THE ROLE OF SKEWED IMMUNE RESPONSES IN THE RESOLUTION OF VIRAL INFECTION, ALLERGIC DISEASE, AND STERILE INJURY  by  MELINA MESSING  M.Sc., Lund University, 2017   A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF  THE REQUIREMENTS FOR THE DEGREE OF   DOCTOR OF PHILOSOPHY  in  THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES  (Experimental Medicine)   THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)   September 2022  © Melina Messing, 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:  The role of skewed immune responses in the resolution of viral infection, allergic disease, and sterile injury  submitted by Melina Messing   in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Experimental Medicine  Examining Committee: Kelly McNagny, Professor, Medical Genetics and School of Biomedical Engineering, UBC Supervisor  Fabio Rossi, Professor, Medical Genetics and School of Biomedical Engineering, UBC Supervisory Committee Member  Pauline Johnson, Professor, Microbiology and Immunology, UBC  University Examiner Alice Mui, Associate Professor, Biochemistry and Surgery, UBC University Examiner  Additional Supervisory Committee Members: Kevin Bennewith, Associate Professor, Pathology and Laboratory Medicine, UBC Supervisory Committee Member Fumio Takei, Professor, Pathology and Laboratory Medicine, UBC Supervisory Committee Member     iii Abstract   The immune system evolved to protect the host from external pathogens as well as manage host internal processes including tissue repair after damage. This thesis explores different aspects of immune responses in the context of viral infection, allergic disease, and internal, sterile muscle injury.  Firstly, the characterization of peripheral blood immune cells in patients admitted to the intensive care unit (ICU) due to an infection with SARS-CoV-2, the virus that causes COVID-19, uncovered immune biomarkers, specifically serum IL-10 and CD11clow classical monocytes, that are predictive of disease outcome. The unprecedented and ongoing burden caused by the COVID-19 pandemic makes this research timely and critical to assist with ICU patient management.  Secondly, the characterization of immune cells in archived umbilical cord blood revealed biomarkers predictive of the development of childhood allergic disease, which occurs commonly and severely and for which disease origin is unclear. Our findings suggest that alterations in naive CD8 T cells and monocytes are already established prior to birth in children who, by the age of 5, develop a combined wheeze and atopy phenotype. The stages of prenatal immune development are rarely explored in the context of allergic disease development and may be a new avenue for early disease detection and prevention.   Lastly, the characterization and manipulations of type-2 immune responses in mouse models of sterile acute and chronic skeletal muscle injury showed that type-2 immune cells are largely expendable in normal tissue regeneration but accelerate muscle pathology. Specifically, IL-33- induced type-2 immunity promoted prominent influx of ILC2s and eosinophils in acutely injured skeletal muscle with no discernable impact on muscle regeneration but accelerated fibrosis  iv deposition in mdx mice – a model of Duchenne muscular dystrophy. These findings add to our understanding of how the immune system supports normal tissue function and how tissue is damaged in disease, which is fundamental for future development of disease treatments and preventative measures.     v Lay Summary   The immune system is essential for protection against environmental threats and to maintain normal organ function. Threats from the environment can include viruses and allergens and normal organ function can be disrupted through injury that requires immune mediators for repair. In this thesis, immune cells and inflammatory molecules were explored in these diverse but related settings. Firstly, human immune cells were characterized in COVID-19 patient blood that led to the discovery of immune biomarkers that predict disease outcome. Secondly, profiling of human immune cells from umbilical cord blood enabled discovery of immune subsets predictive of childhood allergic disease development. Lastly, a specific type of immune response called type-2 immunity that is known for its involvement in tissue repair, was evaluated in mouse skeletal muscle injury. This led to an improved understanding of type-2 cells involved in normal tissue repair and muscle destruction in a mouse model of muscular dystrophy.    vi Preface  As part of the requirements for the Experimental Medicine program, the work presented in this thesis was conducted at the Biomedical Research Centre (School of Biomedical Engineering) on the Point Grey Campus of the University of British Columbia.   Sections of chapter 1 have been published previously as a review in Messing, M., Jan-Abu, S. C., & McNagny, K. (2020). Group 2 innate lymphoid cells: central players in a recurring theme of repair and regeneration. International Journal of Molecular Sciences, 21(4), 1350 and can be accessed at: https://doi.org/10.3390/ijms21041350. I was responsible for writing the manuscript and creating figures and Sia Cecilia Jan-Abu and Kelly M. McNagny provided guidance and edits.    Chapter 2 has been published as a preprint on medrxiv: Messing, M., Sekhon, M. S., Hughes, M. R., Stukas, S., Hoiland, R. L., Cooper, J., Ahmed, N., Hamer, M., Li, Y., Shin, S. B., Tung, L. W., Wellington, C., Sin, D. D., Leslie, K. B. & McNagny, K. M. (2022). Prognostic peripheral blood biomarkers at ICU admission predict COVID-19 clinical outcomes. medRxiv. and can be accessed at: https://doi.org/10.1101/2022.01.31.22270208.  I was responsible for study design, mass cytometry experiments, data analysis and interpretation and manuscript preparation. Kelly M. McNagny and Kevin B. Leslie were instrumental for study design, data analyses, interpretation, and manuscript preparation. Members of the Wellington lab (Sophie Stukas, Jennifer Cooper, Nyra Ahmed and Cheryl Wellington) performed sample preparation and collection of serum cytokine data. Patient samples and clinical information were collected and provided by Mypinder S. Sekhon, Ryan L. Hoiland and Don D. Sin. Mark Hamer,  vii Yicong Li, Samuel B. Shin and Lin Wei Tung assisted with sample preparation and data processing. All authors provided manuscript edits.   A manuscript is in preparation for Chapter 3: Messing, M. Shin, S. B., Tung, L. W., Hughes, M. R., Jan-Abu, S. C., Li, Y., Subbarao, P., Turvey, S. E., Azad, M. B., Mandhane, P. J., Simons, E., Moraes, T. J., Leslie, K. B., McNagny, K. M. Prediction of future childhood allergic disease from alterations in umbilical cord blood immune cell signatures. 2022. I was responsible for conducting all experiments as well as overall project management, data analyses, interpretation, and manuscript preparation. Kelly M. McNagny and Kevin B. Leslie were instrumental for study design, data analyses, interpretation, and manuscript preparation. Samuel B. Shin will be co-first author of the manuscript and is responsible, together with myself and Lin Wei Tung, for the single-cell RNA sequencing portion of this project that is mentioned but not presented in this thesis. Michael R. Hughes, Sia Cecilia Jan-Abu and Yicong Li assisted with writing of ethics protocols and sample preparations. Human umbilical cord blood samples and clinical information were provided by the CHILD study (www.childstudy.ca). CHILD study investigators are listed above as co-authors on the manuscript.    Chapter 4 is a work in progress with a small manuscript in preparation: Messing, M., Theret, M., Hughes, M. R., Li, Y., Rossi, F., McNagny, K. M. IL-33 induced type-2 inflammation worsens Duchenne muscular dystrophy pathology in mdx mice but does not impair acute muscle repair and regeneration. 2022. I was responsible for conducting all experiments. Marine Theret assisted with tissue processing, flow cytometry acquisition and data interpretation. Michael R. Hughes  viii assisted with tail-vein injections of the fetal liver transplants. Yicong Li assisted with tissue sectioning, data imaging and analyses.   All human and animal experiments conducted in this thesis were approved by the University of British Columbia Research Ethics Board and Animal Care Committee in compliance with the Canadian Council on Animal Care (approved protocols: H19-01157, H20-00685, A18-0314, A19-0010).         ix Table of Contents Abstract ......................................................................................................................................... iii	Lay Summary ................................................................................................................................ v	Preface ........................................................................................................................................... vi	Table of Contents ......................................................................................................................... ix	List of Tables .............................................................................................................................. xiii	List of Figures ............................................................................................................................. xiv	List of Abbreviations .................................................................................................................. xv	Acknowledgements .................................................................................................................... xix	Dedication ................................................................................................................................... xxi	Chapter 1:	 Introduction ........................................................................................................... 1	1.1	 The immune system: an overview ............................................................................................ 1	1.2	 Type-1 immunity and viral infections ...................................................................................... 4	1.2.1	 Type-1 immunity: central players and mechanisms ................................................................ 4	1.2.2	 Viral infections: the COVID-19 pandemic ............................................................................. 8	1.2.3	 Immunopathology of COVID-19 ............................................................................................ 9	1.3	 Type-2 immunity and allergic diseases .................................................................................. 12	1.3.1	 Type-2 immunity: central players and mechanisms .............................................................. 12	1.3.2	 Allergic diseases .................................................................................................................... 16	1.3.3	 Postnatal links with allergic disease susceptibility ............................................................... 17	1.3.4	 Prenatal influences on allergic disease susceptibility ........................................................... 19	1.4	 Immune responses in sterile injury ........................................................................................ 24	1.4.1	 Acute skeletal muscle inflammation and regeneration ......................................................... 24	1.4.2	 Chronic muscle inflammation and degeneration ................................................................... 27	1.5	 Research outline ....................................................................................................................... 29	Chapter 2:	 Prognostic peripheral blood biomarkers at ICU admission predict COVID-19 clinical outcome ...................................................................................................................... 31	2.1	 Introduction ............................................................................................................................. 31	2.2	 Materials and methods ............................................................................................................ 33	2.2.1	 Study design .......................................................................................................................... 33	2.2.2	 Specimen collection and isolation ......................................................................................... 33	2.2.3	 Antibody staining and CyTOF data collection ...................................................................... 34	2.2.4	 Cytokine data collection ........................................................................................................ 35	2.2.5	 Data processing ..................................................................................................................... 35	 x 2.2.6	 Statistical analysis and figures .............................................................................................. 35	2.3	 Results ....................................................................................................................................... 37	2.3.1	 COVID-19 patient group selection and optimization of immune profiling .......................... 37	2.3.2	 Serum cytokine analyses as prognostic screens for predicting clinical outcome .................. 42	2.3.3	 Major PBMC subsets fail to distinguish Short-Stay from Long-Stay/Died patients ............ 44	2.3.4	 Levels of a distinct monocyte subset at the time of ICU admission predicts subsequent clinical outcome ................................................................................................................................. 47	2.3.5	 Combined evaluation of immune parameters as a tool to predict clinical outcome ............. 50	2.4	 Discussion ................................................................................................................................. 54	Chapter 3:	 Prediction of future childhood allergic disease based on alterations in umbilical cord blood immune cell signatures ........................................................................... 59	3.1	 Introduction ............................................................................................................................. 59	3.2	 Materials and methods ............................................................................................................ 61	3.2.1	 CHILD Study clinical information and diagnoses ................................................................ 61	3.2.2	 Sample selection .................................................................................................................... 62	3.2.3	 Antibody staining and CyTOF data collection ...................................................................... 62	3.2.4	 CyTOF data processing and analysis .................................................................................... 63	3.2.5	 Statistical analysis ................................................................................................................. 63	3.3	 Results ....................................................................................................................................... 65	3.3.1	 Study design and clinical characteristics of CHILD study participants ................................ 65	3.3.2	 CyTOF reveals detailed CBMC signatures at time of birth .................................................. 70	3.3.3	 T cell compartment analyses suggest expansion of CD8 T cells in WA CBMCs ................ 73	3.3.4	 Sample selection criteria for secondary CyTOF analyses ..................................................... 76	3.3.5	 Naive CD8 T cells and monocytes are predictive of future allergic disease ......................... 77	3.4	 Discussion ................................................................................................................................. 81	Chapter 4:	 Modulation of type-2 immune responses as an approach toward enhancing muscle regeneration and treating muscular dystrophy ........................................................... 86	4.1	 Introduction ............................................................................................................................. 86	4.2	 Materials and methods ............................................................................................................ 88	4.2.1	 Mice ....................................................................................................................................... 88	4.2.2	 Bone marrow chimeras .......................................................................................................... 88	4.2.3	 Muscle injury ......................................................................................................................... 88	4.2.4	 IL-33 treatment ...................................................................................................................... 89	4.2.5	 Histology and imaging .......................................................................................................... 89	 xi 4.2.6	 Flow cytometry ..................................................................................................................... 90	4.2.7	 Serum IgE ELISA ................................................................................................................. 92	4.2.8	 Statistical analysis ................................................................................................................. 92	4.3	 Results ....................................................................................................................................... 93	4.3.1	 IL-33-induced immune skewing does not impact muscle regeneration ................................ 93	4.3.2	 Ror𝜶sg/sg mice show normal muscle regeneration after acute injury ..................................... 97	4.3.3	 IL-33 treatment exacerbates DMD pathology in a chronic muscle injury model ............... 100	4.4	 Discussion ............................................................................................................................... 103	Chapter 5:	 Conclusions ........................................................................................................ 106	5.1	 Chapter 2 ................................................................................................................................ 106	5.1.1	 Research summary .............................................................................................................. 106	5.1.2	 Significance ......................................................................................................................... 108	5.1.3	 Limitations .......................................................................................................................... 108	5.1.4	 Future directions .................................................................................................................. 108	5.2	 Chapter 3 ................................................................................................................................ 111	5.2.1	 Research summary .............................................................................................................. 111	5.2.2	 Significance ......................................................................................................................... 111	5.2.3	 Limitations .......................................................................................................................... 112	5.2.4	 Future directions .................................................................................................................. 113	5.3	 Chapter 4 ................................................................................................................................ 114	5.3.1	 Research summary .............................................................................................................. 114	5.3.2	 Significance ......................................................................................................................... 115	5.3.3	 Limitations .......................................................................................................................... 116	5.3.4	 Future directions .................................................................................................................. 117	Bibliography .............................................................................................................................. 119	Appendices ................................................................................................................................. 146	Appendix A ................................................................................................................................ 146	A.1 List of CyTOF antibodies .......................................................................................................... 146	A.2 Mean absolute counts and p-values (ungated cluster analyses) ................................................ 148	A.3 Mean absolute counts and p-values (gated myeloid and T cell cluster analyses) ..................... 149	A.4 Cell surface protein signatures for cluster assignment .............................................................. 150	A.5 Ungated clustering of Initial Cohort .......................................................................................... 152	A.6 T cell clustering of Initial and Replication Cohorts .................................................................. 153	A.7 Cytokine and length of ICU stay correlations ........................................................................... 154	 xii Appendix B ................................................................................................................................ 155	B.1 CHILD Cohort complete blood counts ...................................................................................... 155	B.2 Parental BMI and atopy status ................................................................................................... 156	B.3 Cell subset frequencies .............................................................................................................. 157	Appendix C ................................................................................................................................ 158	C.1 Engraftment analysis ................................................................................................................. 158	C.2 Gating strategies for ILC2s and eosinophils .............................................................................. 159	     xiii List of Tables  Table 1.1: PBMC population marker signatures and functions .................................................................... 2	Table 2.1: Study participant clinical characteristics. .................................................................................. 40	Table 3.1: CHILD Study participant clinical information .......................................................................... 66	Table 4.1: Flow cytometry staining antibodies and panels ......................................................................... 91	   xiv List of Figures  Figure 1.1: Virally induced type-1 and allergen induced type-2 immune responses. ................................... 5	Figure 1.2: Dysregulation of type-1 IFN responses as drivers COVID-19 immunopathology. ................. 10	Figure 1.3: Prenatal exposures protect from or drive disease susceptibility. .............................................. 20	Figure 1.4: Side-by-side comparison of human fetal and adult immune populations. ............................... 23	Figure 1.5: Skeletal muscle regeneration. ................................................................................................... 25	Figure 2.1: Study design and patient characteristics. .................................................................................. 38	Figure 2.2: Serum cytokine analyses as prognostic screens for predicting clinical outcome. .................... 43	Figure 2.3: Major PBMC subsets fail to distinguish Long-Stay/Died patients. ......................................... 45	Figure 2.4: CD11clow Classical Monocytes are predictive of clinical outcome. ......................................... 48	Figure 2.5: Prognostic cytokine and cellular biomarkers predict clinical outcome. ................................... 52	Figure 3.1: Study design and CHILD subject clinical characteristics. ....................................................... 68	Figure 3.2: CyTOF reveals detailed CBMCs signatures at time of birth .................................................... 72	Figure 3.3: T cell compartment analyses suggest expansion of CD8 T cells in WA CBMCs ................... 75	Figure 3.4: Naive CD8 T cells and monocytes are predictive of future allergic disease ............................ 80	Figure 4.1: IL-33-induced immune skewing does not impact muscle regeneration. .................................. 95	Figure 4.2: Ror𝜶sg/sg mice show normal muscle regeneration after acute injury. ....................................... 99	Figure 4.3: IL-33 treatment exacerbates DMD pathology. ....................................................................... 102	Figure 5.1: Chapter 2 research summary. ................................................................................................. 107	Figure 5.2: Chapter 4 research summary. ................................................................................................. 115	   xv List of Abbreviations  %      Percent <      Less than >      Greater than ±      Plus or minus μl      Microliter μm     Micrometer ACE2    Angiotensin-converting enzyme 2  ARDS    Acute respiratory distress syndrome Areg     Amphiregulin B6     C57Bl/6 BaCl    Barium Chloride BCG    Bacille Calmette-Guerin BMI    Body mass index BMT     Bone marrow transplant CBC    Complete blood count CBMC   Cord blood mononuclear cell CC    Chemokine CD     Cluster of differentiation CHILD   Canadian Healthy Infant Longitudinal Development  CN    Centrally nucleated COVID-19  Coronavirus disease 2019 CRP    C-reactive protein CSA    Cross sectional area C-section   caesarean section CyTOF   Cytometry by time of flight d     Day(s) DAPI     4’,6-Diamidnino-2-phenylindole DC     Dendritic cell DMD    Duchenne Muscular Dystrophy  xvi d.p.i    Days post injection ECM     Extracellular matrix ELISA    Enzyme linked immunosorbent assay EOMES   Eomesodermin EPO    Eosinophil peroxidase FAP     Fibro/adipogenic progenitor FBS     Fetal Bovine Serum FOXP3   Forkhead box protein p3 FcR     Receptor for Fc portion of antibodies FcRn    Neonatal Fc Receptor FL     Fetal liver FLT    Fetal liver transplant FSC    Forward scatter FVD    Fixable viability dye g     Gram GATA3   GATA-binding protein 3 GM-CSF    Granulocyte-macrophage colony-stimulating factor GWAS    Genome-wide association studies HIV    Human immunodeficiency virus HSC     Hematopoietic stem cell ICOS    Inducible T-cell co-stimulator ICU    Intensive care unit IFN     Interferon IFNAR   Interferon α/β receptor Ig      Immunoglobulin IL     Interleukin IL-1RAcP   Interleukin-1 receptor accessory protein IL-2C    IL-2/anti-IL-2 complex ILC    Innate lymphoid cell ISG    Interferon stimulated gene IM     Intramuscular  xvii IP     Intraperitoneal JAK    Janus kinase KLRG    Killer cell lectin-like receptor subfamily G member KO    Knockout LB     Live-bleed Lin     Lineage M1     Classically activated macrophages M2     Alternatively, activated macrophages MAIT    Mucosal associated invariant T MBP    Major basic protein MCP     Monocyte chemoattractant protein mdx    X-chromosome-linked muscular dystrophy  mg     Milligram MHC     Major histocompatibility complex ml     Milliliter Mono    Monocyte MP    Muscle progenitor MuSC    Muscle stem cell NK     Natural killer NF-κB    Nuclear factor kappa-light chain-enhancer of activated B cells ng     Nanogram ns     Not significant PAMP    Pathogen associated molecular pattern PBMC   Peripheral blood mononuclear cell PBS     Phosphate Buffered Saline PD-1     Programmed cell death protein 1 PDGF    Platelet derived growth factor PD-L1    Programmed death-ligand 1  PI     Propidium iodide PMN     Polymorphonuclear PRR     Pattern recognition receptor  xviii PSR     Picrosirius red r     Recombinant  RNA     Ribonucleic acid ROR     Retinoic acid receptor-related orphan receptor RT     Room Temperature Sac    Sacrifice SARS-CoV-2  Severe acute respiratory syndrome coronavirus 2 Sca-1     Stem cell antigen 1 Sg     Staggerer allele SSC    Side scatter STAT    Signal transducers and activator of transcription T1/ST2    IL-33 receptor TA     Tibialis anterior Tbet     Type-1 T-box transcription factor TBX21 Tc      Cytotoxic T cells TCR     T cell receptor TGF     Transforming growth factor Th     T helper TNF     Tumour necrosis factor TLR    Toll-like receptor Treg     Regulatory T cells TSLP     Thymic stromal lymphopoietin TVI    Tail vein injection UMAP   Uniform manifold approximation and projection VEGF    Vascular endothelial growth factor w     week(s) WA    Wheeze & Atopy WT     Wild type x      Times     xix Acknowledgements  I would like to sincerely thank Dr. Kelly McNagny for the opportunity to complete my PhD studies in his lab and without whom none of the work conducted in this thesis would have been possible. Thank you for creating an incredibly kind and open environment that encourages exciting scientific discovery while supporting ideas, trials, and learning. While your expertise as a scientist has been fundamental for my training, it is your unconditional mentorship and kindness that truly makes you a distinguished supervisor. It is because of your mentorship, patience, and support that I have discovered my passion for immunology and the dream to make this a lifelong career.   Further, I would like to thank all the members of the McNagny lab, past and present, who have been my support system throughout the last 5 years and who have helped me grow as a scientist through their guidance and encouragement. I would like to especially highlight and thank Dr. Michael Hughes, Yicong Li and Samuel Shin for offering me their time and expertise which was fundamental for the success of many experiments and projects. I would also like to thank the members of the Rossi lab and especially Dr. Marine Theret for spending many hours to teach me her skillset, discuss experimental design and results as well as the opportunity to take part in her scientific work and discovery. Dr. Theret has become a cherished mentor, colleague and friend that has been essential for my training. Thank you to Dr. Rossi for enabling these collaborations and training opportunities and of course for his mentorship as a member of my committee. Dr. Rossi’s guidance, together with committee members Dr. Fumio Takei and Dr. Kevin Bennewith, have been essential for furthering my career.   I would like to extend a special thank you to Dr. Kevin Leslie with whom I had the privilege to collaborate with on multiple projects over the past two years and who has provided expert mentorship by sharing his expertise in immunology, data science and the design and execution of clinical projects. Dr. Leslie’s enthusiasm for discovery has been an inspiration and motivation, especially throughout those times of trial and error, long hours, and uncertainty.   xx Finally, I would like to thank all the members of the Biomedical Research Centre that collectively have been an incredible support system throughout my studies, both on a professional and personal level. Thank you to Michael Williams for his help with all things regarding antibody panel design and troubleshooting and thank you to all core facilities and staff for their support, guidance, and friendship. I acknowledge that the University of British Columbia is the traditional, ancestral and unceded territory of the Musqueam people.      xxi Dedication  To my family for their unconditional love and support.   1 Chapter 1: Introduction 1.1 The immune system: an overview In an environment populated by countless pathogens and inflammatory agents including viruses, bacteria, fungi, parasitic worms, allergens and toxins, the immune system responds and often protects from this heterogenous set of exposures with specialized cells and inflammatory processes. Broadly, innate (in-born) immunity is the first branch of the immune system and encompasses immune cells and inflammatory mediators that respond instantly and with broad specificity to an infectious agent or environmental insult. Innate cell subsets include myeloid cells (monocytes, macrophages, dendritic cells (DCs), neutrophils, eosinophils, basophils, mast cells) and lymphoid cells (innate lymphoid cells (ILCs), natural killer (NK) cells) (1). Adaptive immunity is the second branch of the immune system and includes immune cells that act more slowly but with exquisite specificity to a similar set of invading pathogens and insults. Adaptive immune cells include T lymphocytes (T cells) and B lymphocytes (B cells). Unconventional immune cells, including invariant natural killer T cells (iNKTs), γδ T cells, and mucosal associated invariant T (MAIT) cells, are functionally on the border of innate and adaptive immunity as they possess properties from both immune branches (2). Lymphocytes and mononuclear myeloid cells (monocytes, DCs) and many of their subpopulation present in blood circulation can be separated from granulocytes, red blood cells and platelets and recovered in the ‘low density’ peripheral blood mononuclear cell (PBMC) blood fraction. These cells are easily accessible from patients and thus are a popular choice for the characterization of human immune responses. An overview of PBMC populations, key marker signatures and main functional properties that are relevant for this thesis are presented in Table 1.1.    2 Table 1.1: PBMC population marker signatures and functions Cell type Subset Marker signature Main functions Lymphocytes    B cells (3) Naive HLA-DR+CD38+CD19+CD27- Respond to novel antigens to generate memory Memory HLA-DR+CD38-CD19+CD27+ Immune memory; Antibody production CD4+  T cells (4, 5) Naive CD3+TCR⍺β+CD38+CD45RA+ CD45RO-CD27+CCR7+ No immune memory; respond to novel antigens   CM CD3+TCR⍺β+CD38-CD45RA-CD45RO+CD27+CCR7+  Immune memory; home to lymphoid organs; ‘helper’ cytokine production EM CD3+TCR⍺β+CD38-CD45RA-CD45RO+CD27-CCR7- Immune memory; home to inflamed peripheral tissues; ‘helper’ cytokine production Tregs CD3+TCR⍺β+CD38+CD45RA+ CD45RO+CD27+CCR7+ CD25+CD127low Immunosuppression and tolerance CD8+  T cells (6–8) Naive CD3+TCR⍺β+CD38+CD45RA+ CD45RO-CD27+CCR7+ No immune memory; respond to novel antigens   CM CD3+TCR⍺β+CD38-CD45RA- CD45RO+CD27+CCR7+ Immune memory; home to lymphoid organs; IL-2 production EM CD3+TCR⍺β+CD38-CD45RA- CD45RO+CD27-CCR7- Immune memory; home to inflamed peripheral tissues; cytotoxic effector function MAIT  CD3+TCR⍺β+IL-18R⍺+ MR1-5-OP-RU+ MR1-dependant recognitions of bacterial metabolites; cytotoxic effector function; MR1-independent cytokine production γδ T CD3+TCR⍺β-TCRγδ+ MHC independent antigen recognition; antigen presentation; cytokine production; cytotoxicity  3 Cell type Subset Marker signature Main functions Natural killer  cells (9) Cytokine producers CD3-CD116-CD56high IL-18R⍺high CD94high CD16lowCD161+ Cytokine production Cytotoxic CD3-CD116-CD56lowIL-18R⍺low CD94low CD16highCD161+ Cytotoxicity; cell killing Myeloid cells    Monocytes (10) Classical CD116+HLA-DR+CD14high  CD16-CD123- Phagocytosis; MPO production; antimicrobial responses Intermediate CD116+HLA-DR+CD14high/int CD16lowCD123low  Antigen presentation and T cell stimulation; ROS production Non-classical CD116+HLA-DR+ CD14lowCD16high Complement and FcR- mediated phagocytosis; anti-viral responses Dendritic  cells (11) cDC CD11c+CD1c+NRP1-CD123+ Fc𝜺R⍺1+HLA-DR+ Antigen processing and presentation pDC CD11c-NRP1+CD123+Fc𝜺R⍺1+ CD116+HLA-DR+ Type-I IFN production; T cell stimulation; cytokine production Other Stem cells/ Progenitors (12) NA Lin-CD34+cKIT+ Pluripotent/ multipotent  CM = Central Memory; EM = Effector Memory; MAIT = Mucosal associated invariant T; MHC = Major histocompatibility complex; MPO = Myeloperoxidase; ROS = Reactive oxygen species; cDC = Conventional dendritic cell; pDC = Plasmacytoid dendritic cell; IFN = Interferon; Lin = Lineage.    Beyond this broad classification, immune cells and their effector functions have been categorized into type-1, type-2, and type-3 immunity that each evolved based on different environmental threats which are targeted with effector functions specific for each type of immunity (13).  The following sections outline the functional properties of both innate and adaptive immune cells specifically in the context of type-1 and type-2 immunity as these are central concepts of this thesis. Type-3 immunity is not discussed further here for the sake of brevity but several excellent reviews describe its role in host defense as well as the growing body of evidence describing type- 4 3 immunity in the context of autoimmune diseases (14). Type-1 and type-2 immune responses are further presented in the context of three different but related settings: viral infection (focused on type-1 immunity), allergic disease (focused on type-2 immunity) and sterile injury (a combined view at type-1 and type-2 immunity in pathogen independent tissue regeneration and repair).   1.2 Type-1 immunity and viral infections  1.2.1 Type-1 immunity: central players and mechanisms Type-1 immunity evolved to detect and eliminate intracellular infections, commonly those of protozoan, bacterial or viral origin. To this end, the main functional outputs of a type-1 immune response are generally cytolytic processes for targeted host cell killing and phagocytic processes for pathogen and cell debris removal (Fig. 1.1 A) (14). Prominent type-1 immune cell subsets include the ‘helper’ lymphocytes ILC1s and CD4+ type-1 T helper (Th1) cells, CD8+ type-1 cytotoxic T (Tc1) cells and NK cells, as well as myeloid cells, particularly proinflammatory monocytes and M1 macrophages (15–17). Through pathogen recognition receptors (PRRs) that bind to relatively conserved foreign structures on broad classes of pathogens (pathogen associated molecular pattern (PAMPs)), antigen presenting cells including monocytes, conventional dendritic cells (cDCs) and plasmacytoid dendritic cells (pDCs) detect and recognize the presence of pathogens. Viral PAMPs include viral genetic material (e.g., SARS-CoV-2 single-stranded RNA) which, following endocytosis, is detected by cytosolic PRRs including toll-like receptors (TLRs) 7 and 8 (18–20). Infected cells also sense viral replication intermediates through the engagement of, in the case of RNA carrying coronaviruses, cytosolic RNA sensors (21).    5  Figure 1.1: Virally induced type-1 and allergen induced type-2 immune responses.  Viral infections are sensed inside of infected cells and elicit a type-I interferon response. This initiates the recruitment of inflammatory cell subsets (ILC1s, NK cells, monocytes, and dendritic cells) and production of inflammatory cytokines and chemokines. Antigen-laden dendritic cells then migrate to lymph nodes to present viral antigen and initiate the development of an adaptive immune response and maturation of antigen-specific T cells and B cells. Th1 and Tc1 cells migrate to the site of infection and participate in cytokine production and targeted killing of infected cells. B cells produce IgM and IgG antibodies that neutralize viral particles and enhance viral clearance. Monocytes recruited to the site of infection differentiate into M1 macrophages which are potent phagocytes and cytokine producers (left, panel A). Allergens and parasites, in contrast, cause epithelial and stromal cell damage that leads to the release of alarmins sensed by ILC2s. In response, ILC2s produce a range of cytokines that are essential for recruitment of other innate immune subsets (eosinophils, mast cells, basophils, macrophages) that release compounds that induce mucus production, smooth muscle contraction and tissue remodelling. Again, antigen-laden dendritic cells mature and migrate to lymph nodes to initiate the adaptive immune response and Th2-biased T and B cell differentiation. Th2 and Tc2 cells migrate to the site of tissue damage to support the cytokine environment. B cells produce IgE and IgG1 antibodies that enhance effector cell functions suitable for parasite clearance or induction of allergic responses. Monocytes differentiate into M2 macrophages that produce factors important for tissue repair typically associated with parasite infections and resolution of damage linked to inflammation (right, panel B). A type-1 immune response suppresses a type-2 immune response and vice-versa. This figure was created with Biorender.com.    6 Viral sensing triggers the production of type-I interferons (IFNs) (e.g., IFNα and IFNβ) which signal through the type I IFN receptor (IFNAR) and the JAK/STAT signalling cascade (22). This prompts transcription of IFN stimulated genes (ISGs) and the production of a range of proteins essential for immediate host defense, inflammation, and immunomodulation (23). Characteristic, early inflammatory mediators of type-1 inflammation include monocyte chemoattractant protein-1 (MCP-1), IL-1β, IL-6, IL-12, IL-15, and IL-18 which are produced by local and infiltrating neutrophils, monocytes/macrophages and DCs upon type-1 IFN stimulation (24–26). ILC1s and NK cells also depend on type-I IFN signalling directly and indirectly (through type-I IFN induced expression of cytokines and chemokines) for their recruitment and activation, and for the production of the signature type-1 cytokines interferon gamma (IFNγ), tumor necrosis factor alpha (TNFα) and lymphotoxin (LT)-α (27–29). ILC1s do not possess endogenous cytotoxic properties, but instead act in support of NK cells that, like ILC1s, express the signature type-1 T-box transcription factor TBX21 (T-bet) but, unlike ILC1s, also express transcription factor eomesodermin (EOMES) and produce perforin and granzymes for targeted cell killing (30–32). Based on these transcription factor profiles and functions, the ‘helper’ ILC1s are the innate counterpart to CD4+ Th1 cells and NK cells are the innate counterpart to CD8+ Tc1 cells, which are the key adaptive cell subsets activated downstream of the innate response to a viral infection (33). Adaptive immune activation also depends on type-I IFN induced cytokine production and subsequent inflammatory monocyte recruitment, DC maturation and increased major histocompatibility complex class II (MHCII) expression and antigen presentation (34, 35). For adaptive cell activation and differentiation to occur, cell subsets capable of antigen presentation through the MHC complex, primarily DCs and monocytes, migrate to proximal lymph nodes where peptide-bound MHCI is recognized by CD8 T cell receptors while peptide-bound MHCII  7 is recognized by CD4 T cell receptors (36). In addition to antigen presentation, cytokines, especially IL-12 and IFNγ are critical for naive T cell differentiation into type-1 T cells (37, 38). Th1 and Tc1 cells migrate through the thoracic duct lymph and blood stream to the site of infection and like their innate counterparts, Th1 cells release IFNγ, LT-α and TNFα to support the pro-inflammatory environment, while Tc1 cells release cytokines and perform targeted killing of infected cells (39).  Another key output of adaptive immune activation is the B cell mediated production of pathogen specific antibodies. B cell activation, differentiation, and subsequent antigen specific immunoglobulin (Ig) production also depends on antigen presentation as well as co-stimulation by T helper cells (40). In response to a viral infection, IgM and IgG are the main antibody isotypes secreted and these bind specific pathogenic structures to enhance pathogen neutralization and detection. These bound antibodies also enhance pathogen and infected cell destruction by cytotoxic and phagocytic processes mediated through the recruitment of phagocytic and cytolytic cells via their expression of antibody Fc receptors (40). The latter process is initiated when cytokines produced by both innate and adaptive lymphocytes enable monocyte differentiation into proinflammatory M1 macrophages, which are effective phagocytes and further aid in viral clearance through release of proinflammatory cytokines, and production of an oxidative environment.   In summary, a type-1 immune response is a carefully orchestrated sequence of events that creates an inflammatory environment designed for pathogen elimination with multiple layers of defense.  8 The following section discusses how a failure to mount or control this response leads to immunopathology with far reaching effects on host as well as species survival.    1.2.2 Viral infections: the COVID-19 pandemic Viruses have long been recognized as the causes for major, globally impactful diseases (including influenza, Ebola, HIV, herpes, hepatitis and many more). However, since early 2020, viral infections and the importance of a functioning immune response received renewed attention with the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiological agent that causes COVID-19 (41, 42). This novel pathogen is driving an ongoing global pandemic due largely to its transmissibility and ability to be transmitted prior to symptom onset.   SARS-CoV-2 belongs to the family of coronaviruses that, in the case of severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV), previously caused outbreaks and severe disease but with smaller global impact (43). SARS-CoV-2 carries a positive-sense single-stranded RNA that is contained by envelope, membrane and spike proteins and the latter are critical for host cell infection. The SARS-CoV-2 spike proteins specifically recognizes the angiotensin-converting enzyme 2 (ACE2) receptor broadly expressed throughout the upper respiratory tract which is the primary site of infection (44). As of July 2022, the estimated number of reported global human infections is 558 million with a death toll of 6.36 million. In countries where advanced medical care is readily available, approximately 20% of infected individuals are hospitalized and 5% require admission to an intensive care unit (ICU) (41). Despite the development of highly effective vaccines and their  9 documented success in preventing death and hospitalization, COVID-19 remains a major threat that can overwhelm effective health delivery in many parts of the world due to a constellation of factors. These include challenges in achieving sufficiently high vaccination rates to prevent community spread, vaccine hesitancy and the emergence of more transmissible viral variants for which current vaccines offer more modest protection (45).  1.2.3 Immunopathology of COVID-19 Individual patient responses to infection by the SARS-CoV-2 virus can vary dramatically ranging from asymptomatic or mild flu-like symptoms to much more severe symptoms including acute respiratory distress syndrome (ARDS) and death (46, 47). Moreover, COVID-19 is now recognized as not only a pulmonary disease but a disease that, for a subset of patients, can cause symptoms and dysfunction systemically and sometimes long-term in the gastrointestinal track, liver and kidneys, heart, and brain (48). This is likely due to the broad expression pattern of the ACE2 receptor that is found in many tissues throughout the body (49). Immunologically, “severe” patients (i.e., those patients who must be hospitalized and/or admitted to the ICU) have been reported to exhibit lymphopenia, neutrophilia, accumulation of lung monocytes, emergency myelopoiesis, and substantial changes in serum cytokine and chemokine profiles likely reflecting a cytokine storm as the result of a delayed, but exuberant, immune response to infection (50–57).  Acute anti-viral type-1 immune responses are clearly critical for host defense, yet, compromised immunity and viral immune evasion mechanism can lead to chronic infection and inflammation with detrimental consequences for tissue integrity and function (Fig. 1.2).    10  Figure 1.2: Dysregulation of type-1 IFN responses as drivers COVID-19 immunopathology. Severe COVID-19 likely occurs due to ineffective type-I IFN responses that may be impaired due to age, medications, viral immune evasion mechanisms or genetic predispositions. This leads to the failure to mount a productive type-1 immune response through immunosuppressive mediators but instead leads to subsequent accumulation of excess innate inflammatory cells that contribute to organ damage instead of effective viral clearance. This figure was created with Biorender.com.  11 Many viruses, including SARS-CoV-2, have evolved to evade immune recognition through mechanisms such as the inhibition of type-I interferon induced signalling pathways (58, 59). Consequently, transcription of ISGs is reduced and COVID-19 patients with severe disease present with lower levels of ISGs in upper airways (60, 61). Type-I interferon signalling can further be disrupted by inborn errors in genes linked to the type-I interferon pathway or the presence of autoantibodies to type-I interferons (62, 63). Additionally, a compromised immune system (through advanced age, medications, or underlying medical conditions), may lead to lower levels of interferons (as well reduced numbers of critical immune cells and other cytokines) (64). One or a combination of these mechanisms may underlie chronic and/or delayed and low-level production of type-I interferons as opposed to an acute and effective induction of type-1 immunity. In the context of lymphocytic choriomeningitis virus (LCMV) infection of mice, chronic type-I interferon production has been shown to have immunosuppressive effects on key cell subsets involved in an anti-viral immune response (65). NK cell IFNγ production is suppressed under chronic type-1 interferon release and NK cells show upregulation of programmed death-ligand 1 (PD-L1) which contributes to the suppression of NK cell antiviral activity (65). Chronic type-I interferon release also reduces DC expansion and differentiation and reduces IFNγ receptor expression on macrophages and thus macrophage sensitivity to this cytokine (66). Additionally, the expression of proteins that inhibit inflammation, including PD-L1 and IL-10, are increased in macrophages and DCs (67, 68). These immunosuppressive features of a faulty IFN response to a viral infection may also explain subsequent T cell lymphopenia as well as the reduced antibody responses that are commonly observed in severe COVID-19 patients (69). However, autopsies have shown that severe organ damage in late stages of disease seem to stem primarily from excessive inflammation that lingers even after  12 most of the viral infection has been cleared. This destructive inflammation may also be the result of an early failure to mount an appropriate or timely type-I interferon response and the resulting delayed viral clearance (61). Type-I interferons control the influx of neutrophils and inflammatory monocytes and limit subsequent tissue damage (70). Additionally, type-2 immunity is largely inhibited by type-I interferon signalling but induces substantial tissue damage in the absence of type-I interferon control (71). Type-2 induced asthma exacerbations often occur after viral respiratory tract infection and evidence suggests that interferon responses may be the mechanistic link between these opposing immune responses (72). This intriguing crossover is of further interest for the topics of type-2 immunity and allergic disease discussed in the following sections.   1.3 Type-2 immunity and allergic diseases  1.3.1 Type-2 immunity: central players and mechanisms The type-2 immune response is targeted toward the clearance of large parasites, allergens and tissue damage and has been studied extensively in the context of barrier organs such as lung, intestine and skin (14). To this end, main functional outputs of this immune response include mucus production, smooth muscle contraction as well as tissue remodelling and repair, known colloquially as the “weep and sweep” response. Distinguishing type-2 immune cell types include the ‘helper’ lymphocytes, ILC2s and Th2 cells, as well as myeloid cells including anti-inflammatory M2 macrophages, eosinophils, mast cells and basophils. Comparable to Tc1 cells, Tc2 cells also emerge during a type-2 immune response, but lack the cytotoxic abilities and instead contribute to cytokine production (73) (Fig. 1.1B).    13 Classically, stroma is considered to be the structural support of tissues and organs as well as a source of trophic factors for tissue homeostasis and repair (74). Stromal cells are highly active in communication with other local cells and release signaling molecules at steady state (in response to metabolic or mechanical changes) as well as under an inflammatory condition and in response to tissue damage. Damage to stromal cells (e.g., by allergen or parasite proteases) at mucosal surfaces triggers a type-2 immune response through the release of alarmins, including IL-33, IL-25 and thymic stromal lymphopoietin (TSLP) from injured or dying cells (75, 76). The IL-33 receptor (T1/ST2) is found on several immune cells and the binding of IL-33 to ST2 and to the co-receptor IL-1RAcP leads to GATA binding protein 3 (GATA3) phosphorylation (77). Comparable to Tbet and type-1 immunity, GATA3 is the signature type-2 immune response transcription factor and interacts with the regulatory regions of type-2 cytokine genes, including IL-4, IL-5, and IL-13 to initiate their transcription (78). Therefore, IL-33 is a potent activator of a type-2 immune response, specifically through the activation of ILC2s (79, 80). ILC2s, which resemble Th2 cells in terms of transcription factor profile and cytokine production, are primarily tissue resident and are prominent in lung, intestine, skin, and adipose tissue, where they participate in generating a type-2 immune response through the production of the cytokines IL-4, IL-5, IL-9, and IL-13 (81, 82). ILC2s rely on the transcription factors retinoic-acid-receptor-related orphan receptor alpha (RORα) and GATA3 for their development and function (83). The downstream effects of ILC2 activation include DC migration, naive CD4 T cell differentiation into Th2 cells and naive CD8 T cell differentiation into Tc2 cells, B cell class switching, and subsequently IgE and IgG1 release, as well as the recruitment of eosinophils, basophils, and mast cells (84–87). These are key features of a type-2 response and lead to symptoms that are commonly associated with that of an allergic response such as histamine release, mucus  14 production, and smooth muscle contraction. Stromal cells and ILC2s also interact bi-directionally since ILC2s have been shown to directly modulate stromal cells through cytokines and growth factors. ILC2-derived IL-13 can induce goblet and tuft cell differentiation in the intestine and disrupt epithelial integrity in the lung (76, 88). ILC2-derived amphiregulin (Areg), a member of the epidermal growth factor family of signaling factors, enables epithelial repair both in the intestine and the lungs following damage and inflammation (89).   The alarmin-mediated ILC2 proliferation and expression of type-2 cytokines is essential for the recruitment and maintenance of eosinophils that are widely recognized as important inflammatory cells in allergic responses including asthma for which eosinophilia is one of the hallmarks (90, 91). ILC2-derived IL-5 and stroma derived eotaxins are critical for eosinophil recruitment, survival, and function (92). Eosinophil specific compounds, such as major basic protein (MBP) or eosinophil peroxidase (EPO) are released following degranulation in response to an inflammatory environment and are needed for the clearance of parasitic infections (93). Additionally, eosinophils are an important source of type-2 cytokines, such as IL-4 and IL-13, and thereby reinforce a type-2 immune response and, in later stages of disease, likely aid in tissue remodeling and repair (94).   ILC2- and eosinophil-derived cytokines further determine the inflammatory phenotype of macrophages, which are likewise recruited in response to inflammatory stimuli (95, 96). Unlike the IFNγ- and TNFα-dominated environment of a type-1 response that leads to classically activated (M1) pro-inflammatory macrophages, eosinophil- and ILC2-elaborated IL-4 and IL-13 favor transition to an anti-inflammatory M2 phenotype. Both IL-4 and IL-13 activate  15 JAK/STAT6 signaling and the downstream production of M2 specific proteins, such as arginase-1 and the mannose receptor, CD206. M2 macrophages have a reparative and resolving phenotype resulting in the production of growth factors such as vascular endothelial growth factor (VEGF) and transforming growth factor beta (TGFβ) but also anti-inflammatory cytokines such as IL-10 (97). This state of macrophage activation is necessary for repair following tissue damage and, in many cases, is also necessary for normal tissue function for which a pro-inflammatory environment would be detrimental (98). However, prolonged M2 survival and activation can lead to excess TGFβ accumulation, which in turn can lead to repetitive fibroblast activation, excess collagen and other extracellular matrix component deposition.  T regulatory cells (Tregs) are another subtype of CD4+ T helper cell and play essential roles in the control and resolution of inflammation. In addition to CD4 expression, Tregs can be identified by the expression of the transcription factor forkhead box P3 (FOXP3), as well as the beta chain of the IL-2 receptor (CD25) (99). Treg subsets include thymus-derived natural Tregs (nTregs) or induced Tregs (iTregs), which arise in the periphery in response to IL-2 and TGFβ signaling (100). These signaling events result in the elevated production of FOXP3 and subsequently TGFβ and IL-10. IL-10, in turn, is a potent anti-inflammatory cytokine that exerts its function on many different cell types, including macrophages as well as ILC2s, which both express the IL-10 receptor (101). In addition to dampening ILC2 proliferation and cytokine production via IL-10, Tregs also inhibit ILC2s through direct inducible T-cell co-stimulator- (ICOS) mediated cell–cell contact (102). This is likely complemented by their competitive sequestration of IL-2 and IL-33 since Tregs express the receptors for both cytokines (103). Stroma-derived IL-33 can therefore also directly act on Tregs and this has been shown to  16 promote both Treg accumulation and differentiation (104–106). In summary, a type 2 immune response involves multiple cell subsets that act in a coordinated fashion to elicit inflammation but also an environment suited to tissue repair processes.  An unbalanced, chronic type-2 immune response is most frequently associated with allergic diseases. Peripheral blood of chronic asthmatic patients shows elevated levels of IL-5- and IL-13- producing ILC2s, which is paralleled in patient airways by increases in IL-33 and eosinophilia (107, 108). Mouse models also confirm the critical role of type-2 immunity in allergic diseases as, for example, mice deficient in ILC2s show greatly attenuated inflammation following intranasal allergen treatment (109, 110). The following sections introduce allergic diseases and the impacts of early life events that may underlie the exacerbated type-2 immune responses that lead to chronic inflammation in children and adults.   1.3.2 Allergic diseases Allergic diseases are a highly heterogenous group of conditions centered around a hypersensitive immune response against non-self, but otherwise harmless, environmental chemical stimuli (allergens) (111, 112). Atopy, defined as the predisposition to develop IgE-mediated sensitivity to common allergens, often precedes and predicts the development of allergic disease, particularly asthma (113). One in every four people in industrialized nations develop some form of allergic disease which include rhinitis, conjunctivitis, rhinosinusitis, asthma, atopic eczema, anaphylaxis, urticaria, angioedema, as well as food, drug and insect allergies (114). These heterogenous phenotypes can be triggered by a host of different allergens (pollen, food, house dust mite, animal fur and many more), which are frequently found in the environment and thus  17 pose a continuous risk of exposure, a serious impact on health and quality of life of the affected individual and a tremendous burden on health care resources (111, 114).   Broadly, an increased incidence of allergic diseases has been linked to changing lifestyles and environmental exposures such as urbanization, increased hygienic practices, antibiotics, westernized diets, pollution, loss of biodiversity and climate change (115–117). Additionally, small nucleotide polymorphisms in genes including IL33, TSLP, and IL1RL1 (ST2) have been associated through genome-wide association studies (GWAS) with an increased susceptibility to certain allergic diseases. Allergic disease heritability estimates range from as low as 30% to as high as 95%, depending on the study and type of allergic disease (118–121). Thus, while there is strong evidence for a genetic component to allergic disease, the rapid rise in recent decades strongly argues for an environmental component as well.  In the clinic, skin prick tests that involve skin exposure to allergens as well as blood tests for IgE antibody titre analysis, are used to diagnose allergic diseases (122). However, while these tests, on the individual level, provide strategic information on how to modify the environment to remove allergic triggers and enable appropriate medical intervention in the case of exposure, they do not clarify, mechanistically, how, and when immune dysregulation and subsequent allergen sensitization is established; the critical information required for disease prevention.  1.3.3 Postnatal links with allergic disease susceptibility  Allergic diseases most commonly present during childhood and, in line with disease heterogeneity, several early childhood events and exposures have been linked to disease  18 susceptibility (123). Central to this link, and articulated in the well-known “hygiene hypothesis”, is that microbial exposures and communities (or lack thereof) shape immune responses to environmental stimuli in the developing infant (124–126). Current evidence suggests that the placenta and umbilical cord lack a unique microbiome and that infant microbial colonization does not occur prior to birth (127). Instead, infants are colonized shortly after birth primarily with maternal microbiota acquired through vaginal birth, breastmilk, and skin (128). Cesarian births have been shown to alter the bacterial colonization due to the lack of exposure to vaginal bacteria and this has been linked to greater future allergic disease susceptibility (129, 130). This susceptibility is also seen in pre-term infants who show altered microbial colonization often due to medical interventions, hospital environments and lack of maternal contact (131). Interestingly, the differences in microbial colonization observed depending on mode of birth and gestational age are only detectable in the short-term and can no longer be observed once a child reaches three months of age (132). Yet, the link between these early life events, microbial dysbiosis and allergic disease susceptibility, is strongly supported by data from both human and animal studies with long-term immune modulation as the mechanistic link (123). At birth, infants are particularity susceptible to infections since the immune system is generally naive and less responsive compared to adult immunity (133). While this leaves children vulnerable in early life, it also enables the development of tolerance to microbial communities and harmless environmental stimuli. In fact, many pathogens, including helminths, bacteria and viruses trigger immune tolerance through the induction of immune regulatory mechanisms that suppress inflammation (133). These mechanisms center around an increased number of Tregs and expression of immunosuppressive molecules such as IL-10 and TGFβ (134).  19 Thus, while modern medical care and a hygienic early-life environment have drastically reduced infant death rates in developed countries due to minimal exposure to harmful pathogens, the trade-off is a frequently dysfunctional immune response with life-long consequences, including increased risk of allergic disease and development of autoimmune disease. Taken together, microbial dysbiosis particularly during the critical time of post-birth bacterial colonization and the close relationship and impact of the microbiome on immune development has emerged as a central theme underlying disease susceptibility.    1.3.4 Prenatal influences on allergic disease susceptibility Based on the evidence presented above, exposures that take place during the early, postnatal period are primarily considered to have an outsized impact on shaping the developing immune system and, in turn, future disease susceptibility. Yet, immune development starts prenatally and the passage of infectious, as well as non-infectious, agents across the placenta and the emergence of fetal antigen specific as well as antigen non-specific responses have been documented (Fig. 1.3) (135–138). While under normal conditions, maternal cells and microbes do not cross the placental barrier, the fetal immune environment is shaped through the transfer of maternal immune modulatory molecules. These include antibodies, antigens and microbial metabolites and can also include the vertical transfer of some pathogens. Specific examples include maternal IgG that crosses the placenta via the neonatal Fc receptor (FcRn) and provides the fetus with passive immunity, which is a critical first line of defence during development (139, 140). In mice, maternal-fetal transfer of IgG has further been shown to provide immunologic tolerance and subsequent protection from allergic disease in the offspring (141). A recent study has further provided evidence that IgE is also able to cross the placental barrier in mice and subsequently  20 prime fetal skin mast cells (142). However, this has not been shown conclusively in human studies and current evidence suggests that if IgE does pass across the placenta, it does so as an immune complex with IgG as IgE alone cannot bind FcRn (142, 143).   Figure 1.3: Prenatal exposures protect from or drive disease susceptibility.  Maternal inflammatory mediators including pathogen induced PAMPs, maternal cytokines, chemokines, antibodies, and bacterial metabolites likely cross the placental barrier during fetal development and shape the development of the fetal immune system. Maternal antibodies are critical for passive immunity while other inflammatory mediators may induce increased levels of inflammation or may induce tolerance in the fetus, likely depending on type, length, and severity of the exposure. Generally, elevated fetal inflammation (e.g., due to maternal viral infection) is associated with increased risk of allergic or autoimmune diseases later in life, while a healthy maternal microbiome and transfer of microbial metabolites as well as parasite infections are generally associated with tolerance and reduced risk of allergies and autoimmunity. This figure was created with Biorender.com.    21 Maternal infections during pregnancy have also been shown to alter fetal immunity even in the absence of fetal infection, which suggests that maternal inflammatory molecules may pass to the fetus. For example, uninfected infants of HIV+, malaria infected or hepatitis B+ mothers show higher levels of inflammation in cord blood and postnatally, while type-1 responses to the bacille Calmette-Guerin (BCG) vaccine of newborns from helminth-infected mothers are impaired (144). The latter may be a result of an induction of higher TGFβ and IL-10 levels in the fetus that have been documented in response to helminth infections (145–148). In mice, maternal bacterial exposure and TLR-mediated maternal signalling has been shown to mediate protection from asthma development in the offspring (136).  While microbes do not cross the placental barrier, several studies in mice suggest that bacterial metabolites, especially short-chain fatty acids (SCFAs), impact susceptibility to allergic disease and that susceptibility of the offspring can be altered through SCFA supplementation during pregnancy (149–151). In humans, low levels of the maternal serum SCFA acetate has been associated with increased risk of preeclampsia and subsequent atopic disease in the offspring (151). This suggests that the transfer of metabolites to the fetus can induce long-term changes in immune responses.   These mechanisms of maternal-fetal molecular interactions indicate that the maternal immune environment can influence fetal immune development and thus it is not surprising that maternal behaviours and exposures have also been linked to either protective or dysregulated immune responses in the offspring. For example, maternal exposure to farm environments reduces allergic disease susceptibility in the offspring and this corresponds to increased counts and  22 efficiency of cord blood Tregs, which, intriguingly have been found to be functionally impaired in cord blood of children from atopic mothers (152, 153).   As mentioned previously, maternal infections also shape the fetal inflammatory landscape, and this has also been shown for medications consumed during pregnancy in response to infections. Accordingly, an increased susceptibility to allergic disease has been documented in mouse models and in children from mothers who were exposed to antibiotics or anti-helminth therapy during pregnancy (154–157).   It is still unclear, how exposures experienced by the fetus can not only shape the immune response during pregnancy but maintain immune changes long term. Compared to adult PBMCs, the fetal immune system, as observed in studies of cord blood mononuclear cells (CBMCs), shows that, broadly speaking, all major immune lineages (including both innate and adaptive subsets) are present in fetal circulation at time of birth. However, fetal cord blood shows near absence of mature subsets including memory B cells, MAIT cells, γδ T cells and memory T cell subsets and instead expanded innate subsets such as stem cells, monocytes, and NK cells (Fig. 1.4). Only throughout childhood, circulating immune cells develop a mature phenotype and, consequently, cells found in fetal circulations are unlikely to contribute to long-term memory underlying subsequent disease susceptibility (158, 159). Thus, the focus has shifted to the study of those cell types that are derived from the fetus, seeded in tissues early in life and that are functionally on the border of inflammation and tissue homeostasis such as ILCs, mast cells, and long-lived macrophages (160, 161). In fact, studies in mice have shown that lung ILC2s subjected to an allergic challenge early in life contribute to an exacerbated response in the adult  23 (162–164) and absence of maternal microbiota during pregnancy alters immune populations, including ILC3s, in the offspring (165). Another intriguing hypothesis includes the sensing of inflammatory stimuli directly by fetal hematopoietic stem cells or progenitor cells which may skew hematopoiesis long term and perhaps lifelong.    Figure 1.4: Side-by-side comparison of human fetal and adult immune populations. Fetal cord blood mononuclear cell (CBMC) populations with broad labels and manual gating (dotted lines) for main populations (A). Adult peripheral blood mononuclear cell (PBMC) populations with nuanced labelling of immune subpopulations (B). This figure was derived from UMAP plots of personally generated CMBC and PBMC mass cytometry data.   Memory CD4 TStem cellsCD4 TMyeloidNKBCD8 TMAIT cellsMemory BClassical MonoIntermediate MonoNon-classical MonoNaive BMemory CD8 Tγδ T Cytotoxic NKCytokine producing NKpDCcDCNaive CD8 TCBMCs PBMCsNaive CD4 TCord blood Adult bloodA B 24 1.4 Immune responses in sterile injury  1.4.1 Acute skeletal muscle inflammation and regeneration The above sections explore how the immune system develops and responds in the context of pathogens and other external stimuli and its critical role in host survival but also detrimental effects if left unchecked. However, external environmental stimuli are not the only triggers that elicit an immune response and immune cells do not just aid in pathogen clearance, but also play essential roles in normal tissue repair and regeneration independent of pathogens.   A tissue that is very well known for its regenerative abilities is skeletal muscle which, in healthy individuals, fully restores muscle structure and function in response to acute, sterile damage that is often experienced after strenuous exercise (166). This efficient and complete regeneration is attributed to a well-orchestrated immune response and interplay of immune and muscle resident cells (Fig. 1.5) (167). Like wound healing, muscle regeneration moves through different stages of immune activation that, immediately after damage, is dominated by a type-1 pro-inflammatory response. Over time, this transitions to a type-2 environment that is thought to enhance repair (168, 169). In this temporal sequence, muscle resident mast cells and macrophages are main sources of cytokines, chemokines and DAMPs that promote leukocyte recruitment and the release of pro-inflammatory cytokines, including IFNγ and TNFα, primarily by infiltrating neutrophils and macrophages (170–172). This enables satellite cell (muscle stem cell) activation and proliferation, which generate the pool of muscle progenitors (MPs) that eventually form new muscle fibers. As a true stem cell, satellite cells also self-renew, allowing the tissue to fully regenerated through life.     25  Figure 1.5: Skeletal muscle regeneration. Acute muscle fiber damage triggers pro-inflammatory immune cells to enter the muscle while satellite cells begin to proliferate and generate a pool of muscle progenitors (MPs). Fibro/Adipo progenitors (FAPs) also proliferate and replace the broken extracellular matrix (ECM) during regeneration as well as orchestrate the recruitment of inflammatory cells through the elaboration of chemokines and growth factors. A shift from a type-1 inflammatory environment to a type-2 anti-inflammatory environment allows MP differentiation into muscle fibers. This figure was created with Biorender.com.   Days post injury (dpi) 1 3 5 7  26 In this early phase, macrophages clear debris and necrotic cells through phagocytosis (173). However, the persistence of pro-inflammatory cytokines prevents muscle cell differentiation and, therefore, a change in immune environment is required for the recruited macrophages to transition towards a pro-repair phenotype (174). Several different events have been explored as possible triggers for this phenotype switch, including intrinsic metabolic changes because of phagocytosis, differential signaling by lipid mediators due to lipid mediator class switching, as well as a change in cytokine environment (169, 175, 176). Eosinophils are known to enter the muscle following damage and are a local source of IL-4 and IL-13 at the time of macrophage phenotypic transition (94). These cytokines signal through the IL-4R expressed on a muscle resident population of mesenchymal progenitors, which have been dubbed fibro/adipo progenitors (FAPs) (177). FAPs support the repair of the basement membrane during normal muscle regeneration. Under chronic inflammatory conditions, FAPs differentiate into adipocytes and myofibroblasts and mediate fibrosis development which is defined by the excess deposition of collagen instead of normal tissue (178, 179). During normal muscle regeneration, IL-4/IL-13 signaling prevents the differentiation of FAPs into adipocytes and enhances their release of trophic factors to support muscle fiber development. Since eosinophil-derived cytokines are known to trigger and maintain an M2 phenotype in other settings, it is likely that eosinophils are recruited to the muscle at this critical time during regeneration to either initiate or support macrophage phenotypic switching in addition to their established interactions with FAPs (96). However, the signaling events that lead to eosinophil recruitment are currently not fully established. In contrast, it is known that, like stromal cells in adipose tissue, FAP-like cells release IL-33 at steady state, which is essential to the maintenance of muscle resident Tregs and the enhancement of Treg accumulation after damage (180, 181). Tregs are an important source of  27 IL-10 as well as Areg during muscle regeneration, and the release of IL-10 is impaired upon Treg depletion. M1 to M2 macrophage phenotypic switching requires IL-10 and the loss of IL-10 prolongs inflammation and impairs muscle regeneration (182, 183). Collectively, these suggest that the key components of a type-2 immune response, including stroma-derived IL-33, eosinophil recruitment and IL-4/IL-13 signaling, and M2 and Treg accumulation, are present in damaged muscle tissue. The role of ILC2s in normal muscle regeneration is not defined but based on ILC2 functions in other settings, these may also enter muscle after injury and potentially maintain eosinophils through IL-5 signaling. ILC2s may further contribute directly to muscle repair through the release of Areg since ILC2s contribute in this manner to the restoration of barrier tissue homeostasis in other settings (89).   1.4.2 Chronic muscle inflammation and degeneration Chronic muscle inflammation is associated with muscular dystrophies and leads to fibrosis at the expense of muscle regeneration (184). One example of this phenotype is the heritable condition Duchenne muscular dystrophy (DMD) which is inherited in a X-linked recessive manner and thus is found almost entirely in males (185). Patients usually do not live past their twenties and symptoms start to show around four years of age in the form of weakness in voluntary muscles that progresses to a complete inability to walk by the time a patient reaches their early teens. The genetic mutation is found on the X chromosome in the DNA region that encodes the protein DYSTROPHIN which is essential for muscle function and stability (185). In DMD patients, DYSTROPHIN is absent leading to myofiber membrane destabilization, chronic muscle damage and inflammation which, eventually, causes the replacement of normal muscle fibers with excess fat and collagen. Thus, fibrosis is a key feature of DMD disease.   28 Mechanistic studies of DMD are conducted commonly in the mdx mouse model in which the animals carry the Dmd mutation and eventually develop muscle fibrosis, although mdx mice present with a milder form of the disease compared to humans (186, 187). The mdx mouse model is further characterized by an inflammatory period in early life (age 3-6 weeks) and a fibrotic period that is seen in adult mice (starting around 8 weeks of age). As inflammation plays a key role for muscle regeneration and is also recognised as an essential component of DMD muscle pathology, some of the key components of chronic muscle inflammation that are relevant for this thesis are presented in the following.   Type-2 immune cells have been studied in the context of muscular dystrophy and, recently, ILC2s were characterized during the inflammatory period in 4-week-old mdx mice. At this time point, ILC2s were found elevated in muscle lesions and essential for maintaining eosinophils through IL-5 production (188). Eosinophils, in turn, have been implicated in having a deleterious role in mdx mice where chronic inflammation triggers eosinophil degranulation and the release of MBP that contributes to chronic muscle damage (189). This is supported by recent findings that IL-5 transgenic mice show worsened pathology in the fibrotic stages of dystrophy in mdx mice (190). However, this contrasts findings from the early inflammatory stage where neither eosinophil absence nor overexpression is associated with a difference in pathology (191). The expansion of Tregs via IL-2C treatment significantly decreases muscle fiber damage in dystrophic mice due to elevated IL-10 production that limits type-1 inflammation (192). The balance between pro-inflammatory and anti-inflammatory macrophages is particularly interesting since muscle fiber damage can be largely attributed to a type-1 immune environment but subsequent fibrosis development is driven by M2 macrophage-derived excess TGFβ that triggers  29 excess collagen production by FAP-derived myofibroblasts (193). However, as discussed above, both types of macrophages serve critical functions for muscle regeneration and therefore the full ablation of one type or one cytokine may not support long-term recovery of damaged or dystrophic muscle. Therefore, the subtle manipulation of innate responses and macrophage phenotypes rather than the ablation of inflammatory cell types may be a more suitable treatment approach. Such a type-2- and innate-response-focused approach could prove pivotal in extending the health and life span of individuals with these devastating diseases.  1.5 Research outline  The literature presented in this chapter exemplifies the complexity of the immune system and its responses in the context of different types of disease: viral, allergic, sterile/genetic. In this thesis, I explore immune responses in these diverse settings to gain a comprehensive understanding of immune development, characteristics, and function both focused on clinically translatable research in human subjects as well as mechanistic studies in mouse models. To this end, I hypothesize that high dimensional immune profiling of the adult human immune response to viral infection (type-1 immunity) and of the fetal human immune system in the context of childhood allergic disease (type-2 immunity), can reveal distinct and predictive biomarkers to aid with disease diagnosis, intervention, and prevention. I further hypothesize that targeted immune manipulation, independent of external pathogens, specifically type-2 immunity in the context of skeletal muscle damage and degeneration, alters internal repair and regeneration processes and the course of genetically inherited pathologies.   These hypotheses were tested as outlined in Chapters 2-4 with the following specific objectives:  30 Objective 1 (Chapter 2): Prospective high-dimensional immune cell and cytokine profiling in COVID-19 patient peripheral blood to predict length of ICU stay and/or death.  Objective 2 (Chapter 3): High-dimensional immune cell profiling in archived umbilical cord blood to predict the development of childhood allergic disease.  Objective 3 (Chapter 4): Modulation of type-2 immune responses in murine acute and chronic skeletal muscle damage, to decipher the role of type-2 immune cells in normal tissue regeneration and pathologic tissue degeneration.    31 Chapter 2: Prognostic peripheral blood biomarkers at ICU admission predict COVID-19 clinical outcome  2.1 Introduction   COVID-19 continues to overwhelm effective health care delivery in most parts of the world due to challenges in achieving sufficiently high vaccination rates, vaccine hesitancy and the emergence of more transmissible viral variants for which current vaccines offer more modest protection. Thus, waves of rapid outbreaks continue to threaten ICU capacities (41, 42).  Individual patient outcomes are remarkably challenging to predict but severe disease has been broadly linked to advanced age, obesity, underlying comorbidities and secondary infections (69, 194–198). Neither symptoms nor conventional clinical laboratory measurements (serum C-reactive protein (CRP), blood D-dimers etc.) have sufficient prognostic power and, thus, approved interventions for severe COVID-19 (including systemic corticosteroids and tocilizumab) are administered broadly to patients admitted to the ICU as clinicians lack the tools to identify accurately patients at risk of long-term complications and death (199–203). Systemic cytokine and chemokine profiles have been of particular interest for the development of prognostic tools but, while some markers have proven useful in measuring the severity of active COVID-19, to date they have lacked the necessary statistical power to prospectively predict the likelihood of incipient severe disease (204–211). For example, serum IL-6 (alone, or together with other inflammatory markers) has most consistently been linked to severe active disease and, by some groups, was shown to predict the need for subsequent mechanical ventilation as well as survival (212–216). In contrast, other studies have struggled to link tightly serum IL-6 (or TNFα, IFNγ or GM-CSF) to an elevated risk of severe disease and instead have proposed various  32 combinations of serum levels of CCL5, IL-1RA and IL-10, EN-RAGE, TNFSF14 and oncostatin M as indicators of incipient severe disease and, in some cases, predictors of disease severity (217–219). While, individually, these studies show several biomarkers capable of triaging patients, the inconsistent and sometimes contradictory results highlight the need for biomarkers with robust statistical power to be clinically useful.  Studies focused on cellular changes have linked a decreased frequency of monocytes (and, more variably, alterations in the frequency of natural killer cells (NK), plasmacytoid dendritic cells (pDC), type-2 conventional dendritic cells (DC2), mucosal associated invariant T (MAIT) cells and other lineages) with active severe disease and poor outcomes (51, 53, 214, 220–224). While these global cellular profiling efforts provide important insights into the immune response to SARS-CoV-2 infection, they have yet to be translated into prognostic tools to assist with individualized care.  This chapter focuses on the development of an immunological biomarker screen that, at ICU admission for COVID-19, predicts length of ICU stay or death. Strikingly, we find that, at ICU admission, measurements of serum IL-10 and simple monocyte subset surface signatures, specifically, CD11clow classical monocytes, can predict with 91% sensitivity and 91% specificity patients who will either die or have a longer stay in the ICU. We offer these biomarkers as a model clinical laboratory test with future potential in gaining insights into variable responses to SARS-CoV-2 infection.    33 2.2 Materials and methods  2.2.1 Study design This study was approved by the University of British Columbia Clinical Research Ethics board (H20-00685) and patient blood was collected at St. Paul’s Hospital and Vancouver General Hospital in Vancouver, BC following informed consent. All COVID-19 patients had a positive nasal or tracheal real time reverse transcription polymerase chain reaction (RT-PCR) SARS-CoV-2 test. To avoid unnecessary virus exposure, patient blood was collected in combination with routine care. Patient samples (n = 90) were collected within 48 hours of ICU admission between November 2020 and February 2021. Patient demographics and clinical information are listed in Table 2.1. Patient samples were transported to the main campus of the University of British Columbia for further processing. Healthy control blood samples (n = 8) were collected from age-matched volunteers who showed no COVID-19 symptoms or other illnesses and who had no history of COVID-19.   2.2.2 Specimen collection and isolation Blood designated for peripheral blood mononuclear cell (PBMC) analyses was collected into citrate-coated BD Vacutainer™ Glass Mononuclear Cell Preparation (CPT) tubes and PBMCs were isolated within four hours following collection according to manufacturer guidelines. Red blood cell lysis was performed with ACK lysing buffer (Gibco) for 10 minutes. Isolated PBMCs were frozen in fetal bovine serum (Gibco) with 10% dimethyl sulfoxide and stored in liquid nitrogen. Blood designated for serum analyses was collected into BD Vacutainer™ Serum Separation Tubes (SST) and allowed to clot for at least 30 minutes prior to centrifugation at 1200 rcf for 10 minutes and serum collection and storage at -80 °C.   34 2.2.3 Antibody staining and CyTOF data collection Frozen PBMCs were thawed at 37 °C and washed with RPMI 1640 containing 10% FBS and 25U nuclease (Thermo Fisher Scientific, Cat. #88700). Between 1-4 x 106 cells per sample were used for antibody staining. Prior to fixation, all centrifugation steps were performed at 500 rcf and 4 °C. All reagent dilutions were prepared according to manufacturer instructions unless stated otherwise. For live/dead cell analysis, PBMCs were stained with Cell-ID™ Intercalator-Rh (Fluidigm, Cat. #201103A) for 15 minutes at 37 °C and washed with MaxPar® MCSB. Prior to surface staining, cells were incubated with human TruStain FcX™ (Biolegend, Cat. #422302) for 15 minutes at 4 °C and stained with a surface antibody cocktail for 30 minutes at RT (see Appendix A.1 for complete list of antibodies). The MR1-5-OP-RU tetramer was incubated together with the antibody cocktail. After incubation, the cells were washed and incubated for 30 minutes at RT with the secondary anti-APC antibody. Prior to fixation and nuclear staining, PBMCs were washed with MaxPar® MCSB (Fluidigm, Cat. #201068) and incubated in MaxPar® Fix and Perm Buffer (Fluidigm Cat. #201067) and Cell-ID™ Intercalator-IR (Fluidigm, Cat. #201192A) for 1 hour. Post fix, all centrifugation steps were performed at 900 rcf and 4 °C. To prepare for CyTOF acquisition, PBMCs were washed with MilliQ water and resuspended in EQ™ Four Element Calibration Beads (Fluidigm, Cat. #201078). Samples were acquired in batches of ~15 samples/acquisition with a CyTOF®2 mass cytometer. Batch effects were controlled with a PBMC run control (separate frozen aliquots from the same blood draw/individual).  An average of 400,000 events were collected for each sample at a flow rate of 45µl/min.     35 2.2.4 Cytokine data collection Serum cytokines IL-6, IL-10, TNFα and IL-1β were quantified using the Simoa HD-1 platform from Quanterix (Billerica, MA) according to manufacturing guidelines and as specified by Stukas et. al. (225).  2.2.5 Data processing  All data files were normalized (https://github.com/nolanlab/bead-normalization) and events of interest were manually gated with the FlowJo gating software (BD Biosciences). Dimensionality reduction and clustering were performed with Uniform Manifold Approximation and Projection (UMAP) and Rphenograph respectively, as provided in the bioconductor package Cytofkit (https://github.com/JinmiaoChenLab/cytofkit2). The input files were equally down sampled (usually 200k for ungated analyses and 100k for pre-gated analyses) and negative value pruned inverse hyperbolic sine transformation (cytofAsinh) was used as the transformation method (to remove negative values introduced artificially during CyTOF acquisition). The Rphenograph k (number of nearest neighbours) was set to the default of 30. The dimensionality reduction and clustering were both performed on the entire data set as well as separately on manually pre-gated populations. The assignment of clusters to specific immune cell subsets based on surface marker expression was guided by the cell-type definitions described in Appendix A4.  2.2.6 Statistical analysis and figures Sample size and statistical tests are indicated in figure legends and all graphs and statistical tests were generated using GraphPad Prism (GraphPad Software, La Jolla California, USA). A test was considered statistically significant at a probability of < 5% (p < 0.05) and we did not assume  36 a Gaussian distribution. Where indicated, the p-values for independent t-tests were adjusted for multiple comparisons (Bonferroni). Data are mean ±  SD. UMAP plots and heatmaps were exported from Cytofkit and experimental outline figures, including the graphical abstract were created using BioRender.com. Figures were assembled in Microsoft PowerPoint.    37 2.3 Results   2.3.1 COVID-19 patient group selection and optimization of immune profiling To identify potential prognostic markers of COVID-19, we obtained, within the first 48 hours of ICU admission, serum samples from 90 ICU COVID-19 patients (the “Cytokine Cohort”) admitted during the “second wave” of COVID-19 (November, 2020–February, 2021) together with serial serum samples across different timepoints during the course of their ICU stay. PBMCs obtained from 14 of these 90 ICU COVID-19 patients (the “Initial Cohort”) were analyzed by mass cytometry (CyTOF) with a training set of 35 monoclonal antibodies (Appendix A.1: Initial Cohort) designed to detect broad shifts in levels of normal PBMC lineages as well as their activation status and the possible mobilization of tissue resident innate immune cells and bone marrow progenitors into peripheral blood. Based on these data we developed a refined, second-generation, 38-antibody CyTOF panel (Appendix A.1: Replication Cohort), which was used on PBMCs obtained from a further 28 of the 90 ICU COVID-19 patients (the “Replication Cohort”). Data from the Replication Cohort were used to validate observations from the Initial Cohort on early alterations in immune responses that could effectively differentiate patients likely to recover after a short ICU stay from those who would either die or have prolonged stays in the ICU (Fig. 2.1A, B). The ICU admission sera from all patients in the Cytokine Cohort (which includes all patients in the Initial Cohort and the Replication Cohort) were analyzed for levels of four cytokines: IL-1β, IL-6, IL-10 and TNFα. PBMC, but not serum samples, were obtained for 8 healthy, age-matched controls for the CyTOF analyses portion of this study.     38  Figure 2.1: Study design and patient characteristics.  Experimental design overview: peripheral blood was collected from COVID-19 patients within 48h of ICU admission; immune cells and serum were isolated and stored followed by immune cell subset and cytokine analyses and clinical data integration (A). Patient cohorts overview: Initial Cohort Short-Stay (SS) patient n = 7, Long-Stay/Died (LS/D) patient n = 7; Replication Cohort SS patient n = 12, LS/D patient n = 16 (B). Patient outcome groups based on length of stay in ICU (C). Patient age, body mass index (BMI), C-reactive protein (CRP) levels and D-dimer levels (D-E). Complete blood counts of patients and healthy controls (F). *, p < 0.05; **, p < 0.01; ***, p < 0.001; ns, p ≥ 0.05 by two-tailed, two-sample unequal variance Student’s t-test.    SS LS/D01020304050BMISS LS/D010203040Days in ICU SS LS/D020406080100Age (years)SS LS/D050100150200250300CRP (mg/L) SS LS/D05000100001500020000D-dimer (ng/ml)Replicationumap_2umap_1Healthy Control (HC)Short-Stay (SS)Long-Stay/Died (LS/D)*Blood collection at ICU admissionBlood component isolation*Immune profiling and data integration to predict disease outcomeA Study cohortsLong term storageD E FhGCH IBn=14 n=28n=90ns ns ns ns ns*******ns******** *** ***Predicted outcome groupsCytokineInitialSS LS/D01234IL-1β (pg/ml)SS LS/D0250500750100012501500IL-6 (pg/ml)SS LS/D020406080 Max TNFα (pg/ml)SS LS/D020406080250300Max IL-10 (pg/ml)SS LS/D020406080250300IL-10 (pg/ml)SS LS/D020406080TNFα (pg/ml)HC SSLS/D0246810PBMCs (x109 /L)HC SSLS/D0510152025PMNs (x109 /L) 39 Clinical and demographic details of all patients and healthy controls are presented in Table 2.1 and include age, sex, body mass index (BMI), requirement for ventilation during ICU admission, admission levels of serum CRP, blood D-dimer levels and white blood cell counts along with their differentials. The average age of the ICU patients was 63.5 years with a 2-to-1 bias towards male patients, consistent with previous patient demographic reports linking more severe COVID-19 with older male patients (226). Table 2.1 also bins patients into two clinical outcome groups of “Short-Stay” and “Long-Stay/Died” based on the length of time in the ICU and survival: “Short-Stay” patients are classified as those spending < 6 days in the ICU and were survivors, while “Long-Stay/Died” patients are defined as patients who spent 6 or more days in the ICU or died during their stay in ICU (Fig. 2.1C). Combining patients who spent 6 or more days in the ICU with patients who died during their stay in ICU into a single clinical outcome category of “Long-Stay/Died” is based on the not unreasonable assumption that both sub-groups of patients can be defined here as having more severe COVID-19 than those spending < 6 days in the ICU and were survivors. The application and use of these two clinical outcome categories were more powerful in identifying immune differences between categories than the use of more simple categories of “survived versus died” or “did or did not require subsequent ventilation” (data not shown). The choice of 6 days as the cut-off was based upon iterative empirical statistical analyses of immune data: the cohorts were sequentially divided into two test sub-groups (for example, “<1 day” and “1+ days”, “<2 days” and “2+ days”, “<3 days” and “3+ days” etc.) and the immune data of the test subgroups were compared by Student’s t-test to identify the sub-groups with distinct clinical outcomes that had the greatest statistical significance (by p-value) with respect to differences in immune markers. While some patients were transferred to the ICU from a general ward rather than being directly admitted, there was no significant difference  40 between the Short-Stay and Long-Stay/Died clinical outcome groups with respect to the mean days spent in a general ward prior to admission to ICU (p = 0.06) and all patients were equally treated with 6mg/day of Dexamethasone.   Table 2.1: Study participant clinical characteristics.  Characteristics Healthy (H) (n = 8)* Cytokine Cohort (n=90)** Short-Stay (SS) (n = 34) Long-Stay/Died (LS/D) (n = 56) Age (median years, min:max) 53, 28:67 64, 28:95 56, 31:95 67, 28:92 Sex (M:F) 5:3 56:34 16:18 40:16 Diagnosis Asymptomatic/ healthy SARS-CoV-2 + SARS-CoV-2 + SARS-CoV-2 + Collection timepoint (days) † NA 0-2 post admission 0-2 post admission 0-2 post admission Severity NA ICU ICU ICU BMI (median, min:max) 22, 19:25 31, 22:44 28, 23:44 32, 22:44 CRP (median mg/L, min:max) < 10 73.5, 7:271 70, 10:135 77, 7:271 D-dimer (median ng/ml) < 250 1056, 311:32460 960.5, 311:13669 1248, 451:32460 Length of ICU stay (days) NA NA < 6 ≥ 6 Days in ICU (median, min:max) NA 7, 0:35 3, 0:5 11, 1:35 Outcome (n; recovered:died) NA 74:16 34:0 40:16  41 Characteristics Healthy (H) (n = 8)* Cytokine Cohort (n=90)** Short-Stay (SS) (n = 34) Long-Stay/Died (LS/D) (n = 56) Mechanically ventilated (n, %) NA 45, 60 12, 38 33, 78 PBMCs (median 109/L) 2.6 1.6 1.6 1.6 PMNs (median 109/L) 3.6 7.7 7.1 8.2  *Healthy control BMI, D-dimer and CRP values stated here are publicly available normal references ranges (227) as these measurements were not obtained from healthy subjects.  **BMI, D-dimer, CRP and ventilation information was not available for 16/90 patients; calculations were adjusted accordingly. †71/90 samples were drawn at day 0, 17/90 at day 1 and 2/90 at day 2.  BMI: Body mass index; CRP: C-reactive protein; PBMCs: Peripheral blood mononuclear cells; PMNs: Polymorphonuclear leukocytes  Importantly, we found no significant differences between the two clinical outcome groups with respect to mean age, BMI, blood clotting parameters (D-dimer levels) or serum CRP levels (Fig. 2.1D, E). At admission, the mean total PMN counts were significantly increased in the Long-Stay/Died group compared to the healthy controls (p < 0.0001) and compared to the Short-Stay group (p = 0.025) (Fig. 2.1F). In our separate analyses of just the Initial Cohort and Replication Cohorts, however, differences in PMN counts were not statistically significant and thus we did not consider this measurement as a useful prognostic biomarker of clinical outcomes in the context of smaller cohort numbers. PBMC counts were also not significantly changed between healthy controls and patients or between our two clinical outcome groups (Fig. 2.1F). Thus, while these routine clinical tests follow a broad spectrum of parameters including inflammation, coagulopathy, hypo-immunity and autoimmunity, none consistently proves prognostic in identifying patients who would require an extended stay in the ICU or die. Accordingly, we  42 conducted more detailed immunological examinations focused on a single process, namely inflammation.  2.3.2 Serum cytokine analyses as prognostic screens for predicting clinical outcome We began by examining serum levels of IL-1β, IL-6, IL-10 and TNFα at ICU admission in all serum samples from our Cytokine Cohort of 90 COVID-19 patients. Strikingly, we found that the mean ICU admission levels of serum IL-10 (p = 0.004) and TNFα (p = 0.0005) were significantly elevated in the Long-Stay/Died group relative to the Short-Stay group (Fig. 2.2A). In the Cytokine Cohort, 43% (39/90) of patients had serum IL-10 ICU admission levels > 15pg/ml and 79% (31/39) of these patients fell into the Long-Stay/Died group. Similarly, 42% (38/90) of patients in the Cytokine Cohort had serum ICU admission levels of TNFα > 10pg/ml and 79% (30/38) of these patients were members of the Long-Stay/Died group. Interestingly, serum IL-10 and TNFα only showed a weak correlation with each other (Pearson correlation coefficient R2 = 0.12, p = 0.270) suggesting that each may represent a different aspect or chronology of the inflammatory process. In parallel analyses of serum samples from 10 healthy controls (not matched for age or sex) the mean IL-10 level was 1.0 pg/ml (range 0.56 to 1.8) and the mean TNFα level was 2.4 pg/ml (range 1.7 to 3.9) (data not shown). Since the Cytokine Cohort represented a significant number of COVID patients (n = 90), a parallel replication cohort was not constructed.	  Given the significant differences in ICU admission levels of TNFα (p < 0.001) and IL-10 (p = 0.004) between the Short-Stay the Long-Stay/Died clinical outcome groups, we also examined the subsequent maximum serum cytokine levels in post admission samples from patients in the  43 two groups and found an even more significant difference between the two groups for both serum TNFα (p < 0.001) and serum IL-10 (p < 0.001) (Fig. 2.2B). Intriguingly, many patients in the Long-Stay/Died group who demonstrated modest admission levels of serum IL-10 and TNFα subsequently developed high levels during their stay in ICU, further reinforcing the importance of these two cytokines as predictive measures of patient outcomes and monitoring the trajectory of disease.  Figure 2.2: Serum cytokine analyses as prognostic screens for predicting clinical outcome. Serum cytokine levels of IL-10, TNFa, maximum IL-10, maximum TNFa, IL-6 and IL-1b (A-C). *, p < 0.05; **, p < 0.01; ***, p < 0.001; ns, p ≥ 0.05 by two-tailed, two-sample unequal variance Student’s t-test.   While admission levels of serum IL-6 were also significantly different between the two clinical outcome groups (p = 0.007) (Fig. 2.2C), we excluded this cytokine from further analyses due to the potential confounding effects of anti-IL-6 receptor antibody (tocilizumab) treatments, which have routinely been administered to COVID-19 patients in British Columbia during ICU admission since February 2021 and such treatments could complicate the interpretation of our results. In this study, only six patients in the combined Initial and Replication cohorts received tocilizumab at the time of ICU admission (two patients in the Short-Stay group and four patients A B Cns******** *** ***SS LS/D01234IL-1β (pg/ml)SS LS/D0250500750100012501500IL-6 (pg/ml)SS LS/D020406080 Max TNFα (pg/ml)SS LS/D020406080250300Max IL-10 (pg/ml)SS LS/D020406080250300IL-10 (pg/ml)SS LS/D020406080TNFα (pg/ml) 44 from the Long-Stay/Died group). These patients were not significant outliers with respect to the biomarkers of interest presented in this study and thus were not excluded from further analyses. Finally, there were no significant differences between the two clinical outcome groups with respect to mean serum IL-1β levels at ICU admission (p = 0.205) and thus this cytokine was also not analyzed further (Fig. 2.2C). In summary, we found that ICU admission levels of serum IL-10 and TNFα were useful and statistically powerful prognostic markers for clinical outcomes in severe COVID-19.  2.3.3 Major PBMC subsets fail to distinguish Short-Stay from Long-Stay/Died patients We examined whether parallel CyTOF analyses of peripheral immune cells sampled at the time of ICU admission could reveal additional prognostic biomarkers that identify patients in the Long-Stay/Died group, particularly among those that had serum IL-10 levels <15pg/ml and/or serum TNFα levels <10pg/ml. PBMC samples were available for 42/90 of the Cytokine Cohort patients and these 42 samples were divided into an Initial Cohort (14 samples) and a Replication Cohort (28 samples).   Using a 35-marker CyTOF panel on the Initial Cohort (Appendix A.5) and a 38-marker CyTOF panel on the Replication Cohort (Fig 2.3), we saw no differences between the Short-Stay patients and Long-Stay/Died patients with respect to major peripheral blood immune populations. The more focused and larger 38-marker CyTOF panel, used to analyze immune cell subsets in the Replication Cohort, permitted clear identification of broad blood cell lineages (B, T, NK and myelomonocytic) as well as major subsets within each cell lineage leading to 41 distinct clusters based on the variable expression of these cell-surface markers (Fig. 2.3A-C).   45 Figure 2.3: Major PBMC subsets fail to distinguish Long-Stay/Died patients.  UMAP projection of ungated CyTOF-derived data from the replication cohort (n=28) (A). Proportion of immune cell subsets in Healthy Controls (HC), Short-Stay (SS) and Long-Stay/Died (LS/D) patient outcome groups (B). Mean marker expression heatmap of clusters shown in A (C). Absolute counts of adaptive PBMC subsets (CD4 T, CD8 T, B) (D), innate and unconventional subsets (NK, MAIT, γδ T, pDC, DC2/3) (E), monocytes and stem cells (F). ns, p ≥ 0.05 by two-tailed, two-sample unequal variance Student’s t-test with Bonferroni adjustment for multiple comparisons. Complete absolute counts and p-values can be found in Appendix A.2.  HC SSLS/D0.000.020.040.060.080.10MAIT cells (x109 /L)HC SSLS/D0.0000.0050.0100.0150.020pDC (x109 /L)HC SSLS/D0.000.010.020.030.040.050.06DC2/DC3 (x109 /L)HC SSLS/D0.00.51.01.52.02.5Monocytes (x109 /L)HC SSLS/D0.00.10.20.30.40.50.6NK cells (x109 /L)HC SSLS/D0.000.050.100.150.200.250.30γδ T cells (x109 /L)HC SSLS/D0.0000.0050.0100.0150.0200.025Stem cells (x109 /L)HC SSLS/D0.00.51.01.52.0CD8 T cells (x109 /L)HC SSLS/D0.00.51.01.52.0CD4 T cells (x109 /L)umap_1umap_2CD4 TCD8 TMyeloidBNKStemCount058001234ValueA B CDE Fns ns nsns ns nsns ns nsnsCD8βCD8⍺CD45ROCD116CD14Fc"R⍺1CD123CD34CLEC10ACD1cNRP1TCR#$TRAV1-2MR1-5-OP-RUCD25CRTH2CD200RKLRG1CD161IL-18R⍺IgDCD19CD45RACD16CD56CD94HLA-DRCD38CD11cCD31CD32CD45CD4CD3"TCR⍺βCD127CD197CD27 41 1 3 7 28 40 27 15 14 24 6 12 2 19 4 21 22 43 39 8 20 17 10 37 34 16 30 36 26 23 35 18 42 32 33 38 13 11 29 31 25 5 9HC SS LS/D0.00.20.40.60.81.0% proportionCD4 TCD8 TBNKMAITγδ TDC2/3pDCMonoStemOtherHC SSLS/D0.00.20.40.60.81.0B cells (x109 /L) 46 The number of clusters is not determined beforehand but does depend on the value of k (fixed, k = 30), as well as the number of input cells/sample (e.g., 10k cells/sample will result in fewer clusters than 100k/sample). Based on the available number of input cells/sample we obtained 41 clusters (relatively few given the known diversity of blood immune populations) in these ungated analyses but performed more granular analyses through gated clusterings (see section 2.3.4). The heatmap in Fig. 2.3C depicts mean marker expression levels which were used to identify broad cell populations that are manually labelled in the cluster plot shown in Fig. 2.3A. Appendix A.4 includes a detailed list of marker signatures that we used to identify cell subsets and these signatures are based on previously published data on cell surface protein expression of blood immune populations. A summary of mean absolute counts of these populations and p-values can be found in Appendix A.2. While these analyses of the Replication Cohort samples and the Initial Cohort samples confirmed previous reports (194, 228) of general lymphopenia in COVID-19 patients relative to healthy controls with respect to both total CD4 T cells and total CD8 T cells, these markers failed to discriminate between the Short-Stay and Long-Stay/Died patient groups. Also consistent with previous reports, we observed no significant differences in total B cells in COVID-19 patients compared to healthy controls or between the two clinical outcome groups (Fig. 2.3D). Although mean total NK cells, MAIT cells, γδ T cells, DC2/3 and pDC were depleted in COVID-19 patients relative to healthy controls these, too, failed to distinguish the Short-Stay group from the Long-Stay/Died group (Fig. 2.3E). Finally, while mean total monocytes and stem cell levels were significantly increased in COVID-19 patients relative to healthy controls, neither total monocyte levels nor total stem levels were able, individually, to distinguish the Short-Stay from the Long-Stay/Died patient groups (Fig. 2.3F, Appendix A.2). In summary, broad immune subset analyses were insufficient to predict COVID-19 patient clinical  47 outcomes with respect to the length of stay in the ICU and/or death in either the Initial Cohort or the Replication Cohort. We, therefore, performed more detailed analyses of immune cell subsets within these broad cell categories to identify more subtle potential differences between the two clinical outcome groups that could assist in the prospective identification of Long-Stay/Died patients.  2.3.4 Levels of a distinct monocyte subset at the time of ICU admission predicts subsequent clinical outcome To reveal a larger diversity of specific immune cell subsets we performed more focused cluster analyses on patient PBMC samples from the Initial Cohort and the Replication Cohort after pre-gating for selected major cellular subsets. These analyses were performed for all major cell types but only the results from the myeloid compartment were of interest for this study and thus only those are presented in depth in the following. To restrict the clustering to the myelomonocytic compartment we performed gated analyses on GM-CSFR+(CD116+) CD19- CD3- cells (Fig. 2.4A) and restricted clustering to shared marker channels (between the first, exploratory panel and the more refined secondary panel) to enable direct comparison between the Initial and the Replication cohorts. A summary of mean absolute cell counts, and p-values can be found in Appendix A.3. These analyses did not reveal subsets that separated Short-Stay from Long-Stay/Died patients with respect to absolute cell counts. To focus more specifically on the monocytic subsets as well as to simplify the cluster analyses, after pre-gating on CD116+ CD19- CD3- cells, we restricted the marker channels selected for clustering to a set of 7 markers useful in defining monocytic subsets (CD45, CD14, CD16, CD11c, HLA-DR, CD123, CD56, see Figs. 2.4B, C).   48   Figure 2.4: CD11clow Classical Monocytes are predictive of clinical outcome. Representative gating of CD116+CD3-CD19- cells (A). Initial Cohort UMAP projections of CD116+ CD3-CD19- gated cells (all samples combined; limited clustering channels) and mean marker expression heatmap of UMAP plots where row numbers on the heatmap correspond to UMAP plot cluster numbers (B). Same as in B but for the Replication Cohort (C). Initial (top) and Replication Cohorts (bottom) absolute counts of monocyte subset predictive of clinical outcome (D). Absolute counts of non-significant monocyte subset identified based on gated clustering (top row: Initial Cohort, bottom row: Replication Cohort) (E-F). Manual gating strategy to view predictive monocyte subset (G). ns, p ≥ 0.05 by two-tailed, two-sample unequal variance Student’s t-test with Bonferroni adjustment for multiple comparisons. Complete absolute counts and p-values can be found in Appendix A.3.  HCSSLS/DCD14CD16 CD11cHC SSLS/D0246810HC SSLS/D05101520HC SSLS/D020406080100Total Interm. Mono (x109 /L)HC SSLS/D0255075100125150Total Interm. Mono (x109 /L)HC SSLS/D020406080100Total Classical Mono (x109 /L)HC SSLS/D010203040Total Classical Mono (x109 /L)HC SSLS/D02461525HC SSLS/D0246818CD19CD3!CD123CD56CD16HLA-DRCD14CD45CD11c8212552674101611126191724213143120151823229CD123CD56CD16HLA-DRCD14CD45CD11cCD116Nuclear stainA B CReplication CohortInitial CohortF G5141116201722132421734108252326191816912152D E500Count5Value40ValueCount40MonocytesDCNon-monocytesNon-monocytesDCMonocytesumap_1umap_2umap_2umap_1HC SSLS/D0246810Total Non-classical Mono (x109 /L)HC SSL/02461525CD11clow Classical Mono (x107 /L)HC SSLS/D02461525CD11clow Classical Mono (x107 /L)HC SSLS/D0246810Total Non-classical Mono (x109 /L)p = 0.076nsns nsns nsnsnsns 49 Interestingly, this strategy revealed a CD11clow classical monocytic subset (CD45+ CD116+ CD3ε- CD11clow HLA-DR+ CD14+ CD16-/low CD123-/low) that, in both the Initial and the Replication Cohorts, was consistently enriched in COVID-19 patients relative to healthy controls (Bonferroni-adjusted p = 0.006, Replication Cohort) and was preferentially enriched in the Long-Stay/Died group relative to the Short-Stay group (Bonferroni-adjusted p = 0.076, Replication Cohort) (Fig. 2.4D), though the latter did not achieve statistical significance. The potential prognostic value of the CD11clow classical monocytic marker was restricted to this subset of classical monocytes in both the Initial and Replication Cohorts and did not reflect underlying changes of total classical monocytes which were unchanged in the two clinical outcome groups (Fig. 2.4E). Moreover, for both Initial and Replication Cohorts, total intermediate monocytes (CD14+ CD16int) and total non-classical monocytes (CD14low CD16+), as well as observed subpopulations of these types of monocytes, did not prove useful prognostically (Fig. 2.4F). Focusing the analyses further on classical monocytes, we found that a three-marker gating strategy was sufficient to identify the CD11clow classical monocyte population identified by our multi-dimensional analyses (shown here for one representative sample from each group in the Replication Cohort) (Fig. 2.4G). Thus, the prognostically useful biomarker of CD11clow classical monocytes was detectable in two dimensions in both the Initial and the Replication Cohorts using antibodies to a small set of cell-surface markers.  Because lymphopenia has been a consistent feature of severe COVID-19 and T cell subset alterations have been described, we performed similar in-depth analyses of the T cell compartments by gating on CD3+ cells prior to clustering. Consistent with our analyses of major subsets in the previous section, we were able to confirm and extend our and other groups’  50 findings that more subtle T cell subsets are significantly depleted in patients relative to healthy controls including subsets in the CD4+, CD8+, MAIT and γδ T cell compartments (Appendix A.6). However, while we gained valuable insight into the altered T cell response in COVID-19 patients, none of these T cell subsets were prognostically useful in separating the Long-Stay/Died patients from the Short-Stay patients.  2.3.5 Combined evaluation of immune parameters as a tool to predict clinical outcome Since deeper analyses of multiple cytokines and cell subsets at ICU admission revealed significant differences between the Long-Stay/Died and Short-Stay groups, we sought to combine these findings to generate a streamlined prognostic tool that could accurately predict whether a patient, newly admitted to the ICU, was likely to have a subsequent longer stay in the ICU or die. Although both serum TNFα and serum IL-10 were significantly elevated in Long-Stay/Died patients relative to Short-Stay patients in the Cytokine Cohort, using Pearson analyses, the length of stay in ICU correlated more significantly with serum IL-10 levels (R2 = 0.48, p < 0.001) and maximum IL-10 levels (R2 = 0.54, p < 0.001) than with serum levels of TNFα (R2 = 0.14, p = 0.19) (Appendix A.7). Thus, we proceeded with only serum IL-10 as our cytokine-based pre-screen portion of a stepwise integrated prognostic tool. As the first step, using a cut-off value of 15pg/ml for serum IL-10 (data from the 90-sample Cytokine Cohort), demonstrated a 79% specificity and 55% sensitivity (positive likelihood ratio 2.6) in predicting that a patient newly admitted to ICU would have a longer stay in the ICU or die (Fig. 2.5G). This prognostic specificity of 79% is somewhat comparable with that seen in the smaller subsets of the Cytokine Cohort, namely the 14-sample Initial Cohort (86%) and the 28-sample Replication Cohort (100%).    51  0 2 4 6 15 25020406080300CD11clow Classical Mono (x107/L)IL-10 (pg/ml)0 2 4 6 15 25020406080300CD11clow Classical Mono (x107/L)IL-10 (pg/ml)0 2 4 6 15 25020406080300CD11clow Classical Mono (x107/L)IL-10 (pg/ml)0 10 20 30 40020406080300TNFα (pg/ml)IL-10 (pg/ml)0 10 20 30 40020406080300TNFα (pg/ml)IL-10 (pg/ml)0 10 20 30 40020406080300TNFα (pg/ml)IL-10 (pg/ml)LONG-STAY/DIEDSHORT-STAYSSLS/DEICU ADMISSIONIL-10 < 15pg/mlAND Mono sub < 2.7 (x107/L)A B CDReplication CohortInitial Cohort Initial and Replication Cohorts combinedFIL-10 ≥ 15pg/ml ORMono sub ≥ 2.7 (x107/L)  G HSensitivity Specificity False Positives False Negatives LR+IL-10 screen(Cytokine Cohort; n = 90)55%(31/56)79%(31/39)21%(8/39)45%(25/56) 2.6IL-10 screen(Initial Cohort; n = 14)86%(6/7)86%(6/7)14%(1/7)14%(1/7) 6.1IL-10 screen(Replication Cohort; n = 28)56%(9/16)100%(9/9)0%(0/9)44%(7/16) N/AIL-10 screen(Combined; n = 42)61%(14/23)93% (14/15)7%(1/15)39%(9/23) 8.7Monocyte screen(Initial Cohort; n = 14)71%(5/7)83%(5/6)17%(1/6)29%(2/7) 4.3Monocyte screen (Replication Cohort; n = 28)69%(11/16)100%(11/11)0%(0/11)31%(5/16) N/AMonocyte screen(Combined Cohorts; n = 42)70%(16/23)94%(16/17)6%(1/17)30%(7/23) 11.8IL-10 plus monocyte screen(Initial Cohort; n = 14)100%(7/7)78%(7/9)22%(2/9)0%(0/7) 4.5IL-10 plus monocyte screen(Replication Cohort; n = 28)88%(14/16)100%(14/14)0%(0/14)12%(2/16) N/AIL-10 plus monocyte screen(Combined Cohorts; n = 42)91%(21/23)91%(21/23)9%(2/23)9%(2/23) 10.1 52 Figure 2.5: Prognostic cytokine and cellular biomarkers predict clinical outcome. Cytokine levels scatter plots of Initial (left), Replication (middle) and combined Cohorts (right) with dashed lines at cut-off value of 15pg/ml for serum IL-10 (A-C). Cytokine and cellular levels scatter plots for Initial (left) Replication (middle) and Combined Cohorts (right) with dashed lines at cut-off values of 15pg/ml and 2.7x107/L of serum IL-10 and CD11clow classical monocytes respectively (D-F). Sensitivity, specificity and positive likelihood ratio (LR+) values for each screen and cohort (G). Prognostic patient screening chart based on serum IL-10 and CD11clow monocyte subset measurements (H). “Combined Cohorts” represent merged Initial and Replication Cohorts. White circles = Short-Stay (SS) patients; Black circles = Long-Stay/Died (LS/D) patients.     Similarly, the prognostic sensitivity of 55% in the Cytokine Cohort is somewhat comparable with that observed in the Initial Cohort (86%) and the Replication Cohort (56%) (Figs. 2.5A-C, G). The variations in estimates of prognostic sensitivity and specificity between cohorts (the Cytokine Cohort and its two subsets of the Initial Cohort and Replication Cohort), however, likely demonstrate variations that reflect the influences of random patient sampling and, very importantly, cohort size. These results validate serum IL-10 levels as a pre-screen to identify patients likely to die or to experience a long ICU stay.  We then explored the utility of combining the separate measurements of serum IL-10 levels (with a cut-off value of 15pg/ml) with levels of CD11clow classical monocytes (with a cut-off value of 2.7x107/L) as a stepwise integrated diagnostic tool. With this approach, 100% of the Long-Stay/Died patients were correctly identified in the Initial Cohort and 88% of Long-Stay/Died patients were correctly identified in the Replication Cohort, the latter with a specificity of 100% (Figs. 2.5D, E, G). These analyses of all 42 patients in the combined Initial and Replication Cohorts (n = 42) allowed us to predict with 91% sensitivity and 91% specificity (positive likelihood ratio 10.1) the clinical outcome of COVID-19 patients newly admitted to the  53 ICU with respect to the likelihood of extended stay or death in the ICU (Figs. 2.5F, G). Thus, our results suggest that a simple screen of two biomarkers at the time of ICU admission allows for rapid identification of those patients who are likely to die or require extended ICU care (Fig. 2.5H) and has clear implications for patient care and health care delivery.    54 2.4 Discussion  The goal of the present study was to identify prognostic biomarkers that, at time of ICU admission, could predict subsequent clinical outcome of COVID-19. Such markers are in urgent need and, with further testing and refinement, could serve to triage patients into specific groups for timely and appropriate care while, at the same time, offer insights into the immune-mediated determinants of disease response. Indeed, two patients (one in the Initial Cohort and one in the Replication cohort) that were admitted to the ICU for less than one day may be examples of clinical mis-triage – using the prognostic biomarkers described here these patients would have been clearly identified as belonging to the < 6 Days clinical outcome group. Like many previous studies, we found that although severe COVID-19 is linked to broad shifts in peripheral blood immune subsets (increased PMNs and T cell lymphopenia) and increased blood inflammatory markers (CRP, D-dimer, etc.), none of these proved prognostic with respect to subsequent length of ICU stay and/or death. Therefore, we used CyTOF-based PBMC immunophenotyping and serum cytokine analyses on samples drawn at ICU admission to focus our attention on more subtle shifts in inflammatory parameters with a view to identifying prognostic biomarkers. We identified two groups of ICU patients who would subsequently have clinically distinguishable disease outcomes: those who would be discharged from the ICU within 6 days and those who would require a longer ICU stay or would die. We then used retrospective analyses to generate a simple set of biomarkers that could easily be applied in the clinic to identify, at the time of ICU admission, those patients at greater risk of death or lengthy stay in the ICU. While the clinical decision to admit a patient to the ICU and indeed the length of stay considered clinically necessary for each patient may vary between ICUs, we sought to normalize these more  55 subjective variables to the extent possible by limiting our patient cohort recruitment to only two ICUs, each ICU located in a major teaching hospital of the same university.  A high admission level of serum IL-10 (> 15pg/ml), alone, was (in a cohort of 90 patients) a limited biomarker that identified patients in the Long-Stay/Died group with 79% specificity, though with a lower sensitivity of 55% (positive likelihood ratio 2.6). Additional high dimensional cell surface protein analyses of 42 patients revealed a simple set of monocyte markers, specifically those identifying CD11clow classical monocytes, that when combined with admission serum IL-10 levels accurately predicted with 91% specificity and 91% sensitivity (positive likelihood ratio 10.1) at the time of ICU admission patients who would subsequently either have a longer stay in ICU or who would die (validated in initial and replication cohorts). This prognostic power of combining two biomarkers into a single test was achieved despite the observation that the second biomarker (CD11clow classical monocytes) alone was not statistically useful in distinguishing between the two clinical outcome cohorts (Bonferroni-adjusted p = 0.076) and provided only limited prognostic sensitivity (Fig. 2.5G). Thus, based on the information from our evaluation of 4 serum cytokines and 38 surface markers and validated on two separate clinical cohorts, we have distilled our prognostic screen down to a composite test of one cytokine and one monocyte subset as predictive biomarkers that could be evaluated in most clinical laboratories. Indeed, our demonstration that the cellular biomarker can be detected and visualized in two dimensional analyses (Fig. 2.4G) using limited markers reinforces the likelihood that this biomarker will be detectable using conventional clinical flow cytometry.   56 Although individually several of the biomarkers examined here have been investigated previously and described as markers of disease severity, there has been a lack of clear consensus on their prognostic utility in the published literature. For example, both IL-6 and IL-10 emerged early as candidate clinical markers of disease severity, but to our knowledge are not widely used in standard prognostic testing at hospital or ICU admission (212, 213, 216, 217). This likely reflects the fact that, used in isolation and without a detailed quantitative evaluation of threshold levels predicting outcome, their presence or absence provides a more superficial indication of current inflammatory status and fails to predict the temporal trajectory of clinical disease (increasing or decreasing severity). This also may explain why anti-IL-6 receptor therapy has shown only limited efficacy as a broad-spectrum therapeutic for severe COVID-19 and fails to reduce overall mortality (201, 229).  Similarly, although corticosteroids have emerged as a standard-of-care for COVID-19 ICU patients and undoubtedly provide improved recovery after infection, they are widely recognized as “double-edged swords”: while they are effective at suppressing excessive inflammation, they also potently suppress adaptive immune responses, potentially reducing viral clearance and increasing susceptibility to secondary infections (200, 230).  With that backdrop, a benefit of the streamlined ICU biomarker panel described here is that it provides a direct prognostic link to patient outcome and may also serve as a biomarker panel for monitoring patient response to therapies, though longitudinal analyses of these markers have not been provided here to measure either changes with resolving disease severity or response to therapies. Interestingly, a genetic association of variants of the IL10Rb gene with critical COVID-19 was recently identified through whole genome sequencing (231). This finding, together with our finding of the association of high levels of serum IL-10 with death and/or long  57 ICU stay, may point to the important involvement of the IL-10 ligand/receptor axis in the evolution of severe COVID-19.  In our study, deep immunophenotyping of the myeloid compartment in COVID-19 patients proved pivotal in defining markers to predict patient outcomes. While we saw no significant early changes in total monocyte numbers or total classical monocyte numbers or frequencies, a prognostically useful monocytic subset was contained within these broader subsets which highlights the need for a high-dimensional evaluation to identify subtle, but informative, changes in immune subpopulations that might otherwise have been overlooked. After identification of such subtle biomarkers using high-dimensional analytic technologies, simpler two-dimensional technologies (using limited markers) can then be used to measure the biomarker clinically. It is noteworthy that previous studies have linked both overall increased numbers of inflammatory macrophages in the lung and increases of specific subpopulations of peripheral blood monocytes to severity of COVID-19 (232–235). In fact, Schulte-Schlepping et. al. specifically showed the accumulation of CD11clow monocytes in severe COVID-19 patients (51). Other studies also reported subtle, monocyte subset-specific changes in severe COVID-19, including dysfunctional pro-inflammatory cytokine production, reduction in HLA-DR transcripts, accumulation of HLA-DRlow monocytes and reduction of non-classical monocytes (51, 214, 218, 222, 236). The data presented here confirm and extend these observations in a manner that facilitates accurate prognostication. They also reveal CD11clow classical monocytes as new target populations for more focused mechanistic studies in future research. While the combination of these two biomarkers certainly provides prognostic information on disease outcome in COVID-19, there is a possible parallel interpretation of the results: since all patients received corticosteroids at the  58 time of admission, the biomarkers described here may be identifying those patients who are, in fact, more responsive to corticosteroid therapy. We leave this intriguing possibility for future investigation.  Although not specifically addressed here, we believe that these prognostic biomarkers provide a roadmap for future studies aimed at guiding and monitoring response to therapy. Such monitoring is particularly important in the context of therapies that have the acknowledged potential of exacerbating clinical disease if given in a temporally inappropriate manner in the COVID-19 cycle of stimulation and progression to clearance and resolution. While we have focused here on the utility of these markers at the time of ICU admission it is possible that these may prove even more valuable as temporal monitoring tools for revealing disease trajectory on this continuum and responses to therapeutic intervention.    59 Chapter 3: Prediction of future childhood allergic disease based on alterations in umbilical cord blood immune cell signatures  3.1 Introduction  Allergic diseases pose a major and growing concern in developed and developing countries in which 1 in every 4 people are diagnosed with some form of allergic disease in their lifetime (111). This high incidence coupled with a heterogenous clinical manifestation that complicates diagnosis and treatment, is a major burden on health care systems and restricts quality of life of the effected individual. Childhood allergic diseases can be particularly severe and, in some cases, fatal. Important problems confounding our ability to address these issues include subclinical allergen-sensitization, overlapping wheeze and atopy phenotypes, absence of truly predictive biomarkers of disease and a lack of effective diagnostic tests and, ultimately, a lack of understanding around the mechanistic and temporal factors that render some individuals susceptible to allergic disease and others resistant (112). These details are pivotal in designing effective strategies for prevention and treatment.  Early, postnatal events and environmental exposures (including mode of birth, premature birth, antibiotics exposures, infant feeding methods/diet, and home environment), that alter microbial communities and infant colonization, have been linked with development of allergic disease later in life (123, 128–130). Thus, the first few months of life have been considered as the critical temporal window for the establishment of allergic disease susceptibility. However, this disregards the strong hereditary component of allergic disease susceptibility and the fact that not all types of allergic disease are consistently linked to specific early life events (121).    60 An allergic reaction is, fundamentally, a dysregulated immune response and thus all aspects of, and influences on, immune system development, starting as early as 4 weeks of gestation, could potentially be determinative of the origin of allergic diseases (135). Maternal health and environmental exposures clearly play an important role in fetal health and while the placental/fetal interface does protect the fetus from most pathogens, the transfer of inflammatory mediators between mother and fetus and microbial derived metabolites is well-documented (136, 137, 139). Thus, prenatal events and exposures may be the key initial drivers of immune-based disease, while the subsequent post-natal secondary exposures serve only to further modify an existing predisposition for disease susceptibility. Indeed, viewed through this lens, the microbiome detected in neonates shortly after birth may in fact simply reflect a snapshot of the maternal microbiome carried by the mother during pregnancy. With that concept in mind, we sought to evaluate the status of the developing newborn immune system in future allergic children and healthy controls prior to direct exposure to the postnatal environment, namely, through high content evaluation of cord blood. We prospectively profiled immune cell signatures of n = 50 archived umbilical cord blood mononuclear cell samples from healthy children compared to children diagnosed with wheeze only, atopy only or both. Strikingly, we found that, at the time of birth, altered monocyte and CD8 T cells subsets could be readily detected in those children diagnosed at age 5 with a combined wheeze and atopy phenotype. Our data suggest that susceptibility to some forms of allergic disease is established prior to birth and can be predicted through evaluation of immune cell subsets at time of birth.    61 3.2 Materials and methods  3.2.1 CHILD Study clinical information and diagnoses This study was approved by the University of British Columbia Clinical Research Ethics Board (H19-01157). Between 2008 and 2012, the CHILD Cohort Study (www.childstudy.ca) enrolled 3455 pregnant women from the general population at four sites in Canada (Edmonton, Vancouver, Toronto, Manitoba) to form a birth cohort of healthy infants born >34 weeks gestational age and who were monitored and tested at multiple timepoints during childhood to enable longitudinal data and sample collections. From this cohort, umbilical cord blood mononuclear cell samples were isolated and stored at time of birth. From these, we selected 50 samples from children distributed in four atopy/wheeze phenotype groups: Healthy (no wheeze, no atopy or atopic disease), Atopy only (any positive sensitization to food or inhalant allergens at 1 year, 3 years and 5 years +/- atopic dermatitis or food allergies at 3 years or 5 years), Wheeze only (wheeze trajectories include infrequent, transient, intermediate and persistent as defined previously (237)) and Wheeze and Atopy (any type of wheeze and atopy). A wheeze phenotype was defined as having more than 2 episodes in the past 12 months. The presence of atopy was determined as any positive sensitization (> 2 mm wheal) to skin prick testing with standardized food or inhalant allergens. Food allergens included peanut, milk, egg and soybean. Inhalant allergens included Alternaria Tenuis, cat hair, cat pelt, dog epithelium, Dermatophagoides pteronyssinus, Dermatophagoides farinae, cockroach, Cladosporium, Penicillium, Aspergillus fumigatus, tree mix, grass mix, weeds, and ragweed. Histamine was used as a positive control and glycerin as a negative control.      62 3.2.2 Sample selection As part of an exploratory study, samples were selected initially to include equal numbers of males and females and span a spectrum of different phenotypes (outlined above). Secondary aliquots were selected based on preliminary data that suggested to focus in on male subjects with a combined wheeze and atopic phenotype. Some more specific information regarding sample selection of secondary aliquots is presented as part of the results in section 3.3.4. Additionally, as this study is part of a larger collaboration between multiple teams that perform different types of analyses, samples were further selected for availability of other data sets (e.g., serum IgE analyses) to allow for the assessment of multiple parameters from the same study participants.   3.2.3 Antibody staining and CyTOF data collection CBMCs were thawed at 37 °C and washed with RPMI 1640 containing 10% FBS and 25U nuclease (Thermo Fisher Scientific). Between 1- 4 x 106 cells per sample were used for antibody staining. Prior to fixation, all centrifugation steps were performed at 500 rcf and 4 °C. All reagent dilutions were prepared according to manufacturer instructions unless stated otherwise. For live/dead cell analysis, CBMCs were stained with Cell-ID™ Intercalator-Rh (Fluidigm) for 15 minutes at 37 °C and washed with MaxPar® MCSB. Prior to surface staining, cells were incubated with human TruStain FcX™ (Biolegend) for 15 minutes at 4 °C and stained with a surface antibody cocktail for 30 minutes at RT (see Appendix A.1 for complete list of antibodies). The MR1-5-OP-RU tetramer was incubated together with the antibody cocktail. After incubation, the cells were washed and incubated for 30 minutes at RT with the secondary anti-APC antibody. Prior to fixation and nuclear staining, CBMCs were washed with MaxPar® MCSB and incubated in MaxPar® Fix and Perm Buffer (Fluidigm) and Cell-ID™ Intercalator-IR (Fluidigm) for 1  63 hour. Post fix, all centrifugation steps were performed at 900 rcf and 4 °C. To prepare for CyTOF acquisition, CBMCs were washed with MilliQ water and resuspended in EQ™ Four Element Calibration Beads (Fluidigm). Samples were acquired in batches of ~15 samples/acquisition with a CyTOF®2 mass cytometer. Batch effects were controlled with a CBMC run control (separate frozen aliquots from the same blood draw/individual). An average of 400000 events were collected for each sample at a flow rate of 45µl/min.    3.2.4 CyTOF data processing and analysis All data files were normalized (https://github.com/nolanlab/bead-normalization) and events of interest were manually gated with the FlowJo gating software (BD Biosciences). Dimensionality reduction and clustering were performed with Uniform Manifold Approximation and Projection (UMAP) and Rphenograph respectively, as provided in the bioconductor package Cytofkit (https://github.com/JinmiaoChenLab/cytofkit2). The input files were equally down sampled and cytofAsinh was used as the transformation method. The Rphenograph k was set to the default of 30. The dimensionality reduction and clustering were both performed on the entire data set as well as separately on manually pre-gated populations. Populations were identified manually based on marker expression (e.g., T cells were identified based on expression of CD3, CD4 or CD8).  3.2.5 Statistical analysis Sample size and statistical tests are indicated in figure legends and all graphs and statistical tests were generated using GraphPad Prism (GraphPad Software, La Jolla California, USA). A test was considered statistically significant at a probability of < 5% (p < 0.05) and we did not assume  64 a Gaussian distribution. Data are mean ± SD. UMAP plots and heatmaps were exported from Cytofkit and experimental outline figures were created using BioRender.com. Figures were assembled in Microsoft PowerPoint.    65 3.3 Results  3.3.1 Study design and clinical characteristics of CHILD study participants To identify potential biomarkers of allergic disease susceptibility, we obtained CBMC samples (n = 50) from a Canadian prospective longitudinal birth cohort study (the CHILD Cohort Study). Since the isolation and storage of their CBMCs between 2008 and 2010, study participants have been assessed for the development of allergic diseases at regular timepoints during their development and diagnosed with variable phenotypes and levels of severity. To cover a broad spectrum of symptoms, we selected CBMC samples from four phenotypically distinct groups: ‘Healthy’ (H, n = 15), ‘Wheeze only’ (W, n = 13), ‘Atopy only’ (A, n = 12) and ‘Wheeze and Atopy’ (WA, n = 10) (see section 3.2.1 for full clinical definitions). CBMCs were analyzed by mass cytometry (CyTOF) with an exploratory antibody panel including those that identify broad cord blood immune cell lineages as well as their activation status (Appendix A.1: exploratory analysis). Based on findings from these exploratory analyses, we designed a more specialized second-generation CyTOF antibody panel to further interrogate candidate cell subsets of interest using secondary aliquots from a subset of the previously analyzed participants (Appendix A.1: secondary analysis). Specifically, we selected samples from groups H (n = 5) and WA (n = 6) for secondary CyTOF analyses and for additional single-cell RNA sequencing analyses (data not shown in this thesis).   Study participant demographics and clinical information are presented in Table 3.1 and include sex, gestational age, PMN and PBMC counts, diagnoses and criteria (wheezing episodes, skin- prick tests) and parental information including parental BMI, prenatal antibiotic usage and parental atopy diagnoses.   66 Table 3.1: CHILD Study participant clinical information  Healthy (n = 15) Wheeze (n = 13) Atopic (n = 12) Wheeze/Atopic (n = 10) Sex (M:F) (7:8) (6:7) (7:5) (9:1) Gestational age (mean months, min:max) 9.13, 8.52:9.57 9.16, 8.45:9.54 9.08, 8.28:9.53 9.02, 8.71:9.57 PBMCs (mean 109/L, min:max) 8.70, 5.48:9.95 5.23, 5.09:5.60 6.85, 3.81:11.3 7.33, 5.08:10.33 PMN (mean 109/L, min:max) 12.66, 9.45:11.46 4.88, 3.5:5.62 6.80, 3.3:7.84 7.90, 4:9.92 Diagnosis No wheeze; no atopy Recurrent wheeze; no atopy No wheeze; persistent atopy Recurrent wheeze and atopy Wheeze (n) Rec. wheeze 1y 0 6 0 4 Rec. wheeze 3y 0 6 0 5 Rec. wheeze 5y 0 3 0 2 Atopy (n) Atopy 1y 0 0 6 4 Atopy 3y 0 0 12 4 Atopy 5y 0 0 12 8 Atopic disease 3y 0 0 9 5 Atopic disease 5y 0 0 6 5 Parental background Maternal BMI (mean, min:max) 24.2, 20.1:27.4 25.6, 19.6:37.9 21.7, 18.1:27.1 22.1, 19.6:27.1 Paternal BMI (mean, min:max) 29.3, 22.9:46.54 25.8, 20.9:34.3 26.6, 23.1:34.2 27.1, 21.8:31.8 Parental Atopy (n, %) Paternal atopy 10 8 9 8 Maternal atopy 10 5 10 7 Prenatal Antibiotics (n, %) Start-18w 3 2 1 1 18w-birth 0 0 1 0 Rec. = recurrent; BMI = body mass index; PBMCs: Peripheral blood mononuclear cells; PMNs: Polymorphonuclear leukocytes   67 Our aim was to include equal numbers of male and female participants for each of the four groups analyzed and this was possible for participants in groups H, W and A, but only one female was available from the WA group, likely reflecting the well-documented male-bias of childhood allergies that limited female sample availability (238) (Fig. 1A).  Four, more selective, wheeze phenotypes (infrequent, transient, intermediate, persistent) established previously (237) were present for participants in the W and WA groups, although the W group predominantly included participants with transient wheeze while the WA group predominantly included participants with an intermediate wheeze phenotype (Table 3.1; Fig. 3.1B).  The presence of atopy (+ skin prick tests, see section 3.2.1 for details), was tested at year 1, 3 and 5 after birth and participants in the A group were atopic either at all three time points or at 3 and 5 years of age. Subjects in the WA group additionally tested positive at 1 year only or 5 years only (Table 3.1; Fig. 3.1C). Atopic diseases, including food allergies and atopic dermatitis, were present for some of the participants in both A and WA groups. Among those, the occurrence of both diseases was slightly more common in the A group compared to WA participants, who predominantly presented with atopic dermatitis only (Table 3.1; Fig. 3.1D). In summary, while participants are broadly categorized into four groups, on an individual level, participants presented with more subtle differences both in terms of symptom onset and severity.  We did not observe a significant difference in gestational age at birth between groups and all pregnancies were carried to term (Table 3.1; Fig. 3.1E).     68   Figure 3.1: Study design and CHILD subject clinical characteristics.  Project design overview (A). Wheeze phenotypes of W and WA groups (B). Atopy and atopic disease phenotypes of A and WA groups (C, D). Gestational age of CHILD subjects (E). FA = Food Allergies; AD = Atopic Dermatitis; 1y = skin prick test to test atopy was positive at 1 year of age; 3y = 3 years of age; 3y, 5y = 3 years of age and 5 years of age; 1y, 3y, 5y = 1 year, 3 years, and 5 years of age; scRNAseq = single-cell RNA sequencing. ns, p ≥ 0.05 by one-way ANOVA with Bonferroni adjustment for multiple comparisons. Data are mean ± SD.    NoneFAADFA+ADWheeze (W)Healthy (H)Atopic (A)Wheeze/Atopic (WA)WAH1. Study groups and samples: Archived CBMCs (n = 50)2. CyTOF analyses of CBMCs with exploratory antibody panel3. Secondary aliquots fromselected groups/subjects (n = 11)4. CyTOF (adjusted panel), scRNAseqanalyses and clinical data integrationADW WA1y5y3y, 5y1y, 3y, 5yInfrequentTransient IntermediatePersistentWheeze AtopyAtopic diseaseA WAH W A WA8.08.48.89.29.610.0Gestational age (months)CnsB E 69 We did observe significant differences in complete blood counts (CBCs), most notably a reduction of PMNs in all three patient groups compared to healthy controls (Table 3.1; Appendix B.1A). However, CBC counts were only available for 19/50 participants selected for this study. Thus, to evaluate if the trend in altered PMN numbers we observed holds for a larger cohort, we requested all available CBCs from the CHILD study (n = 983, Appendix B.1B). In this larger cohort, we observed that only the W group showed a reduction in PMNs while the A and WA groups were not significantly different compared to healthy controls (Appendix B.1B).  Parental clinical information was included to evaluate a potential link to certain exposures/behaviors, particularly those known to cause allergic disease susceptibility, with cord blood signatures and future allergic disease status of the offspring. To this end, we obtained both maternal and paternal BMI as obesity status, particularly since the mother BMI during pregnancy has previously been shown to impact allergic disease susceptibility of the offspring (239). We further obtained maternal and paternal atopy status and prenatal usage of antibiotics since genetic predisposition and antibiotic exposures have previously been linked to allergic disease susceptibility. Importantly, we did not observe a difference in parental BMI and the presence of parental atopy was distributed nearly equally among the four groups, with only a slight increase of the presence of atopy for both parents in the A and WA groups (Appendix B.2A, B). Finally, only a few mothers distributed among all four groups were subjected to prenatal antibiotics which is an important factor to keep in mind but was not preferentially observed within one group and thus will likely not impact the overall results.    70 Finally, it is important to note that while these clinical data are presented for all 50 participants, likely due to the length of storage or challenges at time of cell isolation (subject to time of birth and staff availability for timely sample processing), 4/50 (3x H, 1x WA) samples were not of sufficiently high viability for further analyses and thus, in the following sections, data is only shown for 46/50 samples.  3.3.2 CyTOF reveals detailed CBMC signatures at time of birth We began by profiling cord blood immune cell subsets with our first-generation exploratory CyTOF antibody panel to broadly characterize immune signatures present at time of birth. To this end, we first manually gated on CD45+ immune cells to remove any non-immune cell subsets contained in most samples (5-20% of total cells, likely erythrocyte progenitor contamination) (Fig. 3.2A). We then proceeded with dimensionality reduction and cell subset clustering of CD45+ gated cells to reveal cord blood immune cell subpopulations in the H, W, A and WA groups (Fig. 3.2B). These analyses revealed that, with this broad interrogation, all major populations (T, B, NK, myeloid) as well as more rare populations (stem/progenitor) can be identified in all four of the clinical groups as shown with UMAP plots and the corresponding mean marker expression heatmap (Fig. 3.2B-D). In addition to these visual representations, the analyses of cell subset frequencies showed that, while we can see some degree of heterogeneity between groups, there are no striking differences (Fig. 3.2E and Appendix B.3). This indicates, as would be expected from otherwise healthy births/infants, that any biomarker signatures that may predict allergic disease development later in life, are not discernible upon broad cell subset characterization and likely require more in-depth signature analyses.   71  H W A WA05101520CD8 T cells (% of CD45+)WACD31CD11cCD32CD45RACD45CD38CD4CD3CD8⍺HLA-DRCD19IgDCD16CD56CD94CD161IL-18R⍺KLRG1CD127CD34CD117IL-17RβTRAV1-2CRTH2CD25TCR"#PODO83LAG3CD125CD123Fc$R⍺1CD200RCD45ROCD14CD11635 33 24 3 39 22 21 34 4 17 7 37 6 9 8 1 19 27 40 36 41 31 28 43 26 42 20 13 2 30 18 29 32 10 23 12 25 38 16 15 14 5 11Count058001234ValueBFMyeloidStem/progenitorB  NK CD8 TCD4 TOther(CD45low)umap_1umap_2Nuclear stainCD45C DEH A WA0.0 0.2 0.4 0.6 0.8 1.0WAAWH% proportionCD4 TCD8 TBNKMonoDC2/3pDCBasoStemOtherumap_1umap_2H W A WA01234Non-classical Mono (% of CD45+) ns, p = 0.077 ns, p = 0.100 72 Figure 3.2: CyTOF reveals detailed CBMCs signatures at time of birth Representative manual gating of total immune cells (CD45+/Nuclear stain+) (A). UMAP plots of immune populations identified with CyTOF in Healthy (H), Wheeze (W), Atopic (A) and Wheeze and Atopic (WA) groups (B). Combined UMAP plots of all groups/subjects with manual labelling of main CBMC immune cell populations (C). Mean marker expression heatmap of UMAP plots shown in B and C populations where row numbers on the heatmap correspond to UMAP plot cluster numbers (D). Percent frequencies of main and minor CBMC populations in H, W, A and WA groups (E). Percent frequency quantifications of non-classical monocytes (Mono) and CD8 T cells with statistics indicated specifically for H vs WA groups (F). ns, p ≥ 0.05 by one-way ANOVA with Bonferroni adjustment for multiple comparisons. Data are mean ± SD.    73 Indeed, a closer inspection of the immune compartments revealed interesting trends with regards to both monocyte and T cell subsets that become most obvious when comparing groups H and WA. Specifically, we observed a trend towards decreased numbers of non-classical monocyte (p = 0.077) and an expansion of CD8 T cells (p = 0.100, one outlier was observed in this analysis) in the WA group compared to the H group (Fig. 3.2F). In summary, major immune populations fail to distinguish clinical groups at the time of birth while specific subsets may harbour distinct biomarkers that predict disease outcome of children in the WA group.     3.3.3 T cell compartment analyses suggest expansion of CD8 T cells in WA CBMCs  Since our CD45-gated analyses revealed myeloid and T cell signatures of interest, we proceeded to further interrogate these two immune compartments. To reveal myeloid cell subset diversity, particularly focused on monocytes, we first gated on GM-CSFR+ (CD116+) cells followed by a gating on CD3- CD19- cells to remove all T cells and B cells (Fig. 3.3A). We then repeated dimensionality reduction and clustering on these selected populations which primarily revealed, as intended, monocytes as well as some dendritic cells (Fig 3.3B, C). However, neither the monocyte subsets (primarily characterized based on differential expression of CD14 and CD16) nor the dendritic cell subsets (primarily characterized based on differential expression of CD11c, CD123 and Fc𝜺R⍺1) were significantly different between groups and the trend with regards to non-classical monocytes that we observed above was not reproduced based on these more in-depth analyses (Fig. 3.3D).       74  ns, p = 0.073MonocytesDC2/3pDCCD16CD123IL-18R⍺CRTH2Fc$R⍺1 CD14 CD4HLA-DRCD45CD11c4671035112091621713128191181415CountValue5050H W A WA0.00.20.40.60.8pDC (% of CD45+)H W A WA0.00.51.01.52.0DC2/3 (% of CD45+)H W A WA05101520Classical Mono (% of CD45+)H W A WA01020304050Intermed. Mono (% of CD45+)H W A WA012345Non-classical Mono (% of CD45+)ns ns ns ns nsB CDumap_2umap_1CD45CD116Nuclear stainNuclear stainACD3CD19ECD8CD16TRAV1-2HLA-DRTCR%&CD25CD45ROKLRG1CD4CD45RACD127CD38CD451622195171520101823132642191462425127811213F GHValue100 05CountNuclear stainCD45CD3Nuclear stainH W A WA010203040CD4 T cells (% of CD45+)nsCD8 TCD4 Tumap_2umap_1H W A WA03691215put. Naive CD8 T cells (% of CD45+) 75 Figure 3.3: T cell compartment analyses suggest expansion of CD8 T cells in WA CBMCs Manual gating strategy for myeloid compartment (CD45+/CD116+/Nuclear Stain+/CD3-/CD19-) (A). UMAP plots and mean marker expression heatmap of myeloid populations where row numbers on the heatmap correspond to UMAP plot cluster numbers (B, C). Quantifications of percent frequencies of myeloid populations (D). Manual gating strategy for T cell compartment (CD45+/CD3+/Nuclear Stain+) (E). UMAP plots and mean marker expression heatmap of T cell populations (F, G). Percent frequency quantifications of total CD4 T cells and putative naive CD8 T cells with statistics specifically indicated for H vs WA groups (H). ns, p ≥ 0.05 by one-way ANOVA with Bonferroni adjustment for multiple comparisons. Data are mean ± SD. Put = putative.       76 We followed the same analyses strategy to interrogate the T cell compartment for which we gated on all CD3+ cells (Fig. 3.3E-H). Cluster analyses based on this gating revealed both CD4 and CD8 T cell subset diversity. These analyses revealed no additional trends in the CD4 T cell compartment but, again, a trend towards expansion of CD8 T cells in the WA group (p = 0.073). Moreover, based on these detailed analyses, the CD8 T cells expanded are likely all a naive phenotype (as determined by the expression of CD45RA). However, with this exploratory panel, we lacked sufficient markers for an in-depth characterization of the T cell compartment (e.g., TCR⍺β, CD27, CD197) and thus, at this stage, conclusions regarding T cell subset identity were limited.   In summary, exploratory analyses of CBMCs revealed a trend towards a putative naive CD8 T cell subset as a biomarker signature that distinguishes subjects in the WA group from subjects in the H group at time of birth and spurred us to investigate this further.   3.3.4 Sample selection criteria for secondary CyTOF analyses The above sections outline an exploratory approach that revealed interesting trends towards a putative immune biomarker signature as well as for which subset of patients these signatures may be useful. Thus, these findings were critical in guiding the next steps of this project which consisted of: 1) applying a more specialized CyTOF antibody panel to solidify the findings presented above and 2) single-cell RNA (scRNA) sequencing of cord blood immune cells to interrogate functional properties of subsets of interest. Especially for the latter, choosing the right samples was critical due to sensitivity regarding sample quality as well as costs associated with a large sample size. Thus, while sequencing results are not presented in this thesis, the selection of  77 samples, for which CyTOF results are presented below, was guided by the additional aim of ultimately performing scRNA sequence analyses.   Secondary cord blood aliquots were selected from a subset of subjects evaluated in the above section based on the following criteria: 1) based on the exploratory CyTOF analyses, sample quality and number of cells/sample were deemed as sufficient enough for a secondary analysis with both CyTOF and scRNA sequencing; 2) due to the lack of female subjects in the WA group and the male bias of severe childhood allergies, all female subjects were excluded; 3) as promising trends were observed only in the H versus WA groups only those groups were considered; 4) and finally, cord blood sample availability. Based on these criteria, five subjects from the H group and 6 subjects from the WA group were available for the analyses of secondary CBMC aliquots for which the CyTOF findings are presented.   3.3.5 Naive CD8 T cells and monocytes are predictive of future allergic disease Secondary aliquots of subjects from the H and WA groups were interrogated using a specialized antibody panel (including additional markers to evaluate T cell and myeloid cell compartments) and following the same analyses strategies as presented above (Fig. 3.4A, B, E, F). Details on marker signatures used to characterize each population are presented in Appendix A.4. These analyses, including those of the CD45+ gated total immune cells (data not shown) and the specialized gating strategies to interrogate T cell and monocyte compartments, confirmed and extended findings of the exploratory analyses. Specifically, due to the addition of markers such as CD197 and CD27, central memory and effector memory CD8 T cells could be identified and these confirmed our previous finding that the only CD8 T cell population expanded in the WA  78 group is indeed of a naive phenotype (p = 0.016, Fig. 3.4C, D). Furthermore, unlike the exploratory analyses of the myeloid compartment that only pointed towards a possible decrease in non-classical monocytes, the more in-depth gated clustering of the myeloid compartment showed a decrease in total monocytes (p = 0.003) as well as in all major subsets including classical (p = 0.037), intermediate (p = 0.026), and non-classical monocytes (p = 0.032) (Fig. 3.4G, H). Unlike the T cell analyses, the discrepancy between the initial and secondary analyses of the myeloid compartment is less likely due to additional markers (additional myeloid-specific markers were targeted for DC characterization, e.g., NRP1, CLEC10A, CD1c) but rather due to sample selection and data quality which are discussed in more depth in the next section. Taken together, these frequency changes can be used to clearly distinguish H from WA subjects as shown in Figure 3.4I and represent a novel biomarker signature to predict future development of some forms of childhood allergic disease.       79  CD4CD27CD8βCD8⍺CD16HLA-DRKLRG1MR1-5-OP-RUTCR#$CD25CD45ROCD197CD45TCR⍺βCD127CD38CD45RA11122819153417141822161071920623252451321ValueCount150 05HWAumap_2umap_1CD8 T CD4 TCD14CD11cCD16IL-18R⍺CRTH2CLEC10ANRP1CD1cFc!R⍺1CD123HLA-DRCD45CD4758612210191131421591217232016182441321CountValue150 05WAHumap_2umap_1MonocytesDC2pDCH WA051015202530Total Mono (% of CD45+)H WA02468Classical Mono (% of CD45+)H WA05101520Intermed. Mono (% of CD45+)H WA012345Non-classical Mono (% of CD45+)H WA0102030405060Total T cells (% of CD45+)H WA024681012Naive CD8 T cells (% of CD45+)0 2 4 6 8 10 12051015202530Naive CD8 T cells (% of CD45+)Total Mono (% of CD45+) HWAH WA0.0000.0050.0100.0150.0200.025CM CD8 T cells (% CD45+)H WA0.000.250.500.751.001.251.50EM CD8 T cells (% CD45+)A BDCE F GHIns** ** *ns ns* 80 Figure 3.4: Naive CD8 T cells and monocytes are predictive of future allergic disease UMAP plots of T cell subsets identified with CyTOF in Healthy (H, n = 5, top) and Wheeze and Atopic (WA, n = 6, bottom) subjects (A). Mean marker expression heatmap of plots shown in A where row numbers on the heatmap correspond to UMAP plot cluster numbers (B). Percent frequency quantifications of total T cells and naive CD8 T cells (C) and effector memory (EM) and central memory (CM) CD8 T cells (D). Same as in A and B but for the myeloid compartment (E, F). Percent frequency quantifications of total monocytes (Mono) and classical monocytes (G) and intermediate monocytes and non-classical monocytes (H). Graphical visualization of total monocyte and naive CD8 T cell differential expression in H and WA groups (I). *, p < 0.05; **, p < 0.01; ***, p < 0.001; ns, p ≥ 0.05 by two-tailed, two-sample unequal variance Student’s t-Test with Bonferroni adjustment for multiple comparisons. Data are mean ± SD.     81 3.4 Discussion  In this study, we explored, at the time of birth, immune cell signatures in umbilical cord blood of children diagnosed, during childhood, with a spectrum of allergic phenotypes. Using a high-dimensional, exploratory approach, we profiled cord blood immune cells with CyTOF and an antibody panel for broad and in-depth characterization of major and rare cord blood immune cell subsets. From these analyses, immune biomarker signatures emerged that distinguished, at time of birth, those children diagnosed with a combined wheeze and atopy phenotype from children that remained healthy during childhood. Specifically, children diagnosed with wheeze and atopy, showed an expansion of cord blood naive CD8 T cells and a reduction in cord blood monocyte subsets. These results suggest that, in our cohort, an immune signature predicting a combined wheeze and atopy phenotype, was already present at time of birth and thus established prenatally likely through prenatal/maternal influences.   The first and second rounds of analyses presented in this study show some discrepancies that likely arose from a combination of factors that are addressed in the following. Firstly, as presented in section 3.3.4, sample selection for the secondary analyses was based on strict metrics (allergic phenotype, sex, sample availability) but remained unbiased in terms of immunological changes of the individual subjects. Thus, while of course a smaller sample size offers less power, the reduced variability of subset frequencies and subsequent significance that we observed in the secondary analyses (e.g., significantly reduced monocyte frequencies) did  not arise based on biased removal of immunological outliers but likely due to a more targeted analyses of a less immunologically variable group, i.e., males only (240). The analyses of both males and females may have obscured the initial round and thus certain subset changes, such as the significant decrease in monocytes, were not apparent. Reduced variability of secondary  82 analyses likely also arose due to protocol and antibody panel adjustments that both improved overall staining quality (reduced channel spill over and doublets) and enabled more granular subset characterization through the addition of specific markers, primarily for T cells (e.g., TCR⍺β, CD27, CD197, MR1 tetramer) and DCs (NRP1, CLEC10A, CD1c). The additions enabled clearer distinctions between granular subsets which proved particularly critical for the T cell compartment as only additional markers enabled full characterization and the observation of a significant expansion of naive CD8 T cells. Finally, the smaller sample size enabled the acquisition of samples from the secondary analyses in a single batch. This additionally adds to higher quality due to the elimination of batch variability that may have contributed to obscured results from the primary analyses. Taken together, while the sample size is smaller in the secondary analyses and findings are of course subject to validation, findings may still be considered robust, particularity compared to the initial exploratory analyses. Implications of these findings and how these fit into our current understanding of allergic disease susceptibility is discussed in the following.  Allergic diseases are commonly associated with an exacerbated type-2 immune response that is characterized by eosinophilia, histamine release and innate and adaptive type-2 immune cells, particularly ILC2s and CD4+ Th2 cells (109). To a lesser degree, allergic responses have also been associated with dysregulated type-1 responses as is the case for obesity-linked asthma where low levels of chronic inflammation leads to the accumulation of pro-inflammatory macrophages, neutrophils and ILC3s (241). While these are the immune features describing active disease, the immune features that describe a susceptibility to future disease are not well described. This is due to limitations in the availability of early-life human samples and patient cohorts that are also matched with longitudinal follow-up data and disease diagnoses. Thus,  83 through our collaboration with the CHILD study, we were given the rare opportunity to explore immune signatures that may describe disease origins. The biomarkers described here were identified both in innate and adaptive immune compartments and, intriguingly, the expansion of CD8 T cells as well as preliminary scRNA sequencing analyses (not shown here), suggest a surprising activation of immune cells towards a type-1 immune response.    At first glance, it may appear counterintuitive to observe a pre-natal type-1 signature in children that go on to develop conditions that are predominantly marked by an exacerbated type-2 immune response. Moreover, events that occur postnatally, including specific environmental exposures and microbial colonisation, have most commonly been linked to allergic disease susceptibility (123). It is important to recognize, however, that human immune development and particularly how early exposures drive long-term alterations in immune functions remain largely unexplored. Even though postnatal events are clearly significant, the true origin of dysregulated immunity has yet to be determined. In fact, both human and animal studies have shown significant correlations between maternal exposures and altered immunity in infants with two opposing immune features as central themes: inflammation and tolerance (133, 136, 141). Events that occur during pregnancy and that drive maternal inflammation such as viral exposure or weight gain, have been shown to result in antigen and antibody transfer to the offspring and in some instances increased risks of allergic diseases during infancy and childhood (206, 242). Conversely, events that promote maternal type-2 immune responses such as certain parasitic infections are associated with increased immune tolerance which is characterized by increased levels of Tregs, IL-10 and TGFβ and overall, a reduced risk to develop childhood allergies (141, 153). Thus, a prenatal, proinflammatory maternal insult (potentially marked by a cord blood  84 type-1 immune signature as we see here) may represent the origin of altered immune development, reduced tolerance, and a subsequent inability to mount an appropriate type-2 response.  In this study, even without directly considering specific maternal characteristics or exposures, we directly link immune signatures present at time of birth with future allergic disease. While this certainly will guide future mechanistic studies into potential mechanisms, (and our ongoing studies of the accompanying scRNA data set), at this stage the primary output of this work is the discovery of a predictive cord blood immune signature. As our samples only allowed us to explore relative frequency changes (as opposed to absolute counts), we cannot definitively state which of the two major immune subset changes that we observed occurred due to a true prenatal influence and which occurred simply as a compensatory effect due to loss or expansion of another subset. Again, we hope that the exploration of the scRNA sequencing that will complement our CyTOF data may give us more insights into the underlying functional properties and allow us to make preliminary conclusions in this regard. Armed with this new signature that narrows our subsets of interest, we are much better prepared to extend this study to a larger cohort using simpler and easily reproducible techniques, including standard flow cytometry, to validate the exploratory findings presented here.   Overall, this study demonstrates that with careful and precise study design, in terms of both patient phenotyping/selection and the use of high-dimensional exploratory tools with a specialized antibody panel, we can decipher an incredibly heterogeneous disease and its origin. By shifting our attention to the prenatal window and individualize interrogation of disease  85 development through targeted biomarker signatures, we may fundamentally change our view and approach towards how and when allergic disease is established and how to diagnose, treat and potentially prevent future disease development.   86 Chapter 4: Modulation of type-2 immune responses as an approach toward enhancing muscle regeneration and treating muscular dystrophy  4.1 Introduction Unlike most organs, skeletal muscle completely regenerates after acute injury without the formation of scar tissue (fibrosis) (166). Muscle regeneration is a complex process mediated by both muscle resident and infiltrating cells that facilitate muscle stem cells to proliferate and differentiate into new muscle fibers (168, 169, 173). Central to this process are both type-1 and type-2 immune cells that produce cytokine and chemokines to create the appropriate immune environments, enable removal of debris, and generate new muscle tissue. Strikingly, mice that are immune compromised or where certain immune subsets are absent, show a substantial reduction in the ability to regenerate skeletal muscle (243). However, overstimulation of the immune response, as seen in repeated muscle injury, prevents normal muscle regeneration and instead leads to fibrosis deposition and muscle degeneration. This is the case for Duchenne muscular dystrophy (DMD) patients for whom a genetic defect causes continuous destabilization of muscle fiber connections and injury (185). To prevent the chronic inflammation that is triggered by this type of injury and that plays a substantial role in tissue destruction, steroids and non-steroidal anti-inflammatory drugs have shown limited benefits and even enhance pathology (244). This is likely due to off target effects on the tissue resident muscle cells, or the inhibition of those critical immune functions needed to heal tissue injury. Promising treatments have emerged that target only specific immune subsets and inflammatory mediators, primarily those belonging to the pro-inflammatory, type-1 immune response (neutrophils, macrophages, TNFα, IFNγ). For example, the kinase inhibitor nilotinib blocks fibrotic matrix deposition, which is triggered by macrophages that overexpress TGFβ in mdx mice, a mouse model of DMD (174).  87 Additionally, colony-stimulating factor 1 receptor (CSF1R) blockage-mediated depletion of muscle resident macrophages, enabled an alternative muscle fiber type composition with a damage-resistant phenotype in mdx mice (245). While these are promising avenues, our understanding of the entirety of immune mediators and mechanisms that participate in normal tissue regeneration and pathologic tissue degeneration and thus may be targets for new treatments, remains incomplete. This is particularly true of type-2 immune cells and primarily ILC2s and eosinophils which are present in muscle in response to injury, yet their function is poorly understood (94, 188). It is also unclear, if the manipulation of these cells impacts muscle regeneration and development of fibrosis. Heredia et. al. showed that eosinophils play a supportive role for muscle regeneration and that their depletion leads to an impairment of this process (94). More recently, I co-authored a study in collaboration with M. Theret et. al. which showed that extreme hyper-eosinophilia in IL-5 transgenic mice slows the ability of muscle to regenerate and accelerates pathology in mdx mice (190). To build on these findings and further explore the role of type-2 immunity in the context of muscle injury, I studied normal muscle regeneration following acute injury in mice with a type-2 skewed immune environment. Type-2 skewing was achieved through systemic treatment with IL-33 and, conversely, in Ror𝛼 sg/sg (staggerer(sg)) mice that lack ILC2s (due to Ror𝛼 -deficiency) and show impaired tissue eosinophilia. I further used mdx mice, to study IL-33 induced type-2 immunity in the context of pathologic hyper immune activation and fibrosis development.     88 4.2 Materials and methods  4.2.1 Mice C57BL/6J, B6.C3(Cg)-Ror 𝛼 sg/J (Ror 𝛼 sg/sg), B6.SJL-PtprcaPepcb/BoyJ (CD45.1), C57BL/ 10ScSn-Dmdmdx/J (mdx) strains were purchased from Jackson Laboratory and maintained at the UBC Biomedical Research Centre animal facility in a specific pathogen-free environment and all experimental procedures were approved by the UBC Animal Care Committee. Mice were age-matched, and all experiments were conducted on male mice only. For live bleeds, 100µl of blood was collected from the saphenous vein and stored at room temp for 1-2 hours (for serum analysis as described below) or collected into 1X PBS 2mM EDTA (cell harvest for flow cytometry analyses as described below).   4.2.2 Bone marrow chimeras To generate BM chimeras, fetal livers were harvested at embryonic day 14.5 from WT or Ror𝛼sg/sg littermates (CD45.2 donors) and used to reconstitute lethally irradiated (10 Gy) CD45.1 WT recipients with five million cells/mouse. The engraftment period was 10 weeks and engraftment success was assessed by flow cytometry.  Only mice with ≥  90% engraftment (donor chimerism) were used for experiments (Appendix C.1).    4.2.3 Muscle injury  Acute muscle injury was induced with 40µl intramuscular tibialis anterior (TA) injections of 0.9% barium chloride (BaCl, Sigma-Aldrich #B0750-500G). Chronic muscle injury was developed in the mdx mouse strain: a model of DMD. Muscle harvest of mdx mice was performed at least at 12 weeks of age. To enhance chronic injury, TA muscles of mdx mice were manually micro- 89 damaged with micro-needle pricks (15x/day for 15 consecutive days) as previously described (187).  4.2.4 IL-33 treatment For both short-term and long-term treatment regiments, 1 µg of recombinant IL-33 (rIL-33; Biolegend #580506) in 200µl PBS was intraperitoneally injected either 1x/day for 3 days (short-term) or 1x/day, 2x/week for 9 weeks (long-term). Control mice were injected with 200µl PBS with identical injection timing.  4.2.5 Histology and imaging For picrosirius red (PSR) staining, diaphragm muscles were harvested and fixed in 1% paraformaldehyde (PFA) for 24 hours prior to transfer to 70% ethanol. Following paraffin embedding, muscles were sectioned at a thickness of 5 µm. PSR staining was performed according to standard protocols (Wax-it Histological Services Inc.). For immunostaining with Laminin and SiglecF, TA muscles were harvested, snap frozen in liquid nitrogen pre-cooled isopentane and stored at -80°C. Tissues were sectioned at a thickness of 10µm on a Leica Cryostat. Sections were fixed in 4% PFA for 10 minutes at RT, rinsed with 1X PBS and incubated in blocking buffer (1X PSB, 3% Goat Serum, 0.3% Triton X-100) prior to staining with primary antibodies (Laminin: Abcam #ab11575 and SiglecF: Invitrogen #14-1702-80) at 4°C, overnight. The following day, tissues were incubated for two hours at room temperature with secondary antibodies (Invitrogen #A21247, Invitrogen #A11008) followed by staining of nuclei with 4′,6-diamidino-2-phenylindole (DAPI, Invitrogen #D3571; 0.6µM). Brightfield and fluorescent images were captured at 10X and 20X objectives with a Nikon Eclipse Ni  90 microscope and cross-sectional area (CSA) of centrally nucleated fibers (laminin and DAPI stained) was quantified with the NIS-Elements analysis software. For fiber size analyses, a minimum of 500 nucleated fibers were quantified from acutely injured muscle and a whole TA section was quantified from chronically injured muscle (mdx mice). For cell number quantifications, 3 muscle sections were quantified per mouse and the mean was used as the final result. Fiji (ImageJ, version 2.0.0-rc/69/1.52n, NIH, MD) was used to quantify PSR staining as described previously (246).  4.2.6 Flow cytometry Mice were anesthetized with Avertin (2,2,2-Tribromoethanol, Sigma #T48402-25G) and perfused with 20 ml of 1X PBS containing 2mM EDTA. If blood was collected, cardiac puncture was performed prior to perfusion. Muscle tissues were harvested, facias were removed, and muscles were manually minced followed by digestion with 1.5 U/ml collagenase D (Millipore Sigma; 11 088 882 001), 2.4 U/ml Dispase II (Millipore Sigma; 04 942 078 001) and 10mM CaCl2 at 37°C for one hour. Suspensions were diluted with FACS Buffer (1X PBS, 2% FBS, 2 mM EDTA) followed by filtration with a 40 µm filter. Blood collected in 1X PBS 2mM EDTA was centrifuged at 300g and 4°C for 5 minutes and red blood cell lysis was performed with ACK lysis buffer (Gibco). Isolated cells from blood and muscle tissues were incubated for 20 minutes at 4°C in FACS buffer containing 1:1000 Fc Receptor-blocking antibody (AbLab) and stained for 30 minutes at 4°C with antibody cocktails (Table 4.1).      91 Table 4.1: Flow cytometry staining antibodies and panels    Antibody/Stain Fluorophore Clone Company ILC2 panel    NK1.1 FITC PK136 Biolegend Ly-6G FITC RB6-8C5 AbLab CD11b FITC M1/70 AbLab CD3 FITC 2C11 AbLab CD3 FITC Kt3 AbLab CD19 FITC 1D3 AbLab CD5 FITC 53.73 AbLab TCRb FITC H57-597 eBioscience CD4 FITC RM4-5 eBioscience TER119 FITC Ter119 AbLab CD8 FITC 53.67 AbLab B220 FITC RA-6B2 AbLab CD11c FITC N418 eBioscience CD25 BV605 PC61 Biolegend CD127 PeCy7 SB/199 BD Pharmingen KLRG1 PerCPC5.5 2F1 eBioscience CD90 APC 53-2.1 eBioscience CD45 (Pan) APCFire750 30-F11 Biolegend FVD NA NA ThermoFisher Eosinophil/Macrophage panel Ly-6G (Gr-1) PerCPC5.5 1A8 BD Bioscience NK1.1 PerCPC5.5 PK136 Biolegend CD3e PerCPC5.5 145-2C11 Biolegend SiglecF APC E50-2440 Biolegend CD11b BV605 M1/70 BD Bioscience Ly-6C PeCy7 HK1.4 Biolegend CD45 (Pan) APCFire750 30-F11 Biolegend PI NA NA ThermoFisher Muscle cell panel    CD45 (Pan) PB I3/2 AbLab CD31 FITC 390 Ebioscience A7integrin APC R2F2 Ablab VCAM Biotin 429 (MVCAM.A) Biolegend SCA-1 PE D7 Ebioscience Streptavidin PeCy7 NA ThermoFisher FVD NA NA ThermoFisher  92 Antibody/Stain Fluorophore Clone Company Engraftment panel     CD45.1 PE A20 AbLab CD45.2 APC Ly5.2 AbLab  Depending on the staining panel, dead cells were stained using 1:1000 Propidium iodide (PI, ThermoFisher #P1304MP) immediately before data acquisition or with fixable viability dye 1:1000 (FVD, ThermoFisher #65-0865-14) during the Fc blocking step. Data acquisition was performed with LSRII (BD Biosciences), and data analysis was performed with the FlowJo software (BD Biosciences).   4.2.7 Serum IgE ELISA Blood was allowed to clot at room temperature for 1 hour prior to centrifugation at 600g for 10 minutes. Serum was stored at -20°C. ELISA for total serum IgE was performed according to manufacturer’s instructions (BD Biosciences).   4.2.8 Statistical analysis  Sample size and statistical tests are indicated in figure legends and all graphs and statistical tests were generated using GraphPad Prism (GraphPad Software, La Jolla California, USA). A test was considered statistically significant at a probability of < 5% (p < 0.05) and we did not assume a Gaussian distribution. Data are mean ± SD.    93 4.3 Results  4.3.1 IL-33-induced immune skewing does not impact muscle regeneration  To study the impact of a type-2 skewed immune environment on skeletal muscle regeneration after acute injury, WT mice were injected interperitoneally with IL-33 for three consecutive days (day 0-2) prior to a one-time BaCl intramuscular TA injection (day 3 of IL-33 treatment, day 0 of injury). BaCl intramuscular injections block potassium channels which causes muscle fiber depolarization and subsequent sarcolemma rupture due to calcium overload (247). In this injury model, muscle resident and infiltrating immune cells were assessed at 1, 3 and 5 days and muscle histology was assessed at 7 and 14 days of acute muscle injury (Fig 4.1A).   Following IL-33 treatment, peripheral blood ILC2s (gating strategy shown in Appendix C.2A) were significantly elevated in IL-33 treated mice (p = 0.004, Fig. 4.1B). One day after muscle injury, control mice showed peak numbers of ILC2s and a steady decrease over time with ~36% fewer ILC2s still present at day 5 compared to day 1 (Fig. 4.1C). In IL-33 treated mice, ILC2 numbers were increased ~100-fold in TA muscle compared to controls at day 1 after injury and continued to increase with peak numbers at day 3 after injury (~650% increase compared to day 1), and prior to dropping at day 5 closer to the numbers observed on day 1. Thus, IL-33 treatment not only generally increased the number of ILC2s in injured muscle but also resulted in continuous expansion for multiple days compared to injured controls for which ILC2s did not increase past day 1 after injury. Of note, both in the blood and in muscle tissue, nearly all ILC2s were positive for KLRG1 expression, indicative of an activated and inflammatory phenotype (248).  94  1 3 50510152025d.p.iEosinophils (x105 / g of TA)1 3 50.000.020.51.01.52.0d.p.iILC2s (x105 / g of TA )d7 d140500100015002000CN Fiber CSA (um2 )IL-33ControlControlIL-330.000.020.040.060.08Blood ILC2s % of live CD45+ 14dIM BaClSac.IP IL-333dd0**********A B CLBDH I J KnsnsControl IL-33Controld7IL-33d14SiglecF Laminin NucleiIL-33Controlns******d7 d140100200300400500SiglecF+ cells/ TA section 1 3 5051015d.p.iCD45+ cells (x106 / g of TA)1 3 5010203040d.p.iCD31+ cells (x104 / g of TA)1 3 5051015d.p.iMuSC (x105 / g of TA)1 3 5010203040d.p.iFAPs (x105 / g of TA)IL-33ControlIL-33ControlG1 3 5020406080d.p.iM2 (x105 / g of TA)1 3 5010203040d.p.iM1 (x105 / g of TA)nsnsnsns nsnsnsnsnsnsnsnsnsnsnsns***1 3 502468d.p.iM1/M2 (x105 / g of TA)E Fns ns**d14d7 95 Figure 4.1: IL-33-induced immune skewing does not impact muscle regeneration. Experimental design overview (A). Frequency of blood ILC2s after IL-33 treatment but prior to muscle damage (B). Number of ILC2s and eosinophils in tibialis anterior (TA) muscles at 1, 3 and 5 days after injury (C, D). Number of total CD45+ immune cells, M1, M2, M1/M2 macrophage ratio, CD31+ endothelial cells, fibro/adipo progenitors (FAPs) and muscle resident stem cells (MuSC) in tibialis anterior (TA) muscles at 1, 3 and 5 days after injury of PBS or IL-33 treated mice (E-G). Number of mice per group in A-G: n = 9. Representative histological SiglecF staining and quantification of SiglecF+ cells in TA muscle sections at 7 and 14 days after muscle injury (H, I). Representative histological laminin and nuclei (DAPI) staining and quantification of centrally nucleated (CN) fiber cross sectional area (CSA) of TA muscle sections at 7 and 14 days after muscle injury (J, K). Number of mice per group for histology in H-K: n = 3-5. *, p < 0.05; **, p < 0.01; ***, p < 0.001; ns, p ≥ 0.05 by two-tailed, two-sample unequal variance Student’s t-test or two-way ANOVA with post-hoc testing (Šidak multiple comparison). Scale bars show 100µm and data are mean ± SD.  LB = Live bleed; BaCl = Barium chloride; IP = Intraperitoneal; IM = Intramuscular; Sac = Sacrifice.    96 In other tissues, such as the lung and gut, eosinophils infiltrate secondary to ILC2s as eosinophils depend on cytokines (IL-5) produced by ILC2s for their survival (249). In injured muscle, the number of eosinophils (gating strategy shown in Appendix C.2B) continuously increased in control and IL-33 treated mice with the highest number observed at day 5 for both groups. However, at day 5, eosinophils were increased 5-fold in IL-33 treated mice compared to controls (p = 0.016, Fig. 4.1D).   To address whether IL-33 treatment impacts other cell subsets in muscle after acute injury, other immune cells as well as muscle resident cells were evaluated at day 1, 3 and 5 after damage Interestingly, there was no difference in total immune cells (CD45+ cells) in IL-33 treated mice compared to controls at any of the three time points evaluated (Fig. 4.1E). However, IL-33 mice showed significantly lower numbers of M1 (Ly6C+) macrophages (p = 0.021) as well as significantly lower levels of M2 (Ly6C-) macrophages (p = 0.010) at day 3 after injury compared to controls (Fig. 4.1F). In both IL-33 treated and control mice, M2 macrophages continuously increased over time with similar numbers at day 1 and day 5 at which point numbers of M2 macrophages in IL-33 treated mice became comparable to those seen in controls. The analysis of the M1/M2 ratio showed that, elevated numbers of macrophages with an M2 phenotype are present at day 1 after muscle injury of IL-33 treated mice compared to controls (p <0.001, Fig).  Non-immune cells, including endothelial (CD31+) cells, fibro/adipo progenitors (FAPs) and muscle stem cells (MuSC), remained unchanged between IL-33 treated and control mice throughout the first 5 days of regeneration (Fig. 4.1G) Overall, this indicates that the numbers of muscle resident cell during regeneration remain unaltered by IL-33-induced immune skewing that results in lower monocyte infiltration during the pro-inflammatory stage, a delay in M1 to  97 M2 transitions well as significantly increased numbers of ILC2s and eosinophils. Histological sections stained with SiglecF showed a significantly higher number of SiglecF+ cells in TA muscles of IL-33 treated mice at day 7 (p < 0.001) and 14 (p = 0.004), which suggests that immune skewing persists at least throughout the first 2 weeks of regeneration (Fig 4.1H, I).  To assess the ability of muscle to regenerate under these conditions, histological sections were stained with laminin and DAPI to visualize centrally nucleated muscle fibers. As a component of the muscle fiber connective tissue, laminin staining allows for the visualization of muscle fibers and the assessment of muscle fiber cross sectional area (CSA). CSA measurements of recently damaged and newly regenerating fibers are smaller compared to fibers that are fully regenerated or undamaged. Only regenerating fibers will show centrally located nuclei and thus can be easily separated from those fibers not impacted by the acute injury. The quantification of centrally nucleated fiber cross sectional area (CN-CSA), both at day 7 and day 14 after acute injury, was not significantly different between IL-33 treated and control mice (Fig 4.1G, H) and tissue sections did not show any other obvious histological differences between groups (fat accumulation, collagen deposition, altered number of nuclei per fiber). In summary, while IL-33 treatment potently promotes type-2 immune skewing and innate type-2 immune cells (ILC2s and eosinophils) are significantly elevated in injured muscle at various time points during regeneration, the muscle’s ability to regenerate after acute injury remains unaltered.  4.3.2 Ror𝜶sg/sg mice show normal muscle regeneration after acute injury To address if the absence of type-2 immune cells, specifically that of ILC2s, impacts muscle regeneration, Ror𝛼sg/sg mice were generated through BM chimera of WT mice. Ror𝛼sg/sg mice  98 lack ILC2s and show a profound reduction in the ability to mount local, but not systemic, type-2 immune responses (250, 251). Due to severe neurological defects, mice with this genotype die shortly after birth and thus BM chimera are needed to generate ILC2 deficiency in adult mice. Ten weeks after transplant, mice were injected with IL-33 for three consecutive days before acute BaCl muscle injury (Fig. 4.2A). Serum for IgE analyses was collected at 7- and 14-days post injury to evaluate the immune response to systemic IL-33 treatment. At day 7 after injury (day 10 after the first IL-33 injection), both IL-33 treated Ror𝛼 WT (p = 0.048) and Ror𝛼sg/sg mice (p = 0.001) showed significantly elevated serum IgE levels compared to control-treated mice (Figure 4.2.B). As published previously, ILC2s are needed for local, tissue specific type-2 responses (251) but are dispensable for systemic type-2 skewing strategies and thus it is not surprising that both Ror𝛼  WT and Ror𝛼 sg/sg mice show systemic serum IgE production in response to IL-33 treatment. However, control-treated Ror𝛼sg/sg mice also showed higher serum IgE levels compared to control-treated Ror𝛼 WT mice (p = 0.019) and significantly higher IgE levels in response to IL-33 treatment compared to IL-33 treated Ror𝛼 WT mice (p = 0.049). This surprising result suggests that Ror𝛼sg/sg mice may exhibit an unexpected, skewed type-2 response even in the absence of IL-33 treatment. Conversely, at day 14 after injury, serum IgE levels were still significantly elevated in IL-33 treated Ror𝛼 WT mice compared to control-treated Ror𝛼 WT mice (p = 0.048) while there was no longer a significant difference between IL-33 and control treated Ror𝛼sg/sg mice. A possible explanation could be that Ror𝛼sg/sg mice are initially able to mount a more robust type-2 immune response (potentially through some type of compensatory polarization of other type-2 cells) but lack critical signals that maintain this response.    99  Figure 4.2: Ror𝜶sg/sg mice show normal muscle regeneration after acute injury. Experimental design overview (A). Serum IgE analyses at day 7 and day 14 (time of sacrifice (sac)) post muscle injury (B, C). Quantification of SiglecF+ cells in tibialis anterior (TA) muscles at day 14 after injury (D). Representative histological laminin and nuclei (DAPI) staining and quantification of centrally nucleated (CN) fiber cross sectional area (CSA) of TA muscle 14 days after muscle injury (E-G). *, p < 0.05; **, p < 0.01; ***, p < 0.001; ns, p ≥ 0.05 by two-way ANOVA with post-hoc testing (Šidak multiple comparison). Scale bar represents 100µm and data are mean ±  SD. Number of mice per group: n = 4-5. FLT = Fetal liver transplant; TVI = Tail vein injection; LB = Live bleed; BaCl = Barium chloride; IP = Intraperitoneal; IM = Intramuscular.  IL-33ControlRORα WTRORαsg/sg0500100015002000IgE (ng/ml)RORα WTRORαsg/sg050010001500200025003000IgE (ng/ml)0-200200-400400-600600-800800-10001000-12001200-14001400-16001600-18001800-20002000-22002200-24002400-26002600-28002800-3000010203040CN Fiber CSA (um2) breakdown% of total fibersRORα WT ControlRORαsg/sg ControlRORα WT IL-33RORαsg/sg IL-33RORα WTRORαsg/sg020040060080010001200CN Fiber CSA (um2 )10wTVIFLTRorα WT/sg/sgFL harvest14dIM BaClSac.IP IL-333dLBA B C Dnsnsd7 post IM d14 post IM d14 post IMnsF Gd14 post IMIL-33ControlEControl IL-33RorαWTRorαsg/sgLaminin Nucleins ns*** * ***RORα WTRORαsg/sg0255075100125SiglecF+ cells/ TA section  100 Thus, IgE and SiglecF analyses suggest that the immune environment is type-2 skewed initially, but systemic IgE levels as well as muscle infiltrating eosinophils are not maintained in ILC2-deficient Ror𝛼sg/sg mice in response to IL-33 treatment.  To evaluate the ability of muscle to regenerate, histological sections were processed as described earlier. Due to the nature of the experimental model (lethally irradiated, immune-reconstituted mice) as well as the age of the mice (these experiments require more aged mice due to length of time required for BM engraftment), histological sections were processed at 14 days post injury. Prior to this timepoint, due to damage/debris, muscle sections could not be adequately stained to allow for CN-CSA quantification. At day 14, CN-CSA was not significantly different between IL-33 treated and control mice nor between Ror𝛼sg/sg and Ror𝛼 WT mice and there were no other visible histological differences between groups (Fig. 4.2E-G). In summary, ILC2-deficient Ror𝛼sg/sg mice retain the ability to, systemically, mount a type-2 immune response but appear unable to maintain at least some components of this immune environment. That difference aside, Ror𝛼 loss fails to significantly impact the process of skeletal muscle regeneration.    4.3.3 IL-33 treatment exacerbates DMD pathology in a chronic muscle injury model To investigate type-2 immune skewing in chronic muscle injury, mdx mice were injected 2x/week with IL-33 from 3 to 12 weeks of age. Due to a genetic mutation, mdx mice lack the protein DYSTROPHIN which connects the muscle fiber sarcolemma with the cytoskeleton and thus is critical for normal muscle function. Absence of DYSTROPHIN causes continuous muscle damage, chronic inflammation, and muscle fiber degeneration. To enhance this chronic injury, TA muscles were manually micro-damaged and both diaphragm and TA muscles were harvested  101 at 14 weeks (Fig 4.3A) (187). To assess the effects of long-term IL-33 treatment at time of muscle harvest, blood was collected for serum IgE analyses, and this showed that IgE was substantially increased in IL-33 treated mice compared to controls (p = 0.009, Fig. 4.3B). Micro-damaged TA muscle sections were stained with SiglecF, laminin and DAPI to assess eosinophil numbers and CN-CSA as described above. Additionally, diaphragm tissue sections were stained with PSR for the analyses of collagen deposition – a key feature of muscle degeneration in mdx mice and DMD patients. Mdx mice treated with long-term IL-33 showed significantly increased numbers of SiglecF+ cells in TA muscles compared to controls (p = 0.044) which indicates that, similarly to short-term IL-33 treatment and acute muscle damage, long-term IL-33 treatment changed the immune composition in chronically injured mdx muscles (Fig. 4.3C, D).   However, unlike acute muscle injury, assessment of collagen deposition in diaphragm muscles showed a substantial increase of collagen in IL-33 treated mice compared to controls (p = 0.029, Fig. 4.3E, F). Additionally, while there was no difference in mean CN-CSA, IL-33 treated mice exhibited a tendency towards an increase in the number of smaller fibers compared to controls which was on the verge of significance (p = 0.061, 4.3E, G-H). Taken together, these data indicate that long-term IL-33 treatment in mdx mice results in systemic, long-term type-2 skewing, a changed immune landscape in chronically injured muscle, as well as accelerated DMD pathology. If ILC2s and eosinophils are the link between IL-33 induced type-2 skewing and excess fibrosis deposition in mdx mice remains to be explored.  102  Figure 4.3: IL-33 treatment exacerbates DMD pathology. Experimental design overview (A). Serum IgE analyses at time of sacrifice (sac) (B). Representative histological SiglecF staining and quantification of SiglecF+ cells in tibialis anterior (TA) muscle (C, D). Representative histological Picrosirius Red (PSR) staining of Diaphragm (top) and histological laminin and nuclei (DAPI) staining of TA muscle (bottom) (E). Quantification of % collagen (PSR+ area) in diaphragm muscle sections (F) and of centrally nucleated (CN) fiber cross sectional area (CSA) of TA muscle sections (G, H). Number of mice per group: n = 3-5. *, p < 0.05; **, p < 0.01; ***, p < 0.001; ns, p ≥ 0.05 by two-tailed, two-sample unequal variance Student’s t-Test or two-way ANOVA with post-hoc testing (Šidak multiple comparison). Scale bar represents 100µm and data are mean ± SD. IP = Intraperitoneal.     0-200200-400400-600600-800800-10001000-12001200-14001400-16001600-18001800-20002000-22002200-24002400-26002600-2800051015202530CN Fiber CSA (um2) breakdown% of total fibersControlIL-33 0250500750100012501500CN Fiber CSA (um2 )ControlIL-33 02004006008001000IgE (ng/ml)IP IL-33 2x/week 9w3w 14d…TA microdamage …Sac.A B C DE F G*****ns, p=0.061Control IL-33Laminin NucleiIL-33ControlSiglecFControl IL-33 **Picrosirius RedDiaphragmControlIL-33 024681012Collagen (% of area) Tibialis AnteriorTibialis AnteriorHnsControlIL-33 0100200300400500SiglecF+ cells/ TA section  103 4.4 Discussion   This study explored the impact of a type-2 skewed immune environment in the context of skeletal muscle regeneration and repair. Specifically, I treated WT, Ror𝛼sg/sg and mdx mice with IL-33 (commonly used to induce a type-2 immune response) and I evaluated muscle injury in the context of 1) a skewed type-2 immune environment during normal muscle regeneration, 2) the absence of key type-2 immune cells during normal muscle regeneration, and 3) a skewed type-2 environment during hyperinflammation and muscle degeneration. In the latter, I showed that long-term type-2 skewing with IL-33 significantly worsens DMD pathology, namely fibrosis development, which supports similar findings by Theret et. al. in the context of IL-5 transgenic mice and hyper-eosinophilia in adult mdx mice (190). A separate study investigating the IL-5 transgenic overexpression model in young, 4-week-old mdx mice was not able to show an impact of eosinophilia on DMD pathology in the early inflammatory stage of the disease (191). However, together with the findings of Theret et. al., this study demonstrates that the impact of type-2 skewing is indeed significant in the late, fibrotic stage of the disease (12+ weeks of age).  In the context of chronic inflammation in mdx mice, ILC2s have been shown to be the main source of IL-5 that maintains eosinophils in chronically injured muscle tissue (188). However, in normal muscle regeneration after acute injury the presence and role of ILC2s is not defined. Limited research on the role of eosinophils currently suggests, that eosinophil depletion impairs normal muscle regeneration after acute injury as they are an important source of IL-4 and IL-13 that support FAP function (94). Conversely, Theret et. al. showed that muscle regeneration in IL-5 transgenic hyper-eosinophilic mice is impaired following acute injury which suggests that this, too, interferes with processes that enable normal tissue regeneration. Based on these findings, I expected that systemic IL-33 treatment, which similarly induces eosinophilia, would negatively  104 impact the ability of skeletal muscle to regenerate. Surprisingly, while significantly elevated numbers of ILC2s and eosinophils were present in skeletal muscle after acute injury at various time points, muscle tissue regenerated normally. A possible explanation for this discrepancy could be that IL-33 induced eosinophilia is transient and more closely resembles a real biological process while the life-long, constitutive expression of IL-5 fundamentally changes many immune processes and expression profiles that together impact skeletal muscle regeneration (252, 253).  Without IL-33 treatment, I showed that ILC2s are slightly elevated at day 1 after muscle injury and while this finding was subtle, ILC2s, may also support subsequent eosinophil maintenance during muscle regeneration. To test this, as well as the general impact of absence of type-2 immunity, I used Ror𝛼 sg/sg mice that have previously been shown to serve as a model for impaired type-2 skewing in tissues due to the absence of ILC2s. Serum IgE analyses results presented in this study suggest that, systemically, Ror𝛼 sg/sg may actually be more skewed towards a type-2 immune response (possibly due to compensatory mechanisms). This result is surprising as all previous work in mucosal settings have suggested that the lack of Ror𝛼 leads to a deficit in locally initiated type-2 inflammatory responses. These data warrant further investigation and may suggest a fundamental difference in the way Ror𝛼 deletion impinges upon mucosal and non-barrier organ immunity. Both systemic IgE and muscle tissue SiglecF+ cell number analyses suggest that Ror𝛼 sg/sg mice are unable to maintain this type-2 immune environment. This is based on the observations that IgE is not maintained in circulation and that there are no excess numbers of eosinophils in injured muscle in response to IL-33 treatment. I expected that the absence of tissue ILC2s would result in absence or lower numbers of eosinophils (this is the case in response to IL-33 treatment) and this, in turn, would lead to impaired muscle regeneration due to the lack of IL-4/IL-13 secreting eosinophils to support FAP  105 function (94). Instead, I provisionally conclude that manipulations of the type-2 immune axis, through deletion of ILC2, with or without IL-33, does not significantly impact skeletal muscle regeneration. Further experiments are required such as skeletal muscle damage in STAT6 KO mice (that lack the ability to mount a type-2 immune response), to confirm this striking result. It is possible, that absence of ILC2s does not actually impact eosinophil maintenance and function in skeletal muscle following acute injury which would contrast the ILC2-eosinophil mechanisms shown in mdx mice as well as at mucosal barriers where eosinophils are mostly dependent on ILC2 signaling for their survival and function (188, 254).   As demonstrated in this study, in contrast to the extreme type-2 skewing and impaired regeneration observed in IL-5 transgenic hyper-eosinophilic mice, IL-33 treatment is a robust yet milder and transient method to induce type-2 immune skewing with no impact on muscle regeneration. This raises the possibility that skeletal muscle regeneration, through more subtle, temporal and/or targeted approaches to alter the type-2 immune environment, may not just remain unaltered but possibly improve. This could be achieved through strategies such as variation of IL-33 dosage and duration that may appropriately enhance the previously reported supportive role of eosinophils in the context of muscle regeneration, but this remains to be explored. Similarly, these approaches to alter type-2 immunity in mdx mice, especially in later stages of the disease, may improve rather than worsen DMD pathology and this is another intriguing avenue for future investigation.     106 Chapter 5: Conclusions  In this thesis, I have investigated the role of immune responses in two mucosal-barrier associated diseases and one non-barrier organ disease: COVID-19, early-life allergies and muscle injury and repair. The first two provided me with the opportunity to develop human biomarkers predictive of disease evolution and outcome and generate novel hypotheses for how this is modulated by immune responses. The latter allowed me to perform more mechanistic experiments in mouse models to study how immune responses can resolve damage in a non-barrier, sterile organ. In this chapter, I summarize the conclusions and insights gleaned from each of these studies, their significance and how they inform future studies on treating disease.   5.1 Chapter 2  5.1.1 Research summary  In Chapter 2, I have assessed the prognostic value of cytokine and cell surface biomarkers in the most severe group of COVID-19 patients in need of predictive evaluation, namely, those with sufficient disease severity that warrants admission to the ICU. This arose as a fortuitous opportunity to utilize the very same CyTOF-based tools and approaches I had developed for investigating the development of type-2 allergic disease and apply them to an ongoing pandemic and acute health care crisis. I took this as a rare opportunity to apply my scientific skillset and arsenal of discovery tools to an ongoing, unmet clinical need. I chose ICU patients as my evaluation cohort as this represents those most in need of acute care and greatest burden on our health system. While these patients are perhaps somewhat heterogenous due to variability in when they decided to seek medical attention and subjectivity on the part of care providers in  107 determining when a patient needs to be admitted to the ICU, I sought to limit this heterogeneity by adhering to strict metrics. The “day of ICU admission” was used as a universal biomarker evaluation point with disease outcome defined according to a strict binary endpoint: 1) an ICU stay of < 6 days, or 2) an ICU stay of > 6 days and/or death. Remarkably, when applied to these two distinct clinical outcome groups, a simple and reproducible panel of predictive biomarkers, specifically serum IL-10 and a CD11clow classical monocyte subset, can accurately triage patients at time of ICU admission (Fig. 5.1).       Figure 5.1: Chapter 2 research summary.       108 5.1.2 Significance Biomarkers are needed to triage patients at ICU admission to assist with appropriate resource allocation and individualized treatment regimens. Yet to this date, immunological biomarkers are highly under-utilized in clinics around the world. Clear definitions and unified approaches to biomarker discovery that simultaneously untangle the complex immune response to the SARS-CoV-2 virus, will pave the way towards routine diagnostic tools that capture the disease state of each patient and allow for individualized strategies for improved care.  5.1.3 Limitations A clear limitation of this study is the patient cohort size and the limited number of healthy control subjects. We also were unable to include non-COVID ICU controls, but this does not impact the COVID-19 specific prognostic measurements presented in this study. Additionally, the number of cytokines evaluated in this study were limited - a larger cytokine panel may reveal additional prognostic markers of COVID-19 disease outcome. Finally, we focused our analyses on ‘second wave’ patients for whom steroids were administered routinely at the start of ICU admission unlike the anti-IL-6 therapy (since February 2021 most patients receive tocilizumab upon admission). Early administration of immune suppressive treatments will impact the biomarkers discussed in this study and thus to retain full prognostic value the blood analysis should be done prior to any treatments.   5.1.4 Future directions The findings of this study open opportunities for future work both with regards to clinical translation as well as mechanistic studies of COVID-19 immune responses. Firstly, the  109 biomarker signatures presented here, have the potential to be translated into a tool used routinely in clinics to triage patients at time of ICU admission and thus inform clinicians on resource allocation and individualized patient care. To translate these biomarkers from bench to bedside, additional multi-centre cohort studies are needed for validation. As we have performed the tedious analyses of a complex data set and have distilled this down to a few key prognostic markers, the evaluation of other cohorts would only need to include the markers of interest and their evaluation with standard analyses tools including flow cytometry and ELISA or mesoscale assays. Pending these validations, our data suggest that cellular signatures and cytokine screening at time of ICU admission have real potential to become standard clinical practise. Longitudinal analyses of the biomarkers may also serve as a tool to monitor disease progression which would be an intriguing avenue for a follow-up study. Finally, findings of these chapters may also have implications that reach beyond COVID-19 since immunological biomarkers may further be used to triage ICU patients afflicted with other infectious diseases.   This study also offers the opportunity to further study immunological mechanisms involved in severe COVID-19. Since the beginning of the COVID-19 pandemic, multiple studies reported elevated serum IL-10 levels as well as a dysregulated myeloid compartment in severe COVID-19 patients (51, 255, 256). The observation that IL-10 is elevated early in severe COVID-19 and yet correlates with high levels of inflammation and poor disease outcome appears, in many ways, to be paradoxical. This cytokine has classically been associated with anti-inflammatory and dampened immune responses in a variety of diseases (257). It is noteworthy however, that IL-10 has also been shown to induce inflammation particularly in cancer patients treated with rIL-10, which resulted in an increase in serum cytokines, expansion of effector CD8 T cells and  110 proliferation and expansion of previously exhausted effector CD8 T cells (258). Based on this evidence, as well as the observation that IL-10 is elevated in severe COVID-19, IL-10 was recently proposed as a possible driver of inflammation, rather than an anti-inflammatory agent, in COVID-19 (258). Further insights into whether it is inflammatory or, alternatively, represents an ineffective attempt by the immune system to dampen a rapidly progressing and excessive inflammatory response, is a topic for future investigation. Another intriguing avenue to pursue is whether IL-10 and CD11clow classical monocytes are linked directly (i.e., is IL-10 produced directly by these monocytes or potentially by tissue macrophages derived from these monocytes). Recently, Sefik et. al. published a humanized mouse model of COVID-19 that recapitulates all the major features of severe human disease both in terms of the inflammatory characteristics as well as lung pathology (259). Sefik et. al. further demonstrate that SARS-CoV-2 directly infects lung macrophages which are major contributors to hyperinflammation and tissue damage in response to infection (260). This model would be ideal for mechanistic studies on the role of inflammatory monocytes or macrophages in the production of large amounts of serum IL-10 that is observed in severe COVID-19 patients. If not through the direct infection with the SARS-CoV-2 virus, monocytes and macrophages may be triggered to produce IL-10 due to the persistent type-1 interferon response that has been reported in this mouse model as well as COVID-19 patients and that has been shown to cause immunosuppression in the context of other viral infections.      111 5.2 Chapter 3  5.2.1 Research summary In Chapter 3, we explored the origins of allergic disease with the aim to predict the development of disease during childhood by interrogating immune cell alterations at time of birth. The CHILD study provided us with n = 50 archived umbilical cord blood mononuclear cell samples from four groups of children: 1) healthy non-allergic, 2) children diagnosed with wheeze only, 3) atopy only, and 4) both wheeze and atopy. Using a 38-marker antibody panel, we performed prospective, exploratory mass cytometry analyses that revealed an exquisitely detailed cord blood immune subset profile including abundant and minor populations of myeloid cells and lymphoid cells as well as rare progenitor cell populations. These marker profiles were then analyzed for association with clinical data acquired at 1, 3 and 5 years of life. Strikingly, we identified immune cell subset alterations in the monocyte and T cell compartments that could be specifically linked to children with a combined wheeze and atopy phenotype compared to healthy controls.   5.2.2 Significance  The results presented in Chapter 3 have the potential to fundamentally change the way we consider the origins, treatment, and prevention of allergic disease. Our findings suggest that, while it may be possible to alter the trajectory of allergic disease development after birth, the predisposition to some forms of allergies begins before birth and to truly understand this disease and develop effective interventions, we need to refocus our attention on fetal immune development.   112 5.2.3 Limitations Limitations of the study presented in Chapter 3 include sample size, the diverse range of phenotypes and lack of selected clinical data. Our initial screen included samples from 50 subjects, which, for a human study, is already a small cohort size. Additionally, the 50 subjects were divided into four different and complex clinical phenotype groups as well as included a mix of both male and female participants. Together, this limits the number of n in each group and may have resulted in overlooking significant changes in cellular subsets which would be more apparent with a larger sample size. Equally, the significant trends that we did observe, could be challenged with a larger cohort, particularity with regards to the secondary analyses of the subset of Healthy and Wheeze/Atopic groups.   This study also relied on a specific grouping of patients into phenotype categories that were defined by the CHILD study rather than a standard clinical grouping such as simply ‘healthy’ and ‘allergic’. This is beneficial for the interrogation of a complex disease and indeed we discovered that our findings were restricted to a subset of children with a very specific clinical phenotype. However, this also limits our ability to interrogate other cohorts that fail to collect the same type of clinical data or do not follow a similar phenotyping protocol.   Finally, due to the nature of sample collection, namely, at the time of birth and at multiple centres around Canada, the complete counts of cord blood cells were not always recorded and are thus missing for about half of all the children that we analyzed. Therefore, while we can report on frequency changes of immune subsets, we are unable to also report absolute counts. This does not significantly impact the discovery of biomarker signatures, but it restricts the conclusions we  113 can make on biological processes. For example, when we see an increased frequency of one major cell subset, we cannot be certain whether this represents a true expansion of this subset or an apparent increase due to loss of other major subsets.   5.2.4 Future directions To build on the exciting findings presented in Chapter 3, we performed single-cell RNA sequencing on additional cord blood aliquots from those 11 subjects that were also evaluated with our specialized CyTOF panel. Thus, we have obtained detailed transcriptomic information that complements our findings on the cell surface protein expression and will add significant information on functional properties of the cell subsets of interest (data analyses are in progress). This in turn will allow us to better understand how perturbations in fetal immune cell development can predispose to future chronic disease. Additionally, we already obtained matching PBMC samples from these same children 1 year after birth. We plan to perform CyTOF analyses on these samples to explore whether the immune alterations that we observed in the cords also persist postnatally.   Building on this study and because the CHILD cohort is now approaching adolescence, we are in a unique position to further evaluate the cord bloods of children who develop allergies during young adult life: it is well known that, while pre-pubescent allergic disease occurs predominantly in boys, post-puberty allergic disease develops primarily in girls. This gives us an unprecedented opportunity to compare differences in cord blood populations between these two groups and potentially reveal the mechanisms behind this sex bias.    114 Finally, we plan to experimentally validate the biomarkers and immune subsets identified from the human cord blood analyses with animal models that have been established previously in our lab for the study of a neonatal window of opportunity to enhance the susceptibility to future allergic disease. In these models, we will target pathways pre- and postnatally, specifically with a focus on maternal type-1 inflammation/responses and subsequent allergic disease susceptibility of the offspring. Maternal type-1 immunity may be induced through direct maternal viral infection, type-1 stimulation through treatments such as polyinosinic:polycytidylic (poly I:C) as well as modulation of maternal endogenous retroviruses that have been recognised, depending on type and tissue, as both essential for normal functions (such as maintenance of skin microbiota homeostasis) as well as unfavourable in the context of certain autoimmune and allergic diseases (261–264).  Taken together, these approaches will be used to identify novel therapeutic strategies for early intervention of allergic disease development in human.   5.3 Chapter 4  5.3.1 Research summary In Chapter 4, I explored the role of type-2 immunity in the context of skeletal muscle regeneration and DMD pathology. With IL-33 treatment to induce a type-2 immune response, I showed that ILC2s and eosinophils significantly increase in muscle tissue following injury but that this does not impact the ability of muscle to regenerate. Muscle regeneration also remained unchanged in Ror𝛼 sg/sg mice which lack ILC2s. Finally, IL-33 treatment worsened muscle pathology in the DMD mouse model (Fig. 5.2). Thus, in the context of acute muscle injury and otherwise normal conditions, ILC2s and potentially type-2 immunity in general, may not be the critical inflammatory component needed for the regeneration process. However, in chronic  115 injury and disease, type-2 immunity may be a promising target to enhance muscle repair and prevent fibrosis development.  Figure 5.2: Chapter 4 research summary.   5.3.2 Significance  Skeletal muscle is one of the few tissues that can fully regenerate in response to injury which is a process that relies on a carefully orchestrated sequence of interactions between muscle resident cells and infiltrating immune mediators. To fully elucidate this process has major implications for our understanding of tissue regeneration in the absence of fibrosis and scar formation. If fully understood, this may provide insight into this process in other tissues that normally retain lifelong scaring and exhibit reduced ability to restore tissue function in response to injury. Skeletal muscle is also often studied in the context of aging since aged muscle shows a decreasing ability to fully regenerate but several studies have shown an approved ability to regenerate aged muscle through the manipulations of immune cells and mediators (265). It has been suggested that both type-1 and type-2 immune cells are involved in the process of regeneration, but mechanistic studies of how type-2 immunity alters repair have been far more limited. With the findings presented in Chapter 3, I add to our understanding of the role of type-2  116 inflammation during muscle injury and extend this to a model of DMD for which precisely targeting inflammation may hold promise for the treatment and/or prevention of this currently untreatable and lethal disease.    5.3.3 Limitations A major limitation of this study is the use of the Ror𝛼sg/sg bone marrow chimeric model to assess the absence of ILC2s in the context of muscle damage. Firstly, serum IgE analyses suggest that there is a compensatory systemic shift in Ror𝛼sg/sg chimeric mice towards type-2 inflammation which makes conclusions about type-2 immunity, especially its reduction or absence, challenging. Moreover, while engraftment was assessed prior to muscle damage experiments, the number of ILC2s was not studied in muscle after injury which would confirm if the desired outcome of the transplant (absence of ILC2s and potentially downstream type-2 inflammation) was successful. At time of experimental endpoint, Ror𝛼sg/sg chimeric mice are aged due to the lengthy nature of the transplant model and therefore were not assessed at the 12–14-week (adult) time point of other experiments in this study (instead, mice were evaluated at 24+ weeks of age). Thus, a direct comparison to other experiments is challenging since muscles in aged WT mice have reduced ability to regenerate. The irradiation and transplant may also have impacted other immune subsets which would need to be investigated in detail to be able to make complete conclusions. Additional models should be assessed to further study the role of a type-2 immune response, particularly the absence of ILC2s and/or type-2 immunity, in muscle and are discussed in the following section. Also discussed below are key experiments to explore underlying mechanisms that are clearly still missing from this study at this point.    117 5.3.4 Future directions To further explore type-2 immunity in the context of skeletal muscle injury, I plan to investigate additional mouse models and treatment strategies that elucidate the role of type-2 immune cells and how this branch of the immune response may be specifically targeted to enhance muscle regeneration after acute damage and prevent muscle pathology in mdx mice.   Firstly, I will use the STAT6 KO mouse model to repeat experiments shown in this chapter. These mice lack the ability to generate a functional type-2 immune response due to the absence of STAT6 which is critical to the IL-4/IL-33 signalling axis (266). While ILC2s are not directly targeted in this model, type-2 immunity is affected generally and systemically which may help to better understand how the absence of type-2 immunity alters muscle repair. Unlike the Ror𝛼sg/sg model, STAT6 KO mice are not generated through BM transplant and thus can be evaluated at the desired time point of 12 weeks.   Secondly, it has been shown previously, that anti-PD-1 treatment leads to KLRG1+ ILC2 expansion and thus I have started to explore this treatment approach in the context of muscle injury as an alternative to the IL-33 treatment strategy (267). I have established that this treatment does enhance eosinophil infiltration into the muscle but to a much lower extent compared to IL-33 treated mice. Preliminary data also suggests that with this more subtle treatment, muscles can regenerate more quickly. 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Med. 214, 1663–1678 (2017).    146 Appendices  Appendix A  A.1 List of CyTOF antibodies  Antibody/ Tetramer Metal/ Fluorophore Clone Company Used in Cohort (Chapter 2) Used in analysis (Chapter 3) CD1c 151Eu L161 Biolegend Replication secondary CD3δ 143Nd 7D6 Thermo Fisher NA exploratory CD3𝜺 143Nd OKT3 Biolegend both secondary CD4 174Yb SK3 Biolegend both both CD8⍺ 168Er SK1 Biolegend both both CD8β 141Pr SIDI8BEE eBioscience both secondary CD11c 147Sm Bu15 Biolegend both both CD14 153Eu M5E2 Biolegend both both CD16 158Gd 3G8 Biolegend both both CD19 142Nd HIB19 Biolegend both both CD25 169Tm BC96 Biolegend both both CD27 175Lu O323 Biolegend Replication secondary CD31 145Nd WM59 Biolegend both both CD32 160Gd IV.3 Stemcell Technologies both both CD34 156Gd 581 Biolegend both both CD38 106Cd HIT2 Biolegend both both CD45 89Y HI30 Biolegend both both CD45RA 110Cd HI100 Biolegend both both CD45RO 112Cd UCHL1 Biolegend both both CD56 148Nd NCAM16.2 BD Bioscience both both CD94 161Dy DX22 Biolegend both both CD116 150Nd 4H1 Biolegend both both CD117 171Yb 104D2 Biolegend Initial both CD123 164Dy 6H6 Biolegend both both CD125 141Pr 26815 Novus Biologicals NA exploratory CD127 165Ho A019D5 Biolegend both both  147 Antibody/ Tetramer Metal/ Fluorophore Clone Company Used in Cohort (Chapter 2) Used in analysis (Chapter 3) CD161 159Tb HP-3G10 Biolegend both both CD197 171Dy G043H7 Biolegend Replication secondary CD200R 173Yb 0X-108 BD Bioscience both both CD294 163Dy BM16 Biolegend both both CD301/CLEC10A 154Sm H037G3 Biolegend Replication secondary CD304/NRP1 172Yb 12C2 Biolegend Replication secondary Fc𝜺R⍺1 176Yb AER-37 Biolegend both both HLA-DR 170Er L243 Biolegend both both IgD 116Cd IA6-2 Biolegend both both IL-17Rβ 155Gd 938975 Novus Biologicals NA exploratory IL-18R⍺ 162Dy H44 Biolegend both both KLRG1 144Nd SA231A2 Biolegend both both LAG3 175Lu 11C3C65 Biolegend Initial primary PODO83 149Sm PODO83 AbLab NA primary TCR⍺β 155Gd T10B9.1A-31 BD Bioscience both secondary TCR𝛾𝛿 152Sm B1 Biolegend both both TRAV1-2 115Ln 3C10 Biolegend both exploratory Anti-APC (Secondary) 149Sm APC003 Biolegend both secondary MR1-5-OP-RU (Primary) APC NA NIH Tetramer Facility both secondary     148 A.2 Mean absolute counts and p-values (ungated cluster analyses)  Cohort Total cell subset H SS LS/D H vs SS H vs LS/D SS vs LS/D   Mean absolute counts (x109/L) p-values Initial CD4 T cells 0.936 0.330 0.244 0.067 0.053 0.549 CD8 T cells 0.428 0.304 0.198 0.383 0.049 0.402 B cells 0.267 0.143 0.184 0.176 0.335 0.509 Monocytes 0.392 0.392 0.589 0.997 0.237 0.311 Stem cells 0.002 0.001 0.003 0.247 0.315 0.088 NK cells 0.230 0.234 0.109 0.961 0.032 0.150 MAIT cells 0.057 0.008 0.012 0.122 0.140 0.721 γδ T cells 0.058 0.018 0.014 0.077 0.065 0.705 pDC 0.012 0.000 0.001 0.029 0.028 0.473 DC2/3 0.028 0.002 0.006 0.026 0.034 0.205 Replication CD4 T cells 0.911 0.511 0.309 0.008 0.000 0.112 CD8 T cells 0.386 0.374 0.184 0.914 0.005 0.090 B cells 0.247 0.285 0.237 0.584 0.871 0.565 Monocytes 0.403 0.657 0.740 0.041 0.028 0.618 Stem cells 0.002 0.004 0.003 0.223 0.451 0.360 NK cells 0.259 0.179 0.199 0.108 0.269 0.708 MAIT cells 0.047 0.007 0.003 0.001 0.000 0.076 γδ T cells 0.058 0.022 0.028 0.003 0.130 0.732 pDC 0.009 0.001 0.001 0.000 0.000 0.575 DC2/3 0.037 0.014 0.012 0.003 0.001 0.829 p-values calculated using a two-tailed, two-sample unequal variance Student’s t-test with Bonferroni-adjustment for multiple comparisons. H = Healthy; SS = Short-Stay; LS/D = Long-Stay/Died   149 A.3 Mean absolute counts and p-values (gated myeloid and T cell cluster analyses)  Cohort Cell subset H SS LS/D H vs SS H vs LS/D SS vs LS/D   Mean absolute counts (x109/L) p-values Initial CD11clow Classical 0.004 0.019 0.082 0.536 0.133 0.388 Total Classical 0.108 0.110 0.181 0.962 0.137 0.212 Total Intermediate 0.218 0.247 0.377 0.745 0.176 0.338 Total Non-Classical 0.019 0.020 0.011 0.898 0.227 0.462 CD4 T cells 0.888 0.312 0.219 0.067 0.050 0.506 CD8 T cells 0.403 0.277 0.182 0.344 0.045 0.415 MAIT cells 0.052 0.008 0.011 0.123 0.140 0.724 γδ T cells 0.057 0.016 0.015 0.062 0.058 0.868 Replication CD11clow classical 0.001 0.015 0.058 <0.001 0.010 0.076 Total Classical 0.117 0.193 0.271 0.031 0.008 0.167 Total Intermediate 0.196 0.412 0.422 0.017 0.014 0.925 Total Non-Classical 0.022 0.026 0.015 0.769 0.188 0.404 CD4 T cells 0.899 0.477 0.292 0.009 0.001 0.098 CD8 T cells 0.412 0.341 0.171 0.499 0.003 0.083 MAIT cells 0.035 0.005 0.002 0.006 0.004 0.093 γδ T cells 0.055 0.022 0.028 0.014 0.166 0.769 p-values calculated using a two-tailed, two-sample unequal variance Student’s t-test with Bonferroni-adjustment for multiple comparisons. H = Healthy; SS = Short-Stay; LS/D = Long-Stay/Died     150 A.4 Cell surface protein signatures for cluster assignment  Analyses Cell type Cell surface protein signatures for cluster assignment Ungated (or CD45+ gated) CD4 T cells CD3+ TCR⍺β+ CD4+ CD8- CD8 T cells CD3+ TCR⍺β+ CD4- CD8+ B cells HLA-DR+ IgD+ CD19+ Monocytes CD116+ CD3- CD19- CD11c+ HLA-DR+ CD32+ Stem cells CD34+ NK cells CD94+ CD3- CD116- MAIT cells CD3+ TCR⍺β+ IL-18R⍺+ MR1-5-OP-RU+ γδ T cells CD3+ TCR⍺β- TCRγδ+ pDC CD11c- NRP1+ CD123+ Fc𝜺R⍺1+ CD116+ HLA-DR+ DC2/3 CD11c+ CD1c+ NRP1- CD123+ Fc𝜺R⍺1+ HLA-DR+ Gated on CD3-CD19- CD116+ (Monocytes) CD11clow classical CD116+ CD14high CD16- CD11clow Total Classical CD116+ HLA-DR+ CD14high CD16- CD123- Total Intermediate CD116+ HLA-DR+ CD14high/int CD16low CD123low Total Non-Classical CD116+ HLA-DR+ CD14low CD16high  151 Analyses Cell type Cell surface protein signatures for cluster assignment Gated on CD3+  (T cells) Naive CD3+TCR⍺β+CD38+CD45RA+ CD45RO-CD27+CCR7+ CM CD3+TCR⍺β+CD38-CD45RA- CD45RO+CD27+CCR7+ EM CD3+TCR⍺β+CD38-CD45RA- CD45RO+CD27-CCR7-  Signatures presented here are based on the refined secondary panel that was used both in Chapter 2 (PBMCs: Replication Cohort) and Chapter 3 (CBMCs: Secondary aliquots of Healthy and Wheeze/Atopic groups) for secondary rounds of analyses.    152  A.5 Ungated clustering of Initial Cohort   UMAP plots of ungated CyTOF-derived data from the Initial cohort (n=14) (A). Proportion of immune cell subsets in Healthy Controls (HC), Short-Stay (SS) and Long-Stay/Died (LS/D) patient outcome groups (B). Mean marker expression heatmap of clusters shown in A (C). Absolute counts of adaptive PBMC subsets (CD4 T, CD8 T, B) (D). Innate and unconventional subsets (NK, MAIT, γδ T, pDC, DC2/3) (E). Monocytes and stem cells (F). n, p ≥ 0.05 by two-tailed, two-sample unequal variance Student’s t-test with Bonferroni adjustment for multiple comparisons.  umap_1umap_2CD4 TCD8 TMyeloidBNKStemCount058001234ValueA B CHC SS LS/D0.00.20.40.60.81.0% proportionCD4 TCD8 TBNKMAITγδ T DC2/3pDCMonoStemOtherCD38CD45RACD45CD4CD45ROCD3"CD127TCR⍺βCD8βCD8⍺CD11cCD31CD32CD16CD56CD94TRAV1-2MR1-5-OP-RUKLRG1CD161IL-18R⍺HLA-DRFc"R⍺1CD123CRTH2CD200RCD19IgDCD14CD116CD34LAG3TCR#$CD117CD2529 38 23 6 12 36 28 8 19 25 30 39 18 40 6 4 16 26 10 7 13 27 37 41 34 17 32 16 31 24 35 21 3 14 9 33 22 20 2 11 1  HC SSLS/D0.0000.0050.0100.0150.020pDC (x109 /L)HC SSLS/D0.000.010.020.030.040.05DC2/DC3 (x109 /L)HC SSLS/D0.000.250.500.751.001.251.50Monocytes (x109 /L)HC SSLS/D0.0000.0020.0040.0060.008Stem cells (x109 /L)HC SSLS/D0.00.51.01.52.0CD4 T cells (x109 /L)HC SSLS/D0.00.51.01.52.0CD8 T cells (x109 /L)HC SSLS/D0.00.10.20.30.40.5B cells (x109 /L)HC SSLS/D0.00.10.20.30.40.50.6NK cells (x109 /L)HC SSLS/D0.0000.0250.0500.0750.1000.125MAIT cells (x109 /L)HC SSLS/D0.0000.0250.0500.0750.1000.125γδ T cells (x109 /L)FEDnsns        ns nsns ns ns ns nsns 153 A.6 T cell clustering of Initial and Replication Cohorts  Representative gating of CD3+ cells (A). Initial Cohort UMAP projections of CD3+ gated cells (all samples combined; limited clustering channels) and mean marker expression heatmap (B). Same as in B but for the Replication Cohort (C). Initial Cohort absolute counts of T cell subsets identified based on gated clustering (D). Same as in D but for the Replication Cohort (E). ns, p ≥ 0.05 by two-tailed, two-sample unequal variance Student’s t-test with Bonferroni adjustment for multiple comparisons. HC SSLS/D0.00.51.01.52.0CD4 T cells (x109 /L)HC SSLS/D0.00.20.40.60.81.0CD8 T cells (x109 /L)HC SSLS/D0.00.51.01.52.0CD4 T cells (x109 /L)HC SSLS/D0.00.20.40.60.81.0CD8 T cells (x109 /L)HC SSLS/D0.000.050.100.150.200.250.30γδ T cells (x109 /L)HC SSLS/D0.000.020.040.060.08MAIT cells (x109 /L)HC SSLS/D0.000.020.040.060.080.100.12γδ T cells (x109 /L)HC SSLS/D0.000.020.040.060.080.100.12MAIT cells (x109 /L)ns ns ns ns   nsnsnsnsNuclear StainCD3!52102811231814917271934156261207228122513242116CD45CD8βCD8⍺CD4CD45RAKLRG1CD16TCR#$CD25TRAV1-2MR1-5-OP- RUHLA-DRCD38CD197CD27CD127CD45ROTCR⍺βCD8⍺CD8βCD45RAKLRG1TRAV1-2TCR#$MR1-5-OP- RUCD16CD25HLA-DRCD4CD45CD38CD127CD45RO261615102430321237332743123619141322213451163525203891218281729AB Cumap_1umap_2umap_1umap_2CD8 TCD4 T CD4 TCD8 TValueCount2500 5CountValue2500 5DE 154 A.7 Cytokine and length of ICU stay correlations   Correlations of length of ICU stay with ICU admission serum IL-10 levels (A), maximum serum IL-10 during ICU stay (B) serum TNFa ICU admission levels (C) and CD11clow Classical Monocytes (D). *, p < 0.05; **, p < 0.01; ***, p < 0.001; ns, p ≥ 0.05 by two-tailed, two-sample unequal variance Student’s t-test and R2 by two-tailed Pearson correlation with 95% confidence interval.  0 10 20 30 40020406080300Length of ICU stay (days)IL-10 (pg/ml)SSLS/D0 10 20 30 40020406080Length of ICU stay (days)TNFα (pg/ml)0 10 20 30 40020406080300Length of ICU stay (days)Max IL-10 (pg/ml)A B C 155 Appendix B  B.1 CHILD Cohort complete blood counts  Complete polymorphonuclear (PMN) cell and peripheral blood mononuclear cell (PBMC) counts from a subset (n = 19) available out of the 50 CHILD umbilical cord blood samples analyzed (A). All PMN and PBMC blood counts available from the CHILD study (n = 983) (B). *, p < 0.05; **, p < 0.01; ***, p < 0.001; ns, p ≥ 0.05 by 05 by one-way ANOVA with Bonferroni adjustment for multiple comparisons. H = Healthy, W = Wheeze, A = Atopic, WA = Wheeze and Atopic.      H W A WA024681012PMNs (x109 /L)A BH W A WA0510152025PBMCs (x109 /L)H W A WA0510152025PMNs (x109 /L)H W A WA024681012PBMCs (x109 /L)**** **nsns*nsns*nsns 156 B.2 Parental BMI and atopy status  Maternal and paternal BMI measurements (A) and parental atopy status (B) were obtained from the CHILD study. Distribution of parental atopy. ns, p ≥  0.05 by one-way ANOVA with Bonferroni adjustment for multiple comparisons. H = Healthy, W = Wheeze, A = Atopic, WA = Wheeze and Atopic.   H W A WA01020304050Paternal BMI H W A WANoneMaternalPaternalBothBAH W A WA01020304050Maternal BMIns ns Atopy 157 B.3 Cell subset frequencies   Percent frequencies of main subsets identified with mass cytometry in cord blood mononuclear cell samples from the four CHILD groups (H = Healthy; W = Wheeze; A = Atopic; WA = Wheeze and Atopic). ns, p ≥ 0.05 by one-way ANOVA with Bonferroni adjustment for multiple comparisons. H = Healthy, W = Wheeze, A = Atopic, WA = Wheeze and Atopic.    H W A WA0.00.51.01.52.0Stemcells (% of CD45+)H W A WA0102030405060Monocytes (% of CD45+)H W A WA0.00.51.01.52.0DC2/3 (% of CD45+)H W A WA0.00.20.40.60.8pDC (% of CD45+)H W A WA0.00.51.01.52.02.53.0Basophils (% of CD45+)H W A WA01020304050B cells (% of CD45+)H W A WA01020304050CD4 T cells (% of CD45+)H W A WA010203040NK cells (% of CD45+)ns ns ns nsns ns ns ns 158 Appendix C  C.1 Engraftment analysis  Fetal liver transplants were performed as described in the methods after 10 weeks engraftment was assessed by performing flow cytometry analyses on blood collected from saphenous vein bleeds. Blood immune cells were stained with CD45.1, CD45.2 and viability dye and frequency of CD45.2+ cells were determined with manual FlowJo gating and quantification. Samples below the dashed line were excluded from the experiment. p ≥ 0.05 by two-tailed, two-sample unequal variance Student’s t-Test.     nsRORα WTRORαsg/sg7580859095100% CD45.2 (of live) A 159 C.2 Gating strategies for ILC2s and eosinophils  Representative gating strategy of muscle ILC2s from PBS treated (top) and IL-33 treated mice (bottom) (A). Representative gating strategy for muscle eosinophils from PBS treated mice (top) and IL-33 treated mice (bottom) (B). PI/FVD = viability dyes; Lin = Lineage (contains: CD4, CD5, CD8, CD3 (two clones), TCRb, TER119, Ly6G, CD11b, CD11c, NK1.1, CD19, B220). ABControlIL-33SSC-AFSC-A FSC-AFSC-HFVDCD45 CD90LinCD90CD127CD25KLRG1ControlIL-33SSC-AFSC-A FSC-AFSC-HPICD45 Ly6CLinCD11bSiglecF