sets of genes in these two rather dissimilar developmental contexts. Further, since 3 of 4 cases in Cluster Green were associated with LMO2 translocation (as compared to 0 of 13 in Cluster Teal) another issue may be whether the LMO2/LYL1 signature in Cluster Teal is due to 49 oncogenic dysregulation or alternatively a secondary consequence of developmental arrest at a stage where LMO2/LYL1 are normally expressed at high levels. The observation that certain genetic/TF subgroups tend to cluster within specific developmental windows may indicate a unique requirement of specific TFs for the developmental programs within which they are embedded. Distinguishing between those factors which necessarily depend on specific developmental contexts for survival, and those which are simply a consequence of developmental context, may have implications for differentiation stage-specific therapies, which have shown benefit in AML298. We note that the analyses presented here are based on bulk RNA-seq data and do not fully capture developmental or intra-tumor heterogeneity, which may be better illustrated by methods such as single-cell RNA-seq. We speculate that normal T-cell development represents a continuum of transient differentiation states, and that comparison of T-ALL cases to bulk thymus may result in artificial ‘binning’ of T-ALLs to the nearest normal counterparts as a result of capturing only a limited number of normal T-cell developmental states. Similarly, the existence of sub-clonal genetic events in T-ALL may influence, or be influenced by, the developmental context in which leukemia cells are arrested, and these dynamics are not fully reflected by bulk RNA sequencing of T-ALL. Our analyses suggest that T-ALL may be placed along a trajectory of aberrant T-cell development, which is in contrast to current classification methods which place T-ALL into discrete bins. Accordingly, it may be that even greater resolution afforded by single cell analyses will yield further refinement in the classification of tumors by revealing both inter- and intra-tumor heterogeneity. In sum, patient T-ALLs can be clustered and ordered along a developmental trajectory with improved resolution using transcriptomic data and which outperforms both TF and CD marker-based clustering in prediction of MRD level, a highly relevant clinical outcome measure. We would postulate that the functional outcomes and/or sets of oncogenic effectors induced by a given TF potentially differ based on the developmental context of the cells in which they operate, and thus establishing a robust developmental schema may provide a useful backdrop in interpreting gene expression data and may facilitate design of differentiation state-specific therapies. Leveraging this information will, however, require the use of developmental models to interrogate the effects of genetic perturbations in specific developmental contexts. 50 Chapter 3: Normal human T-cell development using a feeder-free in vitro culture system 3.1 Introduction To study the development of T-cells and the effect of genetic perturbations on normal T-lymphopoiesis, a robust system that effectively models the complex thymic environment and allows for genetic manipulation of developing T-cells is required. Although experimental mouse models provide a feasible means to explore and manipulate mechanisms underlying T-cell differentiation in vivo, several recent studies have highlighted differences between murine and human T-cell development (reviewed in299). These species-specific differences advocate for the use of human T-cell culture systems to study processes related to human T-cell development. While the most accurate models might be those that best approximate primary tissues, such as thymocytes isolated from human thymus, the use of primary tissue is restricted by limited availability and by challenges in manipulation of human thymocytes in vitro. Accordingly, many culture systems have instead focused on the generation of T-cells from human hematopoietic stem/progenitor cells in vitro. Early iterations of in vitro human T-cell culture were limited by the use of cumbersome fetal thymic organ cultures (FTOCs), which required careful extraction of thymic lobes from a fetal mouse and reconstitution with human progenitors300. Ground-breaking efforts have since shown that T-cells can be derived in vitro by culturing human progenitor cells from various sources (e.g., umbilical cord blood, bone marrow, and mobilized peripheral blood) on a monolayer of murine OP9 stromal cells that overexpress Notch ligands (e.g., Delta-like ligand 1; DLL1) and are thus termed OP9-DL1 cells29,301,302. While these cultures may be supplemented with exogenous cytokines to promote T-cell differentiation, such as IL-7, SCF, and Flt3L, parental OP9 cells themselves also secrete factors which promote T-lymphocyte development, including IL-7, SCF, and CXCR12303,304. The OP9-DL1 system efficiently supports the production of CD4+CD8+ double positive (DP) T-cells, and to a lesser extent, mature single positive (SP) CD8+ T-cells with rearranged T-cell receptors (TCRs)305. Importantly, these in vitro-generated T-cells may be readily altered and thus provide a foundation for T-cell production and for exploring processes related to T-cell development. The ease of manipulation afforded by in vitro models, either through genetic 51 perturbation or by addition of exogenous reagents, thus makes them an attractive choice for modeling T-cell development to study the contexts in which normal development may go astray. Although the OP9-DL1 system represents a major breakthrough in the in vitro generation of T-cells and has revolutionized in vitro T-cell culture, it does not come without limitations. The simplicity of the OP9-DL1 monolayer system translates to convenience in cell culture and manipulation, but does not fully appreciate the complex three-dimensional architecture in which normal T-cells develop, nor the distinct cellular zones that are characteristic of the thymus (reviewed in24), and thus may not be suitable for modeling processes such as intrathymic T-cell migration. Furthermore, deviations between variant OP9-DL1 cell lines as a result of extensive in vitro culture may introduce additional uncontrollable heterogeneity; variations in culture conditions, such as the levels of expression of Notch ligand by feeder lines, can elicit demonstrable differences in T-cell outputs306. This may be further compounded by the use of fetal bovine serum, which varies in composition across different batches and sources. Taken together, these limitations may culminate in defects in faithful T-cell differentiation; for example, while the OP9-DL1 system can efficiently generate CD8+ SP cells, the CD4+ SP output is hindered307 (reviewed in308,309). The development of alternative in vitro T-cell differentiation models in more recent years, such as 3D organoid systems, may mitigate these effects by more accurately mimicking the complex thymic architecture and hold promise in generating T-cell populations which faithfully resemble those observed in human thymus300,310–314. The use of these systems have also achieved noticeable improvement over the OP9-DL1 system in the generation of DP and SP cells312. Nonetheless, these models remain limited by the use of stromal cells (e.g., artificial thymic organoids) or primary thymic tissue which may deliver exogenous factors of variable composition and concentration. Although 3D organoid systems are an invaluable tool for modeling T-cell development within thymic tissue, the additional uncontrolled variability introduced by variant feeder lines or primary thymic tissue may confound interpretations when attempting to assess the effect of genetic or molecular manipulation on T-cell development. For this purpose, culture systems with defined components may be more suitable for certain applications. Although primarily developed for clinical use, several groups have developed chemically-defined, feeder-free conditions that support the generation and expansion of T-cells in vitro from umbilical cord blood (CB)-derived progenitors315–317. These systems typically include recombinant human Notch ligand bound to 52 a tissue culture plate and cytokines that encourage T-lineage growth, such as SCF, IL-7, and Flt3L, and provide a scalable means for efficient and reproducible T-cell maturation. Similar to the OP9-DL1 system, these are monolayer culture systems and thus are not suitable for modeling processes related to T-cell migration, but given the tractability and use of synthetic media, may be valuable for modeling the consequences of specific genetic perturbations. Another important limitation of these cultures systems is the relative paucity of SP8 and SP4 T-cells, which is likely due to the absence of thymic epithelial cells bearing MHC molecules which are required for positive and negative selection. In the studies described here, T-cells were generated using an off-the-shelf feeder-free culture system. This culture system yields large numbers of T-cells from CD34+ progenitors from human CB which differentiate in a reproducible manner and are amenable to genetic manipulation. T-cells generated from this system may be captured in large quantities at any stage of T-cell development, therefore allowing precise dissection of events in early T-cell ontogeny, which can be difficult to capture due to limited cell numbers in primary tissues. Importantly, the T-cells derived from this system resemble human thymic populations both phenotypically (based on transcriptomic and immunophenotypic analyses), and functionally (based on the presence of TCR rearrangements and T-cell lineage commitment), and thus may be suitable for use to further understand factors important for early events in T-cell differentiation. As these cells are easily manipulated (i.e., by shRNA-mediated knockdown or CRISPR-mediated modulation), this system may be used to explore the effect of genetic perturbation on T-cell development, as discussed in subsequent text. This chapter describes the molecular and phenotypic characterization of T-cells generated from this system in comparison to primary intrathymic T-cell populations through the use of RNA-seq, whole genome bisulfite sequencing, and immunophenotypic profiling. 3.2 Materials and methods 3.2.1 Cells and cell culture 3.2.1.1 Primary cells Human umbilical cord blood samples were obtained from Stem Cell Assay (Terry Fox Laboratory, BC Cancer Research Centre, Vancouver, British Columbia, Canada) with informed consent from donors. These samples contained pools of cord blood samples from multiple donors (2-10 individual donors). A list 53 of cord blood pools accessed, and the experiments performed for each cord blood pool, can be found in Appendix A. 3.2.1.2 Primary cell culture Cells were cultured in vitro using the StemSpan T cell Generation Kit (STEMCELL Technologies) according to manufacturer’s instructions and maintained in a 37C incubator with 5% CO2. Briefly, non-treated culture dishes were coated with StemSpan Lymphoid Differentiation Coating Material (100X), diluted 1:100 in PBS and left overnight at 4C, or at room temperature for 2 hours, before the coating material was removed and plates were rinsed with PBS. Cord blood samples were resuspended in StemSpan Lymphoid Progenitor Expansion Supplement (10X), diluted 1:10 in StemSpan Serum-Free Expansion Medium II (SFEM II, STEMCELL Technologies), or in StemSpan T cell Progenitor Maturation Supplement (10X), diluted 1:10 in SFEM II (STEMCELL Technologies), according to manufacturer’s protocol. For the myeloid media transfer experiments, T-cells were sorted into non-treated culture dishes containing StemSpan Myeloid Expansion Supplement (100X) or StemSpan Myeloid Expansion Supplement II (100X), diluted 1:100 in StemSpan SFEM II (STEMCELL Technologies). 3.2.2 Limited dilution analysis Cells were sorted at various cell concentrations into non-treated 96-well round bottom plates (Falcon). For all experiments, 3-8 replicates were performed for each media condition and cell concentration. For populations cultured in conditions supportive of granulocytic cell expansion, cells were sorted into wells containing StemSpan Myeloid Expansion Supplement (100X), diluted 1:100 in StemSpan SFEM II (STEMCELL Technologies). For populations cultured in conditions promoting monocyte cell expansion, cells were sorted into wells containing StemSpan Myeloid Expansion Supplement II (100X), diluted 1:100 in StemSpan SFEM II (STEMCELL Technologies). For populations cultured in conditions supportive of T-cell differentiation, cells were sorted into wells coated with StemSpan Lymphoid Differentiation Coating Material (100X), diluted 1:100 in PBS, containing StemSpan Lymphoid Progenitor Expansion Supplement (10X), diluted 1:10 in StemSpan SFEM II (STEMCELL Technologies). Data analysis 54 was performed using R (version 4.1.1) using the elda package (version 1.4.36) using default parameters except for “observed”, which was set to “TRUE”. 3.2.3 Flow cytometry and cell sorting 3.2.3.1 Flow cytometry Samples were stained with fluorochrome-conjugated antibodies as listed in Appendix B.1 and B.6. Data was acquired on the BD LSRFortessa Cell Analyzer (BD Biosciences) and the FACSymphony (BD Biosciences). To obtain cell numbers for each sample, AccuCheck counting beads (Invitrogen/ThermoFisher Scientific) were added prior to sample acquisition. In experiments where samples were acquired over several weeks, per-channel normalization was performed by staining a bead standard with the antibody mastermix at each timepoint. 3.2.3.2 Cell sorting Samples were stained with fluorochrome-conjugated antibodies as listed in Appendix B.4 and B.5. Cell sorting was performed on the BD FACSAria II (BD Biosciences) and the BD FACSAria Fusion (BD Biosciences). For samples collected for RNA-seq and whole-genome bisulfite sequencing, cell populations were cultured in vitro as per the methods outlined in Section 3.2.1.2. At different indicated time points, cells were harvested and sorted according to the markers as listed in Appendix B.4, Appendix C, and Figure 3.3. Sorted populations were pelleted and snap frozen pellets were processed by the Genome Sciences Centre (Vancouver, British Columbia, Canada) for RNA/DNA extraction as outlined in Section 3.2.4.1 and Section 3.2.6.1. For the isolation of DN and DP thymus subsets, CD4 and CD8 selection was performed using the EasySep Human CD8+ T Cell Positive Selection Kit II and the EasySep Human CD4+ T Cell Positive Selection Kit II (STEMCELL Technologies). The eluent (DP-debulked) and magnetic-bound (DP-enriched) fractions were subsequently sorted by FACs. 3.2.3.3 Flow cytometry data analysis Data was analyzed using FlowJo v10 software (Tree Star) and in R (version 4.1.1) using custom scripts. Single-cell data collected from 16 dimensions (FSC-A, SSC-A, CD4, CD8, CD7, CD5, CD1a, CD3, 55 CD13, CD33, HLADR, CD15, CD11b, CD44, CD34, and CD38) were reduced to 2 dimensions using the R package ‘Monocle3’ (version 1.0.0)318 or ‘umap’ (version 0.2.28). Scale data was exported from FlowJo and was subsequently transformed using the arcsinh function from the ‘flowCore’ package (version 2.6.0)319 in R (version 4.1.1) using the cofactor set to 150. For experiments where data was acquired on separate days, per-channel normalization was performed prior to archsinh transformation. Sample normalization was performed by collecting a bead standard stained with the antibody master mix at each timepoint. For generation of UMAP projections, data were generated using the predict function in ‘umap’ (version 0.2.28). All data was visualized using ggplot2 (version 3.3.6). 3.2.4 RNA-seq 3.2.4.1 RNA-seq sample processing RNA-seq samples were processed as part of a collaboration with the Canadian Epigenetics, Environment and Health Research Consortium (CEEHRC) by the Genome Sciences Centre (GSC) in Vancouver, British Columbia, Canada. Briefly, polyadenylated (polyA+) RNA was purified from total RNA obtained from sorted flash frozen pellets using the NEBNext Poly(A) mRNA Magnetic Isolation Module from New England Biolabs. Complementary DNA (cDNA) was then generated from the purified polyA+ RNA for strand-specific sequencing. First-strand cDNA synthesis was performed using Maxima H Minus Reverse Transcriptase (Thermo Fisher) according to manufacturer’s protocol but with the addition of 0.04g/L Actinomycin D and random primers. Second-strand cDNA synthesis was performed using NEBNext Second Strand Synthesis Module (New England Biolabs). Bead clean-up was performed following first-strand and second-strand cDNA synthesis using Ampure XP Beads (Beckman Coulter). Libraries were sequenced on the Illumina HiSeq 2500 instrument (150bp paired-end reads) according to manufacturer’s protocols (Illumina, Haward, CA). On average, approximately 100-200 million reads per sample were achieved. 3.2.4.2 RNA-seq analysis RNA-seq reads which passed the Illumina Chastity filtering were quality assessed using FastQC (version 0.11.9). Quality read trimming was performed using ‘Trimmomatic’ (version 0.40)290. Trimmed reads were aligned to the human reference genome (GRCh38/hg38) using ‘STAR’ (version 2.7.8a) using 56 default parameters291. To obtain read counts, the ‘featureCounts’ function in ‘Rsubread’ (version 2.8.2)292 available in R (version 4.1.1) was used. Genes with <1 read count across all samples were removed for all downstream analysis. To identify differentially expressed genes, the ‘DESeq2’ package (version 1.34.0) in R (version 4.1.1) was used293. Differentially expressed genes were identified based on a BH-adjusted p-value < 0.05 and a log2 fold change >1 unless otherwise indicated. Clustering of gene expression patterns was performed using the R package ‘MBCluster.Seq’ (version 1.0)320. All data was visualized using ggplot2 (version 3.3.6). 3.2.4.3 Public data resources The RNA-seq dataset of human thymic populations as originally published by Casero et al. (2015)286 can be obtained from the National Centre for Biotechnology Information Gene Expression Omnibus (GEO) database under the accession code GSE69239. 3.2.5 TCR analysis TCR rearrangements were determined from the RNA-seq data using MiXCR295, as previously described294,321, and visualized using ggplot2 (version 3.3.6). 3.2.6 Whole-genome bisulfite sequencing 3.2.6.1 WGBS sample processing Samples for WGBS were processed as part of a collaboration with CEEHRC by the GSC in Vancouver, British Columbia, Canada. Briefly, 1g of DNA was spiked using 10ng of unmethylated lambda DNA (Promega) to measure the efficiency of bisulfite conversion. Samples were sheared to an average fragment size of ~300bp by sonication using a Covaris LE220. End-repair was performed using the End-Repair Premix (New England Biolabs) at 20C for 30 minutes. Following end repair, A-tailing was then performed using the dA Tailing Reaction Mix (New England Biolabs) at 37C for 30 minutes, 70C for 5 minutes, and 4C for 5 minutes. Methylated PE adapters were then ligated to the end-repaired DNA using the 2X NEB Ligation Premix (New England Biolabs). The methylated adapters were modified to contain 5-methylcytosine bases in place of cytosine bases. Bisulfite conversion was then performed using the EZ 57 DNA Methylation-Gold Kit (Zymo Research). Enrichment for adapter-ligated DNA was performed with 5 cycles of PCR using KAPA HiFi Uracil+ ReadyMix (Roche) using the following protocol: 98C 1min; 5 cycles of 98C 15sec, 65C 30sec, 72C 30sec; 72C 5min. The final library was analyzed on the Agilent Bioanalyzer and quantitated by Quant-iT. Bead clean-up was performed between each sample preparation step using PCRClean DX (Aline Biosciences). Paired-end DNA sequencing (2x125nt) was then performed using Illumina sequencing technology. 3.2.6.2 WGBS sample analysis Reads were mapped to the human reference genome (version GRCh38/hg38) and estimation of CpG methylation was performed using ‘gemBS’ (version 3.5.0)322 using default parameters. Analysis was restricted to uniquely mapped reads with a minimum MAPQ score > 20. CpGs overlapping with known SNPs, present on XY chromosomes, or supported by fewer than 10 reads were filtered prior out to downstream analysis. Only CpG sites with methylation estimates present in all samples were included in downstream analysis. Approximately 24 million CpGs passed filtering for all samples, with an average mean depth per between 22-28X. Functional annotation of gene regulatory regions was performed using custom scripts. CpG methylation of promoters was determined by calculating the average CpG methylation of all CpG sites located 1500bp upstream and 500bp downstream of the transcriptional start site of protein-coding genes. The location of CpG islands was determined from the UCSC genome browser. Genome-wide CpG methylation was determined by taking the average CpG methylation of all CpG sites within 5kb bins. Plots indicating the level of methylation of individual CpGs in genome browser view were generated using the Gviz package (version 1.38.4) in R(version 4.1.1). 3.3 Results 3.3.1 Phenotypes of in vitro CB-derived T-cell populations To characterize the phenotype of T-cells generated in vitro from CD34+ CB, I collected cells cultured in feeder-free T-cell expansion media for analysis by flow cytometry using a panel of markers chosen to resolve various early T-cell populations (Appendix B.6). Included among the markers analyzed were CD7, CD5, CD1a, CD4, and CD8, which are sequentially expressed in developing thymocytes (Figure 1.1). Cells 58 were collected at several timepoints (up to day 42 of in vitro culture) to assess the kinetics of marker acquisition over the course of in vitro differentiation. From these experiments, I observed expression of CD7 and CD5 markers by day 14, followed by CD1a expression by day 28, and finally surface expression of TCR by the end of the culture period (Figure 3.1). Additional repetitions with more timepoints on a subset of surface markers indicated that the kinetics of CD7/CD5 gain and CD44 loss is reproducible, even when using a different cord blood pool, and furthermore demonstrate the acquisition of CD7 prior to CD5 at day 7 (Appendix F). These results are in accordance with previously published reports using the same culture system323. Importantly, the feeder-free culture conditions supported the growth of double positive (DP) CD4+CD8+ T-cell populations, with the majority of cells displaying CD4 and CD8 co-expression by day 42. This contrasts with T-cells generated using the OP9-DL1 system, where very few DP cells were observed after 42 days in culture (Ann Sun, personal communication; Figure 3.2). While it is possible this discrepancy could be due to a delay in the kinetics of marker acquisition in the OP9-DL1 culture system, no DP cells were present even when cultured for an additional 9 days (Ann Sun, personal communication; Figure 3.2), suggesting that, at least in our hands, the OP9-DL1 culture system does not support the full range of T-cell differentiation. This is in contrast to previous reports, where DP generation from CD34+ CB can be first detected after ~24 days of culture on OP9-DL1 feeders29. This discrepancy may point to variations in OP9-DL1 feeder lines or culture conditions between different research settings. 59 Figure 3.1 Flow cytometric analysis of T-cell subsets derived in vitro from CD34+ cord blood. A Pseudocoloured flow cytometry plots representative of 2 different cord blood donors. Cells were harvested for flow cytometry at the indicated time points throughout in vitro T-cell differentiation (day 0, day 14, day 28 and day 42). B Heatmap representation of CD marker expression at time point. 60 Figure 3.2 Comparison of surface marker expression of cord blood-derived T-cell subsets generated in feeder-free or OP9-DL1 culture systems. A In vitro-derived T-cells generated in a feeder-free culture system. B In vitro-derived T-cells generated from the OP9-DL1 co-culture system. 61 3.3.2 In vitro generated T-cell subsets are transcriptionally similar to human thymic populations To gain an understanding of the transcriptional changes that occur throughout differentiation, T-cell subsets were isolated based on several surface markers at various timepoints throughout in vitro culture for RNA-seq and whole genome bisulfite sequencing (WGBS) analysis as shown in Figure 3.3A (top panel) and Appendix C. Principal component analysis of the top variably expressed genes among these subsets indicated that samples arrange according to the number of days cultured in vitro (Figure 3.3B). To determine if our T-cell subsets recapitulate aspects of T-cell development as observed in human thymocyte populations, I next sought to compare RNA-seq expression profiles of our in vitro generated T-cells to a publicly available RNA-seq dataset of sorted T-cell subsets isolated from human thymus generated by Casero et al. (2015)286, as previously described in Chapter 2 (Figure 3.3A, bottom panel). To first determine which intrathymic populations corresponded to each of our in vitro generated T-cell populations, subsets across both datasets were roughly matched based on immunophenotype as shown in Figure 3.3A. Projection of both datasets into the same principal component space showed similarity along PC1 among the matched samples, suggesting that the sample comparisons were appropriate (Figure 3.3C). Having determined the closest developmental equivalents between our in vitro-derived T-cell subsets and human thymocytes populations, we next examined the gene expression changes associated with T-cell development. Focusing on a set of 2,824 genes related to T-cell development as determined from intrathymic populations (discussed in Chapter 2), I first clustered the 2,824 gene set into a series of 18 expression patterns. These expression pattern clusters were then compared to the in vitro generated T-cell populations (Figure 3.4A). Overall, there were similarities in the gene expression patterns of the in vitro generated T-cells and the intrathymic subsets throughout development. When this analysis was extended to a wider selection of genes (i.e., not limited to the 2,824 gene set), a high correlation among the matched populations was observed (Figure 3.4B). These results suggest that the in vitro-derived T-cell subsets recapitulate certain aspects of human thymic populations. 62 Figure 3.3 Overview of datasets used for the analysis of human T-cell differentiation. A Sample comparison between in vitro-derived T-cell subsets (top, n = 3 replicates for each subset) and human intrathymic subsets (bottom, n = 2 replicates for each subset; data from Casero et al., 2015)286. The defining surface markers for each population are indicated. B Principal component analysis of in vitro-derived T-cell subsets. C Comparison of the in vitro-derived T-cell subsets and human intrathymic subsets when projected onto PC1. 63 Figure 3.4 Transcriptomic comparison of in vitro generated T-cells and human intrathymic populations. A Mean expression profiles of in vitro-derived T-cell subsets (teal) compared to human intrathymic subsets (grey). The number of genes in each gene expression cluster are indicated in brackets. Genes known to be involved in processes related to T-cell differentiation are highlighted. RNA-seq of human intrathymic populations was obtained from GEO accession GSE69239 (published by Casero et al., 2015)286. B Spearman rank correlation of gene expression between matched in vitro-derived T-cell subsets and human intrathymic subsets. 64 3.3.3 In vitro generated T-cell subsets exhibit T-cell receptor rearrangement and myeloid-lineage restriction Although T-cells generated in vitro appeared similar to intrathymic T-cell populations based on expression profile and surface marker expression, we considered whether these cells exhibit developmental checkpoints that are characteristic of T-cell development. Two processes during T-cell ontogeny which can be readily interrogated in vitro include T-cell commitment and TCR rearrangement. T-cell commitment is a process whereby once-multipotent cells selectively restrict access to alternative fates until the T-lineage remains the only available option. To examine if CB-derived T-cells undergo aspects of T-cell commitment, we assessed the growth of various T-cell subpopulations by transferring them to media supportive of the growth of granulocytic and monocytic (“myeloid”) lineages. We specifically chose to test myeloid potential at least in part due to the relatively late divergence of myeloid and T-lymphoid lineages during human T-cell development107. In contrast, the potential of B-cell or erythroid fates are thought to be lost quite early in human and murine T-cell development based on lineage potential assays in which T-cells are plated into conditions permissive of alternative lineages (reviewed in104,324). To this end, cells were sorted into myeloid conditions on the basis of CD7, CD44 and CD1a expression (Figure 3.5A,B). These markers were chosen as previous studies have correlated T-cell commitment in human thymocytes with expression of CD7 and CD1a43,101, and more recently, the loss of CD44dim expression, which precedes gain of CD1a95,107. Consistent with these prior studies, the in vitro generated CD7+ CD44+/-CD1a+/- T-cell subpopulations had nonequivalent myeloid potential as measured by growth and phenotypic changes upon culture in myeloid conditions (Figure 3.5C). Whereas CD7+CD44+CD1a- cells had virtually indistinguishable myeloid and T-lymphoid potential based on limiting dilution analysis, myeloid developmental potential was reduced upon loss of CD44 expression, and even further so upon gain of CD1a. When examining the immunophenotype of myeloid outputs generated from CD7+CD44- cells, the surface marker expression was consistent with granulocytic or monocytic lineages based on the expression of markers including HLA-DR, CD15, CD13, CD11b, CD7 and CD5, and we observed a clear separation of T-cell and myeloid populations by Uniform Manifold Approximation and Projection (UMAP; Figure 3.6). 65 We next wondered if there was a temporal difference in myeloid potential within the CD7+CD44- subset. Our initial experiments were performed when cells were cultured for a total of 14 days in vitro, as this timepoint includes the earliest emergence of CD7+CD44- cells. As cells are cultured for longer, however, the CD7+CD44-CD1a+ population becomes more appreciable (Figure 3.1A). In our next set of experiments, cells were instead cultured for a period of 28 days in order to assess myeloid lineage output among the CD44 and CD1a subpopulations (Figure 3.7A,B). Although the CD44+ fraction was too rare to feasibly examine at this timepoint, the myeloid potential of CD7+CD44-CD1a- cells was greatly reduced when compared to the same phenotypic population cultured for only 14 days (20 – 30-fold decrease; Figure 3.7C). These results suggest that although CD44 loss may correlate with loss of myeloid potential, there is also a temporal factor associated with this lineage exclusion process. Importantly, the T-cell lineage output did not appear to be affected (1 in 4 – 8 cells compared to 1 in 7 cells after culture for 14 days and 28 days, respectively), which might suggest that the differences observed in myeloid output are not related to differences in clonogenic potential across the two timepoints tested. Overall, these findings suggest that myeloid lineage exclusion in in vitro derived T-cells occurs in a CD7+CD44- population, which is similar to what has been described in primary human thymocytes95,107, and this population is thus referred to as “T-lineage restricted” throughout. We then performed a differential gene expression analysis of subsets before and after the T-lineage restriction timepoint (i.e., day 7 and 10 vs day 14, 36 and 42), yielding a set of ~4000 significantly differentially expressed genes (absolute log2 fold change > 1, BH-adjusted p-value < 0.05). Pathway analysis of these differentially expressed genes revealed that programs associated with myeloid differentiation were among the top pathways upregulated in the non-restricted T-cell subsets (i.e., day 7 and day 10), whereas programs associated with T-cell differentiation were upregulated in the restricted T-cell subsets (i.e., day 14, 36 and 42) (Figure 3.8). These results support our functional experiments and further suggest that myeloid lineage exclusion may be important for T-cell differentiation. We next sought to determine if T-cells generated in vitro also undergo TCR rearrangement. To this end, MiXCR analysis was performed to determine the relative proportion of rearrangements within the TRG, TRD, TRB and TRA loci in T-cell subsets collected throughout in vitro culture. MiXCR can be used to infer TCR rearrangement status from RNA-seq data by extracting reads that align to the CDR3 region295. I 66 compared the extent and proportion of rearrangement observed in in vitro-derived T-cells to human intrathymic T-cell subsets (Figure 3.9). Our data suggest that the first instance of reproducible detection of TCR rearrangement occurs by day 10 of in vitro culture, with most rearrangements occurring within the gamma chain. Although rearrangements were present even by day 7, these were supported by very few unique rearrangements, and high variability was observed across sample replicates, which may be suggestive of contaminating mature T-cell populations. Comparison of the percent of total rearrangements between our data and publicly available RNA-seq data of intrathymic human T-cell subsets similarly showed rearrangement of TRG and TRD loci in Thy3, followed by TRB and TRA rearrangement emerging in Thy4. This is further supported by additional studies which report that TCR rearrangement occurs in a sequential order from TRG > TRD > TRB > TRA87–89. Overall, this data suggests that TCR rearrangement occurs in the expected fashion in T-cell subsets derived from in vitro cultures. 67 Figure 3.5 CD44 loss in T-cells correlates with myeloid-lineage exclusion. A General experimental design of media transfer experiments. T-cells were derived in vitro from CD34+ cord blood (CB) and cultured for 14 days in T-cell growth conditions. CD7+ cells were sorted upon the basis of CD44 and CD1a expression into conditions supportive of T-cell (grey) or granulocytic/monocytic lineages (red and burgundy). Cells were scored for growth and harvested for flow cytometry at day 24. B Representative flow cytometry plot indicating the levels of CD44 and CD1a expression within the CD7+ subset after 14 days of culture. C The percent of positive wells for each phenotypic population (i.e., CD7+CD44+CD1a-, CD7+CD44-CD1a-, and CD7+CD44-CD1a+) as a function of the number of input cells per well (top). Limited dilution growth assays measuring the frequency of growth for each sorted T-cell population when transferred into T-cell or myeloid culture conditions (bottom). For all plots, each dot is coloured according to the media condition that cells were sorted into. A minimum of 8 replicates were performed for each condition and cell concentration. 68 Figure 3.6 14-parameter UMAP distribution of flow cytometric immunophenotypes of T-lineage restricted cells after transfer into T-cell or myeloid growth conditions. A Cells were cultured in T-cell conditions for 14 days and subsequently transferred into granulocytic, monocytic or T-cell conditions for an additional 10 days. The UMAP distribution indicates cells harvested after 10 days of growth in the indicated media conditions (red, burgundy, or grey). B The expression of select surface markers which discriminate between the T-cell or myeloid lineage are shown and are coloured according to relative staining intensity. 69 Figure 3.7 T-lineage restriction in in vitro-derived T-cell populations after 28 days of culture. A General experimental design of media transfer experiments. T-cells were derived in vitro from CD34+ cord blood (CB) and cultured for 28 days in T-cell growth conditions. CD7+ cells were sorted upon the basis of CD44 and CD1a expression into conditions supportive of T-cell (grey) or granulocytic/monocytic lineages (red and burgundy). Cells were scored for growth and harvested for flow cytometry at day 38. B Representative flow cytometry plot indicating the levels of CD44 and CD1a expression within the CD7+ subset after 28 days of culture. C Top: The percent of positive wells for each phenotypic population as a function of the number of input cells per well. Bottom: Limited dilution growth assays measuring the frequency of growth in sorted T-cell populations when transferred into T-cell or myeloid culture conditions. For all plots, each dot is coloured according to the media condition that cells were sorted into. A minimum of 8 replicates was performed for each condition and cell concentration. Inf = infinity. 70 Figure 3.8 Pathways associated with myeloid differentiation are upregulated in early T-cell subsets. Pathway analysis was performed using genes differentially expressed between Early (i.e., day 7 and 10) and Late (i.e., day 14, 36 and 42) T-cell subsets. The top 10 significantly enriched pathways are shown for genes upregulated in Early or in Late subsets. The size of each dot indicates the number of genes that overlap with each pathway. 71 Figure 3.9 Comparison of T-cell receptor rearrangement analysis of in vitro-derived T-cell subsets and human intrathymic populations. TCR rearrangements were inferred from RNA-seq data using MiXCR. Each dot indicates a sample replicate and is coloured according to the TCR chain. The size of each dot indicates the number of unique rearrangements as determined from the CDR3 regions. 3.3.4 Interrogation of DNA methylation patterns and gene expression profiles of T-cell populations throughout differentiation Due in part to the relative paucity of DNA methylation analyses in normal T-cell populations, we next performed whole genome bisulfite sequencing (WGBS) to gain a better understanding of the antecedent DNA methylation changes that occur as cells establish and embellish upon the T-cell identity. The dataset described here was collected in conjunction with the RNA-seq dataset described in Section 3.3.2, however the WGBS data includes only one replicate for each time point surveyed, and thus the following methylation analyses are strictly descriptive (Figure 3.10A). To complement this dataset, DN and DP subsets from human thymus were also collected for WGBS analysis as described in Materials and Methods (Section 3.2.3.2, Appendix C). I first performed a genome-wide analysis of DNA methylation patterns across each of the in vitro T-cell subsets (day 7 – day 36) and two human intrathymic populations (DN and DP). When parsed into different 72 genomic features, DNA methylation levels showed expected patterns such as hypomethylation of gene promoters and CpG islands (CGIs), and comparatively higher levels of methylation within gene bodies (Figure 3.10B). Across the various T-cell subsets, global genome-wide methylation levels remained consistent, suggesting that T-cell differentiation does not coincide with major changes in methylation (Figure 3.10B). This is congruent with previously published reports in human thymic populations, which show that methylation of the CpGs does not vary greatly throughout development196. Although the majority of CpGs do not show drastic differences in methylation during development, principal component analysis of the top 8000 variable methylated CpGs revealed that samples segregate both by cell type (in vitro-generated T-cells vs human thymocytes) and differentiation stage, suggesting that methylation differences exist between and within cell types, albeit in a minority of CpGs (Figure 3.10C). Motivated by these results, I sought to determine if differences in DNA methylation correspond to changes in gene expression. As such, I focused on methylation within the promoter region. Using the RNA-seq data generated from each of the in vitro-derived T-cell subsets, I pared down analysis to the subset of genes that are differentially expressed between pre-committed T-cell subsets (“Early”, day 7 and 10) and T-lineage restricted subsets (“Late”, day 14, 36 and 42), as described previously (Figure 3.11A; differentially expressed genes were defined by an absolute log2 fold change > 1, BH-adjusted p-value < 0.05). set of ~4000 differentially expressed genes could be loosely grouped based on expression increase vs decrease during development (Figure 3.11B, red vs blue bars). Inspection of promoter methylation of these two groups (increasing vs decreasing RNA expression during development) revealed that decreases in gene expression were concomitant with increases in promoter methylation, although the opposite trend was not observed among genes that increased in expression throughout development (Figure 3.11B). To determine which genes had coordinated expression and methylation patterns, RNA-seq and WGBS data were integrated (Figure 3.11C, highlighted points indicate average absolute methylation difference > 10% and absolute log2 fold-change > 1 between “Early” and “Late” subgroups from WGBS and RNA-seq, respectively). This approach revealed several genes important for myeloid/B-lineage development, such as TBXA2R, CD179B, CLNK, and SPI1, which showed decreased gene expression and increased promoter methylation among early T-cell subsets (Figure 3.11C, Q2 and Figure 3.11D, middle panel). Although genes which increased in expression throughout development were not consistently associated with decreased 73 promoter methylation, we identified several exceptions including the critical T-cell genes RAG2, PTCRA, and CD1A (Figure 3.11C, Q3 and Figure 3.11D, top panel). The association of gene expression and methylation in some of these genes, including RAG2 and PDCD1, are consistent with previous studies196. Taken together, these results may suggest that promoter methylation can help coordinate the expression and repression of genes associated with T-cell maturation and alternative lineages, respectively, and may implicate a role of DNA methylation in development. Figure 3.10 Whole genome bisulfite sequencing of T-cell subsets. A Overview of samples collected for DNA methylation analysis by WGBS (n = 1 for each subset). DN = double negative, DP = double positive. B CpG methylation analysis measured by WGBS for various genomic features in in vitro-derived T-cell subsets collected at various time points (shades of green, blue, and purple) or human thymic subsets (grey and black). C Principal component analysis of the top 8000 variably methylated CpGs across all samples. 74 Figure 3.11 Comparison of CpG methylation by WGBS and gene expression by RNA-seq in the in vitro-derived T-cell subsets. A Overview of sample comparisons. Subsets collected at day 7 and 10 (Early) or day 14 and 36 (Late) were grouped. Differential gene expression analysis was performed on the two groups (Early vs Late). 75 B Relative expression and promoter methylation of genes significantly upregulated in Early T-cell subsets (blue) or significantly upregulated in Late T-cell subsets (red). C Comparison of CpG methylation (x-axis) and gene expression (y-axis). Only significantly differentially expressed genes (absolute log2 fold-change > 1, adjusted p-value < 0.05) or promoter regions with an absolute CpG methylation difference > 10% are shown. Each quadrant is defined as follows: Q1, genes associated with increased expression and higher CpG methylation in Early; Q2, genes associated with increased expression and lower CpG methylation in Early; Q3, genes associated with increased expression and lower CpG methylation in Late, Q4, genes associated with increased expression and higher CpG methylation in Late. D Exemplary gene expression and CpG methylation patterns of select genes in each quadrant. Top: Q3, Increasing expression / decreasing promoter methylation; middle: Q2, Decreasing expression / increasing promoter methylation; bottom: Q4, Increasing expression / increasing promoter methylation. RNA-seq data is indicated by the dark grey lines and plotted on the y1-axis. WGBS data is indicated by the light grey lines and plotted on the y2-axis. 3.4 Discussion Understanding normal T-cell biology can provide context for what may go awry during disease. Experimentally tractable systems that model human T-cell differentiation provide essential tools for studying context-specific effects of various genetic alterations. The benefits of using in vitro-derived cells to model elements of T-cell development are threefold: (1) These cultures yield large numbers of T-cells which can be harvested at any stage during differentiation, (2) T-cells are generated in a reproducible manner based on RNA-seq expression, and (3) cells obtained from this culture system are amenable to genetic manipulation. The first and third points are particularly salient when considering alternative T-cell sources, such as human thymocytes, in which immature T-cell subsets can be difficult to capture in appreciable number and are more difficult to manipulate in vitro. In this chapter, I employed the use of flow cytometric profiling and molecular profiling (i.e., by RNA-seq and whole-genome bisulfite sequencing) to characterize an established feeder-free system for modeling the development of T-cells. Unlike the “gold standard” OP9-DL1 culture system, the conditions described here are chemically defined and do not require the use of stromal feeder cells, which may increase inter-experimental reproducibility. When compared to human thymocyte populations, cells derived from the feeder-free system recapitulated aspects of T-cell development based on gene expression profiles and on functional evidence such as TCR rearrangement and restriction of the myeloid lineage. Based on our RNA-seq analysis, the in vitro-derived T-cell subsets undergo TCR rearrangement in a predictable manner, marked by the emergence of TRG rearrangement in early T-cell subsets and 76 eventual TRA rearrangements arising in CD4+CD8+ double positive cells. The in vitro-derived T-cell subsets also showed similar gene expression trends compared to human intrathymic populations, displaying a predictable increase in expression of genes important for T-cell function among mature T-cell subsets, such as RAG2, and PTCRA, and a concomitant decrease in genes associated with stem/progenitor cells or alternative lineages, such as LMO2, LYL1, MEF2C, FLT3, SPI1, MPO, and ELANE. Altogether this suggested that, in these cultures, differentiation is associated with initiation of a T-cell program and downregulation of a progenitor program. When we performed pathway analysis of genes significantly upregulated in immature T-cell subsets (i.e., subsets collected after 7 and 10 days of in vitro culture), we noted enrichment of several pathways that pertain to myeloid cell differentiation. This may suggest that myeloid and T-cell developmental programs are at one point simultaneously available in immature T-cells, but myeloid programs become deactivated or repressed as cells later upregulate genes important for specialized T-cell function. This has been long understood by several groups who have focused on lineage potential in human and murine T-cell progenitors43,97,101,107,325,326 (reviewed in104). Yet, the mechanisms underlying repression of the myeloid lineage in developing T-cells remain poorly understood and require further investigation. DNA methylation is a major mechanism of gene repression, but few studies have examined the incipient DNA methylation changes in early human T-cell differentiation196. To interrogate these changes, we performed WGBS on the same T-cell subsets as those collected for RNA sequencing. Our exploratory analysis suggested that although the majority of CpGs do not change in DNA methylation across each subset, promoter methylation most prominently increases among a selection of genes whose expression is significantly downregulated during development. Among these genes include those associated with alternative lineages, such as TBXA2R, CD179B, CLNK, and SPI1. When focusing on genes that were significantly upregulated during development, an inverse correlation between gene expression and promoter methylation less clear, which could suggest that DNA demethylation may not play as large a role in gene activation. This is consistent with the general finding across several tissue types that DNA methylation is primarily – though not strictly – associated with gene repression, and only to a lesser extent with gene activation147,131–138. These results present a model in which DNA methylation may be among the repressive mechanisms which enforce T-lineage restriction through inhibition of non-T-lineage identities. In 77 line with this, previous studies have indicated that inhibition of DNA methyltransferases in various murine T-cell subsets through the use of 5-aza-2’-deoxycitadine result in a modest increase in myeloid, as opposed to T-lineage, output192. This outcome was most evident in immature T-cell stages (i.e., DN1 and DN2 subsets), but not in the mature DN3 subset. This could suggest that DNA methylation may play an important role in maintaining T-lineage fidelity. To provide functional evidence that the molecular profiles that we observed correlated with a loss of stem/progenitor features, I harvested T-cells populations at different timepoints throughout differentiation on the basis of CD7, CD44 and CD1a expression. Each T-cell subset was then transferred into conditions supportive of myeloid-lineage growth. The results from these experiments revealed a reduction in monocytic and granulocytic output which became most pronounced upon loss of CD44dim expression, which in human thymocytes has been previously identified as a proximal marker of T-lineage commitment95,107. The results from this experiment suggest that myeloid lineage potential is reduced upon CD44dim loss. Whether this also correlates with T-cell commitment in these cultures remains to be determined; T-cell commitment implies the exclusion of all alternative fates, including B-cell, erythroid, NK, and DC lineages, but our experiments assess only myeloid lineage potential. At least in murine and human T-cell development, divergence of B-lymphoid, dendritic, and erythroid lineages is thought to occur prior to the divergence of myeloid and NK fates107 (reviewed in104,324), which by extension might imply that B, erythroid, and DC fates have already been excluded in these cultures by the time of CD44dim loss. Nonetheless, a more comprehensive analysis of alternative (non-T) lineage potential will be required in order to directly assess T-cell commitment and restriction. Overall, the findings discussed here provide a blueprint of normal T-cell development modelled in vitro and serve as a framework within which to dissect the effect of genetic perturbations on T-cell differentiation and lineage specification. From the results of our expression profiling and DNA methylation analyses, we observed that increased methylation throughout development correlated with decreased expression of genes operative in alternative lineage programs, indicating that DNA methylation might be an important regulator of the T-cell lineage. This is congruent with previous studies which suggest that the level of DNA methylation could play a role in regulating T-lineage vs myeloid lineage development191,192. The main family of enzymes which are involved in establishing and maintaining DNA methylation are the 78 DNA methyltransferases (DNMTs), and include DNMT3A, DNMT3B, and DNMT1. Accordingly, the focus of the next chapter is to examine the effect of genetic perturbation of DNTM3A, one of two de novo DNA methyltransferases, on T-cell development and lineage regulation. 79 Chapter 4: Characterization of the functional consequences of DNMT3A loss in normal development and in T-ALL 4.1 Introduction The factors that underlie T-lineage restriction and differentiation are highly complex, and hinge on the interplay of epigenetic regulators and transcription factors to orchestrate the sequential elimination of alternative fates (reviewed in327). In normal development, the role of these elements can be highly context-specific and may act to coordinate repression of factors associated with stem/progenitor cells and activation of lineage-specific programs. Failure to properly shut off developmental programs operative in immature T-cells can manifest in diseases such as T-ALL, where alterations in epigenetic patterning or aberrant transcription factor expression may co-operate to circumvent otherwise highly regulated cellular processes such as proliferation, apoptosis, and differentiation. DNA methylation is an epigenetic modification that is often associated with gene repression147,132–138. Furthermore, mutation of the de novo DNA methyltransferase, DNMT3A, is frequently observed in hematological malignancies, including T-ALL. The role of DNMT3A in T-cell development and progression to disease, however, is not wholly understood. Systematic profiling of disease-associated DNMT3A mutations suggest that the majority lead to protein loss-of-function267. Among T-ALL cases, DNMT3A mutations are associated with older age257 and are enriched in ETP-ALL243–245, a disease subtype commonly characterized by unique co-expression of lymphoid and myeloid/progenitor markers, and which has been previously associated with poor prognosis226. Although the frequency of purported loss-of-function mutations would suggest that DNMT3A plays tumor suppressor roles in pathogenesis, the functional implications of DNMT3A loss in T-ALL remains mostly unexplored. At least in the context of constitutively active Notch1, conditional Dnmt3a knockout in murine HSCs has been previously associated with accelerated T-ALL development in mice and hypomethylation of enhancers relative to Dnmt3a-wildtype disease195,268. Although early T-cell arrest is only rarely observed upon Dnmt3a knockout, leukemic blasts display upregulated gene expression signatures associated with the myeloid lineage program, including Elane, Mpo, Csf2rb and Gfi1b195,269. This set of observations might suggest that loss of DNMT3A is associated with reactivation of the myeloid program. Indeed, a connection 80 between T-cell leukemia, myeloid leukemia, and Dnmt3a loss has been previously reported; in these studies, the degree of Dnmt3a loss (i.e., heterozygous vs complete knockout) in murine HSCs influenced the progression to myeloid vs T-cell leukemia in the context of FLT3-ITD mutation, where heterozygous Dnmt3a loss was associated with a higher frequency of myeloid leukemia, and complete Dnmt3a loss resulted in both T-cell and myeloid leukemia269. These results indicate that modulating the level of Dnmt3a expression can guide disease outcome (i.e., AML vs T-ALL) in the context of murine disease models. In this chapter, I embellish and extrapolate upon these previous studies to further understand the role of DNMT3A in the context of normal human T-cell development. The aforementioned observations might posit a role of DNMT3A in influencing T-lymphoid and myeloid lineage decisions. Nonetheless, direct characterization of DNMT3A as a regulator of lineage choice in human T-cells remains to be seen. To this end, I investigate the role of DNMT3A in T-lineage restriction and exclusion of myeloid potential through knockdown and knockout experiments in in vitro-derived T-cells. I also explore the role of DNMT3A in the context of established leukemias through knockdown experiments in T-ALL cell lines. The results of these experiments provide a new perspective of the functional consequence of DNMT3A loss in T-cell development and may lend insight into novel pathogenic mechanisms in DNMT3A-mutated T-ALL. 4.2 Materials and methods 4.2.1 Cell culture and conditions 4.2.1.1 Primary cells Human umbilical cord blood samples were obtained from Stem Cell Assay (Terry Fox Laboratory, BC Cancer Research Centre, Vancouver, British Columbia, Canada) with informed consent from donors. These samples contained pools of cord blood samples from multiple donors (2-10 individual donors). A list of cord blood pools accessed, and the experiments performed for each cord blood pool, can be found in Appendix A. 4.2.1.2 Cell lines HEK293T cells were obtained from John Aster (Brigham & Women’s Hospital, Boston) and cultured in DMEM (Thermo Fisher) supplemented with 10% heat-inactivated FBS (Sigma Aldrich) incubated at 37C 81 and 5% CO2. Cells were passaged at 80-90% confluency. The RPMI-8402 human T-ALL cell line was cultured in RPMI 1640 Media (Thermo Fisher) supplemented with Penicillin/Streptomycin (Stem Cell Technologies), 1mM sodium pyruvate (Thermo Fisher), and 10% heat-inactivated FBS (Sigma Aldrich). All cell lines were authenticated by STR genotyping and confirmed mycoplasma negative. 4.2.1.3 In vitro development of T-cells Cells were cultured in vitro using the StemSpan T cell Generation Kit (STEMCELL Technologies) incubated at 37C and 5% CO2. Briefly, non-treated culture dishes were coated with StemSpan Lymphoid Differentiation Coating Material (100X), diluted 1:100 in PBS (Manufacturer) and left overnight at 4C, or at room temperature for 2 hours, before the coating material was removed and plates were rinsed with PBS. Cord blood samples were resuspended in StemSpan Lymphoid Progenitor Expansion Supplement (10X), diluted 1:10 in StemSpan Serum-Free Expansion Medium II (SFEM II, STEMCELL Technologies), or in StemSpan T cell Progenitor Maturation Supplement (10X), diluted 1:10 in SFEM II (STEMCELL Technologies), according to manufacturer’s protocol. For the media transfer experiments, T-cells were sorted into non-treated culture dishes containing StemSpan Myeloid Expansion Supplement (100X) or StemSpan Myeloid Expansion Supplement II (100X), diluted 1:100 in StemSpan SFEM II (STEMCELL Technologies). 4.2.2 Cell proliferation assays 24h prior to cytokine stimulation, RPMI 8402 cells were cultured in reduced serum conditions (0.5% FBS). Proliferation was assessed by sorting GFP+ cells into 96 well plates containing RPMI 1640 Media supplemented with antibiotics and 100ng/L rhGM-CSF, 100ng/L rhG-CSF, or 100ng/L rhM-CSF (Stem Cell Technologies). After 10 days of culture, GFP+ cell counts were determined by the addition of Accuchek Counting Beads (Thermo Fisher) at sample acquisition. 4.2.3 Media transfer experiments Cells were sorted at various cell concentrations according to the phenotypes indicated into non-treated 96-well round bottom plates (Falcon). For all experiments, 3-8 replicates were performed for each 82 shRNA/sgRNA construct, media condition, and cell concentration. For populations cultured in conditions supportive of granulocytic cell expansion, cells were sorted into wells containing StemSpan Myeloid Expansion Supplement (100X), diluted 1:100 in StemSpan SFEM II (STEMCELL Technologies). For populations cultured in conditions promoting monocyte cell expansion, cells were sorted into wells containing StemSpan Myeloid Expansion Supplement II (100X), diluted 1:100 in StemSpan SFEM II (STEMCELL Technologies). For populations cultured in conditions supportive of T-cell differentiation, cells were sorted into wells coated with StemSpan Lymphoid Differentiation Coating Material (100X), diluted 1:100 in PBS, containing StemSpan Lymphoid Progenitor Expansion Supplement (10X), diluted 1:10 in StemSpan SFEM II (STEMCELL Technologies). Data analysis was performed using R (version 4.1.1) using the elda package (version 1.4.36) using default parameters except for “observed”, which was set to “TRUE”. 4.2.4 Lentiviral vectors and transduction Lentiviral constructs The TRC2-pLKO.5-puro vectors containing shRNA targeting DNMT3A (shDNMT3A-54, shDNMT3A-55, shDNMT3A-56, and shDNMT3A-58; Appendix E) were obtained from The RNAi Consortium (Broad Institute) and were distributed to us by the Center for High-Throughput Biology (University of British Columbia, Vancouver, Canada). The non-silencing control shRNA was gifted from David Sabatini (Addgene #1864)328. The PGK-PuroR cassette was removed from all constructs and replaced with a PGK-GFP cassette, and the cloned constructs were verified by Sanger sequencing. Lentiviral transduction Cells were transduced with high-titre replication-incompetent lentivirus generated by transient co-transfection of HEK 293T cells using polyethyleneimine HCl MAX (Polysciences) with second-generation packaging and envelope vectors (pCMVΔR8.74, Addgene #22036; pCMV-VSV-G, Addgene #8454; and pRSV-Rev, Addgene #12253) as previously described329, along with TRC2-pLKO.5-puro expression vectors containing shRNA targeting a scrambled non-silencing control or targeting DNMT3A. Supernatants from transfected HEK 293T cells were collected 48h post-transfection and concentrated by ultracentrifugation at 25,000 rpm for 90 minutes at 4C in a Beckman SW32Ti rotor. In vitro-derived T-cells 83 and T-ALL cell lines were transduced by adding lentiviral supernatant and 4g/mL polybrene and by centrifuging cells at 300 x g at 32C for 2 hours. 4.2.5 CRISPR knockout experiments Guide RNAs targeting the catalytic loop of DNMT3A (15R, 54R, 59R; Appendix E) or the EMX locus330 were designed using CRISPOR331 and were obtained from Integrated DNA Technologies (IDT). Recombinant S. pyogenes Cas9 nuclease (Alt-R® S.p. HiFi cas9 Nuclease V3) was obtained from IDT. Assembled Cas9 RNP complexes were electroporated into cells using the Neon Transfection System (Thermo Fisher Scientific), at 1600V with 3 x 10ms pulses. The frequency of insertions and deletions (indels) was determined by PCR amplification of the region targeted by the DNMT3A guides followed by Sanger sequencing as described below. 4.2.6 Analysis of CRISPR knockout cells Genomic DNA was extracted from cell pellets by lysing in 1M Tris-HCL, 0.5M EDTA, 10% SDS, 5M NaCl, and 1mg/mL Proteinase K. Cells were lysed overnight at 60C. DNA was precipitated with the addition of ice-cold EtOH/NaCl followed by incubation for 30 minutes. Samples were then centrifuged at 5,000 x g for 20 minutes. Pellets were washed twice with cold 70% EtOH and air dried. PCR amplification of the DNMT3A locus for Sanger sequencing was performed using Q5 High-Fidelity 2X Master Mix (NEB) and PCR primers (final concentration = 0.5M) as listed in Appendix E and according to manufacturer’s protocol. To determine the indel frequency, Sanger sequencing traces were deconvoluted using Inference of CRISPR Edits (ICE)332. 4.2.7 Flow cytometry and cell sorting 4.2.7.1 Intracellular Phospho-flow cytometry RPMI-8402 cells were cultured in RPMI 1640 media (Thermo Fisher) as indicated above. Prior to stimulation with cytokines, samples were starved for 24h in culture media without FBS. Samples were stimulated with 100ng/L rhGM-CSF, 100ng/L rhG-CSF, or 100ng/L rhM-CSF (Stem Cell Technologies) 84 in RPMI 1640 media for 15 minutes in a 37C water bath. Cells were stained with LIVE/DEAD Fixable Yellow Dead Cell Stain (Thermo Fisher) for 15 minutes and subsequently fixed with 1.5% paraformaldehyde (PFA) for 15 minutes at room temperature, followed by permeabilization with pre-chilled 100% methanol for 10 minutes at -20C. In separate panels, cells were stained with AlexaFluor647-conjugated anti-Phospho-Erk1/2 (Thr202/Tyr204) antibodies (clone 19G2, Cell Signaling Technologies), AlexaFluor647-conjugated anti-Phospho-Stat3 (pY05) antibodies (clone 4/P-STAT3, BD Biosciences), AlexaFluor647-conjugated anti-Phospho-Stat5 (pY694) antibodies (clone 47/Stat5, BD Biosciences), in addition to BV421-conjugated anti-Human CD115, BV421-conjugated anti-Human CD116, and BV421-conjugated anti-Human CD114 (BD Biosciences). Equivalent amounts of isotype control antibodies were used. Samples were acquired on a LSRFortessa cytometer (Becton Dickinson). Data was analyzed using FlowJo software (Tree Star) and in R (version 4.1.1). Data was visualized using ggplot2 (version 3.3.6). 4.2.7.2 Flow cytometry Samples were stained with fluorochrome-conjugated antibodies as listed in Appendix B. Data was acquired on the BD LSRFortessa Cell Analyzer (BD Biosciences) and the FACSymphony (BD Biosciences). To obtain cell numbers for each sample, AccuCheck counting beads (Invitrogen/ThermoFisher Scientific) were added prior to sample acquisition. In experiments where samples were acquired over several weeks, per-channel normalization was performed by staining BD CompBead Compensation Particles (BD Biosciences) with the antibody mastermix. 4.2.7.3 Cell sorting Samples were stained with fluorochrome-conjugated antibodies as listed in Appendix B. Cell sorting was performed on ice using the BD FACSAria II (BD Biosciences) and the BD FACSAria Fusion (BD Biosciences). 4.2.7.4 Flow cytometry data analysis Data was analyzed using FlowJo v10 software (Tree Star) and in R (version 4.1.1) using custom scripts. Single-cell data collected from 16 dimensions (FSC-A, SSC-A, CD4, CD8, CD7, CD5, CD1a, CD3, 85 CD13, CD33, HLADR, CD15, CD11b, CD44, CD34, and CD38) were reduced to 2 dimensions using the R package ‘Monocle3’ (version 1.0.0)318 or ‘umap’ (version 0.2.28). Scale data was exported from FlowJo and was subsequently transformed using the arcsinh function from the ‘flowCore’ package (version 2.6.0)319 in R (version 4.1.1) using the cofactor set to 150. For experiments where data was acquired on separate days, per-channel normalization was performed prior to archsinh transformation. Sample normalization was performed by collecting a bead standard stained with the antibody master mix at each timepoint. For generation of UMAP projections, data were generated using the predict function in ‘umap’ (version 0.2.28). 4.2.8 Whole-cell lysate extraction Cell pellets from RPMI 8402 cells were resuspended in RIPA buffer supplemented with activated Na3VO4, PMSF Protease inhibitor, and Protease Inhibitor Cocktail (Calbiochem). Pellets were solubilized for 10 minutes on ice and centrifuged at 15,000 x g for 10 minutes at 4C. Cleared lysates were quantified using the Pierce BCA Protein Assay Kit (Thermo Fisher). Lysates were heated at 70C for 10 minutes prior to separation by gel electrophoresis. 4.2.9 Western blotting Whole cell protein lysates were separated by gel electrophoresis on SDS-PAGE gels (5%/8% acrylamide stacking/separating layers) at 80V for ~30 minutes followed by 100 V for ~1 hour. Proteins were transferred to a methanol-activated PVDF membrane for 90 minutes at 70V. Membranes were blocked with 5%(w/v) skim milk in TBS with 0.1% Tween 20 for 1 hour at room temperature. Membranes were probed with anti-DNMT3A antibodies (1:1000 dilution, clone D23G1, Cell Signaling Technologies), and anti--actin antibodies (1:10000 dilution, AC-74, Sigma Aldrich) followed by incubation with secondary antibodies for one hour at room temperature. Chemiluminescent detection of the HRP-conjugated secondary antibodies was performed using ECL Western Blotting Substrate (Thermo Fisher). 86 4.3 Results 4.3.1 DNMT3A loss enhances the outgrowth of myeloid-like cells We hypothesized that DNMT3A knockdown might delay or exclude T-lineage restriction in developing T-cells. This was motivated by three main observations: first, that inhibition of DNA methylation in murine progenitors results in myeloid skewing at the expense of lymphoid output192; second, that lower global DNA methylation levels are associated with myeloid commitment as opposed to T-lineage commitment191,192; and third, that DNMT3A is frequently mutated in a subtype of T-ALL with myeloid characteristics245. Our previous experiments discussed in Chapter 3 suggested that, at least in a genetically unperturbed state, in vitro-derived CD7+CD44- T-cells cultured for 14 or 28 days exhibit a reduced frequency of cells capable of initiating growth in conditions supportive of granulocytic or monocytic lineages (referred to throughout as “myeloid”) but not in conditions supportive of the T-lineage (Figure 3.5, Figure 3.7). In contrast, no difference in initiation frequency was observed in CD7+CD44+ T-cells obtained from the same cultures when cultured in myeloid conditions (Figure 3.5). These results, consistent with previously reported findings95,100, suggest that CD44 loss also correlates with lineage restriction in in vitro developed T-cells. To assess if DNMT3A loss might alter this restriction point, I performed short hairpin RNA (shRNA)-mediated knockdown of DNMT3A in in vitro differentiating T-cells by lentiviral transduction of 4 different shRNAs tagged with green fluorescent protein (GFP), compared to a scrambled control shRNA. shRNAs targeting DNMT3A showed a reduction of DNMT3A protein by western blot analysis in a human T-ALL cell line (Figure 4.1A). Transduced T-cells were cultured for a total period of 28 days in culture, at which point CD7+CD44-GFP+ cells were harvested and sorted into media supportive of either T-cell, granulocytic, or monocytic lineages (Figure 4.1B,C). Growth was assessed after 10 days of culture and visually inspected under a microscope, and wells were classified as positive or negative upon the basis of cell growth. To verify that classification by visual inspection was accurate, in one experiment, all wells classified as positive or negative were harvested and no viable events were detected by flow cytometry in wells classified as negative by visual inspection (data not shown). Consistent across 4 independent trials, I observed that knockdown of DNMT3A conferred the ability of CD7+CD44-GFP+ cells to grow in media supportive of myeloid lineages (Figure 4.1D), with the proportion of positive wells increasing as a function of the number of input cells. No positive wells were observed in 87 conditions transduced with scrambled shRNA, overall suggesting that DNTM3A knockout correlated with a greater proportion of “T-lineage restricted” CD7+CD44- cells capable of proliferating under myeloid growth conditions. We next considered if growth of CD7+CD44-GFP+ cells in myeloid conditions also corresponded to a change in surface marker expression. The immunophenotype of positive wells was assessed using a 14-colour panel containing T- or myeloid-lineage markers, including CD4, CD8, CD7, CD5, CD1a, CD13, CD33, HLADR, CD15, CD11b, CD44, CD34, and CD38. UMAP analysis revealed two distinct groups that clustered according to the culture conditions (i.e., monocytic, granulocytic, or T cell media; Figure 4.2). When immunophenotypes were parsed in each cluster, we observed that cells cultured in T-cell conditions for the entirety of the experiment maintained expression of T-cell markers such as CD7 and CD5, whereas DNMT3A knockdown cells cultured in myeloid conditions expressed myeloid markers such as CD13 and CD33, and had relatively lower expression of CD7 and CD5, thus appearing “myeloid-like” in immunophenotype. Further segregation of the myeloid-like cell cluster could be achieved on the basis of CD15 expression, which resolved the monocytic and granulocytic lineages. We wondered how the myeloid-like T-cells might compare to bona fide myeloid cells. To this end, we derived monocytic and granulocytic populations in vitro from CD34+ cord blood and visualized them together with the myeloid-like T-cell populations (Figure 4.3A). Based on UMAP visualization, we observed that DNMT3A knockdown cells cultured in myeloid conditions clustered together with bona fide myeloid cells (Figure 4.3B). The myeloid-like populations expressed high levels of markers associated with the myeloid lineage, such as CD13 and CD33 (Figure 4.3C). Nonetheless, these populations still expressed higher levels of T-cell markers relative to bona fide myeloid populations, including CD4, CD3, CD5, CD7, and CD1a. Hierarchical clustering placed the myeloid-like cells at a state intermediate between bona fide T-cell and myeloid populations, possibly suggesting that the myeloid-like populations retain vestiges of a former T-cell phenotype (Figure 4.3D). Altogether, these results suggest that DNMT3A knockdown in “T-lineage restricted” CD7+CD44- cells may increase the proportion of cells capable of proliferating and differentiating in myeloid conditions. 88 Figure 4.1 shRNA-mediated knockdown of DNMT3A in T-cells correlates with increased growth in myeloid culture conditions. A Western blot analysis of DNMT3A protein levels upon shRNA-mediated DNMT3A knockdown (54, 55, 56, 58) compared to a scrambled control (Scr). B-actin was used as a loading control. B General experimental design of media transfer experiments. T-cells were derived in vitro from CD34+ cord blood (CB) and lentivirally transduced at day 14. CD7+CD44-CD1a- populations were sorted into conditions supportive of T-cell or granulocytic/monocytic lineages at day 28. Cells were scored for growth and harvested for flow cytometry at day 38. C Representative flow cytometry plot indicating the levels of CD44 and CD1a expression within the CD7+ subset after 28 days of culture. The CD44-CD1a- population (circled in red) was sorted for media transfer experiments at day 28. D Top: Representative images of cells after culture in the indicated conditions at time of harvest. Bottom: The percent of positive wells for each experiment as a function of the number of input cells per well. Each dot indicates a different experiment and is coloured according to the shRNA. The yellow regression line summarizes all constructs targeting DNMT3A. The grey regression line summarizes the scrambled control. A minimum of 3 replicates were collected for each shRNA, media condition, and cell dilution. 89 Figure 4.2 14-parameter UMAP distribution of flow cytometric immunophenotypes of DNTM3A knockdown T-lineage restricted cells after transfer into T-cell or myeloid growth conditions. A Cells were cultured in T-cell conditions for 28 days and subsequently transferred into granulocytic, monocytic or T-cell conditions for an additional 10 days. The UMAP distribution indicates cells harvested after 10 days of growth in the indicated media conditions (red, burgundy, or grey). B The expression of select markers which discriminate between the T-cell or myeloid lineage are shown and are coloured according to relative staining intensity. 90 Figure 4.3 T-lineage restricted cells transferred into myeloid growth conditions adopt an intermediate T-lymphoid/myeloid phenotype. A General experimental design pertaining to analyzed populations. Unmanipulated CD34+ cord blood (CB) cells were cultured in T-cell conditions and harvested at day 28 (“T-cell”; grey), or cultured in myeloid conditions and harvested at day 10 (“Myeloid”; burgundy). DNMT3A knockdown T-cells were cultured in T-cell conditions for 28 days, at which point the T-lineage restricted population was sorted into myeloid growth conditions for an additional 10 days (“Myeloid-like”; orange). B 14-parameter UMAP of flow cytometric immunophenotypes displayed as density distribution of CD34+ cord blood derived T-cells (grey) or CD34+ cord blood derived myeloid cells (dark red), together with DNMT3A knockdown T-cells transferred into myeloid growth conditions (“myeloid-like”; orange). C Violin plots indicating the relative staining intensity of select lineage-discriminating surface markers. M = myeloid, M-like = myeloid like, T = T-cell. 91 D Hierarchical clustering of myeloid, myeloid-like and T-cell populations based on the median expression of 14 surface markers. Myeloid growth conditions were segregated into granulocytic or monocytic culture conditions. 4.3.1.1 The timing of DNMT3A knockdown influences the frequency of myeloid-like cell growth The developmental context in which DNMT3A is lost might influence the frequency of T-cells that retain myeloid potential. That is, knockdown of DNMT3A in an early stage of development may have a different outcome than DNMT3A knockdown in a later stage of T-cell development. This supposition is based on the observation that de novo DNA methylation changes primarily occur during T-cell commitment196, which possibly indicates that methylation is critical at this stage in T-cell development. To address this possibility, in vitro-derived T-cells were transduced with shRNA targeting DNMT3A after 7 or 14 days of culture. After a total of 28 days, the CD7+CD44-GFP+ population was sorted at various cell dilutions into media supportive of T-cell, granulocytic or monocytic lineages, as before. Among cells cultured in myeloid conditions, a significant increase in well-initiating frequency was observed in cells transduced after 7 days of culture in comparison to cells transduced after 14 days in culture (Figure 4.4). The difference in well-initiating frequency was not apparent in cells that were maintained in T-cell conditions throughout the duration of the experiment, altogether suggesting that the developmental context of DNMT3A loss may influence the frequency of CD7+CD44- T-cells that are capable of proliferating in myeloid conditions. Altogether, our results may suggest that DNMT3A knockdown delays or disrupts T-cell lineage restriction and confers the ability of T-cells to proliferate and differentiate under myeloid growth conditions. This effect was most pronounced when knockdown was performed after 7 days of in vitro differentiation, as opposed to 14 days, which could suggest that expression of DNMT3A is crucial for enforcement of the T-cell lineage during the first 2 weeks of in vitro differentiation. We considered that this may be through its role as a de novo DNA methyltransferase; our analysis of CpG methylation changes during in vitro T-cell differentiation (discussed in Chapter 2) suggested that promoter methylation notably increases in regulatory regions of the myeloid “master regulator” SPI1 beginning around 10 days in culture (Figure 3.12). We suspect that DNMT3A knockdown at day 7 may prevent these methylation events from occurring, however further experiments which directly link DNTM3A with de novo methylation over this region are needed. 92 Figure 4.4 Stage-specific DNMT3A knockdown influences the proportion of T-lineage restricted myeloid-like cells. A Experimental design of media transfer experiments. The jagged arrow indicates the timepoint of lentiviral transduction. Cells were maintained in T-cell media conditions for a total of 28 days prior to sorting into myeloid growth conditions. All wells were harvested for flow cytometry at day 38. B Limited dilution growth assays measuring the frequency of growth in sorted T-cell populations in the indicated culture conditions. Each dot indicates the proportion of positive wells with a minimum of 3 replicates for each tested shRNA, media condition, and cell concentration. Inf = infinity. 4.3.1.2 Selection for DNMT3A loss occurs in myeloid-like populations To further validate our findings, we knocked out DNMT3A in developing T-cells by electroporating Cas9 protein complexed with guide RNA designed to target the catalytic loop of DNMT3A (referred to throughout as sgDNMT3A 59R, 54R and 15R), or a guide designed to target the control gene, EMX1330 (Appendix D). Cells were electroporated at an early timepoint in in vitro differentiation, and as in previous experiments, the CD7+CD44- cells were sorted into pro-myeloid media after a total of 28 days of culture in 93 media supportive of T-cell differentiation (Figure 4.5). Among the CD7+CD44- population, cells expressing CD13 or CD33 were removed during cell sorting to exclude the hematopoietic progenitor or myeloid compartments333. At the end of the experiment, wells were scored as positive or negative based on cell growth, as described for previous experiments. To determine the indel frequency at the DNMT3A locus, we also harvested genomic DNA for Sanger sequencing from each of the positive wells. Genomic DNA from bulk cultures was also collected at various timepoints before cells were transferred into myeloid growth conditions (day 28, and for some cultures, day 7) to compare changes in indel frequency over time. Overall, we observed a gradual increase in indel frequency between day 7 and day 28, which pertains to the period in which cells were cultured in T-cell differentiation media (indicated by filled circles) (Figure 4.5). This was punctuated by a rapid increase in indel frequency upon transfer into myeloid conditions (indicated by open circles). The majority of indels (~70%) were predicted to result in a frameshift mutation. In instances where the indel frequency at day 28 was already high (i.e., sgDNMT3A 59R), the increase in indel frequency upon transfer to myeloid conditions was less apparent; this is likely due to the indel frequency approaching the upper limit of detection. Consistent with our previous knockdown experiments, cells electroporated with the control guide did not grow in myeloid conditions. Altogether, these results suggest that DNMT3A loss may generally enhance cell proliferation based on a gradual increase in indel frequency when cultured in T-cell differentiation conditions. There is, however, a much stronger selection pressure for DNMT3A loss among the T-lineage restricted population that is capable of growth in myeloid conditions. These findings, which are in accordance with our previous shRNA knockdown experiments, provide further evidence that DNMT3A loss may alter myeloid lineage restriction in T-cells. 94 Figure 4.5 Selection for DNMT3A loss occurs in CRISPR/Cas9 edited populations upon transfer to myeloid conditions Cells were electroporated with RNPs targeting DNMT3A or EMX1 (control) in bulk and the T-lineage restricted population was sorted into multiple wells containing myeloid growth media at day 28. Wells were harvested at day 38 for flow immunophenotypic profiling and genotyping to assess DNMT3A indel frequency. Filled dots indicate the DNMT3A indel frequency of bulk cultures cultured in T-cell conditions. Open circles indicate the DNMT3A indel frequency of individual wells after culture in myeloid growth conditions. 4.3.2 DNMT3A knockout does not demonstrably alter T-cell differentiation We considered that the apparent T-cell commitment defect induced by DNMT3A knockdown and knockout could instead be rationalized by a differentiation delay in DNMT3A-depleted cells. To address this concern, cells were electroporated with a guide targeting DNMT3A (sgDNMT3A 59R) or the control gene EMX1330, and were subsequently single-cell sorted (Figure 4.6A; referred to as “DNMT3AWT”). Once a clonally expanded population was achieved, genomic DNA was collected for Sanger sequencing at day 28 95 for each single-cell-derived clone. A portion of each clonal population was also harvested for flow cytometric profiling. We then compared the phenotypes of clonally derived populations to unmanipulated, bulk in vitro-derived T-cells, which were collected at the same timepoint (day 28). As multiple markers were used to interrogate the stage of T-cell development (including CD5, CD7, CD38, CD44, CD1a, CD4, CD8, and CD3), dimensionality reduction was first performed using UMAP to visualize the unmanipulated T-cells in 2D space. k-means clustering was performed to classify cells based on phenotype. Accordingly, each cell was grouped into one of four major clusters (Figure 4.6B). Each cluster could be roughly ascribed to developmental stages throughout T-cell differentiation based upon the pattern of CD marker expression (Figure 4.6C,D). The UMAP clusters generated from unmanipulated cells (i.e., DNMT3A wildtype) were then used as a developmental schema upon which to project the single-cell-expanded CRISPR clones (Figure 4.6E). When comparing the cluster composition from each clonal population grouped by genotype (i.e., DNMT3A knockout, heterozygous, or wildtype), we did not observe a significant difference in the percent of cells in each cluster (Figure 4.6F); DNMT3AKO and DNMT3AHET cells displayed a spectrum of differentiation states that did not differ demonstrably from control cells, suggesting that, on a per-cell basis, there is no clear differentiation defect upon DNMT3A loss. These results are in accordance with previous studies, in which Dnmt3a knockout in murine HSCs did not demonstrate a consistent T-cell differentiation arrest195. 96 Figure 4.6 T-cell differentiation in single-cell-derived CRISPR clones. A General experimental design and analysis. Cells were electroporated with RNPs targeting DNMT3A or EMX1 (control) and were subsequently single-cell sorted. Wells were scored as positive (cell growth was observed) or negative (no cell growth was observed) after 28 days of culture. Positive wells were harvested for genotyping and flow cytometric profiling. Flow cytometric data was then projected into UMAP space defined by unmanipulated T-cells cultured in bulk for 28 days. B 8-parameter UMAP distribution of flow cytometric immunophenotypes of unmanipulated cells cultured in T-cell growth conditions for 28 days. k-means clustering was performed and resulted in 4 phenotypic clusters (1-4). C Expression of select CD markers. 97 D Dot plot indicating the expression of CD markers for each UMAP cluster. Frequency indicates the percent of cells positive for each CD marker based on manual gating and is displayed by the size of each dot. E Single-cell-derived clones projected into the UMAP distribution defined by unmanipulated T-cells and segregated according to genotype (WT, wildtype; HET, DNMT3A+/-, KO, DNMT3A-/-). F Boxplot indicating the percent of genotyped cells in each k-means cluster. No significant differences were observed (One-way ANOVA). 4.3.3 Myeloid-like cell potential is retained in the G-CSF, M-CSF and/or GM-CSF receptor expressing population Although DNMT3A loss in the T-lineage restricted population correlated with enhanced growth in myeloid conditions, not every cell displayed this capability upon DNMT3A knockdown or knockout; the frequency of this population was quite rare (down to 1/100,000 cells) indicating an opportunity to further enrich for this subset of cells. We hypothesized that our population of interest may be enriched among cells expressing surface receptors that bind to cytokines present in the myeloid growth conditions. Most notably, this includes the granulocyte-, macrophage-, and granulocyte-macrophage colony stimulating factors (G-CSF, M-CSF, and GM-CSF, respectively), which promote the proliferation and differentiation of the myeloid lineage334. To determine if the expression of G-, M-, or GM-CSF receptors correlates with enrichment of our population of interest, cells were electroporated with RNPs which target DNTM3A, or the control guide which targets EMX1. After culture for 28 days in T-cell differentiation media, CD7+CD44-CD13-CD33- cells were sorted into myeloid conditions according to G-, M-, or GM-CSF receptor expression (Figure 4.7A). Among the receptor-expressing cells, I observed enrichment of T-lineage restricted cells that were able to grow in myeloid conditions, with a frequency of 1/300 – 1/3000 cells (Figure 4.7C). This number likely represents an underestimate of the true frequency, since the genotypes were not assessed prior to sort, and we expect that many input cells were unedited (i.e., DNMT3A wildtype) and therefore unlikely to grow. When we focused on the population lacking expression of the G-, M-, or GM-CSF receptors, we found a relative depletion of myeloid-competent cells (40-80 wells tested per sgRNA) (Figure 4.7B). Consistent with our previous experiments, growth was not observed in cultures electroporated with the EMX1 control. Altogether, our results suggest that the expression of the G-, M-, or GM-CSF receptor can enrich for T-lineage restricted cells capable of growing in myeloid media upon DNMT3A knockout. 98 A breadth of research suggests that there is an inverse correlation between DNA methylation and gene expression, and altogether implicate DNA methylation in gene repression131–138. We therefore hypothesized that DNMT3A loss during early T-cell development might result in impaired repression of genes encoding for the G-, M-, and GM-CSF receptors, ultimately leading to their increased surface expression. To determine if DNMT3A loss might increase the expression of these receptors at the protein level, we performed DNMT3A knockdown with shRNA targeting DNMT3A, or a scrambled control (Figure 4.8A, orange vs grey). Compared to control, no clear difference was observed in the level of expression of the G-, M-, or GM-CSF receptors, even when comparing the expression levels at the 99th percentile (Figure 4.8B, orange vs grey), suggesting altogether that DNMT3A knockdown does not demonstrably alter the expression levels of these receptors, nor the proportion of cells that express the receptors. These results were also consistent upon DNMT3A knockout with sgRNA (data not shown). I also examined the G-, M-, and GM-CSF receptor expression of DNMT3A knockout myeloid-like cells, which had been transferred into myeloid media at day 28, as indicated in blue (Figure 4.8A). These cells expressed markers consistent with the myeloid lineage, including CD13 and CD33, and displayed increased expression of all three cytokine receptors (Figure 4.8B, blue histograms). This result may indicate that, for a T-lineage restricted cell to grow in myeloid media conditions, there is selection for cells with high levels of receptor expression or for cells capable of eventually upregulating the G-, M-, or GM-CSF receptors upon stimulation. Consistent with our observation that DNMT3A knockout does not alter the expression of the G-, M-, or GM-CSF receptors at the cell surface, we found no increase in de novo DNA methylation within the promoter regions of genes encoding for the G- or M-CSF receptor throughout T-cell differentiation (Figure 4.9). Taken together, our results suggest that DNMT3A may not regulate the expression of the G-, M-, or GM-CSF receptors, and may instead act downstream of receptor activation. 99 Figure 4.7 Myeloid-like cells are enriched in the G-CSF, M-CSF, and GM-CSF receptor-expressing populations within T-lineage restricted compartment. A Representative flow plot indicating the G-CSF, M-CSF, and GM-CSF receptor expression in CD7+CD44-CD13-CD33- T-cells after 28 days of culture. B Limited dilution growth assays measuring the frequency of growth in the receptor negative population (G-, M-, and GM-CSFR-) in granulocytic or monocytic culture conditions after electroporation with RNPs targeting DNMT3A or EMX1 (control). C Limited dilution growth assays measuring the frequency of growth in the receptor dim-positive population (G-, M-, and GM-CSFRdim/+) in granulocytic or monocytic culture conditions after electroporation with RNPs targeting DNMT3A or EMX1 (control). Inf = infinity. 100 Figure 4.8 Analysis of the expression of G-CSF, M-CSF, and GM-CSF receptors by flow cytometry. A Experimental design and sample collection. DNMT3A knockdown or control T-cells were harvested for immunophenotypic profiling by flow cytometry after 28 days of culture in T-cell growth conditions (shDNMT3A, orange; control, grey) or for an additional 10 days in myeloid media conditions (blue). B Left: Contour plots indicating the expression of G-CSFR, M-CSFR and GM-CSFR by flow cytometric analysis; Right: Expression data plotted as histograms. The black line indicates the sample median. C Boxplots indicating the level of expression of G-CSFR, M-CSFR and GM-CSFR by flow cytometric analysis. The staining intensity is shown for populations in the 99th percentile of expression. NS = not significant. C 101 Figure 4.9 Levels of CpG methylation over the CSF3R and CSF1R loci. WGBS data indicating the levels of DNA methylation across the promoter region and gene body of CSF3R and CSF1R (encoding G-CSFR and M-CSFR, respectively). Tracks are coloured according to the sample collection time point. Within each track, the height of each bar indicates the level of CpG methylation for individual CpGs between 0 to 100%. CpG islands are indicated in grey boxes. The RefSeq gene annotation for each gene is displayed at the bottom of the tracks. 4.3.4 Knockdown of DNMT3A induces proliferation in response to G-, M-, and GM-CSF in human T-ALL cell lines It was perhaps surprising that a subset of lineage-restricted T-cells expressed the G-, M-, or GM-CSF receptors. To exclude the possibility that this population was an artifact of in vitro culture, we examined receptor expression in additional T-cell contexts including human post-natal thymus (PNT) and T-ALL cell lines. To limit possible contamination of progenitor populations within the PNT samples, analysis was restricted to CD45+CD3+lin- mature DP or CD4/CD8 SP T-cells. Across 2 PNT donors and 4 T-ALL cell lines analyzed, we observed a population of cells with dim positive expression of the G-, M-, or GM-CSF receptors, suggesting that this population is present in multiple T-cell contexts (Figure 4.10). Motivated by this finding, we wondered if DNMT3A knockdown in a T-ALL context might alter cellular response in myeloid growth conditions, as was observed in our previous experiments in in vitro-102 derived T-cells. To this end, the T-ALL cell line RPMI-8402 was lentivirally transduced with two GFP-tagged shRNAs targeting DNMT3A, or a scrambled control. Cells were cultured for a period of 2 weeks to allow for DNA methylation changes to occur before sorting the GFP+, G-, M-, GM-CSFRdim(+) population into base media, or media supplemented with G-CSF, M-CSF, or GM-CSF (referred to throughout as “myeloid cytokines”; Figure 4.11A). After 10 days, all wells were harvested and the number of GFP+ cells were counted. Compared to the scrambled control, DNTM3A knockdown in RPMI-8402 resulted in a significant increase in cell number when cultured in media supplemented with G- and GM-CSF, or M- and GM-CSF (Figure 4.11B). This was only evident among the G-, M-, or GM-CSFRdim(+) population. These results suggest that DNMT3A loss may enhance cellular response to myeloid cytokines in T-ALL cell lines, as measured by increased proliferation. As the mechanisms underlying enhanced proliferation in response to G-, M-, and GM-CSF in DNMT3A knockdown populations were unclear to us, we considered the possibility that DNMT3A knockdown might result in differential intracellular signaling response downstream of the G-, M-, or GM-CSF receptors. At least in the context of myeloid cells, activation of these receptors can lead to increased phosphorylation STAT3, STAT5, and ERK335–338. To determine if this was also true in DNMT3A knockdown T-ALL cells, we starved DNMT3A knockdown and control RPMI-8402 cells in serum-free media for 24 hours, then stimulated the cells with a cocktail containing G-, M-, and GM-CSF, or with serum-free media as an unstimulated control (Figure 4.12A). We then harvested cells to analyze the levels of phosphorylated STAT3 (pSTAT3), pSTAT5, and pERK. In response to cytokine stimulation, RPMI-8402 cells did not show consistent phosphorylation of ERK or STAT3, even when parsing cells according to receptor expression (Figure 4.12C,D). This suggests that ERK and STAT3 are not activated downstream of G-, M-, and GM-CSF stimulation in a T-ALL context. To our surprise, we did observe an increase in pSTAT5 expression, which was limited to the G-, M-, or GM-CSF receptor expressing population (Figure 4.12E). Notably, there were no discernable differences in STAT5 activation between DNMT3A knockdown and scrambled populations when comparing each population to the unstimulated control. Together, these results may indicate that cellular proliferation in response to G-, M-, and GM-CSF upon DNMT3A knockdown may be regulated instead at the level of transcriptional control, although it will also be of interest to determine if global levels of STAT5 protein are 103 altered upon DNMT3A loss. We suspect that DNMT3A knockdown might render certain loci more susceptible to transcriptional activation, perhaps as a result of DNA methylation changes which increase the accessibility of key target genes downstream of STAT5 activation. Although further experiments are required to address the possibility that DNMT3A regulates loci accessibility of downstream target genes, we hypothesize that a key downstream target of signal activation is SPI1, which encodes the myeloid “master regulator” PU.1. In some contexts, SPI1 upregulation has been observed downstream of the GM-CSF and M-CSF receptors, and PU.1 itself has also been linked to upregulation of the G-, M-, and GM-CSF receptors339–343, which may indicate a positive regulatory loop. In the context of certain hematopoietic progenitor types, PU.1 is important for cell proliferation and growth344,345, and in some T-ALL contexts may be essential for disease development346. Intriguingly, when we analyzed the expression of SPI1 in an RNA-seq dataset containing 129 T-ALL cases, we observed significantly higher expression of SPI1 among the T-ALL cases in which DNMT3A was mutated (Figure 4.13). Many of the mutations in DNMT3A were predicted to result in protein loss-of-function222. This may indicate that a regulatory mechanism exists between SPI1 expression and DNMT3A activity, although further experiments will be required to address these possibilities. 104 Figure 4.10 G-CSF, M-CSF, and GM-CSF receptor-expressing populations are present in human postnatal thymus and T-ALL cell lines. A Primary human thymus samples are gated on CD45+CD3+lin- populations as indicated. The expression of G-CSF, M-CSF and GM-CSF receptors are plotted for the CD4+ single positive (CD4 SP), CD8+ single positive (CD8 SP) and CD4+CD8+ double positive (DP) populations 105 B Expression of G-CSF, M-CSF, and GM-CSF receptors in T-ALL cell lines. Figure 4.11 Populations expressing the G-CSF, M-CSF or GM-CSF receptors expand in response to the addition of cognate ligands upon DNMT3A knockdown in T-ALL cells. A General experimental design. RPMI-8402 T-ALL cells were lentivirally transduced and cultured for 14 days prior to sort into conditions supplemented with G-CSF and GM-CSF (red) or M-CSF and GM-CSF (burgundy). Cells were harvested to obtain cell counts after 10 days of growth. B Numbers of transduced GFP+ cells among the receptor dim (left) or receptor negative (right) populations. (*, P < 0.05; ** P < 0.01; *** P < 0.001; **** P < 0.0001; NS, P > 0.05; Dunn’s post-hoc test, two-sided, BH multiple test correction). Each dot indicates an individual well. 106 Figure 4.12 STAT5 phosphorylation increases upon stimulation of T-ALL cells with G-CSF, M-CSF, and GM-CSF. A General experimental design. RPMI-8402 T-ALL cells were lentivirally transduced and cultured for 14 days prior to serum starvation for 24 hours. Cells were stimulated with a cocktail of G-CSF, M-CSF, and GM-CSF. The activation of phosphorylated intermediates was assessed by flow cytometry. 107 B Expression of the G-CSF, M-CSF, and GM-CSF receptors in unstimulated conditions (left) or upon stimulation with cytokines (right) for each shRNA condition. A representative plot of 2 replicates is shown. C Phosphorylation of pSTAT3 in the receptor negative (left) or dim/positive (right) populations for each replicate upon stimulation (red) or unstimulated (grey). D Phosphorylation of pERK. E Phosphorylation of pSTAT5 shown as a boxplot (left) or as histograms (right). The histograms are representative of 2 replicates. (All significant differences shown; *, P < 0.05; ** P < 0.01; Student’s t-test, two-sided). Figure 4.13 SPI1 is highly expressed in DNMT3A mutated T-ALLs. Expression of SPI1 (encoding PU.1) in DNMT3A wildtype and mutated T-ALL samples. (*, P < 0.05, Student’s t-test, two-sided). 4.4 Discussion In this chapter, we describe the use of an in vitro model of T-cell differentiation to explore the role of DNMT3A in human T-cell development and lineage restriction and extrapolate our findings to human T-ALL. In the context of normal T-cell development, DNMT3A loss enhanced cell proliferation and myeloid-like differentiation in the presence of G-, M-, and GM-CSF, which was only observed among the “T-lineage restricted” (i.e., CD13-CD33-CD7+CD44-) T-cell subset with dim positive expression of the G-, M- and GM-CSF receptors. In human T-ALL cells and PNT, we similarly identified a population of T-cells with dim positive receptor expression, and DNMT3A knockdown in the context of T-ALL cells resulted in enhanced proliferation among this subset. The proliferation and defective lineage-restriction phenotypes that we observed upon DNMT3A loss could not be explained by alterations in levels of surface expression of the G-, M-, or GM-CSF receptors, nor by differential phosphorylation of intracellular signaling factors (i.e., ERK, STAT3 and STAT5), because a discernible difference in DNMT3A knockdown vs control cells was not 108 observed upon analysis of receptor expression and protein phosphorylation. Given the role of DNMT3A in de novo DNA methylation, it thus remains to be elucidated if these phenotypes are instead a consequence of differential loci accessibility leading to aberrant transcriptional activation of a proliferative and/or alternative lineage program upon DNMT3A loss. Our findings suggest that, in the context of normal T-cell differentiation, the developmental stage in which DNMT3A loss occurs has critical consequences on T-cell lineage restriction; knockdown of DNMT3A in CD7+CD44- T-cells at day 14 in culture reduced the overall capacity to give rise to myeloid-like cells compared to knockdown of DNMT3A at day 7 in culture. When taken together with our WGBS data collected from developing T-cells, this presents a model in which DNA methylation accrues over select genes as T-cells differentiate, and interruption of this process (e.g., by DNMT3A knockdown or knockout) results in defective or incomplete myeloid lineage exclusion. This idea is consistent with lineage specification in embryonic stem cells, in which DNA methylation accumulates over genes associated with pluripotency or alternative tissue types but diminishes over tissue-specific genes, thus acting to restrain alternative developmental potentials and allow for activation of cell-type specific programs347–349. Although further studies are required to directly link DNMT3A knockdown or knockout with DNA methylation changes in our cultures, we suspect that de novo methylation by DNMT3A may repress the expression of genes important for myeloid lineage programs, such as PU.1. At least in the context of murine T-cell development, PU.1 overexpression has been previously shown to re-direct T-cells to myeloid or dendritic cell fate upon cessation of Notch signaling109. Though it remains to be determined if the myeloid-like phenotypes that we observe upon DNMT3A loss are a result of PU.1 upregulation, our WGBS and RNA-seq results indicate that methylation of the PU.1 promoter increases, and gene expression decreases, throughout normal T-cell differentiation. This might suggest that repression of PU.1, perhaps through DNA methylation, may be an important early event during T-lineage restriction. Importantly, there remains the possibility that DNMT3A loss does not alter lineage commitment but rather has subtle effects on differentiation, and that the phenotypes we observe may be a consequence of uncommitted progenitor populations remaining in the cultures. Although populations were sorted based on CD7+, CD13/33-, CD44-, and G-, M-, or GM-CSF+(dim) marker expression, we cannot exclude that there is heterogeneity between our DNMT3A knockout and wildtype populations within this sorted subset. Although 109 phenotypic analysis of clonally-derived DNMT3A knockout or wildtype populations harvested after 4 weeks in culture did not reveal discernible differences in marker expression, suggesting that differentiation is not grossly altered upon DNMT3A loss, addressing this possibility will require higher resolution methods. This may be achieved by expanding our panel of flow cytometric markers or through higher-dimensional single-cell analyses such as single-cell RNA sequencing. Alternatively, the use of other markers of T-cell commitment such as TCR rearrangement, which creates an indelible mark in a T-cell’s differentiation history, may provide more compelling evidence that the myeloid-like cells we observe are originating from committed T-cell populations. In the context of clonal and malignant hematopoiesis, the prevalence of DNMT3A mutations have been described extensively (reviewed in198,350). Indeed, functions in clonal expansion have become increasingly evident due to the dominance of DNMT3A mutations (>50%) among the hematopoietic compartment of aging individuals who otherwise display no hematological malignancy351–353. The prevalence of DNMT3A mutation in mature blood subsets might suggest that mutation arises in an early progenitor354,355. This is corroborated by studies in mice, in which Dnmt3a-null HSCs display enhanced self-renewal compared to normal counterparts173,194,356. Collectively, these studies posit that DNMT3A loss provides a selective advantage among the HSC compartment, and malignant transformation may occur upon acquisition of a secondary hit within the clonally-expanded pool. Nonetheless, the functional implication of DNMT3A loss among downstream progeny and mature blood subsets remains poorly understood. Our findings here may suggest a novel role of DNMT3A in restricting proliferative signals in response to myeloid cytokines which appears to hold true in normal and malignant T-cell contexts. In the context of T-ALL, where the majority of leukemias have mutations that result in constitutively active Notch and may thus be T-lineage restricted, DNMT3A knockdown can provide a selective advantage by enhancing cell expansion in the presence of these same cytokines, thus possibly conferring plasticity in signaling response. We speculate that this may be occurring through de-repression of programs operative in alternative lineages. Overall, the results presented here provide a model in which DNMT3A constricts lineage-specific responses in T-cells. This is of particular importance during normal development, in which repression of factors associated with stem/progenitor programs must be coordinated with activation of lineage-specific 110 function. During malignancy, however, failure to properly downregulate early developmental programs may provide growth advantages through inappropriate reactivation of progenitor-associated genes. Thus, in the context of T-ALL, DNMT3A loss may provide a selective advantage among a subset of cells which have exited the nurturing thymic microenvironment and have infiltrated into areas replete with factors that, under normal circumstances, do not explicitly support T-cell proliferation and expansion. 111 Chapter 5: Conclusions The central objective of the work presented in this thesis was to garner a greater understanding of the molecular events in early human T-cell differentiation to allow for dissection of particular genetic events that occur in specific developmental contexts in T-ALL. This was approached in two ways: First, through a comprehensive expression analysis of T-ALL in comparison to human intrathymic populations, and second, through genetic perturbation and functional characterization of in vitro-differentiating T-cell populations. The findings revealed (1) an improved classification of T-ALL which better captures the developmental context in which specific transcription factors operate, and (2) novel insights into a possible role of DNMT3A in restricting lineage-specific signal responses in T-cell development and T-ALL. The results of these approaches and the insight they provide in the greater context of T-cell development and T-ALL, will be highlighted here. 5.1 Incorporation of RNA-seq based developmental profiling improves resolution of phenotypic subsets in human T-ALL T-ALL has long been characterized as a malignancy of developmentally arrested T-cells. As a proof-of-concept, I extracted gene signatures of normal T-cell development from a publicly available RNA-seq dataset of human intrathymic populations generated by the Crooks lab286, as described in Chapter 2. This gene signature was used to superimpose two RNA-seq datasets of T-ALL samples upon the spectrum of normal T-cell development, which resulted in a number of sample clusters that correlated with the developmental stage. Although the application of gene signatures from normal T-cells to T-ALL samples has been previously met with success by other groups226, few have extended this characterization to cover the entire spectrum of T-cell development. The data presented in this chapter thus presents a more comprehensive understanding of T-ALL parsed according to developmental stage. A key finding presented in this chapter is that immunophenotypic profiling of T-ALL blasts is limited in dimensionality (i.e., only a handful of CD markers may be assessed in parallel), and thus classification, particularly of the ETP-ALL subgroup, lacks sensitivity. This might suggest that immunophenotypic classification – the current standard for developmental classification of T-ALL – does not fully appreciate the true number of ETP-ALL cases, and thus advocates for the use of more comprehensive and inclusive 112 classification methods. Accurate subgroup classification has demonstrated consequences on treatment approaches in other cancers357, and precise identification of prognostically poor cases may help guide more prophylactic treatment regimens in T-ALL. Our approach also revealed biological insight into T-ALL pathogenesis; when parsing the sample clusters by TF subgroups, I observed that many TF subgroups correlated with certain stages of T-cell development. While the synchrony between TF overexpression and developmental context may indicate co-operation between oncogenic TFs and developmental context, perhaps as a result of TF-induced differentiation arrest, it may instead more accurately reflect normal molecular features of the stage of differentiation arrest. In other words, certain TFs may be highly expressed primarily as a consequence of specific developmental contexts but may not be oncogenic effectors themselves. The findings presented in Chapter 2 suggest that both mechanisms may be operative; for example, LMO2 overexpression is observed in both immature (i.e., LMO2/LYL) and cortical (i.e., TAL/LMO2) T-ALL, which lie at opposing ends of the differentiation spectrum. LMO2 overexpression in the former subgroup, which is often observed coincident with LYL1 overexpression, is likely a consequence of developmental arrest at an immature stage of T-cell development in which LMO2 and LYL1 are already highly expressed. In the latter instance, LMO2 overexpression is often a consequence of translocation, and is typically observed in the context of TAL1 overexpression. This might suggest the possibility that, in the absence of translocation, high expression of LMO2 more accurately reflects the developmental context and may not indicate pathogenic mechanism. With the advent of emerging personalized therapies, care must be taken to ensure segregation of oncogenic effectors from the prevailing stage of differentiation arrest. Thus, accurate classification of the developmental state may pose an important step in understanding disease mechanism. On one hand, precise characterization of the developmental context in which a cell is arrested may provide a clean background against which to compare the effect of gene overexpression. On the other hand, this information may be leveraged to target specific developmental contexts; the TFs which do correlate with particular stages of development might indicate a unique dependence on a particular developmental context, which itself is a product of epigenetic, genetic, and proteomic circumstance. Targeting the specific developmental context upon which oncogenic TFs rely, as opposed to the TFs themselves, may therefore broaden the list of possible candidates for targeted therapy in a subtype-specific manner. Unlike other 113 disease contexts, targeted therapies are not yet integrated in treatment of T-ALL, although many have been investigated in clinical trials (reviewed in280). Successful application of targeted therapy to T-ALL treatment will require dissection of the molecular mechanisms underlying T-ALL pathogenesis, combined with careful consideration of the developmental contexts in which oncogenic TFs operate. The results presented in this chapter provide a small step towards developing a more comprehensive understanding of T-ALL pathogenesis in the context of normal development. 5.2 Identification of DNMT3A as a regulator of lineage-specific responses in developing human T-cells Specific transcription factors are often operative in a limited range of developmental contexts in T-ALL, but the underlying constraints which may be enforced – by epigenetic factors, for example – are poorly characterized. To better understand how DNTM3A loss might be operating in specific developmental stages of T-cell differentiation, I first characterized a commercially available, chemically-defined system to model in vitro T-cell development. By gene expression profiling, the in vitro-derived subsets recapitulated expression patterns observed in human intrathymic populations. The results presented in Chapter 3 provide a glimpse of these expression profiles, as well as the accompanying DNA methylation patterns. Although an interrogation of early stages of human T-cell development (either through the use of in vitro models or by accessing primary thymic tissue), has been accomplished by a few other groups through the use of RNA-seq196,286,314, and more recently with scRNA-seq107,358, there are very few studies which have in parallel interrogated DNA methylation changes196. The data presented in Chapter 3 represents, to my knowledge, the first RNA-seq and WGBS dataset that has placed more emphasis on characterization of the early stages of human T-cell development that occur prior to CD1a acquisition. Having established the expression and methylation landscape of in vitro-derived T-cells, I then used this system to model the role of DNMT3A loss in early T-cell development, particularly in relation to regulating T-lineage restriction and differentiation. The experiments presented in Chapter 4 were motivated by emerging evidence that: (1) DNA methylation patterning is distinct in lymphoid and myeloid lineages191,192; (2) that mutation of the de novo methyltransferase DNMT3A occurs frequently in lymphoid and myeloid malignancies, and is particularly enriched among ETP-ALL244,245, a subset of T-ALL which 114 displays myeloid characteristics and which may have poorer prognosis226; (3) that the majority of mutations in DNMT3A are purported to result in protein loss-of-function based on functional studies267; (4) that the degree of DNMT3A loss (heterozygous vs complete knockout) can influence the progression to myeloid vs T-cell leukemia269; and (5) that loss of the myeloid fate is among the last to occur prior to complete T-cell commitment during T-cell development107 (reviewed in104,324). Taken together, I hypothesized that loss of DNMT3A during T-cell development impairs restriction of the myeloid fate, and that understanding the consequence of DNMT3A loss might yield important insight into T-ALL biology, and in particular ETP-ALL. The results presented in Chapter 4 revealed that DNMT3A loss correlates with loss of T-lineage restriction associated with an outgrowth of myeloid-like cells under specific experimental conditions, which was most evident when DNMT3A knockdown occurred during earlier stages of T-cell differentiation. This is not likely occurring as a result of a differentiation defect at an early stage of T-cell development, where the myeloid lineage may still remain accessible, because no demonstrable phenotypic difference was observed between clonally-derived DNMT3AWT or DNMT3AKO T-cells. Based on these results, it is tempting to hypothesize that de novo DNA methylation patterns are established during early T-cell development and are critical for exclusion of alternative lineages, such as the myeloid lineage, and that perturbation of this process (i.e., by altering DNMT3A expression during early stages of development), might impede lineage restriction. Nevertheless, the data presented in this thesis is limited because we have not directly assessed changes in DNA methylation upon DNMT3A knockdown; therefore, at present these data cannot directly correlate DNA methylation changes with DNMT3A loss. Regardless, our WGBS analysis of developing T-cells in an unperturbed state suggest that the majority of gene promoters do not undergo de novo DNA methylation throughout T-cell development, which is consistent with reports in human thymic subsets by others196. In the context of the findings reported in Chapter 4, this might indicate that whilst the majority of loci exhibit relatively stable methylation levels throughout differentiation, methylation of key genes associated with alternative (non-T) lineages may occur. Our WGBS and expression profiling data point to PU.1 as a potential candidate due to increased promoter methylation and decreased gene expression of PU.1 throughout normal T-cell differentiation. Additionally, in the context of murine T-cells, PU.1 overexpression correlates with acquisition of a myeloid-like or dendritic cell-like phenotype and expression of genes associated with the myeloid lineage, which 115 occurs at the expense of T-cell development109,359,360. To elaborate upon the possibility of reprogramming by PU.1, further studies which directly link DNA methylation changes over PU.1 regulatory elements or PU.1 target genes as a consequence of DNMT3A loss are required. Of note, reprogramming of murine T-cells by PU.1 overexpression is only evident upon cessation of Notch signaling109, perhaps suggesting that Notch may also play important roles in T-cell lineage restriction. It may therefore be of future interest to explore the possible role of DNMT3A in regulating accessibility of Notch and PU.1 to target loci. To our surprise, the myeloid-like phenotype observed in lineage-restricted T-cells upon DNMT3A knockdown was enriched among a population of cells that express receptors typically associated with myeloid-lineage differentiation (i.e., the G-CSF, M-CSF, and GM-CSF receptors). Expression of these receptors was also observed in mature DP and SP CD4+/CD8+ human thymic subsets and in human T-ALL cell lines, suggesting that expression of these receptors normally occurs among a minority of T-cells. The presence of a G-, M-, and/or GM-CSF receptor expressing population in these T-cell contexts was not expected – historically, activation of the G-, M-, and/or GM-CSF receptors by binding cognate ligands has been associated with the proliferation and differentiation of myeloid populations334. Nonetheless, G-CSF receptor expression has been previously observed by others in mature CD4+ and CD8+ SP T-cell subsets, and treatment with G-CSF resulted in increased STAT5 expression361. In the context of mature T-cells, the expression of the G-CSF receptor may be important for controlling immune responses361. The relevance of this subpopulation in immature T-cells and in T-ALL, however, was only realized upon genetic perturbation. This may point to a general regulatory mechanism in which DNMT3A restricts promiscuous signaling activation prior to T-cell commitment to maintain lineage-appropriate cellular responses. Nonetheless, whether DNMT3A loss alters signaling response in additional populations (i.e., not limited to the G-, M-, and/or GM-CSF receptor-expressing cells), or in response to a different assortment of cytokines, remains an unexplored possibility. In the context of T-ALL, expression of the G-, M-, or GM-CSF receptors bestowed a proliferative advantage in response to the addition of cognate cytokines, which was only evident upon DNMT3A knockdown. This suggests that, under certain circumstances, DNMT3A knockdown may also confer plasticity in signaling response in T-ALL cells. We hypothesize that the proliferative phenotype that we observe in DNMT3A knockdown populations is occurring through increased accessibility of key 116 downstream target genes, since we did not observe an appreciable difference in surface expression of the G-, M-, or GM-CSF receptors, nor did we observe a difference in activation of downstream signaling factors (i.e., phosphorylation of ERK, STAT3, and STAT5), upon DNMT3A loss. Previously, we pointed to PU.1 as a possible downstream target gene given the pattern of methylation and expression during normal T-cell development. In the context of T-ALL, we noted that SPI1, which encodes PU.1, is highly expressed among DNMT3A-mutated cases. Of note, Spi1 has been previously implicated in T-ALL pathogenesis in a Pten-null mouse model, and conditional deletion of Spi1 resulted in increased disease latency. In a subset of murine blasts and human T-ALL cell lines with low SPI1 expression, treatment with the pan-DNMT inhibitor, 5-azacytidine, restored levels of SPI1 expression. Furthermore, treatment of human T-ALL cell lines with 5-azacytidine accelerated development of disease compared to untreated cells upon injection into a mouse346. These results may highlight the relevance of PU.1 in T-ALL, and furthermore point to DNA methylation as a possible regulatory mechanism controlling SPI1 expression. Taken together, our results suggest that in the context of normal T-cell development and in T-ALL, DNMT3A may act to restrict T-lineage responses. In a normal T-cell context, loss of DNMT3A correlates with a loss of myeloid lineage restriction. In the context of T-ALL, loss of DNMT3A is associated with a selective growth advantage through enhanced cellular proliferation in response to a more diverse array of signaling molecules. While we hypothesize that this mechanism may be occurring through regulation of key target genes (e.g., PU.1), further experiments are required to determine the molecular mechanism underlying the phenotypes we observe. For example, to determine if this regulatory axis is operating in the context of developing T-cells, we could interrogate CpG methylation within the SPI1 promoter in DNMT3A knockout/knockdown vs control cells and, in parallel, analyze SPI1 expression changes. Nonetheless, the results presented in this chapter provide novel insight regarding the role of DNMT3A in maintaining lineage-specific responses in the context of T-cells. 5.3 Limitations of the work and future directions Although a few studies have interrogated the role of DNMT3A in the context of murine hematopoietic progenitors173,194,356 and in mouse models of T-ALL195,268,269, there is a relative paucity of data in a human T-cell context perhaps related to limited accessibility of primary thymic tissue and difficulty 117 with manipulation of these tissues. To circumvent these challenges, our experiments were based upon the use T-cells differentiated from CD34+ enriched CB progenitors. Interpretation of our results thus requires careful consideration of the model system, and extrapolation of our findings to human thymocyte populations must be proceeded with caution. Limitations of this research pertaining to the use of CD34+ enriched CB progenitors as a source of T-cells include: (1) the effect of the CD34+ progenitor source (i.e., cord blood, bone marrow, or mobilized peripheral blood); and (2) differences between CD34+ CB progenitors and thymus seeding progenitors. Previous studies have indicated that different sources of CD34+ progenitors display different capacities for T-lymphoid and myeloid differentiation362, which may suggest that tissue source can introduce biases related to developmental capacity. Furthermore, because not every progenitor cell is capable of homing to the thymus, it may be of interest to identify a population of progenitor cells which better approximate the cells which enter the thymus, and which thus initiate T-cell development, in order to more accurately model T-lineage restriction processes. Although several groups have postulated which cells are capable of entering the thymus, identification of the thymus seeding progenitor population remains contentious4–12,6,100 (reviewed in3,99). Nonetheless, identification of a population which more closely resembles thymus seeding progenitors may represent a better progenitor source than cord blood for accurate modeling of T-cell development and lineage-restriction processes. There are several limitations to drawing meaningful conclusions pertaining to the T-lineage restriction (i.e., myeloid exclusion) experiments discussed throughout Chapter 3 and 4. In these experiments, the CD7+CD44-CD13-CD33-(G-, M-, GM-CSFR)+(dim) population was sorted into conditions supportive of T- or myeloid-lineage development. This population was chosen because loss of CD44dim expression was previously identified to be among the first phenotypic changes that mark T-lineage commitment95. Nonetheless, we cannot entirely rule out the possibility that there remain uncommitted T-cell populations within the CD7+CD44-CD13-CD33-(G-, M- ,GM-CSFR)+(dim) in vitro-derived T-cell population after 28 days in culture, and that it is the uncommitted T-cell fraction which is responsible for the myeloid-like phenotypes we observe. Although the myeloid-like cells still displayed some vestiges of the T-cell lineage (i.e., higher expression of CD7, CD5, CD3, and CD1a than granulocytic or monocytic cells), interpretation of the studies presented here would greatly benefit from the use of a trackable, irrevocable marker of T-cell commitment. For example, indication of TCR rearrangement in our myeloid-like 118 populations may provide more compelling evidence that these cells had initiated T-cell commitment, and that DNMT3A loss is able to divert these committed T-cells to adopt myeloid phenotypes. Furthermore, a much greater issue is that the series of lineage potential assays described throughout Chapter 3 and Chapter 4 of this thesis do not necessarily reflect developmental processes that occur in vivo, but rather only describe events which can occur under highly contrived experimental contexts. It thus remains to be determined if there is any physiological relevance of the findings highlighted in our in vitro lineage potential experiments. The experiments demonstrating that expression of the G-, M-, and/or GM-CSF receptor may provide a proliferative advantage in T-ALL cells upon DNMT3A knockdown leave many questions unanswered. For one, the cell line examined displays phenotypic features of an immature T-cell363, and it may be interesting to see if the growth advantage that we observe can be extrapolated to additional immature T-ALLs and ETP-ALLs, or to T-ALL generally. Additionally, we did not assess DNA methylation pattern changes upon DNTM3A loss, nor did we investigate particular loci that may be key in driving proliferation upon stimulation with G-, M-, and GM-CSF. As we did not detect a demonstrable difference in activation of signaling intermediates downstream of the G-, M-, and GM-CSF receptors, the proliferative advantage exhibited by DNMT3A knockdown cells upon cytokine stimulation may be instead related to changes in DNA methylation over genes that contribute to a proliferative phenotype. These possible targets, however, remain uncharacterized. Lastly, the relevance of G-, M-, and GM-CSF receptor expression in normal human intrathymic populations and in T-ALL is yet to be realized. Although the results of these experiments suggest that this population displays a proliferative advantage in a specific context (i.e., upon stimulation with G-, M-, and GM-CSF), whether this relates to plasticity in signaling response generally (e.g., to a wider array of cytokines), remains to be determined. At least in the context of T-ALL, the heterogeneity in response to cytokine stimulation upon DNMT3A knockdown may indicate a survival mechanism in which selective advantages are only realized when T-cells encounter different, perhaps less T-cell supportive, environments such as what might be found outside of the thymic microenvironment. Extrapolation of these findings to other T-ALL models, such as additional cell lines or de novo synthetic leukemias that we have 119 previously described294, and in the context of different receptor/cytokine combinations, may be of future interest. 5.4 Concluding remarks It is long known that T-ALL represents abated T-cell development, but this knowledge is yet to be leveraged into improved therapies. This will require improved classification of the developmental stage of arrest and further understanding of the molecular drivers of different disease subtypes in specific developmental contexts. In this thesis, I have approached these two issues by (1) incorporating high resolution methods (i.e., RNA-sequencing) to developmental-stage classification of T-ALL, and by (2) interrogating the functional consequences of DNMT3A in a defined developmental context. 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(Academic Press, 2001). 149 Appendices Appendix A Cord blood pools accessed Cord pool Experiments performed C9801p Flow immunophenotyping C9663p Flow immunophenotyping C9839p Flow immunophenotyping, media transfer experiments, RNA-seq, WGBS C9891p Flow immunophenotyping, media transfer experiments, RNA-seq, WGBS C9941p Flow immunophenotyping, media transfer experiments, RNA-seq, WGBS 150 Appendix B Flow cytometry antibodies B.1 Antibodies for discrimination of T-lymphoid and myeloid populations Antibody Clone Colour/Channel Manufacturer CD11b ICRF44 Per-CPCy5.5 BioLegend CD13 WM15 PE BioLegend CD15 W6D3 BV421 BD Biosciences CD1a HI149 PE-Cy7 BioLegend CD3 UCHT-1 BV480 BD Biosciences CD33 WM53 PE-CF594 BD Biosciences CD34 581 BUV563 BD Biosciences CD38 HB7 BUV395 BD Biosciences CD4 SK3 BUV661 BD Biosciences CD44 IM7 BV786 BD Biosciences CD5 UCHT2 BUV737 BD Biosciences CD7 M-T701 APC-H7 BD Biosciences CD8 RPA-T8 APC BD Biosciences HLA-DR HIT8a AF700 BioLegend Live/Dead Fixable Yellow BV570 ThermoFisher B.2 Antibodies for analysis of G,M, and GM-CSF receptors Antibody Clone Colour/Channel Manufacturer CD114 LMM741 PerCP-Cy5.5 and APC BioLegend CD114 LMM741 BV421 BD Biosciences CD115 9-4D2-1E4 PerCP-Cy5.5 BioLegend CD115 9-4D2-1E4 BV421 BD Biosciences CD116 4H1 PE-Cy7 BioLegend B.3 Antibodies for phosphoflow Antibody Clone Colour/Channel Manufacturer pSTAT3 4/P-STAT3 AF647 BD Biosciences pSTAT5 Clone 47/Stat5 AF647 BD Biosciences pERK 197G2 AF647 CST Anti-rabbit isotype AF647 CST Anti-mouse isotype AF647 BD Biosciences B.4 Antibodies for cell sorting (in vitro subsets) Antibody Clone Colour/Channel Manufacturer CD7 M-T701 PerCP-Cy5.5 BD Biosciences CD5 UCHT2 BV605 BD Biosciences CD1a HI149 PE-Cy7 BioLegend CD44 IM7 APC-Cy7 BD Biosciences DAPI BV450 ThermoFisher 151 B.5 Antibodies for cell sorting (thymus populations) Antibody Clone Colour/Channel Manufacturer CD45 HI30 AF700 BioLegend CD123 6H6 Biotin BioLegend CD56 5.1H11 Biotin BioLegend CD14 63D3 Biotin BioLegend CD11c 3.9 Biotin BioLegend CD19 HIB19 Biotin BioLegend CD3 UCHT-1 BV480 BD Biosciences CD4 OKT4 PerCP-Cy5.5 BioLegend CD8 RPA-T8 BV650 BD Biosciences Streptavidin Biotin BioLegend DAPI BV450 Thermofisher B.6 Antibodies for discrimination of in vitro T-cell populations Panel Antibody Clone Colour/Channel Manufacturer Early CD38 HIT2 PerCP-Cy5.5 BioLegend CD34 581 APC BioLegend CD44 IM7 APC-Cy7 BD Biosciences CD5 UCHT2 BV605 BD Biosciences CD7 M-T701 BV786 BD Biosciences CD1a HI149 PE-Cy7 BioLegend Late TCRgd B1 PerCP-Cy5.5 BioLegend CD8 RPA-T8 APC BD Biosciences CD3 UCHT1 BV510 BioLegend CD4 RPA-T4 BV605 BD Biosciences TCRab IP26 PE BioLegend 152 Appendix C In vitro-derived T-cell subsets and thymus populations isolated for RNA-seq and WGBS Population # replicates Sorted phenotype RNA-seq WGBS Day 7 3 1 CD7+CD5-CD44+CD1a- Day 10 3 1 CD7+CD44+CD1a- Day 14 3 1 CD7+CD44-CD1a- Day 36 3 1 CD7+CD44-CD1a+ Day 42 3 0 CD7+CD4+CD8+ DN PNT 1 1 CD45dimCD123-CD56-CD14-CD11c-CD19-CD3-CD4-CD8- DP PNT 1 1 CD4+CD8+ 153 Appendix D sgRNA targeting of the DNMT3A locus Location of guide RNAs within Exon 18 of DNMT3A. The region highlighted in orange encodes the catalytic loop within the methyltransferase domain of DNMT3A. 154 Appendix E shRNA, sgRNA, and primer sequences Class Name Sequence shRNA shDNMT3A TRCN0000035754 CCCAAGGTCAAGGAGATTATT shDNMT3A TRCN0000035755 CCGGCTCTTCTTTGAGTTCTA shDNMT3A TRCN0000035756 GCCTCAGAGCTATTACCCAAT shDNMT3A TRCN0000035758 CCACCAGAAGAAGAGAAGAAT shScrambled CTAAGGTTAAGTCGCCCTCG sgRNA DNMT3A 15R CCCCAATCACCAGATCGAATGTTTTAGAGCTATGCT DNMT3A 54R GAGCAGGGTTGACGATGGAGGTTTTAGAGCTATGCT DNMT3A 59R CTTGCGAGCAGGGTTGACGAGTTTTAGAGCTATGCT EMX1 GAGTCCGAGCAGAAGAAGAA Primer DNMT3A FW TGTCCATGGTTGCAGCTAGG DNMT3A RV GCGATCATCTCCCTCCTTGG DNMT3A FW Nested TGGGTCTCCTCTCTTTCGTG DNMT3A RV Nested CAGGAGGCGGTAGAACTCAA EMX1 FW AGCAGCTCTGTGACCCTTTG EMX1 RV TTGTCCCTCTGTCAATGGCG 155 Appendix F Immunophenotypes of in vitro-derived T-cell subsets in additional cord pools Flow cytometric analysis of T-cell subsets derived in vitro from CD34+ cord blood. Cells were collected at the indicated time points throughout differentiation. Results of a second cord blood donor are shown. 156 Appendix G Supplementary figures related to Chapter 2 G.1 TARGET and CGAD k-medoid clustering measures Silhouette width and gap statistic measures of clustering robustness for (A) TARGET and (B) CGAD RNA-seq datasets. 157 G.2 k-medoid clustering of CGAD T-ALL samples. 158 G.3 Projection of CGAD and TARGET T-ALL cohorts into PCA space defined by normal T-cell subsets. T-ALL samples are coloured based on transcription factor (TF) membership (top) or immunophenotype (bottom). 159 G.4 Trajectory analysis of TARGET and CGAD T-ALL cohorts. TARGET and CGAD T-ALL cohorts were analyzed along with normal thymic progenitors by rank normalization of 2,824 genes implicated in T-cell development. Dimensionality reduction was performed by diffusion map. 160 G.5 Derivation of Thy1-4 subset signatures. A) Differential gene expression comparisons performed to generate gene set signatures. B) Gene set variation analysis (GSVA) was performed on the CGAD cohort. C) PCA plot of CGAD samples with optimal k=6 clustering and Thy1-4 signature annotations. A similar result was obtained for TARGET samples. 161 G.6 Clinical outcome correlations. A) Event-free survival probability for each sample cluster in the TARGET cohort (Log-rank test). B) Day 29 MRD divided by ETP type and TF subgroup. C) PCA plot of the TARGET samples with k=6 optimal clustering and annotated by ETP type. D) Day 29 MRD divided by ETP type for Cluster Teal samples only. For panels B and D, all significant differences are shown. *, p<.05; **, p<.01; ***, p<.001; ****, p<.0001. (Dunn’s test with Benjamini-Hochberg multiple test correction).
Thesis/Dissertation
eng
Interdisciplinary Oncology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Weng, Andrew
Exploring mechanisms of T-cell acute lymphoblastic leukemia pathogenesis through molecular characterization of normal T-cell development
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