Medicine, Faculty of
Medicine, Department of
DSpace
UBCV
Tung, Lin Wei (Henry)
Doctor of Philosophy - PhD
University of British Columbia
Heart failure is, in general, an inevitable outcome of ischemic heart disease and various cardiomyopathies. While this is largely attributed to the negligible regenerative capacity of the heart, the deterioration of cardiac function is driven by a myriad of dysregulated processes that culminate in chronic remodeling of the heart. In particular, the cardiac stroma, composed of interstitial mesenchymal cells, represents an important component of the cardiac niche and structure. It acts as a “double-edged” entity that plays a vital role in tissue repair and recovery of cardiac function, yet its involvement in driving pathological alterations leading to complete heart failure cannot be excluded. Thus, understanding the cardiac stroma and identifying mechanisms to alter its behaviour hold significant therapeutic potential. Here, we found that the cardiac stroma is a highly heterogeneous population consisted of multiple anatomically and functionally distinct entities. In response to acute ischemic injury, a subset of perivascular stromal cells known as pericytes acquire a transient signature indicative of increased fibrogenic activity and altered vascular maintenance, which is correlated with their dissociation from the vasculature and the ensuing vascular deterioration in the infarct. This regional response is supported by a niche that perpetuates pro-inflammatory, fibrogenic, and anti-angiogenic signaling beyond the completion of the myocardial scar. Dysregulation of the stroma and its secretome also potentiate the functional deterioration of the cardiac muscle. In chronic contexts such as hypertrophic cardiomyopathy, a subset of fibroblasts adopts an activated state similar to that of acute injuries and upregulates IGF-1 signaling to cardiomyocytes, and therefore inducing a hypertrophic response. Moreover, the abrogation of stromal signals regulating vascular growth and maintenance is indicative of compromised coronary circulation. Collectively, our work underlines the importance to develop strategies that target specific stromal populations and incorporate spatial considerations for effective treatment of cardiac pathologies.
Deciphering the heterogeneity and functional role of the cardiac stroma in health and disease by Lin Wei (Henry) Tung B.Sc., Queen’s University, 2019 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Experimental Medicine) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) April 2024 © Lin Wei (Henry) Tung, 2024 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Deciphering the heterogeneity and functional role of the cardiac stroma in health and disease submitted by Lin Wei (Henry) Tung in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Experimental Medicine Examining Committee: Dr. Fabio M.V. Rossi, Professor, Biomedical Engineering, UBC Supervisor Dr. Kelly M. McNagny, Professor, Biomedical Engineering, UBC Supervisory Committee Member Dr. Zachary Laksman, Professor, Biomedical Engineering, UBC Supervisory Committee Member Dr. Nozomu Yachie, Professor, Biomedical Engineering, UBC University Examiner Dr. Liam Brunham, Associate Professor, Biomedical Engineering, UBC University Examiner Additional Supervisory Committee Members: Dr. Ali Bashashati, Professor, Biomedical Engineering, UBC Supervisory Committee Member iii Abstract Heart failure is, in general, an inevitable outcome of ischemic heart disease and various cardiomyopathies. While this is largely attributed to the negligible regenerative capacity of the heart, the deterioration of cardiac function is driven by a myriad of dysregulated processes that culminate in chronic remodeling of the heart. In particular, the cardiac stroma, composed of interstitial mesenchymal cells, represents an important component of the cardiac niche and structure. It acts as a “double-edged” entity that plays a vital role in tissue repair and recovery of cardiac function, yet its involvement in driving pathological alterations leading to complete heart failure cannot be excluded. Thus, understanding the cardiac stroma and identifying mechanisms to alter its behaviour hold significant therapeutic potential. Here, we found that the cardiac stroma is a highly heterogeneous population consisted of multiple anatomically and functionally distinct entities. In response to acute ischemic injury, a subset of perivascular stromal cells known as pericytes acquire a transient signature indicative of increased fibrogenic activity and altered vascular maintenance, which is correlated with their dissociation from the vasculature and the ensuing vascular deterioration in the infarct. This regional response is supported by a niche that perpetuates pro-inflammatory, fibrogenic, and anti-angiogenic signaling beyond the completion of the myocardial scar. Dysregulation of the stroma and its secretome also potentiate the functional deterioration of the cardiac muscle. In chronic contexts such as hypertrophic cardiomyopathy, a subset of fibroblasts adopts an activated state similar to that of acute injuries and upregulates IGF-1 signaling to cardiomyocytes, and therefore inducing a hypertrophic response. Moreover, the abrogation of stromal signals regulating vascular growth and iv maintenance is indicative of compromised coronary circulation. Collectively, our work underlines the importance to develop strategies that target specific stromal populations and incorporate spatial considerations for effective treatment of cardiac pathologies. v Lay Summary Cardiovascular disease is the leading cause of mortality globally. In Canada, about one in twelve individuals over the age of twenty live with a diagnosed heart condition. Although current therapeutics can control symptoms and improve the quality of life, restoration of normal cardiac function remains an unmet need for many heart conditions. One of the reasons for the lack of a cure is our limited understanding of the cellular environment throughout disease progression. In my proposed research work, we will capture the behaviours of various cell types and how they communicate with each other in response to disease. By understanding the behaviour of cells, we can identify the cell types and specific mechanisms responsible for causing damage and facilitating recovery. Overall, we will initiate the development of a new therapeutic approach centering around promoting tissue regeneration rather than controlling disease progression, a new direction for preventative care in cardiomyopathies. vi Preface Chapter 2 is modified from a published manuscript at Nature Communications, titled “Spatiotemporal signaling underlies progressive vascular rarefaction in myocardial infarction”. E. Groppa and H. Soliman performed scRNAseq experiments. D. Guo and S. Sinha assisted with analyzing the sequencing data. E. Groppa, B. Lin, and L. Rempel, and J. Brassard performed immunostaining and flow cytometry experiments. M. Ritso and L. Rempel compiled graphics and figures. C.W. Cheung and C. Eisner performed confocal imaging, image processing, and quantifications. C. Chang and B. Lin conducted animal surgeries. E. Groppa, F. Rossi, J. Biernaskie, and K. McNagny drafted the manuscript. My contributions include experimental design, data collection, analysis, manuscript preparation, and all of the procedures described above. This study was completed in collaboration with the Biernaskie lab from the University of Calgary and the McNagny lab at UBC. Copyright permissions were granted by the publisher for academic use. Chapter 3 is adapted from a manuscript to be submitted for publication, titled “Multifaceted dysregulation in the cardiac landscape underlies functional deterioration in familial hypertrophic cardiomyopathy”. S. Safabakhsh, F. Sar, A. Haegert, and S. Le Bihan extracted nuclei and generated sequencing data. H. Adomat generated the mass spectrometry data. G. Singhera evaluated tissue fibrosis by Masson’s trichrome staining and quantification. B. Lin performed all immunofluorescence stainings, confocal imaging, and their associated quantifications. Z. Laksman and F. Rossi oversaw the project and drafted the manuscript. My contributions include experimental design, data collection, vii analysis, figure compilation, manuscript preparation, and all of the procedures described above. This study was completed in collaboration with the Laksman lab and the Collins lab at UBC. All research involving animals was conducted in accordance with the protocols approved by the UBC Animal Care Committee (protocol IDs: A19-0316, A22-0245, and A22-0012). Research involving clinical samples was performed with informed consent from patients and in accordance with the protocol, REB# H05-50208, approved by the Research Ethics Board (REB) at UBC (Providence Healthcare, Canada). IDs assigned to patient data in the study are arbitrary and de-identified. viii Table of Contents Abstract ....................................................................................................................................... iii Lay Summary ...............................................................................................................................v Preface ......................................................................................................................................... vi Table of Contents .................................................................................................................... viii List of Tables ................................................................................................................................x List of Figures ............................................................................................................................ xi List of Abbreviations ............................................................................................................... xii Acknowledgements ..................................................................................................................xv Dedication ................................................................................................................................ xvii Chapter 1: Introduction .............................................................................................................1 1.1 Cardiac stromal diversity: an under-appreciated melting pot ................................ 1 1.1.1 Defining stromal subsets by location ................................................................ 2 1.1.2 Defining stromal subsets with identity markers ............................................... 8 1.1.3 Stromal diversity in human hearts ................................................................... 16 1.2 A double-edged stromal response in disease and aging ..................................... 18 1.2.1 Myocardial infarction .......................................................................................... 19 1.2.2 Cardiac aging ...................................................................................................... 25 1.3 Research Aims ........................................................................................................... 34 Chapter 2: Spatiotemporal signaling underlies progressive vascular rarefaction in myocardial infarction. ..............................................................................................................36 2.1 Synopsis ...................................................................................................................... 36 2.2 Introduction .................................................................................................................. 37 2.3 Materials and methods .............................................................................................. 39 2.3.1 Animals ................................................................................................................ 39 2.3.2 Induction of cardiac injury ................................................................................. 40 2.3.3 Perfusion and tissue collection ........................................................................ 41 2.3.4 Tissue digestion and purification of cell populations .................................... 41 2.3.5 Cell proliferation assay ...................................................................................... 42 2.3.6 Immunofluorescence staining .......................................................................... 43 2.3.7 Image acquisition ............................................................................................... 44 2.3.8 Assessment of cardiac vascular network ....................................................... 44 2.3.9 ELISA ................................................................................................................... 45 2.3.10 Single cell library construction and sequencing ............................................ 45 2.3.11 Single cell RNA-seq analysis ........................................................................... 46 2.3.12 Gene Regulatory Network analysis ................................................................. 47 2.3.13 Visium................................................................................................................... 48 2.3.14 Pathway enrichment analysis ........................................................................... 49 2.3.15 Inference of regional communication networks ............................................. 49 2.3.16 Statistical analysis .............................................................................................. 50 2.3.17 Data availability................................................................................................... 51 2.3.18 Code availability ................................................................................................. 51 2.4 Results ......................................................................................................................... 51 2.4.1 Heterogeneity of stromal cells in the heart ..................................................... 51 2.4.2 Myocardial infarction induces cardiac pericyte activation ............................ 56 ix 2.4.3 Fibroblasts and mural cells do not share a lineage relationship in MI ....... 60 2.4.4 Progressive vascular rarefaction and hypertrophy in the infarct zone ....... 61 2.4.5 Spatiotemporal atlas of the infarcted heart .................................................... 64 2.4.6 Inter-regional communication networks regulate cardiac repair ................. 68 2.5 Discussion ................................................................................................................... 71 Chapter 3: Multifaceted dysregulation in the cardiac landscape underlies functional deterioration in familial hypertrophic cardiomyopathy. ...........................100 3.1 Synopsis .................................................................................................................... 100 3.2 Introduction ................................................................................................................ 101 3.3 Material and method ................................................................................................ 103 3.3.1 Ethics and human tissue screening .............................................................. 103 3.3.2 Nuclei isolation .................................................................................................. 104 3.3.3 Immunohistochemistry..................................................................................... 105 3.3.4 Immunofluorescence microscopy .................................................................. 105 3.3.5 Image acquisition ............................................................................................. 106 3.3.6 Protein extraction ............................................................................................. 107 3.3.7 Mass spectrometry ........................................................................................... 108 3.3.8 Single-nucleus RNA-sequencing and library preparation .......................... 109 3.3.9 Single-nucleus RNA-sequencing analysis ................................................... 110 3.3.10 Pathway enrichment analysis ......................................................................... 111 3.3.11 Inference of cell-cell communication ............................................................. 112 3.3.12 Statistical analysis ............................................................................................ 112 3.4 Results ....................................................................................................................... 113 3.4.1 Cellular landscape of the hypertrophic heart ............................................... 113 3.4.2 Heterogeneity of cardiomyocytes in hypertrophic hearts ........................... 116 3.4.3 Pathological alterations of the cardiac stroma ............................................. 120 3.4.4 Cardiac hypertrophy exacerbates vascular dysfunction ............................ 123 3.4.5 Perturbations of cell-cell communication in cardiac hypertrophy ............. 125 3.5 Discussion ................................................................................................................. 128 Chapter 4: Concluding remarks ..........................................................................................149 4.1 The impact of our work ............................................................................................ 149 4.2 Limitations of omics studies .................................................................................... 152 4.3 Current challenges of cell-based therapies .......................................................... 155 4.4 Future directions ....................................................................................................... 157 Bibliography .............................................................................................................................158 x List of Tables Table 1.1. Summary of cardiac stromal nomenclature and reported subsets ................ 33 xi List of Figures Figure 1.1. Graphical summary of heart anatomy and its substructures. ...................... 30 Figure 1.2. Graphical summary of major stromal subsets and their relative spatial positions to the vasculature. .......................................................................................... 30 Figure 1.3. Cellular landscape of the heart. ................................................................... 31 Figure 1.4. Heterogeneity of cardiac fibroblasts. ........................................................... 31 Figure 1.5. Heterogeneity of cardiac mural cells. .......................................................... 32 Figure 2.1. Cardiac fibroblasts and mural cells are distinct stromal entities. ................. 81 Figure 2.2. Characterization of cardiac stromal populations. ......................................... 83 Figure 2.3. GRN signatures of stromal subsets at SS and cell kinetics after MI. ........... 85 Figure 2.4. Pericytes adopt an altered state in MI. ........................................................ 86 Figure 2.5. Mural cell response to MI. ........................................................................... 89 Figure 2.6. Fibroblasts and mural cells do not share a lineage relationship in MI. ........ 91 Figure 2.7. Lineage relationship of stromal subsets and assessment of vascular function. ......................................................................................................................... 93 Figure 2.8. Progressive vascular rarefaction in the infarct. ............................................ 95 Figure 2.9. Spatiotemporal atlas of the infarcted heart. ................................................. 97 Figure 2.10. Signaling networks underlying the infarcted heart. .................................... 99 Figure 3.1. Cellular landscape of the hypertrophic heart. ............................................ 133 Figure 3.2. Dysregulated molecular profiles of cardiomyocytes. ................................. 134 Figure 3.3. Pathological alterations of the stroma. ...................................................... 137 Figure 3.4. Cardiac hypertrophy exacerbates vascular dysfunction. ........................... 138 Figure 3.5. Perturbations of cell-cell communication in cardiac hypertrophy. .............. 140 Figure 3.6. Extended supplementary data of 3.1. ........................................................ 141 Figure 3.7. Extended supplementary data of 3.2. ........................................................ 142 Figure 3.8. Extended supplementary data of 3.3. ........................................................ 145 Figure 3.9. Extended supplementary data of 3.4. ........................................................ 146 Figure 3.10. Extended supplementary data of 3.5. ...................................................... 148 xii List of Abbreviations αSMA Alpha-smooth muscle actin ABCC9 ATP-binding cassette subfamily C member 9 ALP Alkaline phosphatase ANGPT Angiopoietin ANGPTL Angiopoietin-like ANOVA Analysis of variance ATP Adenosine triphosphate BMP Bone morphogenetic protein CCL Chemokine ligand CCR2 Chemokine receptor-2 CD146 Melanoma cell adhesion molecule CM Cardiomyocyte CNN1 Calponin 1 CNS Central nervous system COL1A1 Collagen type 1 alpha 1 CPC Cardiac progenitor cell CSF Colony stimulating factor DEG Differentially expressed gene EC Endothelial cell ECM Extracellular matrix EdU 5-Ethynyl-2'-deoxyuridine EGFP Enhanced green fluorescent protein ELISA Enzyme-linked immunosorbent assay eNOS Endothelial nitric oxide synthase ENTPD1 Ectonucleoside triphosphate diphosphohydrolase-1 FACS Fluorescence-activated cell sorting FAP Fibro-adipogenic Progenitor FSP-1 Fibroblast-specific protein 1 FGF Fibroblast growth factor FIB Fibroblast F-SH Fibroblast-SCA-1-high F-SL Fibroblast-SCA-1-low F-trans Transitionary fibroblast F-WntX Wnt-expressing fibroblast GAL-3 Galectin-3 GDF Growth differentiation factor GEO Gene expression omnibus GLAST Glutamate aspartate transporter 1 xiii GO Gene Ontology GRN Gene regulatory network HCM Hypertrophic cardiomyopathy hESC Human embryonic stem cell H&E Hematoxylin and Eosin HIC1 Hypermethylated in cancer 1 IGF Insulin growth factor IHD Ischemic heart disease IL Interleukin IP Intraperitoneal iPSC Induced pluripotent stem cell IVS Interventricular septum KCNJ8 Potassium inwardly-rectifying channel LAD Left anterior descending lncRNA Long intergenic non-protein coding RNA LOX Lysyl oxidase LVFW Left ventricular free wall MC Mural cell MCP-1 Monocyte chemoattractant-1 MHC Myosin heavy chain MI Myocardial infarction MMP Metalloproteinase MSC Mesenchymal stromal cell NF𝜅B Nuclear factor 𝜅-light chain enhancer of activated B cell NG2 Neural-glial antigen 2 NND Nearest neighbor distance OCT Optimal cutting temperature OSM Oncostatin PCA Principal component analysis PDGF Platelet-derived growth factor PER Pericyte PI Post infarction PSM Peptide spectrum match PTN Pleiotrophin POSTN Periostin PRR Pattern recognition receptors ROI Region of interest ROS Reactive oxygen species SASP Senescent associated secretory phenotype xiv SCA-1 Stem cell antigen-1 SCENIC Single-Cell rEgulatory Network Inference and Clustering SEM Standard error of the mean scRNAseq Single-cell RNA sequencing snRNAseq Single-nucleus RNA sequencing SMAD Suppressor of Mothers against Decapentaplegic SM22α Transgenlin SPARC Secreted protein acidic and rich in cysteine SPP1 Osteopontin SS Steady state STAT Signal transduction and activator of transcription factor TAM Tamoxifen TAZ Transcriptional co-activator with a PDZ-binding domain TCF21 Transcription factor 21 TGFβ Transforming growth factor β THBS Thrombospondin TIMP Tissue inhibitor of metalloproteinase TMT Tandem mass tag TNF Tumor necrosis factor TF Transcription factor UMAP Uniform manifold approximation and projection UMI Unique molecular identifier VEGF Vascular endothelial growth factor VIC Valve interstitial cells VSMC Vascular smooth muscle cell WNT Wingless-related integration site WGA Wheat germ agglutinin WSS Within-cluster-sum of squared errors YAP Yes-associated protein xv Acknowledgements Words cannot convey my appreciation and respect for Dr. Fabio Rossi – a mentor who inspires his students to become the next beacon of scientific research; a scientist who sees challenges as stepping stones to innovate; and a victor who has triumphed over a debilitating injury with his relentless passion to continue as a leader in science. I would not be the person I am today without his guidance. I extend my sincerest gratitude to the senior lab members, especially Dr. Nasim Kajabadi, Dr. Farshad Babaeijandaghi, and Dr. Marine Theret, who have selflessly provided mentorship and overwatched my growth as a scientist. I would also like to thank the students, David Guo and Bruce Lin, and the technician, Chihkai Chang, who I have worked closely with and made significant progress in my research projects. Although the journey had been difficult, all the times we laughed and cried together have truly made the experience unforgettable and worthwhile. I owe particular appreciation to the Horatio Alger Association and the Dennis & Phyllis Washington Foundation for their sponsorship and opportunities. Their generosity and commitment to inspire the next generation of young leaders have made the “American Dream” achievable for students who have faced adversities. Most importantly, despite the hardship and the fear of “uncertainty” in scientific research, it is the unrelenting support and care of my family and close friends that have xvi pushed me to cross the finishing line and continue as a scientist advocating for the collective improvement of public health. xvii Dedication “To my dearest parents who have given up everything to provide me the opportunity to pursue my dreams in a land far away from their upbringing; To my beloved siblings who will grow up, persevere, and find happiness; To my friends who have accompanied and motivated me to undertake challenges and overcome adversity; To those who have lost a dear one to heart diseases; This research is dedicated to you.”1 Chapter 1: Introduction 1.1 Cardiac stromal diversity: an under-appreciated melting pot Pioneering studies of the stromal compartment have elucidated multiple cellular entities that play pivotal roles in maintaining homeostasis and facilitating tissue repair in disease across organ systems. However, due to technical limitations, characterization of stromal populations was often confounded by the use of promiscuous markers and transgenic reporters, which may be sufficient for their delineation from non-stromal populations but not other subsets under the stromal umbrella. Moreover, the immediate proximity of different stromal populations, especially those of the perivascular niche, has further precluded their distinction. For these reasons, the nomenclature developed over decades of research bears inconsistencies and has hampered our ability to understand the identities, cellular functions, and lineage relationships of stromal populations. Fortunately, with the advent of high-throughput omics technologies, we have now acquired the capacity to interrogate stromal diversity at the single-cell resolution on multiple levels of gene expression, including epigenomics, transcriptomics, and proteomics. Multiplexing of cellular profiles while preserving their spatial context has also become a possibility. By leveraging these cutting-edge tools, recent studies have redefined the stromal landscape and provided a more accurate functional assessment of specific subsets, albeit with some degree of lingering confusion. In the following section, we discuss potential sources of stromal heterogeneity and summarize reported entities with the focus on localization and cellular profiles in healthy adult hearts. 2 Additionally, we also discuss potential correlations of cardiac stromal subsets between those identified in animal models and humans. 1.1.1 Defining stromal subsets by location The historical perspective of stromal cells as a homogenous population of interstitial occupants is outdated. Even under the umbrella classifications such as fibroblasts or mural cells, mounting evidence indicates the presence of specialized subsets or cell states with distinct functions within each stromal population at homeostasis. This cellular diversity, in part, is attributed to anatomical locations or relative proximity to particular structures. A brief summary of cardiac anatomy and its substructures discussed in the thesis can be found in Fig. 1.1. In general, the vast majority of cardiac resident fibroblasts are interspersed throughout the interstitium, a fluid-filled space between the internal structures of an organ, (“interstitial fibroblasts”) or reside in the adventitia (“adventitial fibroblasts”) of large coronary vessels. The adventitia represents a layer of connective tissue outside of the external elastic lamina that encases the medial smooth muscle layer. While it is mainly composed of fibroblasts, the adventitia is also endowed with microvasculature (vasa vasorum), nerves (nervi vasorum), and several other cell populations, including immune cells. Functionally, adventitial fibroblasts provide a supporting framework that enables tensile strength in coronary vessels via the deposition of extracellular matrix1–4. These fibroblasts also act as “sensors” of biomechanical stimuli and regulate vascular tone through the secretion of nitric oxide. In response to disease and physiological 3 alterations associated with aging, they orchestrate vascular remodeling by adopting an activated state and communicating with other constituents of the adventitia5,6. In addition to these functional features, growing evidence suggests that adventitial fibroblasts, or at least a subset of the population, harbour progenitor properties. Indeed, we and others have identified a group of fibroblast-like cells in the adventitia expressing platelet-derived growth factor receptor-α (PDGFRα) and stem cell antigen-1 (SCA-1), a marker of hematopoietic precursors in the bone marrow. These cells are capable of differentiating into specialized stromal subsets including myofibroblasts and adipocytes in vivo and possess a greater colony forming capacity than their SCA-1- or SCA-1low counterparts7–9. Similar observations were also made in perivascular cells expressing glioma-associated oncogene 1 (GLI1)10. Given their multilineage potential, adventitial fibroblasts are broadly considered as fibro-adipogenic progenitors (FAP), a term used to describe mesenchymal progenitors originally identified in skeletal muscle, or mesenchymal progenitors of the heart11–15. Although previous findings have supported that SCA-1+ progenitors retain cardiogenic potential, this notion is challenged and, by and large, debunked by recent studies16–18. Specifically, fate mapping experiments utilizing genetic lineage tracing of SCA-1+ cells have indicated that the myogenic contribution of FAPs during development, adulthood, or after myocardial infarction (MI) is limited19–21. Despite reports of their perivascular association, cardiac FAPs are not strictly confined to the vascular adventitia. This is illustrated by immunostainings against endogenous GLI1 where its expression is present, not only in the arterial adventitia, but also in some interstitial constituents. 4 While the perivascular niche represents a major point of distinction, it is believed that fibroblasts may also exhibit differences associated with anatomical locations. Through in vitro assessments, fibroblasts derived from the atrium display an increased proliferative response than ventricular fibroblasts when treated with growth stimuli, including fetal bovine serum, PDGF, basic fibroblast growth factor, and transforming growth factor-β122–25. Examination of their transcriptomes also indicated discrepancies in gene programs related to ECM components, signaling pathways, and metabolism. PDGF receptor, in particular, is more abundantly expressed in atrial fibroblasts. These differential characteristics between atrial and ventricular fibroblasts may explain a heightened fibrotic response in the atria across several pathological contexts such as congestive heart failure. Furthermore, treatment with statins revealed differences in pharmacological responsiveness in atrial and ventricular fibroblasts derived from human hearts26. Nonetheless, it is important to recognize that chamber-specific differences of fibroblasts may also be attributed to the dissimilarity of microenvironment in atrial and ventricular stroma, which are exposed to different mechanical stimuli and characterized by different cardiomyocyte organization and matrix composition. On the other hand, current evidence does not support that fibroblasts exhibit differences transmurally from the epicardial to endocardial face of the adult heart. During embryonic development, cardiac fibroblasts primarily originate from epicardial and endocardial progenitors through epithelial-to-mesenchymal or endothelial-to-mesenchymal transition27. Interestingly, lineage-tracing of fibroblasts specific to each origin indicated preferential distributions spatially whereby the epicardially-derived fibroblasts populate 5 the left ventricular free wall (~92% of total fibroblasts) and endocardial progeny is more prevalent in the interventricular septum (~64%)28. Once adulthood is reached, contribution of fibroblasts from epicardial and endocardial cells is limited, as shown by fate mapping experiments in models of cardiac injury28,29. Despite their separate embryonic origins, both developmental subsets of fibroblasts have been shown to adopt a similar phenotypic profile and functional response to aging and injury29–31, and thus pointing to the convergence of developmentally distinct fibroblasts. However, this is challenged by more recent findings where their contributions to myofibroblasts were disproportionate and dependent on the type of injury32–34. For instance, in the context of MI, multiple lines of epicardial lineage demonstrated that epicardially-derived fibroblasts are an overwhelming source of myofibroblasts33. One potential consideration underlying this discrepancy may be the anatomical location of the injury. In the case of MI, ischemia and fibrosis are predominantly found in the left ventricle, which, as mentioned above, is mostly occupied by the epicardial progeny. It is likely that the environment in which the fibroblasts reside in may pose as the primary determinant of their behaviour irrespective of developmental origins. In addition to those identified in the myocardium, another distinct population of fibroblasts called valve interstitial cells (VIC) has been documented in the cardiac valves. VICs are embedded throughout all three layers of the valve leaflets, namely fibrosa, spongiosa, and ventricularis, and associate closely with endothelial cells lining the valve exterior. Similar to adventitial fibroblasts, these highly specialized fibroblasts are responsible for synthesizing and remodeling the ECM, enabling the capacity of 6 cardiac valves to withstand hemodynamic stress. Indeed, as “mechanocytes”, they are receptive to the local mechanical stimuli and adopt unique phenotypic properties to maintain homeostatic functions of the valves35. An example of this is that VICs assume an elongated morphology to reinforce valve contractility36,37. Furthermore, in vitro cultures have also shown that they can differentiate into adipocytes, osteoblasts, and chondrocytes under specific conditions, indicating a multi-lineage potential reminiscent to those of mesenchymal progenitors38. This is consistent with their implicated role as drivers of valvular calcification through osteogenic differentiation in disease39. Aside from their regional distinction, molecular differences have been reported in VICs when compared to those derived from the myocardium40. While common fibroblast markers like vimentin are expressed in both populations, VICs exhibit an atypical matrisome profile characterized by reduced expression in matrix cross-linking enzymes and fibrillar collagens whereas the expression of elastin and apolipoproteins are more pronounced. Nonetheless, whether these differences are intrinsic to the cells, or a result of the environment remains to be addressed. In contrast to fibroblasts, cardiac mural cells are strictly confined to the perivascular niche (Fig. 1.2). By and large, current literature supports the classification of mural cells as “pericytes” or “vascular smooth muscle cells” (VSMC) that associate with microvessels (capillaries) or large-diameter vessels (arteries and veins), respectively. However, this distinction does not reflect a clear dichotomy but rather a continuum of phenotypic changes along the arteriovenous axis with venules and arterioles as intermediate compartments41,42. Despite their prevalence in a highly vascularized organ 7 like the heart, our understanding of their functional role at homeostasis is limited. Positionally, VSMCs reside in the tunica media, separating from the vascular endothelium and adventitial residents by basement membranes. Under physiological conditions, they adopt a spindle-like morphology and associate with adjacent cells to form mechanical coupling through myofilaments43. This network of contractile units communicates with endothelial cells and directly regulates the vascular tone by initiating vascular contraction. Pericytes, on the other hand, are embedded in the endothelial basement membrane and defined by cell bodies with long cytoplasmic projections that wrap around the endothelial wall44. Their homeostatic functions, like VSMCs, are vital to supporting adequate blood perfusion throughout the cardiac tissue. For one, pericytes facilitate vascular sprouting and pruning required for organized patterning of vascular networks45,46. By associating with endothelial cells, they also provide the necessary signals to stabilize blood vessels and regulate vascular permeability47. Indeed, partial ablation of mural cells (~60%) was sufficient to induce disorganized capillary networks and increased vascular diameter48. Furthermore, pericyte detachment from the vasculature led to increased vascular leakage in MI49. Beyond the classification of VSMC and pericyte, studies have made further subdivisions to characterize subsets or cell states that may be functionally distinct. In healthy vasculature, the majority of VSMCs are considered to maintain a contractile and quiescent phenotype with some differences, mostly derived from transcriptional data, observed between those associated with arteries and veins50,51. Within pericytes, a subset termed “contractile pericytes” has been recounted52,53. These pericytes, as the 8 name implies, exhibit marked contractile potential and are strategically located at arteriolar-capillary junctions, a position that enables them to exert efficient control over the distribution of blood. However, aside from the expression of alpha-smooth muscle actin (αSMA), no other distinguishing features have been identified to reliably characterize contractile pericytes. In summary, spatial location, encompassing anatomical and relative positions to tissue structures, and its underlying microenvironment pose as important determinants that define stromal entities. To create a conclusive nomenclature of stromal entities and decipher the positional implications on their behaviour, it is imperative to characterize the molecular profiles of stromal cells while preserving their spatial information. 1.1.2 Defining stromal subsets with identity markers Accurate delineation of stromal entities with identity markers has been a long-standing challenge in the field. One major obstacle is that many of the markers proposed to distinguish a particular stromal subset are also expressed by other cell types present in the tissue. Moreover, some markers may only be labeling a fraction of the population they are claimed to identify. Adding another layer of complexity, it has become increasingly clear that strategies used to study stromal subsets necessitate tissue-specific considerations given the discrepancy of cell compositions in various organ systems. This is precisely the case for PDGFRα, which is non-specific in the central nervous system (CNS) given its expression in oligodendrocyte progenitor cells but otherwise a well-regarded pan-fibroblast marker in other tissue types54. All and all, these 9 complexities have culminated in inconsistent strategies of defining stromal subsets, and thus exacerbating confusion in the associated nomenclature. Here, we summarize strategies to distinguish cardiac stromal subsets and review reported classification systems defined by gene markers in mice. We have also incorporated recently published single-cell RNA sequencing (scRNAseq) datasets to illustrate the specificity of identity markers and further discuss stromal diversity observed in the transcriptional landscape. A table summarising the location, gene markers, and proposed functions of reported stromal subsets is provided (Table 1.1). Given the heterogeneous nature of cardiac fibroblasts, most of their proposed markers are non-specific or fall short of completely labeling the population. Fibroblast-specific protein 1 (FSP-1), for instance, has been found to be expressed on leukocytes and vimentin marks both mural cells and endothelial cells, to name a few (reviewed in refs55,56). Fig. 1.3 illustrates the transcript expression of reported fibroblast identity markers in published scRNAseq datasets57–59. Of all markers, it is generally accepted that transcription factor 21 (TCF21), and, as mentioned above, PDGFRα, are the more universal and precise markers of fibroblasts at resting state. In particular, transcriptional reporters of PDGFRα, specifically those utilizing histone-fluorophore fusion proteins (JAX# 007669), have been shown to allow unequivocal identification of cardiac fibroblasts7,60. The nuclear-localizing fluorescence limits the ambiguity of assessing gene expression as fibroblasts associate proximally with their neighbors in histology and the stickiness of their cellular projections may confound experiments involving tissue dissociation61. Another common method of labeling fibroblasts is driving fluorophore 10 expression under the collagen type I alpha 1 promoter (COL1A1). One point of caution of using this transgenic line is that other cardiac cell types like mural cells can express COL1A1, albeit at a lesser degree compared to fibroblasts. Nonetheless, the use of multiple markers, including those that negatively select for non-fibroblast cell types, is recommended to confidently distinguish cardiac fibroblasts. Strategies delineating specific fibroblast subsets using gene markers are far less refined. In the case of FAPs, SCA-1 alone is insufficient for demarcating FAPs as its expression is also present on endothelial cells. Furthermore, assessment of SCA-1 expression in PDGFRα+ cells does not provide a dichotomous separation of positive versus negative populations but rather a continuum of cells of varying SCA-1 expression7. Other progenitor markers such as GLI1, and more recently, hypermethylated in cancer 1 (HIC1) have also been proposed to discern FAPs7,62. However, it is unclear if any one of them offers adequate specificity and efficiency to FAPs as transcriptional reporters or proteomic markers. As shown in Fig. 1.4, interrogation of fibroblast heterogeneity with scRNAseq has identified multiple transcriptionally distinct subsets7,59,63. Of which, a subset characterized by increased expression of Ly6a (encoding for SCA-1), Dpp4, Pi16, and Cd248 is consistently described, not only in heart, but also other organ systems. These fibroblasts are referred to as “Fibroblast-SCA-1-high” (F-SH) and corroborates as a corresponding entity to FAPs59,64. Complementing F-SH is a subset with reduced Ly6a expression (termed “Fibroblast-SCA-1-low” or “F-SL”) that exhibits marked levels of Cxcl14 and Hsd11b1. F-SL may be a secretory fibroblast subset given its expression of 11 signaling ligands including Apoe, Bmp4, and Adm. The expression of Wif1, a canonical WNT pathway inhibitor, demarcates another fibroblast subset that displays a profile associated with negative regulation of WNT pathway (termed “F-WntX”). Antibody staining and in situ hybridization against WIF1 indicate that these fibroblasts likely correspond to a subset of VICs65. Interestingly, F-WntX is also characterized by reduced expression of fibroblast markers like Pdgfra and Tcf21, as well as a heightened signature of activation markers consisted of Postn (encoding for periostin) and Acta2 (encoding for αSMA). Based on trajectory inference, a transitionary subset (termed “F-trans”) between F-SL and F-WntX can be distinguished by Fgl2, Inmt, and Srpx expression. These four subsets likely represent the most abundant constituents of cardiac fibroblasts as they are derived from tissue digestion of whole heart ventricles. In studies where rare fibroblast entities are enriched from a specific anatomical location such as cardiac valves, additional fibroblast subsets have been documented66. Although the described subsets harbour discrete transcriptional profiles, whether these programs translate into unique biological functions or reflect a distinctive spatial distribution remain an area to be addressed. More importantly, it is not known if these subsets exist as transient cell states responsive to local stimuli or a result of cell differentiation. Thus, it may be more appropriate to regard them as cell states rather than fully differentiated fibroblast subtypes with distinct phenotypic features. Historically, mural cells are widely recognized as a perivascular entity expressing PDGFRβ. This loosely defined characterization has obscured their distinction from other perivascular entities, and thus confounded our understanding of their identity, diversity, 12 and function. As opposed to fibroblasts, studies of mural cells in the heart are limited and many of their identity markers are extrapolated from other organ systems, especially the CNS. PDGFRβ, for instance, is predominantly expressed in brain pericytes67,68. However, the specificity of PDGFRβ expression to mural cells in other organs may not be comparable. In the heart, PDGFRβ expression is found in both mural cells and fibroblasts69. Despite the discrepancy, PDGFRβ transgenic lines have been adopted as a method of targeting mural cells in some studies but fibroblasts in others70,71. Other than PDGFRβ, genes including neural-glial antigen 2 (NG2), melanoma cell adhesion molecule (CD146), desmin, and regulator of G-protein signaling 5 (RGS5) have also been established as mural cell markers. Nevertheless, many of these markers require concomitant assessment of markers of other cell lineages to accurately pinpoint mural cells. Fig. 1.3 provides an overview of mural cell identity markers and their expression in the cardiac cell landscape. A common transgenic strategy to target cardiac mural cells is the use of transcriptional reporters under the NG2 promoter. Indeed, the established NG2DsRED line (JAX# 008241) shows extensive labeling of both pericytes and VSMCs72. Moreover, when combined with the PDGFRαEGFP reporter, the dual reporter line provides reliable distinction of mural cells from fibroblasts. Despite its specificity, it is not clear if the NG2 reporter can capture all mural cells as NG2- pericytes have been documented near post-capillary venules73–75. Another point of caution is that other cardiac populations, though likely minor, such as Schwann cells may also be labelled by the reporter. 13 Apart from their arteriovenous zonation, several identity markers have been proposed to delineate subsets within the mural cell compartment. Common markers that distinguish VSMCs from pericytes include αSMA, myosin heavy chain 11 (MYH11), and transgelin (SM22α)76. As these markers are mostly associated with the contractile machinery, whether their expressions indicate a precise distinction from pericytes, which can adopt contractile programs, remains an area to be clarified. Excluding the contractile markers, ectonucleoside triphosphate diphosphohydrolase-1 (ENTPD1) has also been shown to isolate VSMCs in non-endothelial CD146+ cells57. Additionally, arterial VSMCs can be differentiated from those of venous origins by calponin 1 (CNN1) expression. On the contrary, pericyte-specific markers are lacking in the heart. Even with the newly discovered markers like potassium inwardly-rectifying channel (KCNJ8) and ATP-binding cassette (ABCC9), neither gene, as transcriptional reporters, provides definitive specificity to pericytes as their expressions are present in αSMA+ mural cells at the transition site between VSMCs and pericytes77. Note that the utility of subset-specific markers discussed above is largely corroborated in our published scRNAseq data (Fig. 1.5). The challenge of identifying discrete markers across mural cell subsets is likely associated with their gradual phenotypic changes along the arteriovenous axis. This is illustrated by Vanlandewijck et al.41 where a continuum of opposing gene expression gradients is observed between venous VSMC and pericytes in the brain. To date, there are very few studies that examine the heterogeneity of cardiac mural cells with an “omics” approach. Moreover, even fewer provide sufficient numbers of mural cells to characterize their transcriptional diversity. As shown in Fig. 1.3, mural 14 cells captured by scRNAseq are far lower in proportion relative to other cell populations even with the exclusion of cardiomyocytes. Despite their prevalence in the heart, the yield of mural cells may be compromised by their susceptibility to cell death during tissue dissociation. Therefore, enrichment of mural cells by transcriptional reporters, such as NG2-Cre/tdTomato49,78, or proteomic markers may prove to be useful. Based on current evidence, the observed transcriptional differences within the mural cell compartment are largely limited to those between pericytes and VSMCs and do not support further subdivisions of transcriptionally distinct subsets. While heterogeneity exists within mural cells, a number of systems have been proposed to specifically address “pericyte” diversity. A series of studies by Bribrair et al. identified two NG2+ pericyte subtypes based on Nestin expression initially in skeletal muscle, but later also in the heart, lungs, kidney, and CNS79–83. Specifically, “type-1” pericytes are Nestin- entities that express fibroblast markers, including PDGFRα, CD34, and SCA-1, and exhibit the potential to differentiate into adipogenic and fibrogenic lineages upon injury84. In contrast, “type-2” pericytes are characterized by Nestin expression and reported to possess myogenic and neurogenic capacity. Both pericyte subtypes express PDGFRβ and CD146 but do not show preferential localization according to vessel caliber82. A similar classification by Goritz et al. and Dias et al. was established in the CNS85–87. Here, “type-A” pericytes are defined as PDGFRβ+desmin- perivascular cells marked by Glutamate aspartate transporter 1 (GLAST) expression. Following spinal cord injury, these pericytes have been shown to infiltrate the lesion with nascent vessels, expand, and contribute to scar formation. “Type-B” pericytes, on the other 15 hand, express desmin but not GLAST and, unlike type-A pericytes, do not detach from the vascular wall after injury. At homeostatic conditions, it is also noted that type-A pericytes reside in abluminal locations relative to type-B pericytes. The reported features of pericyte subtypes in both nomenclatures overlap extensively with characteristics that juxtapose fibroblasts and mural cells. In the cardiac transcriptome, Nestin expression (Nes) appears non-specific, marking mural cells and endothelial cells at high levels but also other populations at low levels (Fig. 1.5). Irrespective of the specificity of Nestin, the expression of fibroblast markers in type-1 pericytes and their contribution to fibroblasts and adipocytes upon injury are consistent with the properties of a fibroblast entity rather than a pericyte subset. Likewise, the pattern of expression in Slc1a3 (encoding for GLAST) and Des (encoding for desmin) provide a convincing delineation of the stromal umbrellas. The relative positions of type-A and type-B pericytes also correspond to that observed between adventitial fibroblasts and mural cells. Collectively, whether pericytes exist as a combination of phenotypically distinct subsets or a homogenous entity requires further investigation. With the advancement of high-resolution systems, accumulating evidence indicates that cardiac fibroblasts and mural cells are umbrella entities with defining sets of identity markers. However, the stromal diversity is not limited to this dichotomous division and is further characterized by specialized subsets with unique expression profiles and, potentially distinct functions. An important consideration of studying stromal subsets is that, in response to disease, they undergo alterations in gene expression and result in 16 the emergence of disease-responsive entities, and thus necessitate careful assessment of their characterization strategies in pathological contexts. 1.1.3 Stromal diversity in human hearts As shown in the above sections, much of what we know about cardiac stromal cells is mostly based on animal models. Although some correlations have been drawn between human and murine systems, the extent of conservation in stromal heterogeneity and functions is largely unexplored. Here, we briefly discuss the latest findings of stromal entities in adult human hearts. Identification of human cardiac fibroblasts is generally based on the expression of TCF21, PDGFRα, and vimentin18,88,89. Additional markers identified through scRNAseq include genes that encode matrix components (DCN and ELN), actin-binding proteins (GSN), and ATP binding cassette transporters (ABCA6, ABCA8, and ABCA9). Much like the murine cardiac landscape, a significant degree of heterogeneity has been observed in recent omics studies. In the study by Litviňuková et al.90, a total of seven transcriptionally distinct subsets were identified within the fibroblast compartment. Of which, three are characterized by signatures associated with TGFβ signaling response (POSTN, TNC, and FAP), ECM remodeling (PCOLCE2 and FBNL2), and cytokine signaling (through receptors OSMR and ILST6). It is also shown that fibroblasts derived from different heart chambers contribute disproportionally to these subsets, indicating that fibroblasts are governed by chamber-specific programs. This is supported by Tucker et al.91 where differential signatures were found to associate with anatomical 17 specifications. However, the fibroblast subsets identified in this study do not seem to correspond to or resemble those identified by Litviňuková et al.90. A meta-analysis of published human heart datasets may better recapitulate fibroblast heterogeneity and provide a more consistent characterization of transcriptionally distinct subsets. Irrespective of the discrepancy in classifying subsets, neither transcriptomic study revealed the presence of fibroblasts with progenitor-like properties seen in FAPs. Despite the lack of an identified human ortholog for SCA-1, previous studies have isolated the so-called “cardiac progenitor cells” (CPC) from cardiac biopsies using anti-mouse SCA-1 antibodies92,93. These “SCA-1+-like” cells are, in parallel to outdated observations in murine systems, proposed to be endowed with cardiomyogenic potential in vitro94. Whether CPCs represent a mixture of cardiac resident cells, or a true fibroblast entity requires validation. In contrast, current literature, though limited, lacks consistent proteomic markers for identifying human cardiac mural cells, most likely owing to their confusion with fibroblasts. Indeed, in vivo assessment by histology has shown contradicting results of PDGFRα expression in pericytes of adult hearts95,96. Nonetheless, pericytes still show robust expressions of PDGFRβ, CD146, and NG2, whereas VSMCs are marked by αSMA, MYH11, and SM22α. Similar to fibroblasts, pericyte heterogeneity observed at the transcript level is mainly attributed to atrial versus ventricular origins90. In particular, ventricular pericytes exhibit increased expression of adhesion molecules (NCAM2 and CD38) and facilitate vascular morphogenesis via communication with endothelial cells. Intriguingly, a transitional state between pericytes and endothelial cells has been reported but whether it represents a true biological subset, or a product of technical 18 error is unclear. VSMCs, on the other hand, segregate into two subsets where one is enriched for signatures indicative of immaturity (LGR6 and RGS5) and the other is likely associated with arterial origin given elevated expression of contractile markers (CNN1, ACTA2, and TAGLN). Together, the information provided by these pioneering omics data serves as the first step to deciphering the human cardiac stroma. 1.2 A double-edged stromal response in disease and aging The regenerative potential of the mammalian heart is, for the most part, limited to fetal development and negligible in the adult heart. In response to trauma, the cardiac stroma plays an indispensable role in orchestrating scar formation to maintain cardiac integrity and prevent rupture. However, this immediate reparative process only offers temporary reprieve as the scar tissue lacks the functional properties of cardiomyocytes, leading to compromised contractility and electromechanical coupling of the heart. Even after the formation of a mature scar, long-term remodeling of the myocardium leads to a progressive decline in cardiac function, and ultimately results in heart failure. The chronic alterations underlying cardiac deterioration have been linked to a dysregulated stroma, which is not exclusive to acute injuries and extends to conditions associated with genetic disorders and even aging. Moreover, growing evidence indicates that cardiac dysfunction is closely linked to co-morbidities, not only within the cardiovascular system, but also those of peripheral organs including the brain, as proposed by the “heart-brain axis” theory. As such, direct intervention or reprogramming of stromal entities may confer clinical benefits on multiple fronts. In the following section, we 19 provide an overview of the stromal response and its associated mechanisms to cardiovascular conditions including MI, as well as physiological aging. 1.2.1 Myocardial infarction MI, as the leading cause of global mortality, is characterized by irreversible myocardial necrosis due to prolonged deprivation of blood perfusion to the myocardium. The upstream trigger of MI is most often associated with atherosclerosis-induced coronary artery occlusion. However, other etiologies of ischemia including physical trauma and vascular stiffening may also result in the onset of infarction. Following MI, tissue repair is coordinated by an intricate network of communication amongst resident populations, as well as infiltrating immune cells, culminating in a sequence of three temporally-regulated phases (reviewed in refs97–99). Beginning with the inflammatory phase (day 1-3 post-MI), cell death in the infarct causes the release of alarmins such as mitochondrial DNA, which bind to pattern recognition receptors (PRR) on both immune and non-immune entities, including fibroblasts and mural cells. Activation of PRRs and the downstream release of inflammatory factors elicit a phenotypic shift in cardiac stromal cells toward a transient state with upregulated pro-inflammatory and ECM-degrading programs100. The secretion of various cytokines and chemokines in fibroblasts have been extensively documented in in vitro studies modeling the injury response. For example, fibroblasts cultured in conditioned medium of necrotic cardiomyocytes exhibit increased release of interleukin-6 (IL-6) and monocyte chemoattractant protein-1 (MCP-1)101. When 20 stimulated with IL-1, a major inflammatory mediator in the infarct, an increased production of metalloproteinases is observed in fibroblasts102. Surface expression of metalloproteinases such as membrane type 1-matrix metalloproteinase (MT1-MMP) may act as a migratory mechanism to mobilize fibroblasts to the infarct103. While the molecular signatures of cardiac mural cells immediately after MI are less established, their proximal association with the vascular endothelium underscores the capacity to directly regulate perfusion in the infarct. Like fibroblasts, mural cells participate in the recruitment of circulating immune cells by producing chemoattractants, including CXCL1, CXCL8, MIF, and CCL-275,104. However, their involvement extends beyond the secretion of chemokines. In the presence of inflammatory factors and alarmins, pericytes guide leukocyte diapedesis by physical interaction via intercellular adhesion molecule-1 (ICAM-1)75,105. After extravasation, pericyte interaction endows leukocytes with pattern-recognition and motility programs, enhancing their survival and homing to the foci of inflammation. Furthermore, mural cells may produce metalloproteinases to degrade vascular basement membrane as the first step to initiate angiogenesis, though current evidence of this phenomenon is limited to transcriptional data49. Together, the immediate response of the cardiac stroma post-MI orchestrates the infiltration of leukocytes, and the subsequent clearance of cellular debris and fragmented ECM in the infarct, which sets the stage for scar-formation in the next reparative phase. Following the inflammatory phase, the cardiac stroma undergoes further phenotypic alterations and adopt a heightened fibrogenic and proliferative response, manifesting in the proliferative phase (day 3-7 post-MI). As the wound clears, the inflammatory milieu 21 polarizes toward a pro-regenerative state governed by anti-inflammatory signals, including growth factors, cytokines (e.g. IL-10), and lipid mediators (e.g. resolvins, lipoxins and maresins). In turn, the change in tissue environment induces an activated fibroblast state commonly known as myofibroblasts. This transition is controlled by a myriad of signaling factors and mechanical cues (reviewed in refs100,106). Of which, transforming growth factor-β (TGF-β) and its downstream signal transducers, SMADs, have been shown to be indispensable for myofibroblast conversion107,108. Acquisition of myofibroblast state is associated with the formation of stress fibers composed of cytoskeletal actin such as αSMA, which contribute to the contractile and motile properties of myofibroblasts. Notably, even though αSMA is widely regarded as a defining marker of myofibroblasts, its deletion is compensated by an increase of other actin isoforms and does not impair myofibroblast function post-MI109. Moreover, myofibroblasts upregulate matrix synthetic programs encompassing matricellular factors (e.g. periostin and connective tissue growth factor) and structural building blocks (e.g. collagens)99 to assemble the matrix scaffold. To amplify matrix production, myofibroblasts undergo significant expansion and become the most abundant cellular constituent of the infarct. Interestingly, myofibroblasts may also share the capacity to engulf cell debris like phagocytes110. While pro-angiogenic mechanisms have been implicated in fibroblasts111, it is not clear if they are specific to myofibroblasts. Indeed, in a study by Saraswati et al.112, pro-angiogenic functions are attributed to αSMA-FSP1+ fibroblasts given their elevated expression of angiogenic mediators such as vascular endothelial growth factors (VEGF) compared to αSMA+ fibroblasts. On the other hand, mural cells, specifically pericytes, dissociate from the vascular endothelium and migrate 22 toward the infarct49, which has also been observed in other ischemic pathologies including stroke and spinal cord injury113,114. Their detachment from the microvasculature is, in part, driven by angiopoietin-2 (ANGPT2), a vascular destabilizing factor known to antagonize ANGPT1/TIE2 signaling. In the absence of endothelial-derived ANGPT2, pericyte association and vascular integrity are preserved, resulting in reduced inflammation and vascular leakage115. Remarkably, mural cells also proliferate and acquire a fibrogenic phenotype similar to that of myofibroblasts49,78. This pro-fibrotic response is partially dependent on TGF-β signalling as TGF-β receptor 1 (TGFβR1) knockout in NG2-traced cells attenuates their expression of Smad3 and Col1a1. However, the loss of TGFβR1 or TGFβR2 in NG2-traced cells does not impact the scar size on day 7 post-MI, suggesting that mural cells may be dispensable in scar-formation. Further remodeling of the matrix scaffold is supported by the cardiac stroma to develop a mature scar during the maturation phase (day 7-28 post-MI). Akin to reinforcing a building with beams and columns, cross-linking of matrix components endows the scar with rigidity and tensile strength, which is facilitated through the production of cross-linking enzymes including transglutaminase and members of the lysyl oxidase (LOX) family by fibroblasts116. Scar maturation is accompanied by progressive deactivation of fibroblasts due to a reduction or inhibition of signals maintaining their activation status. This is partially characterized by myofibroblast shrinkage in the infarct. While apoptosis may drive myofibroblast clearance, the apparent loss of myofibroblasts may also be associated with a phenotypic shift. Indeed, myofibroblasts have been reported to lose 23 αSMA stress fibers and transition into an entity harbouring chondrocyte and osteoblast signatures, termed “matrifibrocytes”, at 10 days post-MI117. Assessment by scRNAseq has substantiated the presence of matrifibrocytes and revealed additional entities, including “late-response” fibroblasts and a transient Cthrc1+ fibroblast subset, that emerge in a similar timeframe58,63,118,119. However, the biological functions of these specialized fibroblast entities and their fates have not been examined. As opposed to fibroblasts, we know very little about mural cell behaviour during the maturation phase. After their dissociation from the vasculature, a progressive decrease in pericytes (NG2+αSMA-) is observed in the infarct while VSMCs (NG2+αSMA+) are increased at day 7 to 28 post-MI in the infarct78. Whether this shift in cell abundance involves pericyte-to-VSMC transition or simply due to a difference in apoptosis rates between the subsets is not known. Despite their reduction, pericytes may re-associate with neovessels in the infarct and facilitate their maturation. While the cardiac stroma is integral to replacement fibrosis, it is not clear if the degree of fibrotic response goes beyond the threshold necessary to preserve cardiac integrity and contributes to adverse remodeling. Studies have shown that targeted ablation of fibroblasts or modulation of their activation programs can attenuate fibrosis without compromising scar stability and improve cardiac function long-term33,120,121. However, these findings remain controversial as disruption of fibroblast activity may also lead to defective scarring, and consequently, cardiac rupture122,123. The conflicting results suggest that cardiac repair rests on an intricate balance where both insufficient and excessive scarring culminates in pathological outcomes. Therefore, fine-tuning the 24 fibrogenic activity post-MI may be an effective therapeutic approach. Nonetheless, this necessitates further investigation of regulatory mechanisms underlying fibrosis and their temporal relevance, as well as the functional heterogeneity of the cardiac stroma. Myocardial fibrosis in MI is not limited to the infarct and extends to the infarct border and remote zones124–126. This peripheral response, referred to as “reactive fibrosis”, contributes to adverse remodeling that precedes heart failure127. Although its mechanisms are elusive, reactive fibrosis may be associated with an elevated mechanical stress, which can induce the activation of latent TGFβ, and subsequently myofibroblast formation128. Furthermore, fibrogenic stromal cells that persist in the infarct may provide a source of paracrine mediators that diffuse to non-infarcted regions and provoke interstitial fibrosis126. A recent study by Baggett et al.129 showed that the accumulation of myofibroblasts in the infarct scar correlates with increased pro-inflammatory and fibrogenic factors. In the presence of these signals, stromal cells distal to the infarct adopt a dysregulated response and drive chronic pathological remodeling in the viable myocardium. On top of fibrosis, extensive cardiac ischemia represents another hallmark of heart failure. Even with the restoration of blood flow through stent placements, ischemia is perpetuated by incomplete reperfusion of microvasculature, a phenomenon known as “no-reflow”. This complication stems from multiple factors exacerbating microvascular dysfunction, including reperfusion injury, inflammation, and obstructions associated with edema, microembolization, and vasospasm130. Subsequently, extended periods of 25 insufficient perfusion lead to progressive vascular rarefaction. Pericytes have been implicated in no-reflow as they contract and cause vasoconstriction of capillaries73. One mechanism underlying this behaviour may be the need to maintain capillary hydrostatic pressure, which ensures adequate exchange of substances with the interstitium131. Nonetheless, inhibition of pericyte contraction has been shown to increase perfusion volume and capillary density, as well as reduce infarct size132. This finding supports that pericyte contraction is, overall, a maladaptive response, and therefore a potential target of pharmacologic intervention to manage ischemia. 1.2.2 Cardiac aging The decline of cardiovascular functions and the onset of heart disease are closely associated with advancing age. Even in the absence of overt injury, the aging heart endures structural and functional alterations that manifest in changes of chamber geometry such as left ventricular hypertrophy and a compromised cardiac cycle typically involving diastolic dysfunction, respectively. These pathophysiological changes are driven by extensive reprogramming of cardiac resident cells, which increases the susceptibility to developing heart complications. Here, we summarize the current knowledge of molecular mechanisms exhibited by aged stromal cells and their contribution to an increasingly pathological microenvironment. Interstitial fibrosis is a prominent hallmark of the aging heart. In humans, total collagen content and extracellular volume fraction, a quantitative indicator of fibrosis, have been shown to increase with age, and thus contribute to ventricular stiffness133,134. However, 26 whether elevated fibrosis in aging is a derivative of increased matrix synthesis is controversial. Indeed, a recent murine study assessing the transcriptome of cardiac fibroblasts shows a progressive decrease in collagen gene expression from juvenile period to late adulthood despite a marked increase of collagen content in aged animals135. Furthermore, no difference in fibroblast proliferation or absolute number is found between early adulthood and advanced age. Rather, age-related fibrosis may be attributed to dysregulated matrix turnover where post-synthetic and degradative processes are impaired. This is concordant with altered activities of matricellular proteins such as MMPs and their inhibitors (TIMPs) in aged animals136,137. Notably, studies have identified secreted protein acidic and rich in cysteine (SPARC), a factor involved in the processing and assembly of collagen fibrils, as a potential mechanism of age-related fibrosis. In SPARC-knockout animals, the age-dependent increase in collagen content and myocardial stiffness are blunted138. Changes in the activity of collagen cross-linking enzymes may also affect turnover as increased cross-linking attenuates matrix degradation139–141. Nonetheless, direct evidence elucidating the specific mechanisms underlying age-related fibrosis is still lacking. Aging is also associated with pathophysiological adaptations of the vascular niche. Growing evidence suggests that mural cells, especially VSMCs, are dysregulated in the aging heart and compromise vascular function. In part, this involves a switch from a contractile to a synthetic phenotype where aged VSMCs lose contractile proteins and acquire migratory capacity142. The upregulation of matrix remodeling programs, including various MMPs, calpain-1, and TGFβ1 signaling, endows VSMCs with the 27 ability to destabilize vascular ECM. Additionally, concomitant expression of the chemokine, monocyte chemoattractant-1 (MCP-1), and its receptor, chemokine receptor-2 (CCR2), has been shown as the primary mechanism driving VSMC chemotaxis. With the activation of these programs, VSMCs infiltrate and condense the vascular intima, an event shared by plaque formation, suggesting a potential role of aged VSMCs in accelerating the development of atherosclerosis. Moreover, VSMCs may induce persisting inflammation of the aging heart, referred to as “inflamm-aging”. Indeed, aged VSMCs have been shown to upregulate the expression of pro-inflammatory cytokines, such as chemokine ligand 5 (CCL-5), CXCL2, and IL-6, as well as adhesion molecules involved in leukocyte diapedesis143. Inflammation is further amplified and sustained by elevated synthesis of superoxide radicals144,145. In turn, heightened oxidative stress exacerbates endothelial dysfunction, which may contribute to the observed decline in arterial density71. VSMCs have also been implicated in the rigidity of aged vasculature. Activation of TGFβ1 signaling in aged VSMCs is associated with an elevated deposition of fibronectin and collagen in the vascular wall146,147. Furthermore, they exhibit an increased propensity to calcify through the activation of osteogenic mediators, including bone morphogenic protein 2 (BMP-2) and alkaline phosphatase (ALP)148. Interestingly, in vitro comparison of VSMCs derived from young and aged non-human primates demonstrated that aged VSMCs harbour a higher intrinsic stiffness and adhesive interaction to ECM149. Collectively, the age-dependent alterations of mural cells underpin the gradual deterioration of the cardiac vascular network and may predispose the onset of cardiovascular conditions. 28 In response to cardiac insults, aged animals have been shown to exhibit defective myocardial repair150,151. For acute injuries like MI, the inflammatory cascade required for wound clearing is dysregulated as the activity of infiltrating immune cells is delayed and prolonged. Subsequently, a partial fibrogenic response results in immature scar formation, and thus leading to an earlier progression to heart failure. The failure to facilitate adequate tissue repair is associated with a blunted responsiveness to environmental cues. Compared to that of young fibroblasts, TGF-β signaling is reduced in aged fibroblasts as demonstrated by a decreased phosphorylation of downstream SMADs after TGF-β stimulation in vitro152. This is partially due to the downregulation of TGF-β receptors153. Similarly, mechanosensing of the ECM has been shown to be compromised by the reduction of Kindlin-2, an adaptor connecting cytoskeletal actins and integrins, in aged fibroblasts154. Given these defective mechanisms, the maturation of myofibroblasts and their scar-forming capacity are attenuated in response to injury. The impairment of cardiac healing also involves perturbations of cell-cell communication. One example of this is the dysregulation of fibroblast-endothelial cell crosstalk where elevated secretion of anti-angiogenic factors, including Serpine1 and Serpine2, act on the endothelium and inhibit neovascularization155. In addition, the aberrant production of cytokines from VSMCs, as described above, and fibroblasts may interfere with the resolution of inflammation post-injury in aged animals156,157. Cellular senescence, a multifactorial process characterized by the arrest of cell growth, resistance to apoptosis, and reprogramming of functional profile, has been proposed to engender cardiac deterioration. The onset of cellular senescence is generally triggered 29 by external stimuli that induce an injury response (“stress-induced premature senescence”) or telomere-shortening from cycles of cell division (“replicative senescence”; reviewed in ref158). While the former is transient and promotes tissue repair after acute injury, the latter contributes to long-term accumulation of senescent cells, which is implicated in many age-related cardiovascular pathologies159. In the case of cardiac stromal cells, senescence is linked to many of the aforementioned pathophysiological features in aging. One notable characteristic of senescent cells is a secretome comprised of pro-inflammatory cytokines, reactive oxygen species (ROS), growth factors, and matricellular proteins, known as senescent associated secretory phenotype (SASP)160. This secretory profile, to some extent, is shared by aged stromal cells and elicits chronic inflammation and fibrosis. However, despite the perceived association with aging, cellular senescence of the cardiac stroma may confer cardioprotective effects. A recent study by Sawaki et al.161 demonstrated that the induction of senescence in cardiac fibroblasts through the depletion of osteopontin, a matricellular factor known for its role in pathological remodeling, modulated age-dependent myocardial fibrosis and improved heart function. Concordantly, attenuation of myofibroblast senescence by genetic ablation of senescence programs, including p53 and p16, led to elevated fibrosis and cardiac dysfunction after transverse aortic constriction162. Moreover, vascular attrition and loss of mural cells observed with advancing age also precedes the presence of senescent markers such as p1671. These findings suggest that the impact of senescence may deviate from the deleterious effects of aging in a cell- and context-specific manner. How senescence affects specific stromal subsets adds another layer of complexity to dissect its role in the aging heart. 30 Figure 1.1. Graphical summary of heart anatomy and its substructures. The diagram illustrates an overview of anatomical structures of the heart and substructures discussed throughout the thesis. Created with Biorender. Figure 1.2. Graphical summary of major stromal subsets and their relative spatial positions to the vasculature. The diagram depicts cellular entities and structures of capillaries (left; blue) and large vessels such as arteries/veins (right; red). Created with BioRender. 31 Figure 1.3. Cellular landscape of the heart. The illustration represents annotated cellular entities projected in uniform manifold approximation projection (UMAP; left) and their expression of reported identity markers of fibroblasts and mural cells (right). Heatmap depicts the relative average normalized expression of genes. Sequencing data acquired from the ArrayExpress database under the accession codes: E-MTAB-6173 (Skelly et al., 2018), E-MTAB-8810 (McLellan et al., 2020), and E-MTAB-7376/E-MTAB-7365 (Farbehi et al., 2019). EC, Endothelial cell; NK, Nature Killer cell. Figure 1.4. Heterogeneity of cardiac fibroblasts. The illustration represents annotated fibroblast subsets projected in UMAP (left) and their expression of reported identity markers of fibroblast subsets (right). Heatmap depicts the relative average normalized expression of genes. Sequencing data acquired from the Gene Expression Omnibus or ArrayExpress database under the accession codes: GSE132146 (Ruiz-Villalba et al., 2018), GSE141929 (Soliman et al., 2020), and IZ RZ 32 E-MTAB-7376/E-MTAB-7365 (Farbehi et al., 2019). F-SH, Fibroblast-SCA-1-high; F-SL, Fibroblast-SCA-1-low; F-WntX, Wnt-expressing fibroblast; F-Trans, Transitory fibroblast. Figure 1.5. Heterogeneity of cardiac mural cells. The illustration represents annotated mural cell subsets projected in UMAP (left) and their expression of reported identity markers of mural cell subsets (right). Heatmap depicts the relative average normalized expression of genes. Sequencing data acquired from the Gene Expression Omnibus database under the accession code, GSE206787 (Tung et al., 2023). PER, Pericytes; Ven. VSMC, Venous/venular vascular smooth muscle cells; Art. VSMC, Arterial/arteriolar vascular smooth muscle cells. 33 Table 1.1. Summary of cardiac stromal nomenclature and reported subsets Cell entity Location Gene markers Functional role Stromal cells (“MSC”) Perivascular and interstitial Hic1, Nt5e, Eng, Thy1 Regulation of niche across all tissue types Fibroblasts Perivascular and interstitial Pdgfra, Tcf21, S100a4 1. Maintenance and remodeling of tissue ECM 2. Regulation of vascular functions 3. Structural support of cardiac valves 4. Scar formation and inflammation in disease FAP (“F-SH”) Perivascular and interstitial Dpp4, Ly6ahigh, Pi16, Cd248, Gli1 F-SL Perivascular and interstitial Cxcl14, Ly6alow, Hsd11b1, Apoe VIC (“F-WntX”) Valve leaflets Wif1, Postn, Pdgfralow, Tcf21low F-Trans Perivascular and interstitial Fgl2, Inmt, Srpx Myofibroblast Infarct core/border Acta2 Mural cells Perivascular Mcam, Pdgfrb, Rgs5, Cspg4 1. Vascular maintenance and remodeling 2. Growth and shrinkage of vascular network 3. Regulation of vascular tone and permeability 4. Scar formation and inflammation in disease VSMC Perivascular (non-capillaries) Tagln, Acta2, Myh11, Entpd1 Arterial VSMC Perivascular (arteries/arterioles) Cnn1, Itga8 PER Perivascular (capillaries) Kcnj8, Abcc9, Vtn Type 1 PER Interstitial Nes-, Pdgfra, Ly6a Type 2 PER Perivascular Nes, Mcam, Pdgfrb Type A PER Perivascular Slc1a3, Pdgfrb, Des- Type B PER Perivascular Slc1a3-, Pdgfrb, Des Contractile PER Arteriolar-capillary junctions Acta2 MSC, Mesenchymal stromal cells; FAP, Fibro-adipogenic progenitors; F-SH, Fibroblast-SCA-1-high; F-SL, Fibroblast-SCA-1-low; VIC, Valve interstitial cells; F-WntX, Wnt-expressing fibroblast; F-Trans, Transitory fibroblast; VSMC, Vascular smooth muscle cells; PER, Pericytes. 34 1.3 Research Aims Our understanding of the cardiac tissue environment in health and disease is continuously expanding. Yet, as elucidated by the literatures reviewed above, much remains to be done to characterize the heterogeneity of the cardiac stroma and the role of its associated cell entities in tissue repair and pathogenesis under specific disease contexts. Here, by leveraging advanced omics platforms and high-resolution imaging systems, we have thoroughly dissected the cardiac stromal diversity at homeostasis in murine models. To examine the stromal response in ischemic heart disease, we profiled the molecular behaviour of stromal subsets temporally and spatially after occlusion-induced myocardial infarction. Taking what we learnt in murine models, we extended our study to assess the cardiac landscape in the explanted hearts of patients diagnosed with familial hypertrophic cardiomyopathy. Through a systemic approach, we have identified pathological alterations in the cardiac stroma and highlighted their potential impact in the pathogenesis of heart failure. Collectively, the aims of this study culminate in five main objectives: Objective I. To decipher the cellular heterogeneity of the cardiac stroma and the functional characteristics of its subsets at physiological state. We hypothesize that the cardiac stroma is a highly heterogenous population composed of multiple entities with distinct expression programs. Objective II. To characterize the temporal response of cardiac stromal subsets and their role in cardiac repair in acute pathologies (Myocardial infarction). We hypothesize that individual stromal subsets exhibits unique programs and contribute to different components of cardiac repair, albeit with some degree of functional redundancy. 35 Objective III. To assess the response of cardiac stromal subsets and their role in cardiac repair in chronic pathologies (Hypertrophic cardiomyopathy). We hypothesize that the behaviour of stromal subsets in chronic disease settings is dysregulated and contribute to the progressive deterioration of cardiac function. Objective IV. To understand the spatial implications of cardiac stromal subsets in disease. We hypothesize that the spatial distribution of stromal subsets plays a significant role in maintaining tissue homeostasis and its perturbations regulate cardiac repair. Objective V. To identify and attenuate potential mechanisms of disease pathogenesis to enhance reparative programs. We hypothesize that targeting specific injury-induced mechanisms can confer therapeutic benefits by controlling their impact on the tissue environment. 36 Chapter 2: Spatiotemporal signaling underlies progressive vascular rarefaction in myocardial infarction. 2.1 Synopsis Therapeutic angiogenesis represents a promising avenue to revascularize the ischemic heart. Its limited success is partly due to our poor understanding of the cardiac stroma, specifically mural cells, and their response to ischemic injury. Here, we combine single-cell and positional transcriptomics to assess the behaviour of mural cells within the healing heart. In response to myocardial infarction, mural cells adopt an altered state closely associated with the infarct and retain a distinct lineage from fibroblasts. This response is concurrent with vascular rarefaction and reduced coverage by mural cells. Positional transcriptomics reveal that the infarcted heart is governed by regional-dependent and temporally regulated programs. While the remote zone acts as an important source of pro-angiogenic signals, the infarct zone is accentuated by chronic activation of anti-angiogenic, pro-fibrotic, and inflammatory cues. Together, our work unveils the spatiotemporal programs underlying cardiac repair and establishes an association between vascular deterioration and mural cell dysfunction. 37 2.2 Introduction Ischemic heart disease (IHD) is the leading cause of mortality globally163. It is characterized by reduced blood perfusion to the cardiac tissue, leading to irreversible myocardial cell death and the development of non-functional scars119. With the advancement of human pluripotent stem cell technologies, strategies aimed at replacing cardiomyocytes by transplanting cells directly into the infarct have been explored. However, these approaches are limited by the ability of transplanted cells to survive, which is likely compromised in hypovascularized regions such as the post-MI scar164. Indeed, to slow disease progression and improve blood perfusion to the ischemic myocardium, “therapeutic angiogenesis” as a strategy that promotes vessel growth through neovascularization by targeting the vasculature and cardiac stroma has been proposed165. One main approach revolves around the administration and targeting of the pro-angiogenic molecule, vascular endothelial growth factor (VEGF)166. While VEGF is a potent inducer of angiogenesis, the success of VEGF-based gene therapies is limited as adequate angiogenic response requires tight control of VEGF level and its duration of effect167,168. Low or transient expression of VEGF has minimal angiogenic effects whereas high or prolonged expression induces dysfunctional vessels with structural abnormalities and may result in the development of angioma. Stimulating endothelial tube formation without adequate perivascular cell recruitment, namely mural cells, is also insufficient for building mature, stable vessels169,170. More recently, cell-based therapy using mesenchymal stromal cells (MSCs) has emerged as a potential approach of limiting the infarct size through the pro-angiogenic activities of MSCs171. However, the results of clinical trials exploring this approach are rarely in agreement. We believe part of this 38 discrepancy stems from the underappreciated phenotypic and functional heterogeneity of stromal cells, which can adopt different functional states over time in response to local stimuli. Thus, elucidating the behavior of these cells in situ and characterizing the signaling programs underlying their environment may better guide future therapeutic attempts. We and others have identified two major cardiac stromal populations at single-cell resolution, namely fibroblasts and mural cells, the latter are a class of perivascular cells consisting of perivascular smooth muscle cells (VSMC) and pericytes172. Upon the initial ischemic event during myocardial infarction (MI), fibroblasts drive the formation of a robust scar that maintains cardiac integrity and function. However, prolonged ischemia post infarction (PI) leads to persistent activation of fibroblasts, resulting in chronic interstitial fibrosis. On the other hand, the behaviour of mural cells in ischemia is much less characterized. While mural cells are implicated as critical drivers of neovascularization PI173, their fate and function within the scar tissue remains elusive. It is also unclear whether they function to potentiate or reduce fibrosis. This is likely due to the use of promiscuous lineage tracing models and a historical lack of consistent distinction between the main stromal cell types, which until recently precluded our understanding of their roles in tissue repair and their lineage relationships. For example, it is unclear if fibroblasts, specifically fibro-adipogenic progenitors (FAPs), given their progenitor-like and multipotent properties7,55, can give rise to mural cells after injury. More importantly, our limited knowledge of spatially-resolved response and signaling networks 39 with respect to the infarct adds another layer of complexity to dissecting scar formation and its underlying organization. In the present study, we demonstrated that cardiac mural cells are anatomically distinct from fibroblasts and exhibit unique transcriptional programs at homeostasis. Following MI, pericytes undergo activation and adopt a pathological transcriptional profile characterized by increased matrix remodeling and dysregulated vascular programs. Furthermore, fate mapping of the stromal subsets PI showed that they do not share a lineage relationship. In the infarct zone, ischemia is reflected by vascular rarefaction and dysfunction, which coincides with the loss of vascular coverage by mural cells and their activation. By creating a spatiotemporal transcriptomic atlas, we mapped the signaling dynamics of tissue repair programs and identified potential mechanisms perpetuating ischemia. Overall, our study provides a clear picture of processes underlying the infarcted heart and highlights a relationship between vascularization and mural cell function. 2.3 Materials and methods 2.3.1 Animals Animal maintenance and experimental procedures were conducted in accordance with the Animal Care Committee’s approval and regulations at the University of British Columbia. Mice were housed under standard conditions (12:12 light-dark cycle, 21-23 °C, and 40-60% humidity level) and provided PicoLab Mouse Diet 20(LabDiet, cat. 3005750-220) and water ad libitum in a pathogen-free facility. Non-inducible mouse lines including PDGFRαEGFP (referred to as “Pdgfra-EGFP”, JAX stock #007669) and 40 NG2DsRedBAC (referred to as Cspg4-DsRed, JAX stock #008241) were purchased from The Jackson Laboratory. Pdgfra-EGFP mice were bred with Cspg4-DsRed mice to generate the double reporter mice, Pdgfra-EGFP/Cspg4-DsRed. Generation of TAM-inducible mouse lines harbouring Hic1-CT2 is described elsewhere7,62. NG2-CreERTM (referred to as “Cspg4-CT2”, JAX stock #008538) and Pdgfrα-CreERT2 (referred to as “Pdgfra-CT2”, JAX stock #032770) were purchased from The Jackson Laboratory. Each CT2 line was interbred with the reporter line B6.Cg-Gt(ROSA)26Sortm14(CAG-tdTomato)Hze/J (referred to as “tdTomato”, JAX # 007914) or B6.129X1-Gt(ROSA)26Sortm1(EYFP)Cos/J (referred to as “YFP”, JAX #006148). For lineage-tracing experiments, additional crosses were made with the non-inducible lines. Cre recombination was induced by intraperitoneal (IP) injections of TAM (Sigma-Aldrich, cat. T5648) at 3 mg/kg mouse weight in 100 uL of corn oil for 5 consecutive days. Mice administered with TAM were given at least 10 days of washout period prior to additional procedures. All experimental mice were maintained on a C57BL/6 background and ranged from 2-6 months in age. Littermates of both sexes were randomly assigned to different experimental groups wherever applicable. No blinding was performed and no data were excluded. 2.3.2 Induction of cardiac injury Experimental induction of myocardial infarctions was performed on animals ventilated with a mixture of oxygen and isoflurane until surgical anesthesia. Skin was surgically scrubbed with alcohol and Bupivacaine was administered subcutaneously at the incision site. An incision through the rib cage was made and held open with a retractor to 41 visualize the heart. Using an 8-0 polypropylene suture, a branch of the left anterior descending (LAD) artery was ligated. Wound was then closed using a 6-0 vicryl suture in a horizontal mattress pattern174. Buprenorphine (20 uL/g) and saline (1 mL) were administered subcutaneously every 8 hours as part of monitoring procedure. 2.3.3 Perfusion and tissue collection Mice were euthanized with 0.5 mg/g tribromoethanol (Avertin; Sigma-Aldrich, cat. T4840-2) IP. Once euthanized, a horizontal incision was made above the sternum through both the skin and musculoskeletal layer to expose the heart. Following transcardiac perfusion with 20 mL of PBS-EDTA (2mM), whole hearts were excised, and their atria were trimmed off. For immunofluorescence staining, heart samples were fixed in 4% PFA for 24 hours at 4°C, washed with cold PBS, and then incubated in 30% sucrose solution overnight. Samples were embedded into Optimal Cutting Temperature (OCT) compound (Tissue Tek, cat. 4583 or Scigen, cat. SGN4585) in plastic cryomolds and snap frozen in an isopentane bath cooled by liquid nitrogen. For Visium experiments, heart samples were washed with cold PBS upon harvest and directly snap-frozen in OCT compound. 2.3.4 Tissue digestion and purification of cell populations Isolation of cells from cardiac ventricles was achieved using our previously established protocol13. After transcardiac perfusion, excised ventricles were cut into 2 mm pieces and digested in collagenase type II solution (Millipore Sigma, cat. C6885; 500 uL per heart at 2.5 U/mL) containing 5 mM CaCl2 for 30 minutes at 37°C. Digested lysate was 42 quenched with cold PBS and centrifuged at 140-g to remove the supernatant. This process was repeated twice followed by a second round of digestion in a solution (500 uL per heart) containing collagenase D (Millipore Sigma, cat. 11088882001; 1.5 U/ml), Dispase II (Millipore Sigma, cat. 04942078001; 2.4 U/ml) and 5 mM CaCl2 for 1 hour at 37°C. Digested lysate was triturated by pipetting, quenched with cold FACS buffer (PBS containing 2 mM EDTA and 2% FBS), and filtered through 40-μm strainer filters. Filtrate was centrifuged at 500-g and washed again with cold FACS buffer prior to incubation with primary antibodies for 30 minutes at 4°C. For all flow cytometric assays and cell sorting, anti-CD45 and anti-CD31 antibodies were used as lineage (LIN) markers to delineate immune and endothelial populations. To assess cell viability, live/dead staining was performed with fixable viability dye or a combination of propidium iodide, Hoechst, and DAPI. Flow cytometry and FACS were performed on BD Influx and Beckman Coulter CytoFLEX or LSR-II, respectively. Sorted cells were collected in DMEM (Gibco, cat. 11965-092) containing 20% fetal bovine serum (FBS; Sigma, cat. F1051). Flow cytometry data were analyzed using the software CytExpert (v.2.4, Beckman Coulter) or FlowJo (v.10.7.1, BD Biosciences). 2.3.5 Cell proliferation assay Cell kinetics were assessed through the administration of EdU (Thermo Fisher, cat. E10415), followed by flow cytometry or immunofluorescence microscopy. For flow cytometry, mice were administered with EdU (20 mg/kg) dissolved in PBS (2 mg/mL) IP one day prior to harvest. Cells were stained for surface markers and EdU using the Click-iT Plus EdU Pacific Blue Flow Cytometry Assay Kit (Thermo Fisher, cat. C10418) 43 as per manufacturer’s directions. To assess the localization of proliferating cells between day 3 and 7 PI, we administered EdU IP on day 2 and 6 PI along with a continuous dose of EdU mixed in drinking water (5 mg/mL) until harvest. Immunostaining against EdU was performed using Click-iT™ Plus EdU Cell Proliferation Kit for Imaging (Thermo Fisher, cat. C10640) prior to antibody staining. 2.3.6 Immunofluorescence staining OCT embedded tissues were sectioned using the Leica CM3050S cryostat at a thickness of 10-μm and placed onto Superfrost Plus slides (Fischerbrand, cat. 22-037-246). Slides were stored at -80°C prior to thawing at room temperature for staining. Thawed sections were incubated in PBS containing 10 mg/mL sodium borohydride (Sigma, cat. 213462) for 30 minutes to quench autofluorescence and washed with PBS. Hydrophobic borders were drawn around tissue sections prior to incubation with blocking buffer containing 5% normal donkey serum, 0.2% Triton X-100, and 1% bovine serum albumin (BSA; Sigma, cat. A7906) for 1 hour at room temperature. Tissue sections were then incubated with primary antibody cocktails overnight at 4°C, followed by incubation with secondary antibody cocktails for 1 hour at room temperature. After each antibody incubation, tissue sections were washed with cold PBS three times for 5 minutes. Cell nuclei were counterstained with DAPI (600 nM). Slides were mounted with ProLong Gold antifade reagent (Invitrogen, cat. P36930) and stored at 4°C. 44 2.3.7 Image acquisition Confocal images were acquired using the confocal laser scanning microscope system LSM 900 with Airyscan 2 (Carl Zeiss) at 10X, 20X, or 40X magnification. The associated software for image acquisition is Zen Blue Edition (v.3.4, Carl Zeiss). Z-stack images were merged in ImageJ (v.1.53q) with Fiji via the method of maximum projection88. Scanning of large areas of interest was achieved by stitching multiple fields at 10X magnification. Merged and split channel images were exported and assembled in Adobe Illustrator 2021. 2.3.8 Assessment of cardiac vascular network To accurately characterize cardiac vasculature, imaging was completed using the same parameters with two technical replicates per sample. Areas were selected to capture continuous regions of the remote and infarct zones. Images shown in Fig 2.8a depict representative regions focusing on the infarct core and were not used for quantification purposes. Image analysis was performed in ImageJ with a script adapted from a published workflow175. Initially, regions of interest (ROIs) were drawn to refine boundaries of each zone and exclude empty gaps in the tissue section. This is followed by the conversion of individual channels, including CD31 and Cspg4-DsRed, to binarized images with the “Threshold” function. The threshold is set at a fixed value for all images stained and imaged in the same batch. Random noises attributed to abnormally bright pixels were minimized using the “Median” function under “Filter” with radius parameter set at 2 pixels. Area fractions of CD31 and DsRed were then calculated separately as the percentage out of non-zero pixels within the defined ROIs. 45 To evaluate perivascular mural coverage, we quantified the ratio of overlapping area between CD31 and DsRed to total DsRed area. Based on CD31 signals, minimum Feret diameter and the number of distinct vessels above 1 μm2 in area were obtained from the “Analyze Particles” tab. The average nearest neighbour distance between vessels was computed using the “NND” plugin. Qualitative assessment of vascular blood flow was evaluated by transcardiac perfusion of lectin-fluorophore conjugate (Vector Laboratories, cat. DL-1178-1). 2.3.9 ELISA LAD heart ventricles were split into two parts: a “distal” segment above the sutures and another below that is “proximal” to the infarct. Tissues were snap frozen and homogenized TissueLyser II (Qiagen, cat. 85300) in extraction buffer (Thermo Scientific, cat. 89900) mixed with Halt Protease and Phosphatase Inhibitor Cocktail (1:100; Thermo Scientific, cat. 78440). Protein concentration was determined using the Pierce bicinchoninic acid (BCA) Protein Assay Kit (Thermo Scientific, cat. 23225) as per the associated user manual. The amount of VEGFA present in the extracted protein was quantified using the ELISA kit (R&D Systems, cat. MMV00). All measurements were evaluated in duplicates. 2.3.10 Single cell library construction and sequencing Cells from purified suspensions were counted using a hemocytometer and recombined to enrich target populations, if applicable. Cell hashing with oligo-tagged antibodies against MHC class I and CD45 (BioLegend, cat. 155801/3/5/7) were applied to 46 maximize cell yield and reduce cross-sample multiplets in samples corresponding to Hic1-expressing cells at steady state and CD146-sorted cells at 3 days PI. Libraries were generated using the Chromium Single Cell 3’ reagent kit (v.2 and v.3, 10X Genomics) as per manufacturer’s protocol. Sample integrity was assessed on the Agilent Bioanalyzer 2100 system using the RNA 6000 Pico or Nano kit (Agilent, cat. 5067-1511 and 5067-1513). Sequencing was performed on an Illumina NextSeq 500 using the High Output 75 Cycle kit to a minimum depth of 30,000 mean reads per cell. Reads were demultiplexed and aligned to a custom mm10 reference genome with transgenic sequences including tdTomato, YFP, and DsRed using the Cellranger pipeline (v.4.0.0, 10X Genomics). The resulting feature-barcode matrices were imported into R (v.3.6.1) for downstream processing and analyses. 2.3.11 Single cell RNA-seq analysis Data processing and differential expression analysis were performed using Seurat (v.3.2.0)176. Wherever applicable, demultiplexing with hashtag oligos (thresholding at 0.99 quantile) was applied to remove cross-sample multiplets. To remove cells of poor quality, metrics including number of genes expressed (>200), total unique molecular identifier (UMI; <7000-15,000), and proportion of mitochondrial transcripts (<10-15%) per cell were tailored to each dataset. Log-normalization was applied to UMIs and the top 2000 variable genes were selected by variance-stabilizing transformation. The proportion of mitochondrial transcripts was regressed out to minimize clustering by cell quality. Anchor-based data integration was applied to appropriate batches based on experimental dates. The number of principal components (PCs) used for Louvain 47 clustering and uniform manifold approximation and projection (UMAP) was determined by the inflection point of variances accounted by PCs (~20 PCs). Clusters were grouped into distinct annotated subsets as advised by pair-wise Pearson’s correlation across all genes, as well as the expression of identity markers reported in past literature. Differential expression analysis was performed using the Wilcoxon rank sum test with P-values adjusted by Bonferroni’s correction. Only genes that were expressed in at least 25% of cells in one of the comparison groups and had a log2-fold change of at least 0.25 were considered. Temporally regulated genes were compiled by iterative differential expression comparisons of each injury timepoint to SS. Scaled expression was averaged to generate strip plots and the associated trend lines. Heatmaps of average normalized expression were generated using pheatmap (v.1.0.12) with scaling applied to each gene. 2.3.12 Gene Regulatory Network analysis Single cell regulatory network inference and clustering (SCENIC; v.1.1.2-2) was applied to scRNAseq data to infer the activity of GRNs in individual cells177. Raw feature-count matrices were QC’ed in Seurat prior to analysis using the recommended settings according to the published vignette (https://github.com/aertslab/SCENIC) and the mm9 Rcistarget reference. Regulon activity scores were appended to the clustered Seurat objects and evaluated on the same UMAP embeddings. Hierarchical clustering was performed on binarized regulon activity scores to assess the regulatory programs unique to each annotated cell population. Networks of cell-specific regulons were drawn 48 according to the inferred regulon targets. Only transcription factors active in at least 50% of any annotated population were considered in the analysis. 2.3.13 Visium Spatial transcriptomics of the infarcted heart was evaluated using the Visium Spatial Gene Expression platform (v2, 10X Genomics). Frozen heart samples were sectioned at 10-μm thickness and mounted onto a pre-cooled (-20°C) glass slide. Note that serial sections were kept for each heart tissue used to generate positional transcriptomic datasets for immunostaining. Sections were fixed in cold methanol prior to H&E staining and imaged on brightfield with a Nikon Eclipse Ni microscope using the NIS Elements software (v.5.11.03, Nikon) as instructed in the Visium CytAssist demonstrated protocol (CG000614). Processing of tissue sections was performed in accordance with the Visium CytAssist user guide (CG000495) using the Visium CytAssist Spatial Gene Expression Kit (PN-100051). cDNA libraries were sequenced on a NextSeq 2000 sequencer (Illumina) with the P2 100 cycles reagents (Illumina, cat. 20046812) to a minimum depth of 40,000 mean reads per spot. Reads were aligned to the mm10 genome reference with Space Ranger (v.2.0.1, 10X Genomics) to generate positional feature-barcode matrices. Downstream processing with Seurat was completed as described in the spatial data vignette using SCTransform178. Integration across timepoints was applied to identify spots of similar expression profiles. Note that the apparent presence of spots from clusters 3 and 4 at SS is likely due to technical limitations of integration. Follow-up analysis shows that these spots do not resemble those of the “true” infarct zone after MI and do not demarcate areas of myocardial loss 49 or fibrosis. Differential expression analysis was performed as described in “Single cell RNA-seq analysis” with the SCT assay. K-means clustering using the average expression of temporally regulated genes was implemented to categorize genes of distinct patterns and identify their associated functions. The optimal number of clusters was estimated with the inflection point of within-cluster-sum of squared errors (WSS) plotted against clusters. To deconvolve the identity of voxels, data integration was applied using scRNAseq data as a reference to transfer cell type annotations and compute their prediction scores per spot. 2.3.14 Pathway enrichment analysis Pathway enrichment analysis was performed using DAVID Bioinformatics Resources (2023 Update)179,180 and results were extracted from under the “GO Direct ” category. P-values were adjusted by Benjamini-Hochberg correction and log-transformed for plotting. Values adjacent to bars indicate the number of DEGs annotated in each GO term. To score the activity of general biological processes, the average expression of DEGs found in the Gene Ontology terms, “inflammatory response” (GO: 0006954), “ECM organization” (GO:0030198), and “blood vessel development” (GO:0001568), was calculated per timepoint. 2.3.15 Inference of regional communication networks Communication networks of the annotated Visium data were assessed using CellChat181 (v.1.6.1) in accordance with the published vignette (https://github.com/sqjin/CellChat) in R (v. 4.1.2). Briefly, the integrated Seurat object 50 was directly imported to create CellChat objects split by timepoint. All ligand-receptor interactions were queried from the CellChatDB database for Mus musculus. Over-expressed ligands and receptors across annotated regions were identified with P-value threshold set at 0.1 and passed to communication assessment. Communication probability was calculated with distance constraints whereby interactions beyond 5 voxels (~275 μm) were excluded. Strength of communication was normalized to the abundance of regional voxels during the probability calculation. Weak interactions with less than 10 voxels expressing the ligand or receptor in each region were filtered out. Pathways active in at least one timepoint were considered for presentation. Active interactions (i.e. interactions with P-value < 0.05) related to inflammation, matrix remodeling, and vascular development were manually selected and their signaling strengths were presented as a sum of all communication probabilities for each ligand. The digit adjacent to dots in dot plots represents the number of unique receptors that interact with the ligand. 2.3.16 Statistical analysis Statistical testing and graphing of data associated with flow cytometry, immunofluorescence microscopy, and ELISA were performed using Prism 9 (GraphPad Software). Error bars represent deviation by standard error of the mean (Mean ± SEM). The type of test sample size, and adjusted P-values of each experiment is provided in respective figure legends. P-value < 0.05 was considered statistically significant. Degrees of significance are denoted as follows: P < 0.05 (*), P < 0.01 (**), P < 0.001 (***), P < 0.0001 (****). Figure assembly was performed in Adobe Illustrator 2021. 51 2.3.17 Data availability The single-cell and spatial transcriptomic data generated in this study have been deposited in the Gene Expression Omnibus (GEO) database under the accession code GSE206787. All databases associated with the softwares and packages used in the study are described in the Methods section. To promote readership, the processed data can be accessed and probed on our publicly accessible platform, Integrated Single-cell NAvigation Portal (ISNAP) at https://isnap.rossilab.dev/GSE206787/. Source data are provided with this paper. 2.3.18 Code availability All data processing and analysis scripts are constructed as per the vignettes of their respective sources and accessible on the GitHub repository at https://github.com/rossilab/spatial_temporal_atlas. Please refer to methods above for details of key parameters and package versions. 2.4 Results 2.4.1 Heterogeneity of stromal cells in the heart Distinction of mural cells from adventitial fibroblasts has been typically overlooked due to their anatomical proximity around blood vessels and overlapping gene signatures182,183. To elucidate their distinction in vivo, we generated a double reporter mouse line, Pdgfra-EGFP/Cspg4-DsRed, that labels fibroblasts with nuclear EGFP under their canonical marker, platelet-derived growth factor receptor α (Pdgfra)184, and mural cells with cytoplasmic DsRed under the chondroitin sulfate proteoglycan-4 52 (Cspg4) promoter185. Taking advantage of this system, we showed that EGFP and DsRed expressing cells are clearly distinct in the heart at homeostasis (Fig 2.1a, left). In fact, perivascular DsRed+ cells are embedded under the vascular basement membrane (BM) whereas EGFP+ cells are not (Fig 2.1a, right). This is evident in large vessels where DsRed+ cells associate closely with endothelial cells while EGFP+ cells are found mostly in the adventitia. The mutual exclusivity of labelled cells and their difference in localization with respect to blood vessels support the notion that mural cells are a distinct cell type from fibroblasts and likely exhibit a different functional role in the heart. To explore the heterogeneity of cardiac stromal cells, we adopted the established mouse strain, Hic1-CT2/tdTomato that allows for the labeling of multiple subsets of mesenchymal stromal cells in various organ systems7,62,186. Following tamoxifen (TAM) administration, Hic1-expressing cells and their progeny are labeled with tdTomato (Fig 2.1b). Ventricles of the heart were collected at 10 days post-TAM treatment and enzymatically digested. Using fluorescence-activated cell sorting (FACS), tdTomato+ cells were isolated for single-cell RNA sequencing (scRNAseq). To ensure high purity and adequate representation of all stromal subsets, in addition to gating out immune cells and endothelial cells, mural cells were enriched based on a pan-mural cell marker, CD146 (Mcam)187, prior to library preparation. To minimize cross-sample multiplets, we applied cell hashing with oligo-tagged antibodies specific to each mouse sample. Louvain clustering of 11,489 cells generated 12 clusters (Fig 2.2a). We noted that cells derived from each mouse were similarly distributed throughout the clusters, suggesting 53 that hashing did not introduce significant sampling bias (Fig 2.2b). Consistent with what was observed in previous publications7,62, Hic1 transcript expression was low but virtually all cells in the dataset carried tdTomato transcripts (97.2%, Fig 2.2c), validating the purity of sorted cells. To identify distinct cell populations, hierarchical clustering of 12 clusters showed that 4 shared an expression profile distinct from the other 8, in line with the division of cells in two major superclusters (delineated by dashed boxes, Fig 2.2d). Based on known markers of stromal cell types, these superclusters correspond to fibroblasts and mural cells (Fig 2.2e). This distinction in transcriptomic space underscores significant differences between the transcriptomes of fibroblasts and mural cells. Within the fibroblast supercluster, we further classified a small subset of cells (cluster 9) as FAPs (Fig 2.1c; Fig 2.2a). As described by our previous study7, FAPs are multipotent mesenchymal progenitors that give rise to SCA-1- matrix-producing fibroblasts upon injury. These cells express elevated levels of markers indicative of progenitor-like characteristics including, Ly6a (SCA-1), Pi16, Dpp4, and Anxa3188,189, but not Gli1 despite extensive reports of it being a marker of fibroblast progenitors10,55,190(Fig 2.2f). In contrast, the Ly6alow counterpart likely represents a more mature fibroblast state with increased expression of functional markers such as Vcam1, Cxcl14, Apoe, and Col6a3. Vcam1, in particular, has been regarded as a marker of pro-fibrotic fibroblasts in skeletal muscle191,192. 54 Mural cell clusters were regrouped into a VSMC subset and two pericyte subsets, herein annotated as PER-1 and PER-2 (Fig 2.1c). Common gene markers associated with contractility, notably the expression of Acta2, Myh11, and Tagln41,65, were used to identify VSMC (Fig 2.1d). Likewise, the pericyte subsets were distinguished using a set of established pericyte markers including Kcnj8, Abcc9, Colec11, and Vtn41. PER-2 is set apart from PER-1 given their separation in transcriptomic space. Indeed, we found the expression of Cspg4 to be largely absent in PER-2. Further comparison between the pericyte subsets revealed that PER-2 displays a contractile program similar to that seen in VSMC (Fig 2.1e). Additionally, it exhibits a fibrogenic program consisted of genes encoding for structural matrix proteins and matricellular factors typically found in fibroblasts. These overlapping signatures with other stromal subsets suggest that PER-2 may exhibit multiple functional roles. Interestingly, PER-2 also retains a unique signature consisting of genes related to immune cell recruitment (Lgals3 and Ccl11) and vascular development (Ccn3, Flt1, and Angpt2), amongst others (Fig 2.2g). To localize this rare pericyte subset, we examined galectin-3 (GAL-3; encoded by Lgals3) expressing mural cells in Hic1-CT2/tdTomato/Pdgfra-EGFP hearts (Fig 2.2h, top). While PER-2 did not appear to be confined to a particular anatomical region, co-staining with CD31 indicated their close association with small vessels (Fig 2.2h, bottom). Interrogation of relative proportions between the pericyte subsets by flow cytometry confirms the rare presence of PER-2 (Fig 2.2i). We noted that nestin (Nes) and GLAST (Slc1a3), previously described as markers differentiating subtypes of pericytes82,87,193, do not differ in expression across mural cell clusters. In fact, both genes appear to 55 emphasize the distinction between fibroblasts and mural cells rather than identify different types of pericytes (Fig 2.2j). Next, we sought to examine the regulatory programs governing each stromal subset. We applied Single-Cell rEgulatory Network Inference and Clustering (SCENIC) to reconstruct gene regulatory networks (GRNs) termed “regulons” and infer their activities in individual cells177. Each regulon is defined as a set of genes and a transcription factor (TF) predicted to regulate their expression. Hierarchical clustering with regulon activities confirms that mural cells exhibit a different set of regulatory programs from fibroblasts (Fig 2.3a). Specifically, many of the differentially active TFs in both fibroblast subsets (Fig 2.1f) include those critical for fibroblast fate (Tcf21 and Twist2)194,195, matrisome production (Egr1, Nfkb1, and Sox9)62,196,197, and regulation of fetal-specific programs (Nfix, Nfic, and Nfia)198. In contrast, the active regulons in mural cells are predominantly those involved in regulating angiogenesis (Bach1, Ets1, Elk3, Mef2a, Mef2c, Meox2, and Yy1; Fig 2.1g)199–203. We noted that the transcripts of several of these TFs are expressed in both mural cells and fibroblasts at similar levels. However, their regulons are preferentially active in one but not the other, revealing additional layers of regulation in the maintenance of distinct cell identities. Variations in GRNs were also observed within mural cells and fibroblasts, albeit to a lesser degree. Together, our results showed that mural cells are distinct from fibroblasts in the heart at homeostasis. Anatomically, mural cells, unlike the vast majority of fibroblasts, are embedded under the vascular basement membrane. This positional distinction is 56 consistent with their differences in transcriptomes, and therefore impart unique biological functions in the heart stroma. 2.4.2 Myocardial infarction induces cardiac pericyte activation During MI, cardiac extracellular matrix (ECM) undergoes extensive remodeling where the loss of cardiomyocytes results in tissue displacement with a collagen-based scar204. Within the cardiac stroma, fibroblasts have been shown as the main source of matrix building blocks and matricellular factors PI. However, the role of mural cells in scar formation and, in general, their behaviour in response to MI remain unclear47. Here, we assessed changes in the mural cell landscape following left anterior descending (LAD) coronary artery ligation, a murine model of MI. To evaluate the proliferation kinetics of mural cells, we administered 5-ethynyl-2’-deoxyuridine (EdU) at multiple timepoints post-LAD surgery in Hic1-CT2/YFP mice (Fig 2.4a). Heart ventricles were harvested and EdU incorporation was analyzed by flow cytometry (Fig 2.3b). We found that proliferation of Hic1-expressing mural cells (YFP+/CD146+/LIN-) peaks at day 4, at a level comparable to that of fibroblasts (YFP+/CD146-/LIN-). At day 7, proliferation of both stromal populations is reduced and, by day 21, it is returned to steady state (SS) level. In the same timeframe, endothelial cells (YFP-/CD146+/LIN+) exhibit peak proliferation at day 7 PI, indicating active vessel formation in the infarcted heart. Interestingly, EdU incorporation in YFP-CD146+LIN- cells is minimal. To pinpoint the identity of these cells, we sequenced CD146+ cells and compared the resulting cell landscape to that of Hic1-labelled cells. Our comparison 57 demonstrated that Hic1-labeling excludes not only Schwann cells, but also a subset of VSMCs expressing high levels of Cnn1, a marker of arterial and arteriolar VSMCs, herein collectively termed as art. VSMC65 (Fig 2.3c). Immunofluorescence staining with calponin 1 (CNN1) and alpha smooth muscle actin (αSMA) validated that CNN1 expression is exclusive to art. VSMC (Fig 2.3d). Furthermore, we did not observe co-localization between tdTomato and CNN1 signals in the vessel walls, confirming that the Hic1 transgenic model does not trace all mural cells. Based on Hic1-labelling and CD39 (Entpd1) expression, which differentiates between VSMCs and pericytes at both the transcript and protein level57 (Fig. 2.3e-f), we were able to discern CD146+ populations as pericytes (tdTomato+CD39-), Schwann cells (tdTomato-CD39-), venous/venular VSMCs (ven. VSMC; tdTomato+CD39+), and arterial/arteriolar VSMCs (tdTomato-CD39+). Using this gating strategy, EdU incorporation assay was repeated at day 3 PI to evaluate proliferation in specific mural cell subsets, which showed that the majority of proliferating mural cells are pericytes (Fig 2.3g). Next, we generated a scRNAseq time series to explore the transcriptional dynamics of mural cells. CD146+ cells were isolated from day 3, 7, and 42 PI, and sequenced. Together with the uninjured dataset, we applied Seurat’s anchor-based integration across the four datasets, an approach used to harmonize datasets and minimize technical variation. Louvain clustering revealed that the annotated subsets we identified in Hic1-expressing mural cells were present at all timepoints PI (Fig 2.4b). As we anticipated, sorting with CD146 independent of Hic1-labelling yielded a much more prominent population of art. VSMCs. Remarkably, we observed the emergence of an 58 injury-induced mural cell cluster at day 3 PI (cluster 6, Fig 2.5a). Correlation analysis indicates substantial overlap in its transcriptome compared to those of the pericyte subsets (cluster 0 and 7, Fig 2.5B). In fact, many of the markers used to identify pericytes are co-expressed in cluster 6 (Fig 2.5c). Given these characteristics, we annotated this cluster of cells as “activated pericytes” (act. PER), reflecting its injury-responsive origin. Active proliferation in these cells is evident as shown by the expression of mitotic genes (Fig 2.4c). Follow-up assessment by histology indicated that cell division from day 3 to 7 PI, including that of Cspg4+ cells, is largely confined to the myocardial scar, and thus associates the localization of act. PER with the infarct (Fig 2.4d). Differential expression does not reveal any gene marker exclusive to act. PER. However, many genes involved in matrix remodeling and vascular programs, as well as Acta2 are upregulated in this subset compared to the rest of pericytes irrespective of injury timepoint (Fig 2.5d). These changes point to a pathological response resembling that of fibroblast-to-myofibroblast transition. To validate the origin of act. PER, we repeated the sequencing experiment at day 7 PI by sorting CD146+ cells from the Cspg4-CT2/tdTomato transgenic line to specifically label Cspg4+ mural cells and their derivatives (Fig 2.5e). Much like the time series data, we captured the same activated subset in the mural cell compartment. This subset expresses comparable level of tdTomato as other mural cells, corroborating its identity as an injury-induced progeny of Cspg4+ mural cells. In addition to a discernible response in act. PER, temporal assessment of all pericytes indicated a global change in their transcriptional profile PI. Matrix-associated programs, 59 encompassing matrix building blocks (Col1a1, Col3a1, Eln, and Fn1), metalloproteinases (Adamts4, Mmp2, and Mmp11), crosslink-inducing enzymes (Loxl1 and Loxl2), and other matricellular factors regulating fibrosis (Ccn1, Ccn2, Postn, and Sparc), are activated PI (Figure 2.4e). This aligns with the period during which fibroblasts expand and amplify their fibrotic response, suggesting that pericytes may exhibit an overlapping role in scar formation and collagen turnover. Interestingly, upregulation of Pdgfa, a potent mitogen of fibroblasts, also points to their potential role in inducing fibroblast expansion205. Furthermore, changes associated with regulators of vascular function were also observed (Fig 2.4f). These include elevated expression of TFs (Ets1, Meox2, Prrx1, and Tbx20), cell surface proteins (Apold1 and Thy1), cytoskeletal components (Actg1, Myl9, and Mylk), angiogenic elements (Angpt2, Jag1, Notch1, and Thbs2), and regulators of vascular tone (Ednra and Ednrb), reflecting an adaptive response in pericytes that affects vessel growth and stability. Angpt2, in particular, has been shown to mediate mural cell detachment from the vascular endothelium115. Thbs2 may play a role in dampening pro-angiogenic mechanisms PI206. Notably, mediators of inflammation (e.g. Cxcl1, Hmgb2, Il1r1, and Il6), are also upregulated, and therefore do not completely exclude pericytes’ role in inflammatory response (Fig 2.5f). Together, these injury-induced alterations in transcriptional programs are indicative of a shift in pericyte behaviour toward a pathological state. While similar changes are seen in VSMCs, they are observed to a much lesser extent compared to pericytes (Fig 2.5g). 60 Collectively, it is evident that MI induces a significant transcriptional response in pericytes while VSMCs appear less affected. This response is characterized by increased matrix remodeling and alterations in vascular programs. However, an activated subset associated with the infarct goes beyond this partial response and acquires a qualitatively similar but quantitatively distinct transcriptional program. 2.4.3 Fibroblasts and mural cells do not share a lineage relationship in MI Fibroblasts, specifically FAPs, are multipotent progenitors capable of differentiating into a number of cell types in response to injury. In arrhythmogenic cardiomyopathy, we had previously shown that cardiac FAPs can give rise to intramyocardial adipocytes7. Others have also described myocardial calcification as a consequence of fibroblasts adopting osteoblast-like properties207, supporting their multipotency. Nonetheless, whether FAPs or fibroblasts contribute to mural cells, or vice versa, remains elusive. Here, we adopted the Pdgfra-CT2/YFP mouse line to label cardiac fibroblasts at SS and follow their progeny at different timepoints post-LAD (Fig 2.6a). Additionally, we crossed this mouse line with Cspg4-DsRed mice to generate a model that would accurately capture the acquisition of Cspg4 expression in the lineage traced progeny. Interrogation of YFP+ cells by immunofluorescence microscopy showed no co-localization events between YFP and DsRed signals in the infarct (Fig 2.6b). In fact, YFP+ cells are interspersed throughout the infarct whereas DsRed+ cells associate closely with endothelial cells as we had observed previously. Similarly, to address whether mural cells 61 can give rise to fibroblasts, we generated a Cspg4-CT2/tdTomato mouse line that is crossed to the Pdgfra-EGFP reporter (Fig 2.6c). After LAD ligation, tdTomato+ cells do not show overlapping signals with EGFP and remain in immediate proximity to endothelial cells in the infarct (Fig 2.6d). This is supported by flow cytometry data where EGFP expression in the lineage-traced mural cells is largely negligible and unchanged throughout the course of injury (Fig 2.7a). To substantiate these findings, we sequenced mural cells and fibroblasts using the Hic1-CT2/tdTomato system on day 7 PI and examined for changes in the gene expression of identity markers characteristic to either stromal cell types after MI (Fig 2.7b). Comparison of fibroblasts at SS and after injury did not show significant changes in the expression of mural cell-specific markers, including Cspg4, Mcam, and Notch3 (Fig 2.7c). Their expression remained largely confined to mural cells when compared to fibroblasts after injury. Likewise, the expression of fibroblast-specific markers such as Pdgfra, Cd34, and Lum was minimal and unchanged in mural cells. Together, our results strongly support that fibroblasts and mural cells do not share a lineage relationship in response to MI. Instead, both stromal populations can adopt an activated state, which may share a similar set of injury-induced programs. 2.4.4 Progressive vascular rarefaction and hypertrophy in the infarct zone Heart failure, including cases caused by MI, is closely associated with vascular dysfunction and rarefaction208. This, in part, has been attributed to the detachment and 62 loss of mural cells, specifically pericytes, from microvasculature in the infarct209. However, the onset and progression of vascular impairment across the initial phases of tissue repair post-MI have not been investigated extensively. Moreover, it is unclear as to whether regions with distinct stromal compositions are established within the infarct over time. Here, we assessed changes in the vasculature and stroma by histological analyses using the Pdgfra-EGFP/Cspg4-DsRed line (Fig 2.8a). Co-staining of CD31 shows progressive deterioration of vasculature in the infarct zone. This is characterized by a drastic reduction in vascular density starting at day 5 PI (Fig 2.8b), which is complemented with increasing nearest neighbor distance (NND) throughout the vascular network (Fig 2.8c). Despite the loss of vessel density, we did not observe a significant decrease in the proportion of CD31 area until day 14 PI, suggesting that the difference may be masked by a shift in vessel size (Fig 2.8d). Indeed, quantification of the minimum vessel feret diameter indicated increasing vascular hypertrophy from day 5 PI (Fig 2.8e). It is also noted that vessels adopt a more misaligned and disorganized morphology over time. Through transcardiac perfusion of lectin-fluorophore conjugate, we found some of these vessels failed to be labeled by circulating lectin on day 14 PI, indicating a lack of blood perfusion and loss of vascular function (Fig 2.7d). In line with the EdU assay, mural cell expansion is reflected by a transient upsurge in the proportion of NG2 area on day 5 PI, which plateaus until a clear reduction is seen on day 21 PI (Fig 2.8f). Interestingly, the proportion of NG2 area overlapping with CD31 shows an initial drop on day 3 PI, followed by a temporary recovery on day 5 PI and a continuous decline from day 7 PI onward (Fig 2.8g). These data suggest that the initial 63 impact from the infarction and inflammation cause mural cell detachment from the vascular endothelium. As the stroma undergoes a burst of expansion, nascent mural cells attempt to stabilize the vessels by re-associating with the vascular endothelium. Yet, this attempt is transient as mural cell proliferation ceases and likely becomes outpaced by cell death. Ultimately, the resulting vascular network at 3 weeks PI is sparse with limited coverage by mural cells. After the initial expansion at day 3 PI, fibroblasts persist in elevated numbers and appear to be evenly distributed throughout the infarct. In contrast, we have observed a two-layer organization within the infarct zone with respect to vascularization on day 5 and 7 PI in a subset of experimental mice (Fig 2.7e). This division is characterized by a vascularized inner layer and an outer layer that is solely populated by fibroblasts. Such organization points to a regional-specific response within the infarct whereby vessel loss is more prominent on the epicardial face than regions closer to the endocardium. The lack of mural cells in the outer layer also substantiates their role in maintaining viable blood vessels. Overall, our temporal analysis of the infarct indicates that vascular impairment is exacerbated with time post-MI. Although the proliferation of vascular cells points to active revascularization PI, the vascular network displays morphological abnormalities and remains much less permeated compared to that of physiological state or remote zone. More importantly, there is a strong correlation between mural cell coverage and vascular dysfunction whereby mural cell detachment precedes vessel loss. 64 2.4.5 Spatiotemporal atlas of the infarcted heart Single-cell transcriptomics offer insight into cellular mechanisms at single-cell resolution. However, the spatial information of cells is not preserved. In MI, the distribution of cells in the infarct zone and its surrounding regions is crucial to understanding the organization of the myocardial scar. More importantly, identification of local processes and their underlying communication networks are vital for interpreting regional remodeling events, including inflammation, fibrosis, and vascular rarefaction. Here, we generated a time series of spatially resolved transcriptomic data using the 10x Genomics Visium platform. Transverse sections were cut from the hearts of Pdgfra-EGFP mice at SS and day 3, 7, and 14 PI. The resulting data contained 3852 uniquely barcoded “spots” or voxels, each of 55 µm in diameter, overlaid on the tissue sections. Louvain clustering of Visium spots elucidated regional differences in transcriptional programs (Fig 2.9a). In the infarcted hearts, clusters are discerned into areas within the infarct and remote zones. Indeed, immunostaining on the same heart sections after library preparation shows the precise boundary of where viable myocardium meets the infarct based on autofluorescence (Fig 2.10a). Furthermore, the distribution of stromal cells and vasculature is elucidated by immunostaining with the respective serial sections. As expected, an increased density of fibroblasts and attenuation to the vascular network are evident in the infarct zone. Intriguingly, the infarct appears to be composed of two transcriptionally distinct layers at all timepoints PI: an inner layer (cluster 3; herein annotated as “infarct-1”) interfaced with remote zone and an outer 65 layer (cluster 4; annotated as “infarct-2”) with complete loss of cardiomyocytes. Broad assessment of gene signatures indicated that both regions of infarct are characterized by elevated matrix, immune, and cell proliferation programs, as well as reduced gene expression in sarcomeric units (Fig 2.10b). However, infarct-1 exhibits a hybrid profile between infarct-2 and remote zone, representing a region with intermediate remodeling programs that retains some residual cardiomyocytes. In contrary to the infarct zone, we did not capture significant changes amongst clusters in the remote zone or at steady state, and thus they are collectively annotated as “remote” or “SS” for further analysis. To dissect the temporal response in the infarct and remote zones, we compiled a list of genes that are differentially expressed in at least one zone relative to SS for any given timepoint PI. These temporally regulated genes are clustered by k-means to identify gene sets with distinct expression patterns over time and across zones (Fig 2.9b). Based on the clustering, we deduced four unique expression patterns and identified their top associated biological processes by pathway enrichment analysis. Cluster 1 (C1) is characterized by genes displaying progressive upregulation in the infarct zone with time. Its underlying processes are related to key tissue repair programs, including matrix remodeling and angiogenesis, and indicate changes to transcriptional regulation. Cluster 2 (C2) shows marked gene expression in the infarct zone on day 3 PI, followed by downregulation in subsequent timepoints but does not return to SS level. The associated pathways in C2 point to concurrent activation of cell division and apoptosis programs, which likely result from the expansion of stromal cells and cell death due to inflammation or ischemia. Genes in cluster 3 (C3) are progressively downregulated in 66 the infarct zone and associate with processes required for maintaining energy metabolism and contractile function. Lastly, cluster 4 (C4) captures gene programs that are globally downregulated irrespective of zonation. Their associated pathways imply alterations in mechanisms governing gene expression such as post-translational modification and RNA splicing, the efficiency of which has been reported to reduce in heart failure210. In addition to examining holistic patterns of temporally regulated programs, we assessed signatures of known tissue repair processes underlying MI, specifically inflammatory response, matrix remodeling, and vascular development. In general, inflammation is increased in the infarct zone whereas the remote zone remains largely unaffected PI (Fig 2.9c). This increase corresponds to upregulated expression of pattern recognition receptors (PRR) and their downstream effectors, including NFκB and interferon regulatory factors. Moreover, complement cascade and cytokine signaling involving Csf1, Tnf, and interleukins are pronounced. Of note, Il33, a type-2 cytokine, peaks in expression at later timepoints, suggesting a potential shift from type-1 to type-2 inflammatory response. Likewise, chemokines and adhesion glycoproteins driving leukocyte recruitment are upregulated, along with programs related to oxidative stress regulation and endocytosis. Similar to the inflammatory response, the upregulation of matrix remodeling signatures is predominantly associated with the infarct zone (Fig 2.9d). In parallel with the fibroblast kinetics, Pdgfa shows peak expression on day 3 PI and contributes to fibroblast expansion in the infarct during early stages of MI. With stroma activation, the production of matrix structural components, metalloproteinases, 67 and enzymes that induce crosslinks to facilitate scar maturation is amplified. This remodeling process is supported by various matricellular factors and signaling cascades involving TGFβ and WNT/β-catenin pathways. It is noted that a minor increase in some inflammatory and matrix remodeling signatures is seen in the remote zone on day 14 PI. Whether this response reflects adaptive changes endemic to the remote zone induced by “spillover” of factors like alarmins from the infarct requires further investigation. In terms of vascular development, there is an overall upregulation of its associated programs in the infarct zone (Fig 2.9e). However, as a whole, it is not clear if these changes cultivate a tissue environment that reinforces or inhibits neovascularization in the infarct. The chemotactic factor of mural cells, Pdgfb, is downregulated in the remote zone on day 3 PI, which may create a gradient that promotes mural cell recruitment toward the infarct zone211. Hif1a, as a master switch upstream of many angiogenic factors, shows an initial drop in gene expression in both the remote and infarct zones but recovers by day 14 PI. Moreover, the well-established drivers of neovascularization, Vegfa and Vegfb, are downregulated at all timepoints in the infarct zone. Their cognate receptors also display transient downregulation at certain timepoints. On the other hand, the upregulation of sulfatases, Sulf1 and Sulf2, may enhance VEGF signaling by increasing its bioavailability to the infarct212. Intriguingly, Vegfc and Vegfd demonstrate pronounced upregulation on day 7 and 14 PI, indicating a potential increase of lymphangiogenesis during the remodeling process. Genes involved in other major angiogenic pathways, including NOTCH and ROBO/SLIT signaling, show consistent upregulation in the infarct zone but not those of YAP/TAZ signaling. A number of 68 secreted factors with pro-angiogenic properties like Ang are, too, upregulated with the exception of Fgf1. Comparably, we also observed apparent alterations in signals antagonizing neovascularization. For instance, members of the thrombospondin family, which confer antiangiogenic effects, are jointly upregulated in the infarct zone. Angpt1, a factor known to promote vessel stability and maturation, is downregulated in the infarct whereas its cognate receptor, Tek, exhibits the opposite pattern of expression. Interestingly, hormonal regulators of blood pressure such as Agt and Nppa display an early onset of expression in the remote zone, and therefore represent another adaptive response to MI. Collectively, these results indicate that tissue repair in MI is driven by regional-dependent and temporally regulated programs. 2.4.6 Inter-regional communication networks regulate cardiac repair Given the substantial differences in regional programs, we sought to recapitulate potential cellular interactomes underlying the annotated regions using a permutation-based approach213. From the inference, crosstalk amongst the infarct layers and remote zone is extensive with a significant proportion of active ligand-receptor interactions identified between and within the infarct layers (Fig 2.10c). The relative proportions of active interactions show that a subset of signaling pathways is exclusive to certain timepoints while others are approximately equal throughout the time series (Fig 2.10d). However, the relative information flow, defined as the sum of communication probability from all interactions under a signaling pathway, indicates that the strength of signaling is 69 the highest on day 3 PI for the majority of pathways, reflecting a timepoint where regional communication is the most pronounced (Fig 2.9f). To understand the “directionality” of information flow, we examined the “outgoing” (secretion of ligands) and incoming (reception of ligands) strengths of signaling pathways driven by secreted factors, specifically those involved in the tissue repair processes (Fig 2.10e). Consistent with the upregulated expression of inflammatory signatures, signaling of various cytokine groups (CCL, OSM, IL, CSF, and TNF) is the most active in the infarct zone. These signals are largely sourced from within the infarct with infarct-2 as the primary sender and receiver. Comparison of individual ligands reveals that a subset of interactions is only active in earlier timepoints and a decreasing trend of communication strength over time, indicating diminishing, but not complete termination of, inflammatory response (Fig 2.10f). Further analysis of interactions involving non-secreted factors shows that signaling of adhesion molecules for leukocyte diapedesis, such as Icam-1, Icam-2, and Sele, are limited to the infarct (Fig 2.10g). Similarly, signaling groups known to associate with matrix production and remodeling (TGFb, BMP, GDF, WNT, POSTN, ACTIVIN, TENASCIN, and SPP1) are the most active in the infarct zone. While all ligands under these groups are sourced from the infarct layers, some are also derived from the remote zone, albeit at a reduced level of signaling strength compared to those local to the infarct (Fig 2.9g). Additionally, the reception of these signals is present at reduced levels in the remote zone, which may contribute to reactive fibrosis in the uninjured myocardium. It is noted that certain 70 signals display a stringent window of activation. For instance, signaling through Bmp6, a factor found to alleviate cardiac remodeling by attenuating fibroblast collagen production214, is limited to day 7 and 14 PI. Likewise, Spp1 signaling is activated only in earlier timepoints, which may be critical for myofibroblast differentiation215. As expected, signaling through physical contact with ECM structural components, including collagens, laminins, and fibronectin, is restricted to the infarct. Of the secreted signals that confer regulation of vascular development, the infarct zone is generally receptive to both inducers (VEGF, FGF, IGF, PTN, and ANGPT signaling via Angpt2) and inhibitors (ANGPTL, THBS, and ANGPT signaling via Angpt1) of neovascularization (Fig 2.9h). However, the source of these angiogenic ligands can be regionally-specific. Indeed, Vegfa and Vegfb are exclusively sourced from the remote zone and act on both infarct layers across most timepoints. Interrogation by enzyme-linked immunosorbent assay (ELISA) indicated an overall increase of VEGFA in the heart at day 7 PI but no difference is seen between cardiac tissue proximal and distal to the infarct (Fig 2.10h). A similar signaling pattern is also exhibited by Angpt1, which has been shown to act in concert with the Vegf ligands to increase vascular density in the infarct zone216. Notably, the outgoing strength of FGF signaling is much higher in the remote zone and its reception in the infarct may pose as another pro-angiogenic mechanism217. On the contrary, the infarct zone is a dominant source of thrombospondins and Angpt2. Thbs1, in particular, has been implicated to induce impaired angiogenesis in MI206. Furthermore, its elevated expression is closely linked to the induction of Angpt2218, which antagonizes Angpt1 signaling and exacerbates 71 abnormal vascular remodeling. In addition to secreted signals, angiogenic signals requiring cell contact (NOTCH and Ephrins) are localized to the infarct layers. Of note, this included Jag1 signaling, which induces vessel sprouting and tip cell formation during angiogenesis219,220. Yet, its pro-angiogenic effect may be counteracted by coinciding Dll4 activity. Together, these differential patterns of regional communication suggest that, in response to MI, the remote zone acts as an important source of signals for stimulating neovascularization. While the infarct zone is receptive to these pro-angiogenic signals, it fails to maintain vascularization due to the presence of signals that attenuate vessel formation and integrity. Sustained inflammatory and pro-fibrotic signaling in the infarct may also contribute to vascular dysfunction. The resulting rarefaction, in part, is closely associated with the acquisition of a pathological state by pericytes and reduced mural cell coverage in the infarct. Indeed, localization of activated PER is best predicted to reside within the infarct layers on day 3 PI (Fig 2.10i). Our present data established a spatiotemporal atlas of the cardiac transcriptome and potential signaling networks underlying the tissue repair processes post-MI. These findings underscore the importance of regional and temporal considerations for the development of future therapeutics. 2.5 Discussion Cardiac stroma plays a critical role in tissue repair during myocardial ischemia. However, much of this regenerative process has been attributed to fibroblasts given 72 their role of forming a matrix scaffold is indispensable for sustaining cardiac function. On the contrary, mural cells were rarely in the spotlight despite their known involvement in facilitating reparative processes in other disease contexts such as spinal cord injuries221. Our study addressed this gap of knowledge by first examining the heterogeneity of mural cells and accentuating that they are distinct from FAPs/adventitial fibroblasts with a unique transcriptomic profile, regulatory programs, and localization pattern. Upon injury, we showed that pericytes undergo significant expansion and transcriptional alterations in matrix remodeling and vascular development programs. This, in part, is closely associated with progressive vascular rarefaction along with reduced perivascular coverage by mural cells. Lineage tracing of mural cells and fibroblasts also demonstrated a lack of lineage relationship between the stromal cell types in MI. By leveraging spatial transcriptomic assays, we uncovered an unappreciated spatiotemporal landscape of the infarcted heart and the communication networks underlying its tissue repair processes. In particular, we identified the remote zone as an important source of angiogenic factors for neovascularization. This is contrasted by an enriched production and reception of inflammatory, pro-fibrotic, and anti-angiogenic signals in the infarct zone. Overall, these data underpinned a correlation between vascular function and mural cell behaviour, and more importantly, described regional-dependent mechanisms driving tissue repair PI. Our spatiotemporal atlas, specifically, is an invaluable resource for guiding potential avenues of improving therapeutic angiogenesis in MI. 73 A number of strategies have been used to profile stromal subsets in different organ systems. However, the lack of a consistent nomenclature has clouded our understanding of their identities, cellular functions, and lineage relationships. From our study, we demonstrated that the cardiac stroma is composed of non-overlapping cell types, fibroblasts and mural cells, using the double reporter system, Pdgfra-EGFP/Cspg4-DsRed. Pdgfra7,222,223 and Cspg4224 have been widely used as lineage reporters and transcriptomic markers for the identification of fibroblasts and mural cells, respectively. While this system is sufficient in distinguishing stromal subsets in the heart, the same cannot be said for other tissues such as the brain41,225, and thus it is critical to evaluate reporter lines on a tissue-specific basis. At the transcriptomic level, it is clear that fibroblasts and mural cells retain specific sets of identity markers. However, we found that functional markers such as Col1a1, Angpt1, and Vegfa, are not always confined to the cell type they have been traditionally associated with, especially after injury. This is partly due to some degree of redundancy in cellular functions, which may fluctuate depending on the environmental context. One example of this is the upregulation of contractile components (eg. Acta2) in fibroblasts and, as we showed here, act. PER post-MI. As such, it is imperative to carefully consider the implications of using these functional markers as reporters given the temporal and context-dependent restrictions of their utility. Further characterization of cells within the mural and fibroblast compartments revealed the presence of multiple subsets. Consistent with previous literature, fibroblasts can be classified into a progenitor-like subset (herein named “FAP”) and a more mature derivative (“fibroblasts”). Although mural cells are a more homogeneous population than fibroblasts65, we found considerable diversity in cardiac 74 mural cells along the vascular axis. Specifically, we were able to distinguish those associated with arterial/arteriolar vessels (“art. VSMC”), venous/venular vessels (“ven. VSMC”), and capillaries (“PER-1” and “PER-2”). This level of diversity was rarely observed in previous studies as the yield of mural cells is typically low from tissue digestion. As mural cells are much smaller in proportion to other cardiac cells and more difficult to isolate given their long projections, future research examining mural cells should consider enrichment strategies using selective markers such as CD146. Numerous studies have made claims about the presence of multiple pericyte subsets across a number of organ systems. Notably, Bribrair et al. identified 2 subtypes of pericytes based on Nestin-EGFP expression in lung, kidney, heart, and central nervous system (CNS)82,193. More recently, a similar characterization with GLAST (Slc1a3) expression was proposed to identify “type A pericytes” as the progenitor of scar-forming fibroblasts in the CNS87. From our data, the expression of either marker does not appear to differentiate between groups of pericytes. Rather, both genes are differentially expressed between the major stromal subsets where Nes is expressed throughout mural cells and Slc1a3 in fibroblasts. Whether these discrepancies in pericyte characterization are attributed to true biological differences or inconsistency in nomenclature needs to be revisited in future studies. Here, we consistently identified two pericyte subsets with distinct patterns of expression in our baseline datasets: PER-1, which closely resembles canonical pericytes, and PER-2, which exhibits increased fibrogenic and contractile characteristics on top of a unique set of markers. Given its profile, it is likely that PER-2 corresponds to a rare cell state involved in physiological 75 modifications of the perivascular matrix. Indeed, examination by histology indicated their association with microvasculature. Further research is required to characterize the biological function of this pericyte subset. To date, the role of mural cells in cardiac injury and repair remains largely unknown. In fact, their proposed functions are mostly inferred from other organs or in vitro systems. This, in part, is due to the challenge of their identification in an injured environment where they adopt an altered morphology, expression profile, and location depending on the phase of tissue repair. In general, the stroma plays a multi-faceted role in response to acute injury. Immediately after the trauma, mural cells are speculated to participate in leukocyte trafficking by modifying vascular permeability and secreting cytokines, culminating in a pro-inflammatory environment that clears the wound of cell debris and matrix fragments. This is followed by the induction of activation signals to stimulate myofibroblast expansion, which amplifies matrisome production and formation of a matrix scaffold that upholds cardiac integrity. From our study, we found that pericytes undergo significant expansion at a level comparable to that of fibroblasts on day 3 and 7 PI whereas proliferation of VSMCs is negligible. Temporal comparison of their transcriptome revealed that, collectively, pericytes adopt an injury-induced profile. This is characterized by a shift in programs associated with matrix remodeling, vascular development, and inflammation, most of which exhibit a transient window of upregulation. It is noted that this response is much more pronounced in a proliferative pericyte subset (“act. PER”) localized in the infarct. The increase in matrix signatures, in particular, suggests that pericytes may acquire some degree of functional redundancy 76 with fibroblasts and contribute to scar formation in MI226. Importantly, this fibrogenic response is not associated with a lineage transition from or to fibroblasts, as shown by our lineage tracing experiments using mural cell- and fibroblast-specific inducible systems. A similar finding in the context of diabetic fibrosis also demonstrated a lack of cardiac pericyte-to-fibroblast conversion227. However, these results contradict the findings of a recent study where a small fraction of infarct-associated pericytes has been shown to acquire Pdgfra expression78. Alterations in vascular programs may also point to the acquisition of a dysfunctional state that fails to provide adequate vascular maintenance. Despite the palpable adaptive response in mural cells, whether they are indispensable for scar formation or other tissue repair processes remains an area to be investigated. One major hallmark in patients with heart failure is impaired vascular perfusion manifested in microcirculatory rarefaction and dysfunction. However, the processes underlying its onset are complex and likely result from perturbations in biochemical signals, biomechanical cues, and structural configurations. Inflammation, for instance, has been proposed as a key driver of endothelial dysregulation. Physiologically, vascular endothelium maintains a dilated state and exerts protective effects through preserving a fine-tuned bioavailability of nitric oxide (NO). This balance is lost during prolonged inflammation whereby pro-inflammatory cytokines such as TNFα reduce endothelial NO synthase (eNOS) expression and induce “eNOS uncoupling”, a state to which eNOS produces superoxides instead of NO due to the depletion of tetrahydrobiopterin228. The resulting surge in oxidative stress causes endothelial cell 77 death through apoptotic programs like ferroptosis229. In our study, we found a significant decrease in the number of vessels in the infarct as early as day 5 PI. These compromised vessels exhibit morphological anomalies and, in some cases, are not perfused by blood circulation, which may be a consequence of the “no-reflow” phenomenon caused by mural cell hypercontraction. Moreover, we detected a drastic reduction in mural cell coverage around the vascular endothelium. Together, these results establish a convincing correlation whereby mural cell association with the vasculature is indispensable for preserving vascular function in MI. Indeed, given the role of mural cells in maintaining vascular quiescence and stability, their absence or detachment from the endothelial basement membrane has been associated with compromised vascular function in the kidney, brain, and more recently, heart49,230,231. While we believe pathological signaling in the infarct leads to a dysfunctional state in mural cells, which in turn may induce detachment from the vasculature, the exact mechanism to which this occurs remains an important subject to be explored. The potential to modify the ischemic scar and rebuild functional myocardium has been the primary focus of therapeutic intervention for IHD. Therapeutic angiogenesis, in particular, has gained traction as a strategy to repair myocardial injury and restore cardiac function. Indeed, an extensive body of research has established an irrefutable association between heart failure and functional impairment of the vascular network. By promoting cardiac perfusion, morphological and functional improvements in the infarcted heart have been observed. This is illustrated by a recent study in a rat model of MI where transplantation of human induced pluripotent stem cell-derived EC resulted 78 in de novo formation of perfused vessels232. In turn, the increased vascular density in the infarct conferred cardioprotective effects as indicated by enhanced left ventricular function and reduced myocardial fibrosis. Comparably, pre-clinical investigations utilizing MSCs in cell-based therapies have also found a similar reparative response233,234. These effects are likely facilitated by paracrine signaling of factors such as VEGF and exosomes containing microRNAs (miRNA)235. Remarkably, the extent of clinical improvements can be enhanced by modifying MSCs prior to transplantation236–238. While cell-based therapies present a promising avenue for treatment, the effectiveness is inconsistent and often not sustainable long-term. This may be partly attributed to the poor engraftment and short retention of transplanted cells239. Moreover, different strategies of cell differentiation, isolation, and delivery may pose as a major confounder of therapeutic efficacy. Another important consideration is the location of transplantation relative to the infarct. Indeed, studies have reported regional differences in cell engraftment and regeneration240. In our spatial transcriptomic atlas, we have identified transcriptionally distinct regions in the infarcted heart: a remote zone with normal myocardium and an infarct core that can be subdivided into two layers (infarct-1 and infarct-2) with differing degrees of ischemia. Comparison between the remote and infarct zones revealed regional-dependent programs. While inflammatory and matrix remodeling signatures are focal to the infarct, signatures related to vascular development exhibit complex patterns that involve regulation by both the infarct and remote zones. This is further substantiated by regional communication networks underlying the infarcted heart. In general, signals propagating inflammation and fibrosis are largely produced and receptive within the infarct layers. On the other hand, the 79 remote zone likely acts as an imperative source of angiogenic factors required for neovascularization. These differential signaling patterns underline the importance of “location” for localized treatments such as the application of acellular patches. In addition to positional considerations, the therapeutic efficacy is also contingent upon the timing of treatment. A meta-analysis of clinical trials indicated that left ventricular ejection fraction is much higher in patients who received MSC transplantation during the first week after MI241. In line with this conclusion, our data demonstrate that processes driving irreversible myocardial impairment may begin as early as day 3 PI. As such, development of interventions for MI should focus on attenuating pathological mechanisms during the early stages of tissue remodeling. Historically, promoting neovascularization has been the primary focus of therapeutic angiogenesis. However, the role of perivascular cells, especially mural cells, in vascular growth and regulation after injury is often overlooked. From the results of our study, deciphering the mechanisms behind mural cell activation, detachment, and subsequently, loss will provide new angles to curtail vascular rarefaction, and thus increase cardiac perfusion. For a multifocal disease like MI, these mechanisms are closely associated with the environment to which the mural cells reside in. This, in turn, raises the prospect of improving the efficacy of cell-based therapies by concurrent modifications of regionally-dependent programs in the infarcted heart. 80 81 Figure 2.1. Cardiac fibroblasts and mural cells are distinct stromal entities. a, Confocal images of fibroblast (EGFP, green) and mural cell (DsRed, red) localization with respect to the vascular endothelium (CD31, grey; left) and basement membrane (Laminin, magenta; right) in Pdgfra-EGFP/Cspg4-DsRed hearts. Arrows indicate VSMC (S), fibroblast (F), pericyte (P), and basement membrane (BM). DAPI (blue) was used as nuclear counterstain. Scale bar = 100 µm. Representative image of three biological replicates. b, Graphical illustration of the Hic1-CT2/tdTomato reporter construct and experimental scheme. LIN-tdTomato+ fraction with enrichment for CD146+ cells was isolated from heart ventricles of tamoxifen (TAM) treated mice for library preparation. LIN = CD45 and CD31. c, UMAP projection of 11,489 tdTomato+ stromal cells colored by subset annotations. FAP, fibro-adipogenic progenitors; VSMC, vascular smooth muscle cells; PER, pericytes. d, Heatmap depicting the relative gene expression of selected genes (top) and top five upregulated DEGs per subset (bottom). e, Violin plots depicting normalized expression of contractile (top) and matrisome markers (bottom) in each annotated subset. f-g, Web diagrams of GRNs preferentially active in fibroblasts (f) and mural cells (g) inferred from SCENIC. Regulatory links amongst TFs are distinguished as high or low confidence based on annotation source (database, orthology, and motif similarity). 82 83 Figure 2.2. Characterization of cardiac stromal populations. a-b, UMAP projection of 11,489 tdTomato+ stromal cells colored by clusters (a) and their distribution from each hashed sample and non-hashed “negative” cells (b). Each hash represents an individual biological replicate (n = 3) pooled for library preparation and sequencing. c, Projection of normalized expression of Hic1 and tdTomato on the UMAP. d, Pearson’s correlation analysis showing the similarity of global transcriptional profiles across clusters. Dashed boxes delineate two major superclusters: fibroblasts (red) and mural cells (blue). Heatmap illustrates the relative magnitude of correlation coefficient. e-g, Normalized expression of known mural cell and fibroblast markers in clusters (e), reported progenitor and mature fibroblast markers (f), and markers unique to PER-2 (g) in annotated subsets. FAP, fibro-adipogenic progenitors; VSMC, vascular smooth muscle cells; PER, pericytes. h, Immunofluorescence staining with GAL-3 (magenta) and DAPI (grey, top) or CD31 (grey, bottom) in Hic1-CT2/tdTomato/Pdgfra-EGFP (red/green) hearts. Cells highlighted by arrows indicate GAL-3 expressing perivascular cells labelled by tdTomato (red) but not EGFP (green). Scale bar = 100 µm. Representative images of two biological replicates. PER, pericytes. i, Relative proportions of PER-1 (“P7”) and PER-2 (“P6”) out of all Hic1-labelled pericytes (n = 5). Representative gating strategy of all biological replicates. Data are presented as mean ± standard error of the mean. j, Normalized expression of Nes and Slc1a3. 84 85 Figure 2.3. GRN signatures of stromal subsets at SS and cell kinetics after MI. a, Hierarchical clustering of inferred gene regulatory network (GRN) activities from SCENIC and lists of active transcription factors (TF) unique to fibroblasts (top) and mural cells (bottom) coloured by their associated subsets. Heatmap reflects the proportion of cells active for a particular TF in given a subset. Bracketed numbers indicate the number of downstream targets inferred to be regulated by the TF. FAP, fibro-adipogenic progenitors; VSMC, vascular smooth muscle cells; PER, pericytes. b, Representative gating strategy of cell subsets and their EdU-incorporated fractions PI. Gates are coloured by subset annotations corresponding to Fig 2a. LIN = CD45 and CD31. c, UMAP projection of cell transcriptomes between Hic1-labelled (red) and CD146+ (green) cells (left). Projection of normalized expression of Cnn1 on the UMAP (right). d, Immunofluorescence staining of αSMA (green), CD31 (grey), and CNN1 (magenta) in Hic1-CT2/tdTomato (red) hearts. Arrows indicate vessel types: capillary (C), artery (A), and vein (V). Scale bar = 100 µm. Representative image of three biological replicates. e, UMAP projection of integrated cell transcriptomes between Hic1-labelled and CD146+ cells coloured by annotated subsets (left). Violin plots show normalized Acta2 and Entpd1 expression (right). Ven. VSMC, venous/venular vascular smooth muscle cells; Art. VSMC, arterial/arteriolar vascular smooth muscle cells; PER, pericytes. f, Immunofluorescence staining of αSMA (green), CD31 (grey), and CD39 (magenta) in Hic1-CT2/tdTomato (red) hearts. Arrows are indicative of VSMC (S), endothelial cells (E), and pericyte (P). Scale bar = 10 µm. Representative image of three biological replicates. g, Assessment of EdU incorporation in mural cell subsets on day 3 PI (n = 3). A one-way ANOVA with Tukey’s multiple comparisons post-hoc test was used to compare the means of % EdU+ cells. Representative gating strategy of all biological replicates. P-value: <0.0001, 0.0396, and <0.0001. LIN = CD45 and CD31. Asterisks indicate statistically significant changes: P < 0.05 (*), P < 0.01 (**), P < 0.001 (***), P < 0.0001 (****). Data are presented as mean ± standard error of the mean. 86 Figure 2.4. Pericytes adopt an altered state in MI. a, Experimental scheme and assessment of cell proliferation by EdU incorporation on gated populations (Hic1- mural cells/Schwann cells = LIN-YFP-CD146+, Hic1+ mural cells = LIN-YFP+CD146+, Fibroblasts = LIN-YFP+CD146-, Endothelial cells = LIN+YFP-CD146+) at steady state (SS) and day 4, 7, and 21 after LAD ligation (n = 8, 3, 4, and 4, respectively, pooled from ≥ 3 experiments). LIN = CD45 and CD31. Mice were administered with EdU IP one day before harvest. A two-way ANOVA with Dunnett’s post-hoc test was used to compare the means of % EdU+ cells to SS in each respective gate. P-value: <0.0001, <0.0001, <0.0001, <0.0001, and 0.0395. Asterisks indicate 87 statistically significant changes: P < 0.05 (*), P < 0.01 (**), P < 0.001 (***), P < 0.0001 (****). Data are presented as mean ± standard error of the mean. b, UMAP projection of 15,355 LIN-CD146+ cardiac cells colored by subset annotations at SS and day 3, 7, and 42 PI. Ven. VSMC, venous/venular vascular smooth muscle cells; Art. VSMC, arterial/arteriolar vascular smooth muscle cells; PER, pericytes. c, Normalized expression of cell cycle genes differentially upregulated in act. PER. d, Immunofluorescence microscopy of the infarcted heart stained for CD31 (grey) and EdU (magenta) from Pdgfra-EGFP/Cspg4-DsRed (green/red) mice on day 7 PI. The infarct zone (IZ) is delineated from the remote zone (RZ) by dotted lines and a magnified inset (blue box) is provided to show EdU+ pericytes indicated by arrows. Scale bar = 100 µm or 25 µm (inset). Representative image of three biological replicates. e-f, Assessment of temporally regulated DEGs associated with “ECM organization” (e) and “blood vessel development” (f) in pericytes. Venn diagrams represent the number of overlapping and unique DEGs mapped to each GO term across timepoints. Strip plots display the average scaled expression of DEGs with the means taken to generate the trend line. Heatmaps represent the relative average normalized expression of genes of interest. 88 89 Figure 2.5. Mural cell response to MI. a, Experimental scheme and UMAP projection of 15,355 LIN-CD146+ cardiac cells from Hic1-CT2/YFP mice coloured by clusters at SS and day 3, 7, and 42 PI. Libraries were integrated to preserve cells of similar biological states. LIN = CD45 and CD31. b, Assessment of cluster similarities by Pearson’s correlation analysis. Dashed box highlights pericyte clusters. Heatmap illustrates the relative magnitude of correlation coefficient. c-d, Normalized expression of pericyte (PER) markers in clusters (c), as well as markers associated with matrix remodeling (d, top) and vascular functions (d, bottom) in pericyte subsets. e, Experimental scheme and UMAP projection of 12,171 LIN-CD146+ cardiac cells from Cspg4-CT2/tdTomato mice coloured by annotated subsets at day 7 PI. Violin plots depict normalized expression of tdTomato and Cspg4. LIN = CD45 and CD31. Ven. VSMC, venous/venular vascular smooth muscle cells; Art. VSMC, arterial/arteriolar vascular smooth muscle cells; PER, pericytes. f-g, Heatmaps illustrating relative average normalized expression of temporally regulated genes associated with inflammation in pericytes (f) and selected temporally regulated genes in VSMCs (g). Source data are provided as a Source Data file. 90 91 Figure 2.6. Fibroblasts and mural cells do not share a lineage relationship in MI. a, Experimental scheme of lineage-tracing fibroblasts in MI. b, Confocal images of labelled fibroblasts (YFP+, green) and mural cells (DsRed+, red) with respect to vascular endothelium (CD31, grey). Scale bar = 100 µm. Representative images of two to three biological replicates. c, Experimental scheme of lineage-tracing mural cells in MI. d, Confocal images of labelled mural cells (tdTomato+, red) and fibroblasts (EGFP+, green) with respect to vascular endothelium (CD31, grey). DAPI (blue) was used as nuclear counterstain. Scale bar = 100 µm. Representative images of one biological replicate. Arrows are indicative of VSMC (S), fibroblast (F), and pericyte (P). Dotted lines delineate the infarcted heart into remote (RZ) or infarct (IZ) zones. 92 93 Figure 2.7. Lineage relationship of stromal subsets and assessment of vascular function. a, Assessment of Pdgfra-EGFP+ lineage-traced mural cells using Cspg4-CT2/tdTomato/Pdgfra-EGFP mice (n = 3 per group pooled from 3 experiments). A one-way ANOVA with Tukey’s multiple comparisons post-hoc test was used to compare the means of % CD146+EGFP+ cells out of LIN-tdTomato+ cells. NS, non-significant results. Representative gating strategy of all biological replicates per timepoint. LIN = CD45 + CD31. b, Experimental scheme and UMAP projection of LIN-tdTomato+ (with enrichment by CD146) cells from Hic1-CT2/tdTomato mice at SS and day 7 PI. LIN = CD45 + CD31. c, Normalized expression of known mural cell (top) and fibroblast (bottom) markers split by stromal cell type and coloured by timepoint. d, In vivo assessment of vascular perfusion in the remote (left) and infarct (right) zones by fluorophore-conjugated lectin (magenta) injection on day 14 PI. Heart sections were co-stained with CD31 (grey) in the presence of endogenous reporters, Pdgfra-EGFP (green) and Cspg4-DsRed (red). Arrows indicate instances of non-perfused vessels. Scale bar = 100 µm. Representative image of two biological replicates. e, Confocal image illustrating a two-layer division based on the distribution of vessels (CD31, grey), mural cells (Cspg4-DsRed, red), and fibroblasts (Pdgfra-EGFP, green) in the infarct zone at day 7 PI. Cell nuclei were counterstained with DAPI (blue). White and blue dotted lines delineate the infarct zone and its partition, respectively. Scale bar = 100 µm. Representative image of three biological replicates. 94 95 Figure 2.8. Progressive vascular rarefaction in the infarct. a, Immunofluorescence microscopy of the infarct zone at multiple timepoints PI. Heart sections from Pdgfra-EGFP/Cspg4-DsRed (green/red) mice were stained for CD31 (grey) and DAPI (blue). Dotted lines delineate boundaries between the remote (RZ) or infarct (IZ) zones. Scale bar = 100 µm. Representative images of three to four biological replicates. b-g, Quantification and statistical testing of vascular parameters including: the number of vessels per 1000 μm2 (b; P-value: <0.0001, 0.0009, <0.0001, and <0.0001), nearest neighbor distance (NND) between vessels (c; P-value: 0.006, 0.0011, <0.0001, and <0.0001), proportion of CD31 area (d; P-value: 0.0112 and 0.0089), minimum average vessel feret diameter (e; P-value: 0.0008, 0.001, <0.0001, and <0.0001), proportion of NG2 area (f; P-value: 0.005 and 0.0036), and proportion of NG2 area overlapped with CD31 signals (g; P-value: 0.0078, 0.0008, 0.0003, and <0.0001). n = 3 (SS, D3, D5, and D14) or 4 (D7 and D21) pooled from 2 experiments. A two-way ANOVA with Šídák's multiple comparisons post-hoc test was used to compare the means of each parameter between the remote and infarct zones for every given timepoint. Data points derived from zones of the same sample are paired. Asterisks indicate statistically significant changes: P < 0.05 (*), P < 0.01 (**), P < 0.001 (***), P < 0.0001 (****). Data are presented as mean ± standard error of the mean. 96 97 Figure 2.9. Spatiotemporal atlas of the infarcted heart. a, Experimental scheme of generating the spatial transcriptomic time series. Voxels are colored by clusters after data integration across timepoints with clusters 3 and 4 annotated as infarct-1 (I1) and infarct-2 (I2), respectively. Confocal images of serial sections complementing each dataset are presented and include stainings for NG2 (red) and CD31 (grey), along with endogenous Pdgfra-EGFP (green) signals. Dotted lines delineate the infarct zone. b, K-means clustering of temporally regulated genes representing general patterns of gene expression. Elbow plot of within-cluster-sum of squared errors (WSS) against number of clusters indicated inflection at four clusters (C1-4). Heatmap depicts relative average expression of cluster-specific genes per zone and timepoint. Segment-dot plots display the top 10 biological processes associated with each pattern. Length of segment and dot color intensity represent log-transformed adjusted P-values and the relative number of genes mapped to the pathway, respectively. c-e, Assessment of temporally regulated genes associated with “inflammatory response” (c), “ECM organization” (d), and “blood vessel development” (e) per zone and timepoint. Strip plots display the average scaled expression of genes with the means taken to generate the trend lines split by zone. Heatmaps represent the relative average normalized expression of genes of interest. f, Relative information flow of all active pathways scaled by signaling strengths across timepoints. g-h, Directionality and signaling strength of ligands associated with matrix remodeling (g) and vascular development (h). Dot colour intensity reflects the sum of communication probabilities of each ligand and the digit adjacent to each dot indicates the number of unique receptors associated with the ligand. 98 99 Figure 2.10. Signaling networks underlying the infarcted heart. a, Confocal images of the heart sections used for positional transcriptomics after RNA extraction. DAPI (blue) was used as nuclear counterstain and tissue autofluorescence (green) represents regions of viable myocardium. b, Normalized gene expression of selected markers. Dot size and colour intensity represent the proportion of spots expressing the gene and the average normalized expression in each annotated region, respectively. c, Network diagrams depicting the number of active interactions amongst regions for each timepoint. Width of edges reflects relative number of interactions. R, remote zone; I1, Infarct-1; I2, Infarct-2. d, Relative information flow scaled to the number of active signaling pathways across timepoints. e, Heatmaps depicting the relative outgoing and incoming strengths of active signaling pathways. Top bars summarize the collective signaling strengths of pathways per region and side bars summarize the total signaling strengths of each pathway irrespective of regions. f-g, Directionality and signaling strength of ligands associated with inflammation (f) and selected non-secreted interactions (g). Dot colour intensity reflects the sum of communication probabilities of each ligand and the number of unique receptors associated with the ligand is supplemented. h, Graphical illustration of tissue collection and normalized VEGFA protein level in segments proximal and distal to the infarct. A two-way ANOVA with Šídák's multiple comparisons post-hoc test was used to compare the means of VEGFA level (n = 5). Data points derived from portions of the same sample are paired. P-value: 0.0427 and 0.001. Asterisks indicate statistically significant changes: P < 0.05 (*), P < 0.01 (**), P < 0.001 (***), P < 0.0001 (****). Data are presented as mean ± standard error of the mean. i, Prediction scores of annotated cell populations from scRNAseq data in the infarcted heart on day 3 PI. Ven. VSMC, venous/venular vascular smooth muscle cells; Art. VSMC, arterial/arteriolar vascular smooth muscle cells; PER, pericytes. 100 Chapter 3: Multifaceted dysregulation in the cardiac landscape underlies functional deterioration in familial hypertrophic cardiomyopathy. 3.1 Synopsis Hypertrophic cardiomyopathy is an inherited heart disease commonly associated with genetic variants in the sarcomeric genes. Apart from myocyte hypertrophy and disarray as its primary pathological features, the disease often manifests in secondary abnormalities including myocardial fibrosis and vascular dysfunction, and thus predisposes life-threatening conditions such as heart failure. Nonetheless, the mechanistic alterations underlying its pathogenesis remain elusive. Here, we profiled the molecular signatures of cardiac populations in the hypertrophic heart by utilizing single-nucleus RNA sequencing and mass spectrometry. Comparative assessment with location-matched, non-disease biopsies revealed substantial differences of functional programs and cellular heterogeneity across various cell types. Cardiomyocytes, in particular, are delineated into two distinct disease-specific subsets, which exhibit marked adaptations in energy metabolism, muscle contraction, and neuronal processes. Despite clinical indications of extensive fibrosis, the stromal cells, specifically fibroblasts, adopt an exhausted state with altered, but not augmented, fibrogenic characteristics. Importantly, dysregulation of maintenance and angiogenic profile in the endothelium also indicate vascular dysfunction. These molecular patterns are sustained by a compromised network of cell-cell communication. Together, our study provides an 101 in-depth characterization of disease-associated mechanisms in cardiac hypertrophy and underscores the importance of targeting multiple cell types in therapeutic development. 3.2 Introduction Hypertrophic cardiomyopathy (HCM) is a genetic disease with underlying mutations in genes that encode sarcomeric components, the most predominant ones being myosin heavy chain 7 (MYH7; 30-35% of all cases) or myosin binding protein C (MYBPC3; 20-30%)242. Despite its genetic basis, carriers exhibit a broad spectrum in phenotype with variable penetrance and age of disease onset. In patients with signs of cardiac dysfunction, myocyte hypertrophy and disarray, which typically manifest in the interventricular septum (IVS) and left ventricular wall, are pathological hallmarks that have developed with time. Additionally, the histological features extend to diffuse fibrosis and coronary vascular anomalies, including wall thickening and narrowing of the lumen. Ultimately, progressive remodeling of the myocardium predisposes the development of lethal conditions such as heart failure or arrhythmia. Recent omics studies have profiled changes in gene expression patterns throughout the cellular landscape from clinical samples. However, the comparability of findings is limited by a multitude of factors that differ between studies, including the pathogenic variants harboured by the study cohort, stage of disease, and spatial location of tissue biopsies. In general, HCM is a dynamic disease where the hypertrophic heart initially maintains near-normal cardiac function, followed by a continuous decrease of ejection fraction, adverse remodeling of the stroma, and the onset of diastolic dysfunction, 102 before proceeding to end-stage heart failure243,244. The severity of morphological phenotypes is also dependent on the tissue location. As such, the disease mechanisms may be stage- and location-specific, and thus necessitate careful assessment of molecular adaptations and cellular heterogeneity with these factors considered. Although the etiology of HCM revolves around mutations in the contractile elements, our understanding of its pathogenesis is minimal. Studies have proposed that the mutations directly impair the functionality of sarcomeres and instigate a cascade of compensatory response resulting in hypertrophy245. One example of this is that a partial loss of function in sarcomeres leads to increased ATP demand, and thus results in the accumulation of reactive oxygen species (ROS). This, in turn, induces increased glutathione-mediated modifications to the myofilament, which interferes with cross-bridge cycling, further exacerbating contractile dysfunction. Similarly, others have suggested that the mutations may alter the sarcomere’s affinity for calcium and dysregulate durations of contraction-relaxation cycles. Nonetheless, the disease mechanisms of HCM are not limited to cardiomyocytes as other cell populations also undergo significant alterations in molecular signatures246. Here, we have adopted a stringent screening strategy for patient samples based on clinical diagnosis, genetic testing, and tissue location. The screened HCM samples were each paired with a “control” sample from patients with no known cardiac complications for single-nucleus RNA sequencing (snRNA-seq) and mass spectrometry. Comparison between disease conditions uncovered extensive alterations in gene 103 programs and disease-specific subsets in multiple cell populations, including cardiomyocytes, stromal cells, and endothelial cells. These differential signatures pointed to malfunctional adaptations secondary to cardiac hypertrophy. Moreover, inference of cell-cell communication elucidated a network of dysregulated interactions that support the maladaptive molecular patterns. Overall, our findings highlight potential mechanisms that may contribute to the pathogenesis of HCM and emphasize the need to develop treatments that target more than cardiomyocytes. 3.3 Material and method 3.3.1 Ethics and human tissue screening Collection of disease samples was performed on explanted hearts from patients receiving heart transplantation. Immediately after collection, biopsies, specifically at the lateral left ventricular free wall, were processed and stored in collaboration with the Bruce McManus Cardiovascular Biobank (BMCB), St. Paul’s hospital. In total, 27 human cardiac samples of both sexes were collected and screened. The selection of disease patient cohort (n = 5) is based on: 1) clinical diagnosis of HCM by a cardiac pathologist and; 2) the presence of genetic mutations in the MYH7 locus. On the other hand, cardiac biopsies from subjects with no known history of cardiomyopathy were sourced from the International Institute for the Advancement of Medicine (IIAM) as controls for the study. The age of patients enrolled in the study range from 47~64 and 20~58 for the HCM and control groups, respective. All patients provided informed consent and procedures were performed in accordance with the protocol, REB# H05-50208, approved by the Research Ethics Board (REB) at the University of British Columbia 104 (Providence Healthcare, Canada). IDs assigned to patient data in the study are arbitrary and de-identified. 3.3.2 Nuclei isolation Our methods to harvest nuclei from frozen human heart tissues have been published elsewhere247. In short, frozen heart tissues were placed on a 60mm petri dish on ice and minced with a sterile razor blade. 500 μL of pre-chilled EZ Lysis Buffer (Sigma-Aldrich, Cat. NUC101-1KT), supplemented with 0.2 U/μl of RNAse inhibitor, was added to the minced tissue, mixed, and incubated on ice for 5mins. The homogenate was then filtered through a 70-μm strainer into a 50-mL Falcon Tube and subsequently transferred to a 1.5-mL centrifuge tube. The sample was centrifuged at 500-g for 5 minutes at 4°C. After removal of the supernatant, 300 μL of a density gradient medium was used to suspend the pellet. Please refer to associated methods publication for full recipe of gradient medium. The sample was then underlaid with 1mL of the gradient medium and centrifuged at 8000-g for 20 minutes at 4°C to isolate nuclei as a pellet and remove contaminating membranes into the gradient medium. The supernatant was removed and 1 mL of chilled nuclear wash and resuspension buffer (NWRB) (1x PBS, 1.0% BSA, and 0.2U/μl of RNAse inhibitor) was added to the pellet but not used to resuspend the nuclei. The nuclei were then centrifuged at 500-g for 5 minutes at 4°C. After filtering with a 20-μm or 40-μm strainer to remove any remnant debris, the nuclei were then centrifuged at 500-g for 5 minutes at 4°C. The supernatant was removed, and the pellet was resuspended in 300-600 μL of chilled NWRB depending on the pellet size. Nuclei were counted and underwent an initial quality control stage using DAPI staining and fluorescence microscopy to assess nuclear morphology, size, and 105 abundance. Only samples fulfilling this initial quality control phase for nuclear counts and morphologies would proceed to single nucleus sequencing. 3.3.3 Immunohistochemistry Human tissue samples were fixed in 10% formalin for 24 hours and transferred to 70% ethanol prior to paraffin-embedding (FFPE). FFPE blocks were sectioned at 4-µm thickness for subsequent histological procedures. Masson’s trichrome staining was outsourced to the Histology Facility at the Centre for Heart Lung Innovation (HLI). Stained slides were cover-slipped and scanned using the Aperio imaging system (Leica Biosystems). Image analysis was performed as per instructions provided by the Aperio ImageScopeTM User's Guide to quantify the percentage of collagen content. Briefly, using the Aperio image software, the number of positive (red stain) and negative (bluish-green stain) pixels were recorded. The proportion of negative area (PNA) was calculated by taking the ratio between negative pixels and all detected pixels. The average of PNAs from the control samples was used to normalize collagen content. Wheat germ agglutinin (WGA) staining was outsourced to Wax-it Histology Services at the University of British Columbia. Stained tissue sections were captured by confocal microscopy described below. 3.3.4 Immunofluorescence microscopy Human tissue samples were embedded into Optimal Cutting Temperature (OCT) compound (Scigen Cat. SGN4585) in plastic cryomolds and snap frozen in an isopentane bath cooled by liquid nitrogen. OCT embedded tissues were sectioned using 106 the Leica CM3050S cryostat at a thickness of 10-μm and placed onto Superfrost Plus slides (Fischerbrand, Cat. 22-037-246). Slides were stored at -80°C prior to thawing at room temperature for staining. Thawed sections were fixed with 1% paraformaldehyde (PFA) in PBS for 5 minutes. Hydrophobic borders were drawn around tissue sections before incubation with blocking buffer containing 5% serum, 0.2% Triton X-100, and 1% bovine serum albumin (BSA; Sigma A7906) for 1 hour at room temperature. To reduce autofluorescence signals from lipofuscin pigments, TrueBlack® Lipofuscin Autofluorescence Quencher (Cell Signaling Technology 92401; 5% v/v in 70% ethanol) was applied to tissue sections for 3 minutes after complete removal of blocking buffer. Tissue sections were then incubated with primary antibody cocktails overnight at 4°C, followed by incubation with secondary antibody cocktails for 1 hour at room temperature. After each antibody incubation, tissue sections were washed with cold PBS three times for 5 minutes. Lipofuscin quencher was applied one more time and cell nuclei were counterstained with DAPI (600 nM). Slides were mounted with ProLong Gold antifade reagent (Invitrogen P36930) and stored at 4°C. 3.3.5 Image acquisition Confocal images were acquired using the confocal laser scanning microscope system LSM 900 with Airyscan 2 (Carl Zeiss) at 10X or 20X magnification. Z-stack images were merged in ImageJ with Fiji via the method of maximum projection. Scanning of large areas of interest was achieved by stitching multiple fields at 10X magnification. Merged and split channel images were exported and assembled in Adobe Illustrator 2021. 107 3.3.6 Protein extraction Archived frozen heart samples (10-60 mg) were minced with a scalpel in an extraction buffer with cOmplete™ Protease Inhibitor (Roche, Cat. 11873580001), 3% SDS, 1% NP40, 1% Triton X100, 2.5 mM EDTA, 5 mM dithiotheitol (DTT), and 100 mM Tris (pH = 8). Samples were sonicated for 1 min at 50% with a 1/8-inch tip using a Fisherbrand™ Model 705 Sonic Dismembrator and incubated at 65°C for 1 hour at 1400-rpm. Tissue suspensions were cooled on ice and iodoacetamide (400 mM) was added to obtain a final concentration of 15 mM, followed by vortex mixing at 800-rpm for 30 min at room temperature (RT) in the dark. DTT (200 mM) was added to yield a final concentration of 10 mM and incubated for 30 min at RT. Clean-up and digestion were carried out by a variation of single-pot, solid-phase-enhanced sample-preparation (SP3; Hughes et al., 2019). Briefly, SeraMag™ Speedbeads (GE Healthcare, Cat. ) were added to each sample (1 mg/10µl of lysate) along with acetonitrile to reach 67%. Lysates were vortexed for at 800-rpm for 10 min at RT to allow the binding of peptides onto beads. Beads were magnetically isolated, washed with 200 µl of 80% ethanol three times, and mixed in 50 µl of 50 mM HEPES (pH = 8) containing 2 µg of trypsin/LycC (ProMega, Cat. ). Samples were incubated overnight at 37°C in an Eppendorf Thermomixer at 1400-rpm and their protein concentrations were determined using a Nanodrop/BCA assay. Tandem mass tag labeling (10-plex) was carried out as per manufacturer’s directions (Thermo, Cat. ). TMT-labeled samples were dried with a centrifugal evaporator (Centrivap) and reconstituted in 300 μL of 0.2% trifluoroacetic acid (TFA). Following reconstitution, samples were desalted using a 50-mg, 1 cc Waters Sep-Pak column (Cat.) and reconstituted in 30 µl of 0.1% TFA. Multiplexed samples were 108 subjected to high pH fractionation using a Waters Acquity UPLC coupled with an Acquity Photodiode Array detector, dried with a Centrivap, and reconstituted in 0.1% TFA to a final concentration of approximately 0.7 µg/µl. 3.3.7 Mass spectrometry Analysis of peptides was carried out on an Orbitrap Fusion Lumos MS platform (Thermo Scientific). Samples were introduced using an Easy-nLC 1200 system (Thermo) with an ES804A column (Thermo, Cat. 15929007). Prior to each sample injection, the analytical column was equilibrated to 650 bar with 6 μL mobile phase A. The injection volumes for all samples were 3 µl and sample loading was in a total volume of 7 μL. Separations were done using a gradient of mobile phase A (water/0.1% formic acid) and B (80% acetonitrile/0.1% formic acid) at 0.3 µl/min as follows: 2% B, 0-4min; 2-28% B, 4-115 min; 28-40% B, 115-130 min; 40-95% B, 130-140 min; 95% B, 140-150 min at 50°C. Data acquisition (control software v. 3.1.2412.25) was carried out using a data-dependent method with Synchronous Precursor Detection (SPS). The Lumos was operated with a positive ion spray voltage of 2100 V and a transfer tube temperature of 325°C. All data files were processed with Protein Discoverer (v. 2.5.0.400). Spectrum files were recalibrated using a precursor mass tolerance of 20 ppm and fragment tolerance of 0.6 Da with static peptide N-terminus and K TMT 6-plex modification in addition to C carbamidomethyl modification. Searches were carried out using Sequest HT against the SwissProt TaxID=9606 (v2017-10-25) database with a maximum missed cleavage of 2, 109 minimum peptide length of 5, precursor mass tolerance of 10 ppm, and fragment mass tolerance of 0.6 Da. Decoy database strict and relaxed FDR targets were 0.01 and 0.05 based on q-value. Unique + razor peptides were selected and a co-isolation threshold of 75% with S/N cutoff of 10 was used with total peptide amount normalization mode. Quan roll up was based on summed abundance with a maximum allowable fold-change of 100 and no missing value imputation and p-values calculated using the background-based ANOVA test. Output was filtered for master protein and exported to Excel for further analysis. Protein abundance and QC matrices were imported into R (v. 4.1.2) and analyzed using DeqMS248 (v. 1.10.0). Proteins with less than 1 unique peptide count and low confidence of peptide match to reference sequences were removed. Log2 transformation was applied to the protein abundance values for normalization. Variance estimates were corrected using peptide spectrum matches (PSMs). Differential protein expression was performed with Benjamini-Hochberg correction and only significant proteins (adjusted p-value < 0.05) were examined for interpretations. 3.3.8 Single-nucleus RNA-sequencing and library preparation Nuclei from purified suspensions were counted using a hemocytometer and subjected to library preparation using the Chromium Single Cell 3’ reagent kit (Chromium Next GEM Single Cell 3' Kit v3.1, Cat. 1000268; Chromium Next GEM Chip G Single Cell Kit Cat. 1000120; Chromium Controller Cat. 120263, 10x Genomics, Pleasanton, CA, USA) as per manufacturer’s protocol. After nuclei partitioning and barcoding on the Chromium 110 Controller, the integrity of the barcoded cDNA pool was assessed on the Agilent TapeStation 42100 using the D5000 assay (Agilent, Cat. 5067-5588 and 5067-5589) and quantified using the Qubit dsDNA HS Assay (ThermoFisher, Cat. Q32854). The integrity of the sample-specific indexed, adapter-ligated library of cDNA ready for sequencing was measured using the Agilent D1000 assay (Agilent Cat. 5067-5582 and 5067-5583) on the Agilent TapeStation 42100 and quantified with the Qubit as above. Sequencing was performed on an Illumina NextSeq500 using the High Output 75 Cycle kit or on an MGI G50RS using the DNBSEQ-G50RS High-throughput Sequencing set for App-A FCL PE100 (MGI Tech, Cat. 1000021229) to a minimum depth of 25,000 mean reads per nucleus. For sequencing on the MGI G50RS, the sample-specific indexed, adapter-ligated library of cDNA was converted to a MGI library using the MGIEasy Universal Library Conversion Kit (MGI Tech, Cat. 1000004155). Reads were demultiplexed and aligned to the human genome reference, GRCh38 (GENCODE v32/Ensembl 98), using the Cell Ranger pipeline (v4.0.0, 10X Genomics). The resulting feature-barcode matrices were imported into R (v.4.1.2) for downstream processing and analyses. 3.3.9 Single-nucleus RNA-sequencing analysis Analysis of sequencing data including normalization, integration, dimensionality reduction, Louvain clustering, and interrogation of gene expression was conducted with Seurat176,249 (v.4.3.0). Quality control was applied to all datasets to remove artifacts or nuclei of poor quality with the following metrics: number of genes expressed (> 200), UMIs (< 25000; > 2500 for CM and 500 for all other cell types), and proportion of 111 mitochondrial transcripts (< 0.5%). Heterotypic doublets, or artifacts created from appending multiple nuclei of different cell types into the same gel-in-emulsion, were removed manually through iterative rounds of sub-clustering. Data normalization was performed according to the SCTransform vignette (v2) with regression against the number of UMIs and mitochondrial genes. Integration was applied across experiments of nuclei extraction and library preparation. Differential gene expression analysis was performed with the SCT assay after UMI correction using the Wilcoxon rank sum test with P-values adjusted by Bonferroni’s correction. Only genes that were expressed in at least 25% of nuclei in one of the comparison groups and had a log2-fold change of at least 0.25 were considered. Consensus comparisons only included DEGs identified in at least three of the five pairs of disease-control samples. Heatmaps of average normalized expression were generated using pheatmap (v.1.0.12) with scaling applied to each gene. 3.3.10 Pathway enrichment analysis Pathway enrichment analysis was performed using DAVID Bioinformatics Resources180,250 (2023 Update) and results were extracted from under the “GO FAT” category. P-values were adjusted by Benjamini-Hochberg correction and log-transformed for plotting. Values adjacent to bars indicate the number of DEGs annotated in each GO term. 112 3.3.11 Inference of cell-cell communication Communication networks of the annotated populations, excluding adipocytes due to low numbers, were assessed using CellChat181 (v.1.6.1) in accordance with the published vignette (https://github.com/sqjin/CellChat) in R (v. 4.1.2). Briefly, the integrated Seurat object was split based on disease condition and converted to CellChat objects. All ligand-receptor interactions were queried from the CellChatDB database for Homo sapiens. Over-expressed ligands and receptors across annotated regions were identified with a P-value threshold set at 0.1 for further assessment of cell-cell communication. Strength of communication was normalized to the relative abundance of annotated cell populations during the probability calculation. Weak interactions with less than 100 cells expressing the ligand or receptor in each population were filtered out. Pathways active in at least one disease condition were considered for the assessment of information flow and differential signaling activity. Differentially active signals discussed in the manuscript were selected for plotting. The signaling strengths associated with each ligand were presented as a sum of all communication probabilities with up- and downregulated interactions (depicted by up and down arrows) computed separately. The digit adjacent to the arrow(s) represents the number of unique receptors that interact with the ligand. 3.3.12 Statistical analysis Statistical testing and graphing of data associated with immunohistochemistry and immunofluorescence microscopy were performed using Prism 9 (GraphPad Software). Error bars represent deviation by standard error of the mean (Mean ± SEM). The type of 113 test, sample size, and adjusted p-values of each experiment is provided in respective figure legends. P-value < 0.05 was considered statistically significant. Degrees of significance are denoted as follows: P < 0.05 (*), P < 0.01 (**), P < 0.001 (***), P < 0.0001 (****). Graphical schemes were created using BioRender. Figure assembly was performed in Adobe Illustrator 2021. 3.4 Results 3.4.1 Cellular landscape of the hypertrophic heart Understanding cardiac remodeling and the development of heart failure in the hypertrophic heart necessitates the assessment of cellular heterogeneity and its underlying molecular mechanisms. To this end, we profiled the cardiac transcriptome of patients diagnosed with HCM and investigated alterations in gene expression programs by single-nucleus RNA sequencing (sn-RNAseq, Fig 3.1a). Briefly, we collected location-matched transmural biopsies in the lateral left ventricular free wall (LVFW) near the mid-ventricular layer of cardiac explants from patients who received transplantations. In a pool of 27 subjects, we narrowed down the study cohort to 5 subjects of whom carry mutations in the MYH7 locus through genetic testing. Assessment by histology confirms that their tissue samples exhibit hallmarks of HCM, including elevated myocardial fibrosis (Fig 3.6a) and cardiomyocyte disarray (Fig 3.1b, Fig 3.6b). In addition to the disease cohort, we have also analyzed cardiac samples from patients with no prior history of cardiomyopathy as baseline controls. With our previously published protocol, we isolated intact nuclei from flash-frozen biopsies for 114 library preparation and sequencing. This was performed in five independent experiments, each with one pair of sex-matched control and HCM sample. Prior to the analysis of gene expression, all datasets were subjected to an exhaustive and stringent quality control procedure, encompassing the removal of ambient artifacts (Cellbender), heterotypic multiplets, and nuclei of poor quality. To minimize the effects of batch variation, we applied Seurat’s anchor-based integration across datasets derived from different experiments. Louvain clustering of 72,641 viable nuclei (CTRL, n = 39,384; HCM, n = 33,257) manifested in 18 nuclei clusters, many of which belong to continuous groups of clusters in projected space, indicating the presence of multiple transcriptionally distinct entities (Fig 3.6c). In general, the influence of ribosomal or mitochondrial gene expression on clustering appears to be minimal as the clusters do not display biases in the proportions of their transcripts (Fig 3.6d). However, we found that the abundance of transcripts, or unique molecular identifiers (UMI), is significantly higher in a set of clusters (1, 5, 8, 9, and 11), which we later identified as cardiomyocytes (CM). Notably, the expression of MYH7 transcripts in CM is not abrogated in the presence of mutations and does not differ between the control and HCM groups (Fig 3.6e). Interrogation of established gene markers characteristic to broad cardiac populations revealed the identity of clusters (Fig 3.1c-d). These included CM (TNNI3, TNNT2, and MYBPC3), endothelial cells (EC; PECAM1, CDH5, and VWF), fibroblasts (FIB; PDGFRA, DCN, and LUM), mural cells (MC; MCAM, PDGFRB, and NOTCH3), immune 115 cells (IC; PTPRC, HLA-DRA, and ITGAM), and Schwann cells (SC; SOX10, PLP1, and MPZ). Further categorizations informed by independent sub-clustering of each cell population were made to distinguish known cell types. In addition to blood vessel EC (BEC), two minor subsets of EC correspond to those associated with lymphatic vessels (LEC; LYVE1, PROX1, and FLT4) and the endocardium (EndoEC; NPR3, CDH11, and DKK3). A small population of adipocytes was discerned from fibroblasts given their expression of ADIPOQ, PLIN1, and CIDEA. Within MC, distinction was clear between pericytes (PER; CSPG4, KCNJ8, and ABCC9) and vascular smooth muscle cells (VSMC; MYH11, TAGLN, and ENTPD1). Likewise, immune cells can be delineated into those of myeloid (MRC1, MERTK, and MS4A7) and lymphoid (THEMIS, IL7R, and CD247) origins. The proportions of these annotated populations are approximately equal between the control and HCM cohorts with the exception of adipocytes, which are virtually undetected in the control samples (Fig 3.1e). However, principal component analysis (PCA) of pseudo-bulk expression indicates that the global transcriptome of the hypertrophic hearts is significantly different from those of control group (Fig 3.1f). Indeed, the staggered distribution of cells in the projected space suggests that this difference is reflected across multiple cell types (Fig 3.6f). To substantiate the transcriptomic data, we have evaluated the relative protein composition of the lateral LVFW by mass spectrometry. Similar to that observed in the transcriptome, distinction of samples between disease conditions is apparent, albeit less prominent. Comparison of samples by differential expression analysis yielded 191 upregulated and 348 downregulated proteins. 116 Together, these results confirm that the hypertrophic heart exhibits significant alterations at the molecular level, which are likely driven by changes of gene expression in multiple cardiac cell types. 3.4.2 Heterogeneity of cardiomyocytes in hypertrophic hearts As the functional unit of the heart, CMs are vital to cardiac function and their dysregulation has been observed across various cardiac pathologies including HCM. Here, we subsetted 26,009 CM nuclei to perform an in-depth assessment of their heterogeneity and disease-induced programs. Global comparison of gene expression generated 434 upregulated differentially expressed genes (DEGs) and 345 downregulated DEGs in the disease CMs (Fig 3.7a). Notably, only “consensus” DEGs identified in at least three control-disease sample pairs were considered to ensure that the observed differences are conserved in majority of patients. Assessment of these transcriptional changes by pathway enrichment analysis indicated that the downregulated DEGs are enriched for processes associated with heart contraction (Fig 3.7b). Strikingly, this included gene signatures encoding for key components of the sarcomere (MYBPC3, MYH6, TNNI3, and TNNT2; Fig 3.7c). As shown above in Fig 3.6e, MYH7 expression is not impacted despite the presence of mutations in its locus. However, a significant reduction in the expression of its isoform, MYH6, is observed, indicating a potential association between MYH7 mutations and the regulation of MYH6 expression. In addition to sarcomeric constituents, the expression of genes encoding for ion channels on both the sarcolemma (e.g. KCNN2, CACNA1C, and 117 SLC4A3) and sarcoplasmic reticulum (RYR2, ATP2A2, and TMEM38A) is collectively dampened. Similarly, several structural components of the intercalated disc (GJA1, PLN, TMEM65) and adhesion molecules (FLNA, DMD, and SGCD) important for maintaining the cardiac syncytium are downregulated. Of note, the expression of GATA4, a transcription factor with protective effects in load-induced cardiac hypertrophy251, is decreased in the disease CMs. In general, the downregulation of heart contraction signatures is corroborated at the protein level as shown by comparative analysis of the mass spectrometry data (Fig 3.7d). A closer examination of differentially expressed proteins revealed a defined reduction in genes involved in energy metabolism, especially those required to facilitate oxidative phosphorylation (Fig 3.7e). The reduced capacity in energy production may attribute, in part, to the compromised contractile function in the hypertrophic heart. In contrast, the processes associated with the upregulated consensus DEGs are less consistent but encompassed cell development, locomotion, enzyme-linked receptor signaling, and neurogenesis (Fig 3.7a). In particular, insulin signaling, a known inducer of cardiac hypertrophy, appears to be enhanced in the disease CMs. This is demonstrated by the upregulation of its downstream targets such as IRS2, FOXO1, and PIK3R1, and thus reflect a heightened response to insulin ligands (Fig 3.7f). Interestingly, several of the upregulated DEGs are related to neuronal development and synaptic plasticity, suggesting that there may be adaptations in the neuron innervations of the hypertrophic myocardium. However, none of these upregulated signatures is detected by mass spectrometry. 118 To assess whether the changes in gene expression are specific to particular subsets of CMs, sub-clustering was applied to CMs to identify representative cell states (Fig 3.7g). Consistent with that observed in the overall landscape, CM clusters are distributed into two groups that largely correspond to each disease condition. Correlation analysis indicated that the clusters predominantly derived from control patients (0, 2, 5, and 6) share a much higher degree of similarity than those of HCM patients (1, 3, 4, 7, and 8), implicating that CMs become more heterogenous in the hypertrophic heart (Fig 3.7h). Although the control clusters exhibit minor transcriptional differences, they are assigned as four CM states, namely CM-1 to CM-4, as subsequent analysis showed that they share gene markers with CM states reported in previous studies. On the contrary, clusters 3 and 7 are delineated from the other disease clusters as CM-5 and CM-7, respectively, given their marked transcriptional distinction. The remainder disease clusters are collectively termed CM-6. With these observations, CMs of both HCM and control samples are broadly classified into seven cell states for further analysis (Fig 3.2a-b). Comparison of gene expression profiles confirmed that the control CM states harbour subtle differences and many of their defining markers encode for long intergenic non-protein coding RNAs (lncRNA), which may pose as regulators of gene expression through epigenetic and transcriptional modifications252. CM-1 and CM-2, for instance, are marked by the expression of LINC02248 and AC009878.2 (Fig 3.2c). The expression pattern of reported markers, including PRELID2, CDH13, and GRXCR2, 119 indicated that CM-2 corresponds to a population identified in the human heart atlas90 (“vCM2”). Likewise, CM-3 and CM-4 display increased expression of AC093912.1 and LINC02388, respectively, along with GRIK2 and BRINP3, which are markers indicative of another CM population identified in a separate study (“CM5”)253. Nonetheless, the limited number of gene markers unique to each control state indicates a lack of functional heterogeneity in CMs of the LVFW. In contrast to the control CMs, differences in the transcriptional profiles of disease CMs are much more pronounced and impart unique functions. CM-5 is characterized by a core signature consisted of BMPR1B, SLIT2, and EYS. Association of genes specific to CM-5 revealed that the upregulation of neuronal programs in disease CMs is primarily attributed to CM-5 (Fig 3.2d). This is substantiated by the distinct expression of cell adhesion molecules (NEGR1 and NRXN3) and ephrin receptors (EPHA4, and EPHA7) known to facilitate neurite outgrowth and maintenance in addition to majority of the neurodevelopmental genes from the consensus comparison (Fig 3.2e). Furthermore, the confined expression of SLIT2 and ROBO1 (albeit expressed at a proportion below the assigned threshold for DE), suggests that CM-5 may have partially reactivated cardiac developmental programs254. By and large, CM-6 does not retain unique signatures as its top gene markers were also expressed in the other disease states, and therefore interpreted as an intermediate population between CM-5 and CM-7. On the other hand, CM-7 is marked by stress-induced genes identified in heart failure (CRYAB, NPPB, and FTH1)255–257. The top processes associated with CM-7 point to increased energy metabolism and a bias toward oxidative phosphorylation (Fig 3.2f). This shift in 120 cellular metabolism coincides with a surge of expression in genes involved in protein synthesis, including ribosomal components and peptide elongation factors (Fig 3.2g). Remarkably, assessment of contractile programs showed that the expression of genes encoding for sarcomeric components in CM-7 is higher than the other disease states and even exceeds that of control CMs (Fig 3.2h). Conversely, a decreasing trend of expression in these contractile genes is observed in CM-5. Despite an overall reduction in signatures associated with energy production and heart contraction, CM-7 may represent a small, yet distinct, subset that attempts to compensate for the loss of functional capacity in the hypertrophic heart. 3.4.3 Pathological alterations of the cardiac stroma One major hallmark of HCM is extensive myocardial fibrosis, or the chronic deposition of extracellular matrix (ECM). This process, to a large extent, has been attributed to the aberrant activation of the cardiac stroma in many cardiac pathologies. To interpret the stromal response in the hypertrophic heart, the stromal populations, including fibroblasts and mural cells, were isolated for deeper examination. Pathway analysis of consensus DEGs in fibroblasts (165 up, 195 down) pointed to changes associated with ECM-related and neurodevelopmental programs (Fig 3.8a). Although ECM organization is inferred to be a downregulated process, assessment of all DEGs mapped to the term did not indicate a convincing trend of reduction. Instead, a mixed pattern of gene expression is observed in matrix building blocks and matricellular factors (Fig 3.8b). The concurrent upregulation of FAP and downregulation of fibroblast 121 identity markers (DDR2 and PDGFRA) is indicative of an activation response in fibroblasts. However, the decrease in SMAD3, a key component of the transforming growth factor-β (TGF-β) signaling pathway107, suggest that myofibroblast transition is likely impaired. This is consistent with a lack of change in stress fiber expression (ACTA2). As opposed to the ambiguous trend seen in the fibroblast transcriptome, the matrix-associated genes at the protein level are predominantly upregulated in the hypertrophic heart (Fig 3.8c). These results indicate that fibroblasts may be arrested in a state with limited fibrogenic activity at the time of sample collection. Much like the ECM-related programs, a mixed pattern of expression is also observed in the neurodevelopmental signature (Fig 3.8d). On the other hand, comparison of mural cells showed far less differences (31 up, 56 down) and only the downregulated DEGs displayed enrichment for pathways, which are mostly limited to vascular programs (Fig 3.8e). Specifically, the decreased expression of pro-angiogenic genes (e.g. NOTCH3, EPAS1, and THBS4) implicates that mural cells, as proximal regulators of the vascular niche, do not support vascular growth in the hypertrophic heart (Fig 3.8f). Interestingly, the vasoconstrictor hormone, angiotensinogen (encoded by AGT), is also downregulated. Independent clustering of fibroblasts and correlation analysis revealed five transcriptionally distinct populations, excluding a minor population of adipocytes that is mostly derived from the HCM samples (Fig 3.3a-b; Fig 3.8g-h). FIB-1 and FIB-2 correspond to control-specific states and are characterized by an elevated signature typical of resting fibroblasts (ELN, PDGFRA, and PDGFRB) along with a subset of 122 matrix-associated genes identified in the consensus comparison (Fig 3.3c). FIB-2, in particular, likely plays a role in the basal turnover of ECM given its expression of metalloproteinases (e.g. ADAMTS2, ADAMTSL1, and TIMP3). Though, its fibrogenic capacity may be constrained by the distinct expression of SMAD6 and SMAD7258, which are known negative regulators of TGF-β signaling. FIB-3 represents a shared state between the disease conditions and is characterized by increased expression of select structural matrix components (FBN1 and MFAP5) and the pro-collagen modifier, PCOLCE2. While FIB-4 and FIB-5 are delineated as disease-specific states, the fibroblast activation programs (FAP, FN1, POSTN, and THBS4) are much more pronounced in FIB-5, and thus designating it as a dysregulated stromal entity in the hypertrophic heart. This activated subset also adopts the expression of a unique set of matrix constituents (e.g. COL8A1, COL14A1, and LTBP2), which may reflect a fibrogenic profile specific for disease pathogenesis. FIB-4 shares the signatures of FIB-5, albeit at a lower level of transcription, but exhibits defined expression of the metalloproteinase, TLL2. Notably, the insulin ligand, IGF1, is upregulated in FIB-5, though its expression is the highest in adipocytes. Similarly, mural cells were sub-categorized into five populations, all of which are shared in similar proportions between the disease conditions with the exception of a PER subset (PER-2; Fig 3.3d-e; Fig 3.8i-j). Briefly, within the VSMCs, those of arterial or arteriolar origin (Art-VSMC) are demarcated based on unique expression of established arterial markers65 (ITGA8 and CNN1; Fig 3.3f). Conversely, the reduced expression of contractile genes (MYH11, ACTA2, and TAGLN) and the absence of PER markers are 123 leveraged to identify venous or venular VSMCs41 (Ven-VSMC). PER-1 represents the most abundant pericyte state in the heart and PER-3 may correspond to pericytes in transition between capillaries and venules given its overlapping profile with Ven-VSMC. PER-2, in contrast, is absent in the hypertrophic heart and retains a unique profile consisted of AGT, interferon induced proteins (IFITM2 and IFITM3), matricellular proteins (TIMP1, TIMP3, and SPARC), and angiogenic factors (EPAS1 and THBS4). These signatures suggest that PER-2 may be a mediator of vascular remodeling at homeostasis and its deprivation in disease implicates pathological changes in the vasculature. Collectively, our data supports that the cardiac stroma exhibits pathological adaptations in HCM. This response is characterized by the acquisition of an activated phenotype in fibroblasts and the loss of a rare pericyte subset that may play a role in basal remodeling of the vasculature. 3.4.4 Cardiac hypertrophy exacerbates vascular dysfunction Adequate perfusion of the heart muscle is indispensable for cardiac physiology. This is especially true in ischemic heart diseases where vascular health is closely associated with the deterioration of cardiac function. Here, we asked whether the behaviour of vascular ECs is altered in the hypertrophic heart. Consensus comparison of ECs (51 up, 69 down) indicated a decreasing trend of genes associated with vascular development (Fig 3.9a). Of note, this includes the angiopoietin 124 receptor, TEK, which plays a crucial role in stabilizing nascent vessels during neovascularization, and NRP1, a co-receptor of vascular endothelial growth factor (VEGF) essential for the induction of angiogenesis259 (Fig 3.9b). Intriguingly, the concomitant upregulation of INSR (encoding for insulin receptor) and downregulation of CXCL12 (encoding for stromal-derived factor-1) suggest that the CXCR4-mediated recruitment of immune cells may be impaired260. Standalone analysis of ECs showed that, beyond the distinction by lymphatic, endocardial, and blood vessel origins, significant transcriptional diversity exists within the BECs (Fig 3.9c-d). By correlating the gene expression profile of each state with established identity markers253,261,262, it is clear that the heterogeneity of BECs is largely attributed to their anatomical location along the arteriovenous axis (Fig 3.4 a-b). This also encompassed intermediate states with overlapping signatures, such as ECs expressing venous/venular and capillary markers (referred to as “Cap-Ven-EC”), as well as those expressing arterial/arteriolar and capillary markers (referred to as “Cap-Art-EC”). While the majority of BEC states are common to both control and disease samples, the true capillary ECs (“Cap-EC-1” and “Cap-EC-2”) are segregated based on disease condition (Fig 3.4c). Direct comparison between the two states corroborated with the differences observed from the consensus analysis, indicating that the disease-associated changes in ECs are mostly confined to the capillaries (Fig 3.4d). In addition to the consensus signatures, a marked reduction in the VEGF receptor, KDR, and the chemotactic factor involved in mural cell recruitment, PDGFB, is observed in Cap-EC-2, substantiating a compromised response for the formation of stable neovessels. Other 125 genes implicated in vascular development, including surface adhesion molecules (PECAM1, EMCN, and ADGRE5) and the metalloproteinase, ADAMTS9, are also downregulated. Further assessment of EndoEC highlighted reduced expression in a similar set of surface molecules (Fig 3.4e). Strikingly, the expression of CD34 and NPR3, a receptor specific to EndoEC that modulates vascular tone via the clearance of circulating natriuretic peptides263, is completely abrogated. Downregulation of several TGF-β receptors (BMPR1A, BMPR2, and TGFBR2) are also observed. Comparison of LECs did not yield sufficient differences for meaningful interpretations, likely owing to a low number of captured nuclei. 3.4.5 Perturbations of cell-cell communication in cardiac hypertrophy From the divergence of cell-specific programs between disease and control donors, we sought to recapitulate altered cellular interactions underlying cardiac hypertrophy. With a permutation-based approach181, cell-cell communication through ligand-receptor complexes are inferred and compared between the disease conditions. In general, a global reduction in the number of active interactions is observed amongst most of the annotated cell populations (Fig 3.5a; Fig 3.10a). It was noted that CM receive fewer incoming signals but provide a higher number of outgoing signals to BEC, EndoEC, and FIB as a sender (Fig 3.5b). BEC, in contrast, exhibit increased reception of paracrine signals from specific populations such as CM, VSMC, and Schwann cell. Nonetheless, the decline in the number of interactions is consistent with a decrease of the relative information flow, or the sum of communication probabilities, in the majority of the 126 signaling pathways, suggesting that cell-cell communication is compromised in the hypertrophic heart (Fig 3.10b). To interpret the dysregulation of cell-cell communication, the activity of specific ligand-receptor pairs for each sender-receiver combination was evaluated. In line with the upregulated expression of genes downstream of insulin signaling, the reception of IGF1 in CM is enhanced from multiple sources such as LEC, myeloid IC, and most profoundly, FIB (Fig 3.5c). This indicates that, in addition to the liver as the primary source of IGF1 in circulation, the role of locally derived IGF1 in promoting cardiac hypertrophy cannot be excluded. Despite increased IGF1 signaling, the reception of other known inducers of hypertrophy such as fibroblast growth factor 2 (FGF2), neurogrelin-1 (NRG1), and TGFB1 produced by various populations is dampened. Interestingly, signaling of the lymphoangiocrine factor, reelin264 (encoded by RELN), from LEC and Schwann cells is completely abrogated, indicating that the hypertrophic CM may be more prone to cellular damage and apoptosis. Moreover, several other signals that confer cardioprotective effects (FGF7, FGF10, and SEMA3A) are also decreased265–267. The stromal cells, by and large, receive less signals typically involved in their activation and repair programs, encompassing various ligands under the FGF, TGF-β, PDGF, WNT, and activin signaling families (Fig 3.10c). The decreased reception of TGFBs, especially TGFB1, and INHBA underscores a limited fibrogenic activity in the stromal cells (Fig 3.5d). Likewise, the chemotactic and proliferative capacity may be 127 compromised given the reduced signaling of PDGFA, PDGFB, and FGFs. The repression of signaling necessary for activating fibrogenic pathways may explain the lack of global upregulation of matrix-associated signatures in the stromal cells. Fibroblasts, in particular, also do not exhibit characteristics of myofibroblast transition. On the other hand, interrogation of differential signaling patterns in BEC revealed that the signals required for vascular maintenance may be abrogated in the disease environment. Strikingly, the signaling of VEGFA, inferred to be primarily sourced from CM and FIB, is blunted relative to that of control (Fig 3.5e). Similarly, the incoming strength of FGF signals, a family of ligands with known pro-angiogenic effects, decreased with the exception of FGF1. Importantly, it is noted the reception of ANGPT1 signals from the stromal cells is reduced, which may lead to destabilization of the vasculature. In addition to the secreted factors, signaling of cell surface ligands involving those related to the NOTCH pathway, an essential regulator of stalk and tip cell specification during sprouting angiogenesis, is altered. Indeed, the opposing changes of JAG1 (decrease) and DLL4 (increase) signals may implicate suppressive effects on vascular growth220. Interactions between class B ephrin (Eph) and Eph receptors, a group of tyrosine kinases that participate in vascular sprouting and motility, are also reduced. Taken together, the extensive alterations of cellular crosstalk contribute, in part, to the dysregulated programs exhibited by the cardiac populations in the hypertrophic heart. 128 The perturbations of vascular signals, in particular, underscore the potential role of ischemia in disease pathogenesis of HCM and progression toward heart failure. 3.5 Discussion The advent of high-throughput omics technologies has enabled a deeper understanding of the cellular landscape and molecular signatures in the human heart. Our present study provides a detailed comparison between the hypertrophic and non-diseased hearts, using location-matched tissue biopsies from carriers with MYH7 mutations. We uncovered extensive alterations of gene programs and cellular heterogeneity, not only in CM, but also across other non-parenchymal populations. Importantly, these changes in molecular patterns are, in part, perpetuated by a compromised network of intercellular communication. Collectively, our findings underscore the multiple facets of dysregulation that contribute to the pathogenesis of HCM. Of all familial cases of HCM, mutations in the MYH7 locus constitute the most prominent pathogenic variants. MYH7 encodes for the β isoform (slow twitch) of myosin heavy chain (MHC-β), which is primarily expressed in the adult heart ventricles. Our data showed no difference in MYH7 expression at the transcript and protein levels in the disease CMs and heart tissue lysate, respectively, indicating that the genetic variants do not impact its expression. This contradicts recently published findings where MYH7 expression is elevated. Though, the underlying cause of HCM in the patient cohorts involved in these studies was aortic valve stenosis268 or non-MYH7 mutations269 (some with MYBPC3 mutations and others with unknown genotype). Instead, we observed a 129 strong reduction in the α isoform of MHC (α-MHC), encoded by MYH6, that is predominantly found in the atrial and developmental CM along with several other sarcomeric components. Genomically, MYH6 maps in tandem with MYH7270. The expression of each isoform is dependent on regulatory elements, some of which may impose antithetic regulation where the expression of one isoform suppresses the other271. Moreover, the ratio between the two isoforms has been used as an indicator of disease progression in HCM. The decrease of MYH6 expression likely indicates that our data recapitulate the cardiac landscape at end-stage HCM, near complete heart failure, rather than an earlier disease phase where MYH7 may potentially be upregulated272,273. Nonetheless, whether the shift in MYH isoforms is a direct result of MYH7 mutations remains an area to be investigated. It is known that the effects imposed by sarcomeric mutations on CM are spatially-dependent as specific anatomical locations of the heart are more prone to myocyte dysfunction. However, whether the spatial context extends to differential effects at focal regions with respect to micro-structures such as vasculature and fibrotic cores is unclear. From our analysis, we identified two distinct cardiomyocyte subsets in the hypertrophic, one of which is characterized by increased neurodevelopmental programs, and the other displays enhanced signatures associated with energy metabolism by oxidative phosphorylation and sarcomere components at a level beyond that of non-diseased CMs. The latter subset may correspond to a minority group of CMs that is attempting to compensate for the loss of contractile function in the surrounding CMs whereas the former may be a subset that is reactivating developmental programs. 130 The control-derived CMs, in contrast, are largely homogenous with minor differences in the transcription of several lncRNAs. Overall, the presence of multiple distinct subsets supports that the CMs exhibit phenotypic variability even in a localized region of the LVFW. Chronic remodeling of the hypertrophic heart involves excessive deposition of the matrix, culminating in the displacement of viable myocardium (“replacement fibrosis”) and diffuse scarring of the interstitium (interstitial fibrosis). In turn, the accumulation of fibrotic tissue leads chamber stiffening and predisposes diastolic dysfunction274. The cardiac stromal cells, including fibroblasts and, more recently, mural cells, have been identified as key matrix-producing entities in response to cardiac injury78. Here, we showed that the disease fibroblasts retain a state of activation but do not exhibit a clear increase in matrisome production. Rather, these fibroblasts adopt a different set of matrix constituents that are not expressed by those of control donors. Although elevated tissue stiffness, as reflected by a strong increase of structural matrix proteins in our proteomics data, is known to stimulate a pro-fibrotic response through mechanosensory pathways, this effect may be counteracted due to diminished signaling of factors involved in matrix production such as TGF-β ligands275. The absence of an amplified fibrogenic response may also indicate that the fibroblasts at end-stage HCM reside in a state of exhaustion rendered by prolonged activation. Of note, we found that the disease fibroblasts, along with a minor population of adipocytes, act as the primary source of cardiac IGF1, implicating a potential role of the cardiac stroma in driving cardiac hypertrophy. 131 Extensive remodeling and dysfunction in the coronary vasculature are frequent anomalies of the hypertrophic heart. For one, small vessel disease (SVD) characterized by arterial or arteriolar stenosis has been reported, not only in adult failing hearts, but also in infants of less than one year of age276,277. This suggests that, in some cases of HCM, SVD may already be present during early development. At the microvasculature level, the density of capillaries is commonly reduced in HCM patients278. Interestingly, microvascular dysfunction, evaluated by changes in the myocardial blood flow after the administration of a vasodilator, has been found to occur in asymptomatic patients prior to clinical deterioration279. These observations indicate that vascular abnormalities may contribute to the pathogenesis of HCM as tissue ischemia exacerbates myocyte death and fibrosis. Our assessment of the endothelial transcriptome pointed to functional alterations in capillary ECs whereas minimal differences are detected in those of larger vessels. Concordant with vascular deterioration in the hypertrophic heart, the receptors of VEGF and ANGPT1 are downregulated in the capillary ECs, compromising their potential for vascular growth and the subsequent stabilization of neovessels. This is substantiated by a blunted VEGFA signaling from multiple sources, most notably CMs and fibroblasts. Moreover, the signaling activities of NOTCH ligands also confers inhibition of sprouting angiogenesis. In general, these results indicate that the capillary ECs retain a state of impaired angiogenic capacity in the hypertrophic heart. The pathogenesis of HCM is a multifactorial process with multiple facets of clinical outcome. Despite an inherent flaw to the sarcomeres, the disease manifests in 132 pathological features beyond dysfunctional myocytes. The emergence of these features, including fibrosis and vascular rarefaction, is elucidated by our findings where, in addition to CMs, dysregulations of molecular mechanisms are evident in various resident populations, most notably the stromal cells and vascular endothelial cells. The disease-associated alterations within each cell population are accompanied by a malfunctional intercellular communication network that promotes a progressively damaging environment. All and all, our study accentuates the potential benefits of treating HCM through a multi-target approach to prevent its progression toward complete heart failure. 133 Figure 3.1. Cellular landscape of the hypertrophic heart. a, Experimental scheme of generating and processing of the single-nucleus RNA sequencing datasets. Location-matched transmural biopsies from the left ventricular free wall (LVFW) of patients diagnosed with HCM or patients with no prior history of cardiomyopathy were subjected to nuclei isolation and library preparation. b, Confocal images showing wheat germ agglutinin (WGA; green) assay of the myocardium. DAPI (blue) was used as nuclear counterstain. Scale = 100 µm. Representative images of five patient samples per group. c, Uniform manifold approximation and projection (UMAP) of 72,641 nuclei pooled from both CTRL and HCM samples. Colours indicate cell type annotations. CM, Cardiomyocyte; BEC, Blood vessel endothelial cell; EndoEC, Endocardial endothelial cell; LEC, Lymphatic endothelial cell; PER, Pericyte; VSMC, Vascular smooth muscle cell; M. IC, Myeloid immune cell; L. IC, Lymphoid immune cell. d, Heatmap depicting the relative average expression of identity markers in annotated populations. e, Pie chart of relative proportions of annotated populations in baseline (“CTRL”) and disease (“HCM”) samples. f, Principal component analysis (PCA) of pseudo-bulk expression profiles of each patient sample. Batch correction applied across control-disease sample pairs. 134 Figure 3.2. Dysregulated molecular profiles of cardiomyocytes. a, UMAP projection of cardiomyocyte nuclei pooled from both CTRL and HCM samples. Colours indicate subset annotations. CM, Cardiomyocyte. b, Bar plot displaying the relative proportions of cardiomyocyte subsets per patient sample. C = CTRL; H = HCM. 135 c, Heatmap depicting the relative average expression of identity markers in annotated subsets. d, Segment-dot plots showing the top 5 biological processes associated with CM-5. Length of segment and dot color intensity represent log-transformed adjusted P-values and the relative number of genes mapped to the pathway, respectively. e, Heatmap depicting the relative average expression of select neurodevelopmental genes in annotated subsets. f, Segment-dot plots showing the top 5 biological processes associated with CM-7. g-h, Heatmap depicting the relative average expression of select genes associated with oxidative phosphorylation and protein synthesis (g), as well as sarcomeric genes (h) in annotated subsets. 136 137 Figure 3.3. Pathological alterations of the stroma. a, UMAP projection of fibroblast nuclei pooled from both CTRL and HCM samples. Colours indicate subset annotations. FIB, Fibroblast. b, Bar plot displaying the relative proportions of fibroblast subsets per patient sample. C = CTRL; H = HCM. c, Heatmap depicting the relative average expression of identity markers in fibroblast subsets. d, UMAP projection of mural cell nuclei pooled from both CTRL and HCM samples. Colours indicate subset annotations. PER, Pericyte; Ven. VSMC, Venous/venular vascular smooth muscle cell; Art. VSMC, Arterial/arteriolar vascular smooth muscle cell. e, Bar plot displaying the relative proportions of mural cell subsets per patient sample. C = CTRL; H = HCM. f, Heatmap depicting the relative average expression of identity markers in mural cell subsets. 138 a, UMAP projection of endothelial cell nuclei pooled from both CTRL and HCM samples. Colours indicate subset annotations. Cap, Capillary; EC, Endothelial cell; Ven, Venous/venular; Art, Arterial/arteriolar; EndoEC, Endocardial endothelial cell; LEC, Lymphatic endothelial cell. b, Heatmap depicting the relative average expression of identity markers in endothelial cell subsets. c, Bar plot displaying the relative proportions of endothelial cell subsets per patient sample. C = CTRL; H = HCM. d, Heatmap depicting the relative average expression of DEGs associated with vascular function in capillary endothelial cells between disease states. e, Violin plot depicting the normalized expression of dysregulated signatures in endocardial endothelial cells between disease states. Figure 3.4. Cardiac hypertrophy exacerbates vascular dysfunction. 139 140 a, Network diagrams depicting the number of active interactions amongst annotated cell types in each disease condition. Width of edges reflects relative number of interactions. CM, Cardiomyocyte; BEC, Blood vessel endothelial cell; EndoEC, Endocardial endothelial cell; LEC, Lymphatic endothelial cell; PER, Pericyte; VSMC, Vascular smooth muscle cell; IC, Immune cell. b, Heatmap depicting the relative change in the number of pair-wise interactions between disease conditions. Blue/red scale indicate a decrease/increase of interactions in HCM samples. c, Signaling strengths of differentially active signals received by cardiomyocytes. Arrows indicate the presence of upregulated and/or downregulated interactions associated with the ligand. Colour intensity reflects the sum of communication probabilities for upregulated and/or downregulated interactions, independently, and the number of unique receptors associated with the ligand is supplemented. Circos plots summarize the overall interaction strength of IGF and RELN signals in control (left) and disease (right) samples. d, Signaling strengths of differentially active signals received by fibroblasts (green), pericytes (blue), and VSMCs (black). Circos plots summarize the overall interaction strength of TGF-β signals in control (left) and disease (right) samples. e, Signaling strengths of differentially active signals received by endothelial cells. Circos plots summarize the overall interaction strength of VEGF and ANGPT signals in control (left) and disease (right) samples. Figure 3.5. Perturbations of cell-cell communication in cardiac hypertrophy. 141 a, Masson’s trichrome assay and quantification of collagen staining (proportion of collagen area/total tissue area). Representative images of five patient samples per group. b, Confocal images of wheat germ agglutinin (WGA) staining (green) for every patient sample. DAPI (blue) was used as nuclear counterstain. Scale = 100 µm. C = CTRL; H = HCM. c, UMAP projection of 72,641 nuclei pooled from both CTRL and HCM samples. Colours reflect clusters. d, Projection of quality control metrics including the proportion of mitochondrial (% mito) and ribosomal transcripts (% ribo), as well as the absolute number of unique molecular identifiers (UMIs) on the UMAP. e, Violin plot depicting the normalized expression of MYH7 in cardiomyocytes between control and HCM samples. f, Projection of nuclei corresponding to each disease group on the UMAP. g, Principal component analysis (PCA) of proteomic data generated from mass spectrometry. h, Volcano plot summarizing the number of differentially upregulated and downregulated proteins. Figure 3.6. Extended supplementary data of 3.1. 142 a, Graphical summary of consensus comparisons. Only differential expressed genes (DEGs) identified in at least three control-disease sample pairs are considered for further analysis. b, Segment-dot plots showing the top 10 biological processes associated with upregulated or downregulated consensus DEGs in cardiomyocytes. Length of segment and dot color intensity represent log-transformed adjusted P-values and the relative number of genes mapped to the pathway, respectively. c, Heatmap depicting the relative average expression of consensus DEGs encoding for sarcomeric Figure 3.7. Extended supplementary data of 3.2. 143 components, ion channels, and intercalated discs (ICD) or adhesion molecules across patients. C = CTRL; H = HCM. d-e, Heatmap depicting the relative abundance of proteins associated with heart contraction (d) and oxidative phosphorylation (e) across patients. f, Heatmap depicting the relative average expression of consensus DEGs associated with insulin signaling and neuronal development across patients. g, UMAP projection of cardiomyocyte nuclei pooled from both CTRL and HCM samples. Colours reflect clusters. h, Assessment of cluster similarities by Pearson’s correlation analysis. Heatmap illustrates the relative magnitude of correlation coefficient. 144 145 Figure 3.8. Extended supplementary data of 3.3. a, Segment-dot plots showing the top 10 biological processes associated with upregulated or downregulated consensus DEGs in fibroblasts. Length of segment and dot color intensity represent log-transformed adjusted P-values and the relative number of genes mapped to the pathway, respectively. b, Heatmap depicting the relative average expression of consensus DEGs associated with matrix structure and matricellular factors in fibroblasts across patients. C = CTRL; H = HCM. c, Heatmap depicting the relative abundance of proteins associated with matrix organization across patients. d, Heatmap depicting the relative average expression of consensus DEGs associated with neuronal development in fibroblasts across patients. e, Segment-dot plots showing the top 5 biological processes associated with upregulated consensus DEGs in mural cells. f, Heatmap depicting the relative average expression of consensus DEGs associated with angiogenesis in mural cells across patients. g, UMAP projection of fibroblast pooled from both CTRL and HCM samples. Colours reflect clusters. h, Assessment of fibroblast cluster similarities by Pearson’s correlation analysis. Heatmap illustrates the relative magnitude of correlation coefficient. i, UMAP projection of mural cell nuclei pooled from both CTRL and HCM samples. Colours reflect clusters. j, Assessment of mural cell cluster similarities by Pearson’s correlation analysis. 146 Figure 3.9. Extended supplementary data of 3.4. a, Segment-dot plots showing the top 10 biological processes associated with upregulated or downregulated consensus DEGs in endothelial cells. Length of segment and dot color intensity represent log-transformed adjusted P-values and the relative number of genes mapped to the pathway, respectively. b, Heatmap depicting the relative average expression of genes associated with vascular development across patients. c, UMAP projection of endothelial cell nuclei pooled from both CTRL and HCM samples. Colours reflect clusters. d, Assessment of endothelial cell cluster similarities by Pearson’s correlation analysis. Heatmap illustrates the relative magnitude of correlation coefficient. C = CTRL; H = HCM. 147 148 a, Bar plots comparing the number of incoming and outgoing signals between disease conditions. CM, Cardiomyocyte; BEC, Blood vessel endothelial cell; EndoEC, Endocardial endothelial cell; LEC, Lymphatic endothelial cell; PER, Pericyte; VSMC, Vascular smooth muscle cell; IC, Immune cell. b, Relative information flow of all active pathways scaled by signaling strengths. Blue and red labels indicate differentially active pathways in control and disease samples, respectively. c, Circos plots summarize the overall interaction strength of select signaling pathways in control (left) and disease (right) samples. Figure 3.10. Extended supplementary data of 3.5. 149 Chapter 4: Concluding remarks 4.1 The impact of our work The heart is an essential organ at the crossroad to all organ systems. Its deterioration is, not only life-threatening, but also associated with many chronic co-morbidities in peripheral organs. Thus, restoration of the heart function represents a promising avenue to improving population health, especially in the aging population. Of the resident constituents in the heart, the stromal cells actively maintain homeostasis by regulating basal processes including matrix turnover and vascular pruning. In acute injuries (as in the case of MI), they orchestrate tissue repair and induce the formation of a matrix scaffold that prevents heart rupture. However, this immediate, yet double-edged, response elicits the onset of prolonged cardiac remodeling, and consequently, deterioration in the long-term. A similar situation occurs in chronic conditions (as in the case of HCM) where the remodeling process actively contributes to the pathogenesis of disease. Beginning with the second chapter, we first established that the cardiac stromal populations, namely fibroblasts and mural cells, are discrete entities with distinct positional and molecular characteristics, and thereby dispelling confusions regarding their identification and nomenclature. In addition to the stromal subsets described in previous studies, we uncovered a novel and rare pericyte subset that harbours both contractile and fibrogenic signatures, akin to a functionally-plastic entity in the homeostatic heart. With the focus on mural cells, a “forgotten” cell type that is understudied in the cardiovascular field, we, for the first time, elucidated the spatial and 150 functional response of mural cells in MI. Similar to fibroblasts, we found that pericytes at the infarct zone adopt a transient state of activation characterized by increased proliferative and fibrogenic capacity but do not transition into true fibroblasts, or vice versa. The phenotypic alteration extends to the acquisition of a pathological vascular program that induces their dissociation from the vasculature. We confirmed the loss of mural coverage, mostly in the capillaries, with histological analyses, which is closely associated with progressive vascular rarefaction and dysfunction after MI. By assessing the spatial transcriptome of the healing heart through time, we have identified potential signaling mechanisms underlying vascular deterioration. In general, the infarct exhibits overwhelming pro-fibrotic and pro-inflammatory signaling whereas the viable myocardium provides an important source of pro-angiogenic factors necessary for revascularization, and thus elucidating the importance of spatial considerations in therapeutic interventions. Moving forward, it would be invaluable to pinpoint these spatiotemporal mechanisms at the cellular level as it will add another layer of specificity when it comes to designing therapeutic applications such as cardiac patches. In the third chapter, we extended our work to clinical samples where we assessed dysregulations of molecular mechanisms in the context of HCM. With a holistic approach, we established a comprehensive transcriptomic atlas of the non-disease and hypertrophic heart. Comparative analysis showed extensive phenotypic alterations across major cellular constituents. Cardiomyocytes display a global reduction of contractile capacity, indicating that the patients had progressed to end-stage HCM at the time of transplantation. However, a minor but distinct subset overshoots the 151 expression of sarcomeric programs, and thus represents a compensatory entity in the failing heart. In the stroma, fibroblasts are arrested in a state of chronic activation but do not exhibit a definitive upregulation of matrix deposition. Importantly, the growth and maintenance of the microvasculature are likely blunted as angiogenic programs are attenuated in endothelial cells, specifically those of capillaries, and pericytes. This multifaceted pathological response is supported by a dysregulated network of cell-cell communication. Fibroblasts, in particular, act as the primary producer of IGF1 and participate in the pathogenesis of HCM. To evaluate the role of fibroblast-derived IGF1 in cardiac hypertrophy, a pressure overload model such as aortic constriction can be adopted using a transgenic mouse line with fibroblast-specific knockout of IGF1. Collectively, our study has advanced the understanding of the cardiac stroma and its role in health and disease. To date, there are no other omics studies that have evaluated mural cells and their behaviour in MI to the same degree of accuracy and representation. The current literature is, to a large extent, also constrained by a lack of spatial and temporal considerations, which are crucial information that can drastically change the efficacy of cardiac therapeutics. Moreover, very few clinical data generated from explanted hearts of patients diagnosed with familial HCM have been published. As such, our omics datasets and the associated interpretations, not only provide novel insights, but also serve as an invaluable resource to the cardiovascular field. 152 4.2 Limitations of omics studies The recent advancement of omics technologies has revolutionized experimental and clinical research. Through the commercialization of next-generation sequencing, we have acquired the capacity to interrogate molecular profiles, spanning epigenomics, transcriptomics, and proteomics, at the single-cell, and even sub-cellular, resolution. This has propelled a new wave of research, collectively termed “omics studies”, where omics data are used as a discovery, and often validation, tool to guide the direction of further experimental work. However, omics studies are not without limitations and challenges. For one, the cells captured by an omics platform may not be representative of an unbiased distribution present in the tissue due to technical challenges. Cell types with elongated and branched morphologies are typically not compatible with scRNAseq. In the case of our study and likely in other research teams, we found that mural cells are much more susceptible to cell lysis during enzymatic digestion and FACS than other populations, and thus the yield is much lower than their reported proportion in histology. To account for this issue, we have enriched for LIN-CD146+ cells in the final cell suspension prior to library preparation. However, the use of this enrichment strategy may also result in biased sampling if the surface marker expression is variable across subsets within the population it is claimed to distinguish. Other cardiac cell types like cardiomyocytes and neurons are generally considered nonviable post-tissue digestion. An alternative approach is snRNAseq where nuclei are isolated through mechanical dissociation for sequencing. While this approach may produce a more representative 153 cell landscape, its compatibility with FACS is limited, and consequently, rare populations are yielded in numbers too small for meaningful analysis. Moreover, snRNAseq also generates “multiplets”, or data points derived from more than one nucleus, at a higher frequency than scRNAseq. Together, these technical intricacies necessitate careful considerations when designing sequencing experiments. Another challenge of working with omics data is determining whether the observed differences are representative of biology or a result of technical confounders. One major determinant of the detection in gene expression is sequencing depth, a parameter usually calculated as the number of read pairs detected per cell or nucleus. Samples with low sequencing depth may not provide sufficient information for accurate data interpretation. A common phenomenon reflective of this is “dropouts” where the expression of specific genes is detected in one cell but not in others of the same cell type. As a result, downstream analysis with differential expression may miss genes that exhibit “true” biological differences. Although increasing the sequencing depth can reduce the rate of dropouts, reaching the optimal sequencing saturation is not feasible given its costs. One potential solution to this is the use of imputation algorithms to artificially correct for gene expression based on its average detection in similar cells. However, the reliability and accuracy of imputation methods remain a highly debated topic. In addition to sequencing depth, batch variation presents another obstacle without an ideal solution. Generally, samples derived from different experiments exhibit discrepancies in expression patterns and obscure true differences, resulting in a disjointed clustering and projection of cells during data analysis. These discrepancies 154 are largely attributed to technical differences in the steps leading up to library preparation, including enzymatic digestion and FACS. To resolve this, numerous algorithms have been developed to correct for batch variation. In our study, we have utilized Seurat’s anchor-based integration where “mutual nearest neighbors” are identified across gene expression subspaces and used as points of reference to classify cells of similar biological state. The downside of employing batch correction algorithms is that they may also reduce biological differences, and thus mask the presence of disease- or treatment-induced entities. The advancement of omics technologies is also accompanied by incessant development of computational methods and algorithms often claimed to be “better” than their predecessors. However, much emphasis has been placed on the performance of tools benchmarked using statistical approaches rather than their relevance from a biological perspective. The increasing complexity of computational models also creates a “language barrier” for biologists working with omics data. While general workflows have been established with popular packages like Seurat and Scanpy, finding the gold standard approach to work with more complicated experimental designs can be challenging without sufficient statistical or computational background. As such, people often find themselves questioning the validity of their interpretations and whether there is a more appropriate tool or method of examining the data. Understandably, developing a “one-fit-for-all” pipeline is a difficult, if not impossible, task that accounts for variations introduced from technical and biological sources. Beyond the evaluation of methods and their suitability for analysing specific types of data, it is more important to utilize omics 155 data as a guide to substantiate the biological interpretations with additional experimental approaches. In summary, omics tools have enabled the assessment of molecular signatures at a resolution previously unattainable. Yet, the associated technical challenges have compromised the quality of data and led to misinterpretations in published studies. Therefore, a strong emphasis to corroborate key findings derived from omics data with other experimental evidence should be normalized. 4.3 Current challenges of cell-based therapies Given the limited potential for heart regeneration, myocardial trauma results in tissue displacement by a non-functional scar. As such, incorporation of induced pluripotent stem cell-derived cardiomyocytes (iPSC-CM) or human embryonic stem cell-derived cardiomyocytes (hESC-CM) into the injured heart has emerged as a strategy to directly reconstruct the cardiac architecture. Several pre-clinical studies in non-human primates showed significant remuscularization and recovery of left ventricular ejection fraction (LVEF) in the infarcted heart post-transplantation280,281. Remarkably, blood perfusion and electromechanical coupling with the host myocardium were observed in grafts. However, this level of success is not found in the majority of transplantation studies. In many cases, improvement of cardiac function is minimal with short retention of grafted CMs due to immune rejection. Even in cases where the CMs survive long-term, current evidence does not support adequate graft-host electromechanical integration as observations of arrythmias post-transplantation persist. One reason for the 156 inconsistency of transplantation studies is the lack of a standardized method to assess the differentiation and maturity of lab-grown cardiomyocytes. Hence, the utility of engineered CMs in regenerative therapies remains limited. Similarly, MSCs have also been considered as a candidate for cell-based therapies. They have been proposed to confer cardioprotective effects through their immunomodulatory, anti-apoptotic, anti-fibrotic, and pro-angiogenic properties171. Intravenous delivery of MSCs, in particular, has been shown to attenuate myocardial inflammation and improve left ventricular functions despite their entrapment in the lung282, suggesting that an increase of MSC-derived factors may impose a paracrine effect through circulation. However, the therapeutic benefits of MSC infusions are, too, short-lived and variable due to poor retention239. As discussed throughout the thesis, stromal cells are a heterogeneous population of cells consisting of subsets with distinct properties. Although clinical applications of MSCs are generally sourced from the umbilical cord, bone marrow, or placenta, the level of diversity in these organs is likely comparable, if not more, than that of the cardiac tissue283. Moreover, the traditional method of isolating MSCs by broad markers including CD73, CD105, and CD90, as well as the lack thereof CD45, CD14, or CD11b does not yield a functionally uniform entity. For this reason, addressing whether the cardioprotective effects are conferred by a particular subset of the stroma is critical to improving the precision of treatment and its efficacy. 157 4.4 Future directions This thesis work has added profound insights to the growing body of knowledge on the diversity and role of the cardiac stroma in health and disease. Through the lens of single-cell omics and corroborating evidence from non-informatics experiments, we have identified the molecular mechanisms of specific stromal subsets that contribute to disease pathogenesis and tissue repair. One important perspective underscored by our study is the need to assess cellular response in a spatiotemporal context. The tissue environment is governed dynamic alterations that often exhibit regional or temporal characteristics, which are often overlooked in studies and lead to premature interpretations of data. One example of this is shown by another published work of ours where we showed that VEGF derived from specific cell types can impose different effects on tissue regeneration and this is conferred by spatial restrictions of VEGF. The incorporation of spatial and temporal considerations in the development of therapeutics will enhance their precision, and therefore effectiveness. To conclude, we hope that this work will propel cardiovascular research and path the way to improving interventions against cardiac diseases. 158 Bibliography 1. Stenmark, K. R. et al. The adventitia: essential regulator of vascular wall structure and function. Annu. Rev. Physiol. 75, 23–47 (2013). 2. Tallquist, M. D. Cardiac Fibroblast Diversity. Annu. Rev. Physiol. 82, 63–78 (2020). 3. Kuwabara, J. T. & Tallquist, M. 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Thesis/Dissertation
eng
Experimental Medicine
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Rossi, Fabio M. V.
Deciphering the heterogeneity and functional role of the cardiac stroma in health and disease
Text