Functional coordination of non‐myocytes plays a key role in adult zebrafish heart regeneration

Article15 September 2021free access Source Data Functional coordination of non-myocytes plays a key role in adult zebrafish heart regeneration Hong Ma Hong Ma orcid.org/0000-0003-3419-4627 McAllister Heart Institute, University of North Carolina, Chapel Hill, NC, USA Department of Pathology and Laboratory Medicine, University of North Carolina, Chapel Hill, NC, USA These authors contributed equally to this work Search for more papers by this author Ziqing Liu Ziqing Liu McAllister Heart Institute, University of North Carolina, Chapel Hill, NC, USA Department of Pathology and Laboratory Medicine, University of North Carolina, Chapel Hill, NC, USA These authors contributed equally to this work Search for more papers by this author Yuchen Yang Yuchen Yang orcid.org/0000-0001-5977-1617 McAllister Heart Institute, University of North Carolina, Chapel Hill, NC, USA Department of Pathology and Laboratory Medicine, University of North Carolina, Chapel Hill, NC, USA Department of Genetics, University of North Carolina, Chapel Hill, NC, USA These authors contributed equally to this work Search for more papers by this author Dong Feng Dong Feng McAllister Heart Institute, University of North Carolina, Chapel Hill, NC, USA Department of Pathology and Laboratory Medicine, University of North Carolina, Chapel Hill, NC, USA Search for more papers by this author Yanhan Dong Yanhan Dong McAllister Heart Institute, University of North Carolina, Chapel Hill, NC, USA Department of Pathology and Laboratory Medicine, University of North Carolina, Chapel Hill, NC, USA Search for more papers by this author Tiffany A Garbutt Tiffany A Garbutt orcid.org/0000-0001-7134-5821 McAllister Heart Institute, University of North Carolina, Chapel Hill, NC, USA Department of Pathology and Laboratory Medicine, University of North Carolina, Chapel Hill, NC, USA Search for more papers by this author Zhiyuan Hu Zhiyuan Hu orcid.org/0000-0002-2963-2453 Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, USA Search for more papers by this author Li Wang Li Wang McAllister Heart Institute, University of North Carolina, Chapel Hill, NC, USA Department of Pathology and Laboratory Medicine, University of North Carolina, Chapel Hill, NC, USA Search for more papers by this author Changfei Luan Changfei Luan McAllister Heart Institute, University of North Carolina, Chapel Hill, NC, USA Department of Pathology and Laboratory Medicine, University of North Carolina, Chapel Hill, NC, USA Search for more papers by this author Cynthia D Cooper Cynthia D Cooper orcid.org/0000-0003-3992-027X School of Molecular Biosciences, Washington State University Vancouver, Vancouver, WA, USA Search for more papers by this author Yun Li Yun Li Department of Genetics, University of North Carolina, Chapel Hill, NC, USA Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA Search for more papers by this author Joshua D Welch Corresponding Author Joshua D Welch [email protected] orcid.org/0000-0002-5869-2391 Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA Search for more papers by this author Li Qian Corresponding Author Li Qian [email protected] orcid.org/0000-0001-7614-5618 McAllister Heart Institute, University of North Carolina, Chapel Hill, NC, USA Department of Pathology and Laboratory Medicine, University of North Carolina, Chapel Hill, NC, USA Search for more papers by this author Jiandong Liu Corresponding Author Jiandong Liu [email protected] orcid.org/0000-0003-0035-3570 McAllister Heart Institute, University of North Carolina, Chapel Hill, NC, USA Department of Pathology and Laboratory Medicine, University of North Carolina, Chapel Hill, NC, USA Search for more papers by this author Hong Ma Hong Ma orcid.org/0000-0003-3419-4627 McAllister Heart Institute, University of North Carolina, Chapel Hill, NC, USA Department of Pathology and Laboratory Medicine, University of North Carolina, Chapel Hill, NC, USA These authors contributed equally to this work Search for more papers by this author Ziqing Liu Ziqing Liu McAllister Heart Institute, University of North Carolina, Chapel Hill, NC, USA Department of Pathology and Laboratory Medicine, University of North Carolina, Chapel Hill, NC, USA These authors contributed equally to this work Search for more papers by this author Yuchen Yang Yuchen Yang orcid.org/0000-0001-5977-1617 McAllister Heart Institute, University of North Carolina, Chapel Hill, NC, USA Department of Pathology and Laboratory Medicine, University of North Carolina, Chapel Hill, NC, USA Department of Genetics, University of North Carolina, Chapel Hill, NC, USA These authors contributed equally to this work Search for more papers by this author Dong Feng Dong Feng McAllister Heart Institute, University of North Carolina, Chapel Hill, NC, USA Department of Pathology and Laboratory Medicine, University of North Carolina, Chapel Hill, NC, USA Search for more papers by this author Yanhan Dong Yanhan Dong McAllister Heart Institute, University of North Carolina, Chapel Hill, NC, USA Department of Pathology and Laboratory Medicine, University of North Carolina, Chapel Hill, NC, USA Search for more papers by this author Tiffany A Garbutt Tiffany A Garbutt orcid.org/0000-0001-7134-5821 McAllister Heart Institute, University of North Carolina, Chapel Hill, NC, USA Department of Pathology and Laboratory Medicine, University of North Carolina, Chapel Hill, NC, USA Search for more papers by this author Zhiyuan Hu Zhiyuan Hu orcid.org/0000-0002-2963-2453 Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, USA Search for more papers by this author Li Wang Li Wang McAllister Heart Institute, University of North Carolina, Chapel Hill, NC, USA Department of Pathology and Laboratory Medicine, University of North Carolina, Chapel Hill, NC, USA Search for more papers by this author Changfei Luan Changfei Luan McAllister Heart Institute, University of North Carolina, Chapel Hill, NC, USA Department of Pathology and Laboratory Medicine, University of North Carolina, Chapel Hill, NC, USA Search for more papers by this author Cynthia D Cooper Cynthia D Cooper orcid.org/0000-0003-3992-027X School of Molecular Biosciences, Washington State University Vancouver, Vancouver, WA, USA Search for more papers by this author Yun Li Yun Li Department of Genetics, University of North Carolina, Chapel Hill, NC, USA Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA Search for more papers by this author Joshua D Welch Corresponding Author Joshua D Welch [email protected] orcid.org/0000-0002-5869-2391 Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA Search for more papers by this author Li Qian Corresponding Author Li Qian [email protected] orcid.org/0000-0001-7614-5618 McAllister Heart Institute, University of North Carolina, Chapel Hill, NC, USA Department of Pathology and Laboratory Medicine, University of North Carolina, Chapel Hill, NC, USA Search for more papers by this author Jiandong Liu Corresponding Author Jiandong Liu [email protected] orcid.org/0000-0003-0035-3570 McAllister Heart Institute, University of North Carolina, Chapel Hill, NC, USA Department of Pathology and Laboratory Medicine, University of North Carolina, Chapel Hill, NC, USA Search for more papers by this author Author Information Hong Ma1,2,9, Ziqing Liu1,2, Yuchen Yang1,2,3, Dong Feng1,2, Yanhan Dong1,2, Tiffany A Garbutt1,2, Zhiyuan Hu4, Li Wang1,2, Changfei Luan1,2, Cynthia D Cooper5, Yun Li3,6,7, Joshua D Welch *,8, Li Qian *,1,2 and Jiandong Liu *,1,2 1McAllister Heart Institute, University of North Carolina, Chapel Hill, NC, USA 2Department of Pathology and Laboratory Medicine, University of North Carolina, Chapel Hill, NC, USA 3Department of Genetics, University of North Carolina, Chapel Hill, NC, USA 4Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, USA 5School of Molecular Biosciences, Washington State University Vancouver, Vancouver, WA, USA 6Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA 7Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA 8Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA 9Present address: Department of Cardiology, 2nd Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China *Corresponding author. Tel: +1 734 615 0618; E-mail: [email protected] *Corresponding author. Tel: +1 919 962 0340; E-mail: [email protected] *Corresponding author. Tel: +1 919 962 0326; E-mail: [email protected] EMBO Reports (2021)22:e52901https://doi.org/10.15252/embr.202152901 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Cardiac regeneration occurs primarily through proliferation of existing cardiomyocytes, but also involves complex interactions between distinct cardiac cell types including non-cardiomyocytes (non-CMs). However, the subpopulations, distinguishing molecular features, cellular functions, and intercellular interactions of non-CMs in heart regeneration remain largely unexplored. Using the LIGER algorithm, we assemble an atlas of cell states from 61,977 individual non-CM scRNA-seq profiles isolated at multiple time points during regeneration. This analysis reveals extensive non-CM cell diversity, including multiple macrophage (MC), fibroblast (FB), and endothelial cell (EC) subpopulations with unique spatiotemporal distributions, and suggests an important role for MC in inducing the activated FB and EC subpopulations. Indeed, pharmacological perturbation of MC function compromises the induction of the unique FB and EC subpopulations. Furthermore, we developed computational algorithm Topologizer to map the topological relationships and dynamic transitions between functional states. We uncover dynamic transitions between MC functional states and identify factors involved in mRNA processing and transcriptional regulation associated with the transition. Together, our single-cell transcriptomic analysis of non-CMs during cardiac regeneration provides a blueprint for interrogating the molecular and cellular basis of this process. Synopsis Using single cell technology, this study delineates the cellular and transcriptomic dynamics of major non-myocyte populations during cardiac regeneration and demonstrates a critical role of functional coordination of non-myocytes in adult zebrafish heart regeneration. Multiple novel subpopulations for major non-myocyte cell types are identified that exhibit distinct tempo-spatial dynamics during cardiac regeneration. Highly cooperative interactions of non-myocyte subtypes through cell-cell signaling are observed. The data indicate an important role for macrophages in inducing the activated fibroblast and endocardial endothelial cell subpopulations. Development of the new computational algorithm Topologizer reveals the topological relationship of the cellular manifolds. Dynamic transitions between macrophage functional states and factors involved in mRNA processing and transcriptional regulation associated with the transition are uncovered. Introduction Adult mammalian hearts exhibit limited regenerative capacity and are therefore susceptible to massive and irreversible cardiomyocyte (CM) loss due to myocardial infarction (Laflamme & Murry, 2011). In contrast, adult zebrafish and neonatal mice can efficiently regenerate their injured hearts through activation of CM proliferation (Poss et al, 2002; Jopling et al, 2010; Kikuchi et al, 2010; Porrello et al, 2011; Tzahor & Poss, 2017). Thus, much of the basic research on cardiac regeneration has focused on CMs, aiming to unravel cardiac renewal mechanisms for future development of therapeutic interventions to stimulate CM proliferation and regeneration in human patients (Mahmoud et al, 2013; D'Uva et al, 2015; Tao et al, 2016; Wu et al, 2016; Bassat et al, 2017; Leach et al, 2017; Morikawa et al, 2017; Nakada et al, 2017; Price et al, 2019). Yet, the heart as a whole contains many other cell types including endothelial cells, fibroblasts, and a wide variety of immune cells. In particular, it is increasingly recognized that non-myocytes (non-CMs) play active roles in regulating CM behaviors (Kikuchi et al, 2011b; Riley, 2012; Klotz et al, 2015; Lai et al, 2017). Despite substantial advances in understanding genetic regulation of zebrafish heart regeneration (Gonzalez-Rosa et al, 2017), the cardiac non-CM composition and its dynamic changes in response to injury remain largely unexplored. A better understanding of how diverse cells compose zebrafish heart to maintain its homeostasis will shed lights on the mechanisms underlying its robust regenerative capacity and is required for the development of therapeutic strategies. In this study, using the newly developed LIGER algorithm (Welch et al, 2019) that allows flexible modeling across highly diverse single-cell datasets, we analyzed the transcriptome dynamics of 61,977 individual non-CMs isolated at multiple time points during zebrafish heart regeneration. Through this analysis, we identified major non-CM cell types, including multiple macrophage, fibroblast, and endothelial cell subpopulations with unique tempo-spatial distributions and highly cooperative interactions during the process of cardiac regeneration. Interestingly, perturbation of macrophage functional dynamics resulted in compromised interactions among non-CMs concomitant with reduced cardiomyocyte proliferation and defective cardiac regeneration. Furthermore, we developed a computational algorithm Topologizer and revealed the topological relationship of the cellular manifolds. Combining Topologizer and RNA velocity analyses, we uncovered dynamic transition between macrophage functional states and identified factors involved in mRNA processing and transcriptional regulation associated with the transition. Together, our single-cell transcriptomic analysis of non-CMs during cardiac regeneration provides a blueprint for interrogating the molecular and cellular basis of cardiac regeneration. Results Single-cell transcriptome atlas of cardiac non-CMs in adult zebrafish heart The lack of detailed information on the cellular identities and cell states of the non-CMs associated with tissue regeneration is a major hurdle to precisely delineating the biological events underlying the regeneration process. To address this challenge, we first sought to generate a single-cell map of non-CMs in wild-type adult zebrafish ventricle. Following cell dissociation and low-speed centrifugation to remove CMs (Materials and Methods, and Fig 1A), we enriched non-CMs and performed single-cell RNA sequencing (scRNA-Seq) using the 10× Genomics Chromium platform (Fig 1A). In total, we obtained 7,041 high-quality non-CMs that passed quality control and filtering criteria (Appendix Table S1). We then performed unsupervised dimensionality reduction and clustering, and identified 12 distinct cell clusters (Appendix Fig S1A). Cells from two independent experiments intermingled well, suggesting minimal batch effects (Appendix Fig S1B). Based on known marker gene expression, we found eight major non-CM cell types, including endothelial cells (ECs; cdh5, kdrl, fli1a, flt1) (Habeck et al, 2002; Lawson & Weinstein, 2002; Larson et al, 2004), epicardial cells/fibroblasts (FBs; tcf21, fn1b, col1a1a) (Snider et al, 2009; Kikuchi et al, 2011a; Wang et al, 2013; Moore-Morris et al, 2014a; Ivey & Tallquist, 2016), resident mesenchymal cells (Mes; angptl7, rspo1, mgp) (Gore et al, 2011; Costa et al, 2017), macrophages (MCs; mpeg1.1, mfap4, c1qb, cd74a) (Spilsbury et al, 1995; Ellett et al, 2011; Walton et al, 2015), neutrophils (Neutro; lyz, mpx) (Walters et al, 2010; Harvie & Huttenlocher, 2015), T/NK/B cells (T/NK/B; sla2, irf4b, ccl36.1, cxcr4a, lck, nkl.2, zbtb32, cd79a) (Athanasiadis et al, 2017; Carmona et al, 2017), erythrocytes (Eryth; cahz, slc4a1a) (Paw et al, 2003; Moore et al, 2018), and thrombocytes (Throm; itga2b, gp1bb) (Kato et al, 2004; Lin et al, 2005) (Fig 1B and C; Appendix Fig S1C). Figure 1. Single-cell transcriptomics reveals heterogeneity of zebrafish cardiac non-myocytes A. Experimental workflow of non-CM isolation from zebrafish hearts and scRNA-seq (10× Genomics). B. scRNA-seq data of adult zebrafish cardiac non-CM visualized on tSNE and colored by cell types. EC, endothelial cells; FB, fibroblasts; MC, macrophages; Mes, resident mesenchymal cells; T/NK/B, T/NK/B cells; Neutro, neutrophils; Eryth, erythrocytes; Throm, thrombocytes. C. Violin plots showing expression of canonical markers for each cell type. GO analysis (DAVID) of upregulated genes in each population was performed and representative GO terms were listed on the right. D–F. Non-CMs expressing canonical EC markers in panel b were zoom-in analyzed with LIGER. (D) Cells visualized on tSNE and colored by cell types. eEC, endocardial EC; lEC, lymphatic EC; cEC, coronary EC; Mural, mural cells. (E) Pie chart showing contribution of each cell type. (F) Expression of newly identified markers of each EC subpopulation shown on tSNE. G. Pie chart showing non-CM composition (erythrocytes and thrombocytes excluded). H. Dotplot showing expression of top eight positive markers identified for each non-CM population. Download figure Download PowerPoint Interestingly, the EC cells are the non-CM cell type that is grouped into distinct clusters. Because zebrafish hearts contain three types of highly specialized ECs—endocardial ECs (eECs), lymphatic ECs (lECs), and coronary ECs (cECs)—we performed a second round of analysis on non-CMs expressing the canonical EC marker genes cdh5 and kdrl and identified three EC populations and mural cells based on the expression of marker genes—gata5 for eECs (Nemer & Nemer, 2002), lyve1a and prox1a for lEC (Okuda et al, 2012; van Impel et al, 2014), aplnra for cEC (Cui et al, 2019), and cd248a, acta2, and tagln for mural cells (Bagley et al, 2008; Santoro et al, 2009) (Fig 1D and E; Appendix Fig S1D–F). The molecular signatures defining these three types of zebrafish ECs have not been fully explored. With the high resolution of our scRNA-seq data, we found that the transcriptome of cECs is more similar to that of their associated mural cells—including both cells expressing pericyte markers and cells expressing smooth muscle cell markers—than to those of the eECs and lECs (Fig 1D; correlation analysis in Appendix Fig S1H). Further differential gene expression analysis identified highly expressed and specific markers for each EC type (Fig 1F; Appendix Fig S1G). In zebrafish, vascularization of the ventricle is driven by angiogenesis of eECs (Harrison et al, 2015). However, due to limited numbers of cECs in zebrafish hearts and the lack of genetic tools to isolate and enrich this population, whether and how cECs differ from eECs at the molecular level is unclear. Our single-cell study revealed that adult zebrafish heart had a similar cellular composition to that of adult mouse heart (Pinto et al, 2016) (Fig 1G). To identify new markers for each non-CM cell type, we performed differential gene expression analysis for each cell type and identified panels of highly expressed genes specific for each non-CM population (Fig 1H). Gene Ontology (GO) analysis demonstrated that each cell population was associated with distinct biological functions and supported the assignment of cell identities based on canonical markers (Fig 1C; Appendix Fig S2). Therefore, our results provide a new benchmarking dataset for defining zebrafish cardiac non-CM identities. The newly characterized markers promise to increase the feasibility and resolution of functional studies on zebrafish non-CM populations. Mapping of coordinated responses of non-CMs during heart regeneration We next sought to resolve the composition and dynamics of non-CMs during cardiac regeneration. To this end, we performed scRNA-seq at multiple time points (2 days post-injury [dpi], 7 dpi, and 14 dpi) that correspond to major pathophysiological events post cardiac injury (Poss et al, 2002; Cao et al, 2016; Lai et al, 2017). We obtained transcriptomes of 20,124 non-CMs that passed quality control and filtering criteria from the three time points post-injury (Appendix Table S1). These cells were then jointly analyzed with the non-CMs obtained from uninjured ventricles. Integrating scRNA-seq datasets containing a variety of cell types from multiple biological time points proved challenging: Cells separate by a combination of dataset of origin and cell type, suggesting the existence of technical and biological differences (Appendix Fig S2A). We thus applied the recently published algorithm LIGER (Welch et al, 2019) that delineates each cell by shared and dataset-specific features (metagenes) and allows for jointly defining shared cell populations even across multiple heterogeneous datasets. A further advantage of LIGER is interpretability—the ability to associate each factor (metagene set) with specific populations of cells, which is unique among currently available integration analysis methods. The interpretability of the LIGER factorization allowed us to exclude technical (e.g., ribosomal, mitochondrial, and stress genes) and biological (e.g., cell cycle states) confounding factors during joint analysis of all cell types (Appendix Fig S2B–E). Using the aforementioned markers (Fig 1C and H), we assigned cell type identity to LIGER clusters and found that various non-CM types identified in the uninjured hearts were present post-injury, albeit with varying frequencies (Fig 2A and B; Appendix Fig S2F–H). Overall, the higher alignment uniformity of mixing samples not only accurately preserved the cell type architectures, but also enabled us to assemble an integrated atlas of cell states using datasets from multiple replicates and time points. Figure 2. Transcriptome dynamics of macrophages during zebrafish heart regeneration A, B. Joint analysis of non-CM scRNA-seq data from uninjured hearts and hearts at 2, 7, and 14 dpi with LIGER. (A) All non-CM visualized on tSNE. (B top) Non-CM from each time point visualized on the tSNE embedding in (A). Data were down-sampled to the same cell number at each time point. (B bottom) Pie charts showing contribution of different cell types at each time point. C–F. Zoom-in analysis of macrophages identified in (A). (C) tSNE plot colored by MC subpopulations. (D) Expression levels of representative markers of each MC subpopulation color-coded and mapped to tSNE embeddings in (C) (top), and corresponding representative GO terms for each subpopulation (bottom). (E) Dynamics of MC subpopulations during heart regeneration as shown by the proportion of each subpopulation in total non-CM or total MC. (F) Expression of tnfa and csf3b in MC at each time point. G. Fluorescent in situ hybridization for cd74a, tnfa, and ctsc at 7 dpi, respectively. mpeg1 was used to label all MC cells. The blue boxed region is highlighted in the zoom-in image to the right. Scale bar = 50 μm. A stands for atrium, V stands for ventricle, and OFT stands for outflow tract. White dashed lines outline the heart and the yellow dashed lines indicate approximate resection plane. Download figure Download PowerPoint Changes in non-CM composition occurred most dramatically at 2 dpi, remained pronounced at 7 dpi but became minimal by 14 dpi. Among all non-CM cell types, MCs showed the most significant frequency change (Fig 2B), suggesting an acute expansion of the MC population followed by gradual resolution of immune response as the heart regenerates. To comprehensively chart the behavior of MCs over time, we jointly analyzed MCs from all time points and discovered significant diversity within this population, including five distinct subpopulations (Fig 2C; Appendix Fig S3A and B). All MC subpopulations shared common MC marker genes such as mfap4 (Walton et al, 2015) and mpeg1 (Ellett et al, 2011), yet each subpopulation expressed distinct marker genes (Fig 2D; Appendix Fig S3C–E). Interestingly, while MC1 and MC2 appeared across all examined stages, MC3–5 became more apparent post cardiac injury at 2 and 7 dpi (Fig 2E; Appendix Fig S3F). Among all MCs, MC1 cells exhibited the highest level of il1b expression and specifically expressed pro-inflammatory factors tnfa and csf3b with enriched GO terms related to inflammatory response and neutrophil chemotaxis (Fig 2D; Appendix Fig S3D and E). Largely due to an expansion of the number of MC1 cells, the overall expression of tnfa, csf3b, and other pro-inflammatory cytokines ccl35.1, ccl34.4, and cxcl11.1 was transiently upregulated at 2 and 7 dpi (Fig 2E and F; Appendix Fig S3G). MC1 therefore represents the major MC population that mediates the critical acute phase of pro-inflammatory activation post cardiac injury. In contrast, MC2 cells highly expressed genes involved in antigen presentation such as cd74a, cd74b (Schroder, 2016), and mhc2dab (Wittamer et al, 2011) and related to GO terms like antigen processing and presentation, suggestive of a role in immune surveillance (Epelman et al, 2014) (Fig 2D; Appendix Fig S3D and E). MC2's relative frequency within the MC population decreased initially but subsequently increased over time, and MC2 became the dominant MC subpopulation at 14 dpi (Fig 2E). Furthermore, we found that MC2 marker genes cd74a and mhc2dab, after being initially downregulated at 2 dpi, were continuously upregulated until reaching a peak level at 14 dpi (Appendix Fig S3H), suggesting a gradually enhanced activation of MC2 cells. MC3 cells highly expressed cd9b, which encodes a tetraspanin family protein that interacts with Fcγ to activate phagocytosis (Kaji et al, 2001; da Huang et al, 2011) (Fig 2E). Consistently, MC3 cells also highly expressed other genes involved in phagocytosis and proteolysis including cd63 and cathepsins (ctsc, ctsd, and ctsla) (Aderem, 2003; Pols & Klumperman, 2009) (Fig 2D; Appendix Fig S3I). Though barely present in the uninjured heart, MC3 emerged as a major cluster at 2 dpi and then gradually decreased its frequency (Fig 2E). The MC4 subpopulation showed cell cycle activity (Fig 2D; Appendix Fig S3D, E, and J), likely representing a proliferating pool of cardiac MCs to replenish the MC pools post-cardiac injury (Davies et al, 2013). The remaining minor cluster MC5 (0.5–3.9%) highly expressed granulin genes grn1 and grn2 and mostly existed at 2 and 7 dpi (Fig 2D and E; Appendix Fig S3D, E and K), likely representing MCs actively engulfing and degrading cell debris (Altmann et al, 2016; Tsuruma et al, 2018; Yoo et al, 2019). In support of MC subpopulation clustering, fluorescence in situ hybridization (FISH) indicated that the marker genes for MC subpopulations were expressed in subsets of the mpeg1.1 expressing MCs. Consistently, we also found that tnfa and ctsc marked non-overlapping MC populations (Fig 2G). In summary, MCs exist in multiple definable states that exhibit dynamic functional changes from homeostatic conditions to acute immune response until inflammation resolution (Fig 2E). This dynamic change in the number and composition of MCs may reflect differential requirements for temporally regulated functions of MC subpopulations in cardiac repair and regeneration. Next, we determined whether and how other non-CM populations change their cellular composition in response to the temporal dynamics of MC activation and function. We first characterized FB, a cell type traditionally regarded as responsible for extracellular matrix (ECM) production. Unbiased clustering identified 4 FB subpopulations expressing the canonical fibroblast marker genes tcf21, fn1b, and col1a1a (Ivey & Tallquist, 2016) (Fig 3A; Appendix Fig S4A–C). However, these four FB subpopulations clearly exhibited distinct ECM gene expression profiles (Fig 3B). Upon cardiac injury, fibroblasts became activated as evidenced by their transient upregulation of ECM genes expression (Appendix Fig S4D). FB1 and FB2 upregulated essentially the same ECM genes (i.e., fn1b, dcn, and sparc), yet FB2 consistently demonstrated higher expression level than FB1 (Appendix Fig S4E). FB3 was a unique cluster that drastically and acutely expanded in response to cardiac injury (Fig 3C; Appendix Fig S4F). In silico cell cycle assignment and expression of proliferation markers also suggest that FB3 is a highly proliferating FB subpopulation (Appendix Fig S4G and H). Compared to the other FB subtypes, FB3 cells transiently upregulated a unique set of ECM genes col12a1a, col12a1b, postnb, and fn1a (aka, fn1) as well as gstm.3, which encodes the mu class glutathione S-transferase that functions to detoxify, among others, the products of oxidative stress (Glisic et al, 2015) (Fig 3D; Appendix Fig S4I and J). FB3 also transiently expressed the smooth muscle marker gene tagln (aka, sm22), suggesting a transformed phenotype (Fig 3E). FB3 likely corresponds to the postnb-positive “activated fibroblasts” that when ablated, led to reduced CM proliferation after cardiac injury (Wang et al, 2013; Sanchez-Iranzo et al, 2018). Interestingly, double FISH for tcf21- and FB3-specific marker postnb