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The Journal of Heart and Lung Transplantation
International Society for Heart and Lung Transplantation.

The dynamic cellular landscape of grafts with acute rejection after heart transplantation

  • Author Footnotes
    ⁎ These authors equally contribute to the work.
    Deqiang Kong
    Footnotes
    ⁎ These authors equally contribute to the work.
    Affiliations
    Department of Surgery, Second Affiliated Hospital of Zhejiang University, Hangzhou 310009, China.

    Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou 310014, China
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  • Author Footnotes
    ⁎ These authors equally contribute to the work.
    Siyuan Huang
    Footnotes
    ⁎ These authors equally contribute to the work.
    Affiliations
    Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
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  • Author Footnotes
    ⁎ These authors equally contribute to the work.
    Xiaolong Miao
    Footnotes
    ⁎ These authors equally contribute to the work.
    Affiliations
    Department of Surgery, Second Affiliated Hospital of Zhejiang University, Hangzhou 310009, China.

    Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou 310014, China
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  • Jiaxin Li
    Affiliations
    Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.
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  • Zelai Wu
    Affiliations
    Department of Surgery, Second Affiliated Hospital of Zhejiang University, Hangzhou 310009, China.

    Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou 310014, China
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  • Yang Shi
    Affiliations
    School of Mathematical Sciences, Peking University, Beijing 100871, China
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  • Han Liu
    Affiliations
    Department of Surgery, Second Affiliated Hospital of Zhejiang University, Hangzhou 310009, China.

    Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou 310014, China
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  • Yuancong Jiang
    Affiliations
    Department of Surgery, Second Affiliated Hospital of Zhejiang University, Hangzhou 310009, China.

    Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou 310014, China
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  • Xing Yu
    Affiliations
    Department of Thyroid Surgery, Second Affiliated Hospital of Zhejiang University, Hangzhou 310009, China
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  • Mengyao Xie
    Affiliations
    Department of Otolaryngology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
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  • Zhonghua Shen
    Affiliations
    Department of Cardiovascular Surgery, Second Affiliated Hospital of Zhejiang University, Hangzhou 310009, China
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  • Jinzhen Cai
    Affiliations
    Division of Hepatology, Liver Disease Center, Organ Transplantation Center, The Affiliated Hospital of Qingdao University, Qingdao 266003, China
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  • Ruibin Xi
    Correspondence
    Corresponding Author: Dr. Weihua Gong, Jiefang Road #88, Hangzhou, Zhejiang Province 310009, China
    Affiliations
    School of Mathematical Sciences and Center for Statistical Science, Peking University, Beijing 100871, China
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  • Weihua Gong
    Correspondence
    Corresponding Author: Dr. Weihua Gong, Jiefang Road #88, Hangzhou, Zhejiang Province 310009, China
    Affiliations
    Department of Surgery, Second Affiliated Hospital of Zhejiang University, Hangzhou 310009, China.

    Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou 310014, China
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  • Author Footnotes
    ⁎ These authors equally contribute to the work.
Open AccessPublished:November 02, 2022DOI:https://doi.org/10.1016/j.healun.2022.10.017

      Abstract

      Background

      : Acute cellular rejection (ACR) is a major barrier to the long-term survival of cardiac allografts. Although immune cells are well known to play critical roles in ACR, the dynamic cellular landscape of allografts with ACR remains obscure.

      Methods

      : Single-cell RNA sequencing (scRNA-seq) was carried out for mouse cardiac allografts with ACR. Bioinformatic analysis was performed, and subsequent transplant experiments were conducted to validate the findings.

      Results

      : Despite an overall large depletion of cardiac fibroblasts (CFBs), highly expanded cytotoxic T lymphocytes and a CXCL10+Gbp2+ subcluster of CFBs were enriched within grafts at the late stage. CXCL10+Gbp2+ CFBs featured strong interferon responsiveness and high expression of chemokines and major histocompatibility complex molecules, implying their involvement in the recruitment and activation of immune cells. Cell‒cell communication analysis revealed that CXCL9/CXCL10-CXCR3 might contribute to regulating CXCL10+Gbp2+ CFB-induced chemotaxis and immune cell recruitment. In vivo transplant studies revealed the therapeutic potential of CXCR3 antagonism in transplant rejection.

      Conclusions

      : The findings of our study unveiled a novel CFB subcluster that might mediate acute cardiac rejection. Targeting CXCR3 could prolong allograft survival.

      Keywords

      Abbreviations

      ACR
      Acute cellular rejection
      scRNA-seq
      Single-cell RNA sequencing
      CFBs
      Cardiac fibroblasts
      HSC
      Hematopoietic stem cell
      CTLs
      Cytotoxic T lymphocytes
      APCs
      Antigen-presenting cells
      DEGs
      Differentially expressed genes
      TCR
      T cell receptor
      Treg
      Regulatory T cells
      NKT-like
      Natural killer T cell-like cells
      Th17
      T helper type 17
      IFN-γ
      Interferon-gamma
      EndMT
      Endothelial-mesenchymal transition
      MI
      Myocardial infarction
      NK
      Natural killer
      ISHLT
      International Society of Heart and Lung Transplantation

      Introduction

      Although heart transplantation has been successfully used for treating end-stage heart disease, allograft rejection remains a major barrier to long-term graft survival
      • Khachatoorian Y.
      • et al.
      Noninvasive biomarkers for prediction and diagnosis of heart transplantation rejection.
      ,
      • Novak J.
      • Machackova T.
      • Krejci J.
      • Bienertova-Vasku J.
      • Slaby O.
      MicroRNAs as theranostic markers in cardiac allograft transplantation: from murine models to clinical practice.
      . Acute cellular rejection (ACR), one of the most common complications, is characterized by the infiltration of mononuclear cells in the perivasculature and interstitium
      • Stewart S.
      • et al.
      Revision of the 1996 working formulation for the standardization of nomenclature in the diagnosis of lung rejection.
      . Donor antigen-reactive CD4+ and CD8+ T cells have long been recognized as the principal mediators in ACR. However, the underlying pathogenesis of ACR is not fully understood. The dynamic cellular landscape of grafts might unveil the process of ACR and potential therapeutic targets.
      Single-cell RNA sequencing (scRNA-seq) has become a powerful tool to profile gene expression and decipher intercellular signalling networks at single-cell resolution
      • Hesse J.
      • et al.
      Single-cell transcriptomics defines heterogeneity of epicardial cells and fibroblasts within the infarcted murine heart.
      ,
      • Wu F.
      • et al.
      Single-cell profiling of tumor heterogeneity and the microenvironment in advanced non-small cell lung cancer.
      , such as the identification of novel cell subtypes and investigation of intercellular communication after acute rejection. Accumulating evidence has demonstrated its capability to effectively analyse alloimmune responses posttransplantation
      • Wang Y.
      • Wang J.Y.
      • Schnieke A.
      • Fischer K.
      Advances in single-cell sequencing: insights from organ transplantation.
      • Dong F.
      • et al.
      Differentiation of transplanted haematopoietic stem cells tracked by single-cell transcriptomic analysis.
      • Malone A.F.
      • et al.
      Harnessing Expressed Single Nucleotide Variation and Single Cell RNA Sequencing To Define Immune Cell Chimerism in the Rejecting Kidney Transplant.
      • Snyder M.E.
      • et al.
      Generation and persistence of human tissue-resident memory T cells in lung transplantation.
      • Yang X.
      • et al.
      Single-cell profiling reveals distinct immune phenotypes that contribute to ischaemia-reperfusion injury after steatotic liver transplantation.
      . Donor macrophages express unique transcriptional profiles compared to their recipient counterparts that are capable of persisting for several years after renal transplantation
      • Malone A.F.
      • et al.
      Harnessing Expressed Single Nucleotide Variation and Single Cell RNA Sequencing To Define Immune Cell Chimerism in the Rejecting Kidney Transplant.
      . Persistent donor tissue-resident memory T cells can be a prognostic biomarker when the T-cell landscape is dynamically compared between the lungs of donors and recipients in lung transplantation
      • Snyder M.E.
      • et al.
      Generation and persistence of human tissue-resident memory T cells in lung transplantation.
      . Transcriptional analysis has revealed that myeloid-derived cell subsets could transition under different states and mutually interplay
      • Yang X.
      • et al.
      Single-cell profiling reveals distinct immune phenotypes that contribute to ischaemia-reperfusion injury after steatotic liver transplantation.
      . The cellular landscape of heart allografts remains obscure. Furthermore, the dynamic alterations of the immune system must be clarified after acute rejection.
      Several scRNA-seq studies have shown the heterogeneity of cardiac fibroblasts (CFBs) in myocardial infarction (MI)
      • Hesse J.
      • et al.
      Single-cell transcriptomics defines heterogeneity of epicardial cells and fibroblasts within the infarcted murine heart.
      ,
      • Wang L.
      • et al.
      Single cell dual-omics reveals the transcriptomic and epigenomic diversity of cardiac non-myocytes.
      ,
      • Wang Z.
      • et al.
      Cell-Type-Specific Gene Regulatory Networks Underlying Murine Neonatal Heart Regeneration at Single-Cell Resolution.
      . Fibroblasts undergo phenotypic and functional transitions, ranging from proinflammatory and anti-inflammatory to pro-scar producing profiles, for effective scar formation
      • DeLeon-Pennell K.Y.
      • Barker T.H.
      • Lindsey M.L.Fibroblasts
      The arbiters of extracellular matrix remodeling.
      . Activated fibroblasts and myofibroblasts are well recognized as significant contributors to the process of graft fibrosis
      • Schiechl G.
      • et al.
      Basophils Trigger Fibroblast Activation in Cardiac Allograft Fibrosis Development.
      . Nevertheless, it remains unclear how CFBs, as potential producers of proinflammatory cytokines, contribute to cardiac allograft rejection.
      CXCL9 (MIG) and CXCL10 (IP10) are generally regarded as IFN-γ-inducible chemokines that exert their biological functions by binding to a G protein-coupled receptor, CXCR3
      • Altara R.
      • et al.
      Emerging importance of chemokine receptor CXCR3 and its ligands in cardiovascular diseases.
      . CXCR3 chemokines are potent chemoattractants for various immune cells, such as macrophages, dendritic cells, type 1 T helper cells and natural killer (NK) cells
      • Romagnani P.
      • Crescioli C.
      CXCL10: a candidate biomarker in transplantation.
      . Previous studies indicated that the expression level of CXCL10 could reflect the transplant recipient's immune status and are associated with episodes of acute rejection
      • Romagnani P.
      • Crescioli C.
      CXCL10: a candidate biomarker in transplantation.
      ,
      • Shino M.Y.
      • et al.
      Correlation between BAL CXCR3 chemokines and lung allograft histopathologies: A multicenter study.
      . Although the vast literature supports the critical role of CXCR3 chemokines in the pathogenesis of allograft rejection, the underlying molecular mechanisms remain to be further elucidated.
      In this study, we depicted the cellular landscape of murine allografts at different time points posttransplantation by using scRNA-seq analysis, revealing the strong temporal heterogeneity of T cells and CFBs. The number of CXCL10+Gbp2+ CFBs, a novel CFB subpopulation, was increased upon IFN-γ stimulation. Furthermore, the CFB subpopulation highly expressed major histocompatibility complex (MHC) molecules and chemokines, which acted as antigen presenting cells in the process of ACR. We further investigated whether the CXCL9/CXCL10-CXCR3 signalling pathway played a critical role in the CXCL10+Gbp2+ CFB-T-cell interaction. Our findings showed that IFN-γ-responsive CFBs mediated ACR by interacting with T cells.

      Materials and Methods

      Animals

      Six-week-old inbred male wild-type BALB/c (B/c; H-2d) and C57BL/6 (B6; H-2b) mice were purchased from Beijing Vital River Laboratory Animal Technology (Beijing China). All animals were housed in a pathogen-free facility and a constant humidity and temperature environment in the Zhejiang University Laboratory Animal Center and allowed free access to food and water during the experimental interval.

      Single-cell RNA library construction, sequencing, and data processing

      According to the manufacturer's instructions, single-cell RNA-seq and T cell receptor sequencing (TCR-seq) libraries were prepared by using the Chromium Single Cell 5’ Library and Gel Bead Kit. For the scRNA-seq data, the Cell Ranger toolkit (v6.0.1) provided by 10x Genomics was applied to align reads and generate the gene-cell unique molecular identifier (UMI) matrix using the mouse reference genome mm10. Details of the bioinformatics analysis of scRNA-seq data are provided in the Supplementary Materials.

      Flow cytometry

      CFBs were analyzed by flow cytometry for the expression of MHC molecules. Single-cell suspensions were prepared, and the cells were incubated with CD16/32 Ab at 4°C for 15 minutes. Subsequently, the cells were stained with the corresponding antibodies, MHC I (Invitrogen, 34-1-2S) and MHC II (Biolegend, 107625), at 4°C for 30 minutes. Isotype controls were performed for both stains. Flow cytometric analysis was performed using LSRFortessa™ X-20 (BD Bioscience) and FlowJo software.

      Statistical analysis

      Data are presented as the mean±SD or SEM and were analysed with GraphPad Prism 8. Continuous variables were analysed by unpaired Student's t test. The difference in heart graft survival was analysed by the log-rank test. P<0.05 was considered to be statistically significant.
      Additional detailed methods and materials are shown in the Supplementary Materials.

      Results

      Single-cell atlas of posttransplant murine hearts

      To dissect the composition of allografts and explore the regulatory mechanism of acute transplant rejection, we performed scRNA-seq of noncardiomyocytes from murine hearts at days 0, 1, 3, and 5 postheart transplantation (Fig. 1a). We also evaluated inflammatory infiltration of grafts based on the International Society of Heart and Lung Transplantation (ISHLT) criteria at days 0, 1, 3, and 5 after heart transplantation (Fig. 1b, Fig. S1a). The grafts were obviously destroyed over time after transplantation. After quality control, we obtained 45,959 cells, including 9,936 cells from day 0, 12,202 cells from day 1, 12,427 cells from day 3, and 11,394 cells from day 5. Unsupervised clustering and annotation by known lineage markers revealed 16 clusters of cells, consisting of 10 major cell types, CFBs, smooth muscle cells, pericytes, glial cells, endothelial cells and immune cell types (myeloid cells, NK cells, T cells, granulocytes and B cells) (Fig. 1c-e, Fig. S2b-d). Increased proportions were observed for T cells, NK cells, and myeloid cells at day 5. The proportion of CFBs and granulocytes displayed a sharp decrease at day 5 (Fig. 1f). After the first day, almost all immune cells were from the recipient, while most of the nonimmune cells were from the donor (Fig. S3a, b). These results indicated that donor antigen-reactive immune cells might be involved in the tissue damage of the graft
      • Rosenblum J.M.
      • et al.
      CXCR3 antagonism impairs the development of donor-reactive, IFN-gamma-producing effectors and prolongs allograft survival.
      .
      Figure 1
      Figure 1Single-cell atlas of posttransplant murine hearts. a. Schematic of the workflow for scRNA-seq depicting graft transplantation, single-cell suspension preparation and subsequent analysis. Hearts from C57BL/6 mice were grafted into the carotid artery of BALB/c mice. Heart grafts were harvested at days 0, 1, 3, and 5 after surgery. b. Representative HE staining of murine hearts at days 0, 1, 3, and 5 posttransplantation, bar=200 µm. c, d Ten major cell types were identified and are distinguished by different colours (c). The right panel is split based on the 4 different time points (d). e. Dot plot showing the average expression levels of canonical marker genes related to cell types, including B cells (Ms4a1), T cells (Cd3e, Cd3g), NK cells (Ncr1), granulocytes (S100a9, Ccr1), myeloid cells (Fcgr1, Cd163, Aif1, Cd68), glial cells (Plp1), pericytes (Pdgfrb, Kcnj8), smooth muscle cells (Acta2, Myh11, Tagln), CFBs (Pdgfra, Colla1, Col3a1) and endothelial cells (Pecam1, Cdh5). The dot size represents the percentage of cells expressing the related canonical marker genes in each cell type. f. Bar chart showing the number of cells in each cell type at different time points.

      Analyses of T cell clusters and characterization of single-cell TCR repertoires within grafts

      To study the diversity and dynamics of the T cell repertoire postheart transplantation, we performed high-resolution clustering analyses and analysed TCR clonality. T cells could be subdivided into seven clusters, including CTLs, proliferative T cells (Mki67+ T), regulatory T cells (Tregs), natural killer T cell-like cells (NKT-like), mixed IL17a+ T cells (γδ T cells and T helper type 17 cells) and mixed T cells (Lgfbp4+ T, Tnfrsf4+ T) (Fig. 2a, b). The proportion of T cell subclusters at different time points and the proportion of the 4 time points in each T cell subcluster are shown in Fig. 2c and 2d, respectively. Increased proportions of CTLs and NKT-like cells were observed at day 5.
      Figure 2
      Figure 2Analyses of T cell clusters and characterization of single-cell TCR repertoires within grafts. a. UMAP plot displaying colour-coded T cell subclusters in heart grafts (left). The right panel is split based on the time point (right). b. The expression levels of the top 3 DEGs within each T cell subcluster are shown, including Lgfbp4+ T cells (Lgfbp4, Ccr7, Sell), Tnfrsf4+ T cells (Tnfrsf4, Ctla4, Maf), CTL cells (Ccl5, Gzma, Gzmk), Mki67+ T cells (Mki67, Top2a, Ube2c), IL17a+ T cells (Il17a, Rora, Il1r1), Treg cells (Foxp3, Il2ra, Izumo1r) and NKT-like cells (C1qbp, Nolc1, Srm). c. Bar chart showing the proportion of different T cell subclusters at different time points. d. Bar chart showing the proportion of each of the 4 time points in each T cell subcluster. e. UMAP projection of the clonally expanded T cells coded by colour. f. Pie chart showing the proportion of colour-coded clonotypes, unique TCRs (clonotype 1), duplicate TCRs (clonotype 2), and clonal TCRs (clonotype ≥3) among the different T cell subclusters at different time points. The circle size reflects the number of cells. g. Bar plot showing the diversity of clonotypes among the different T cell subclusters. h. Sankey plot showing the V(D)J gene usage of primary TCR α and β chains. Each bar reflects a specific V(D)J segment, and each bar section refers to a specific V, D, or J gene. i. Volcano plot showing the DEGs between expanded (clonotype ≥2) and nonexpanded (clonotype 1) CTL cells. The gene names with the most significant differences are indicated in the plots.
      We further assessed T cell clonal expansion by TCR-seq analysis. The TCR clonotype was obtained from 84.70% (70.57-92.33%) of T cells in each mouse heart sample and from 33.14-94.54% of the cells in each T cell subcluster. We observed that CTLs and partial Mki67+ T cells showed greater expansion than other T cell subclusters (Fig. 2e). Next, we investigated the expanded clones among different T cell subclusters from day 0 to day 5 (Fig. 2f, Fig. S4c, d) and found that clonal expansions occurred in almost all T cell subclusters over time and that the CTL subcluster was the most significant.
      The diversity of clonotypes of different T cell subclusters is shown in Fig. 2g. The CTL subpopulation had the least alpha diversity, consistent with the highly clonal expansion of CTLs. We next characterized the diversity of the TCR repertoire through the V(D)J gene usage of TCR α and β chains and found that T cells had a broad adaptive immune response in transplant rejection (Fig. 2h). Finally, we compared the expression levels of expanded and nonexpanded CTL cells (Fig. 2i). Expanded CTLs expressed higher levels of GZMK and GZMB. These results might demonstrate that there was a vigorous adaptive immune response and highly expanded clonal CTLs during transplant rejection.

      CFBs demonstrated transcriptional and potential functional heterogeneity

      To investigate the roles of CFBs in grafts, we examined the transcriptional changes in CFBs during the progression of transplant rejection (Fig. 3a, b). The genes correlated with the pseudotime inferred by Monocle2
      • Trapnell C.
      • et al.
      The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells.
      can be clustered into 5 clusters. Gene clusters downregulated along the pseudotime axis were involved in the vital activity of CFBs, while those upregulated were involved in immune response-mediated transplant rejection (Fig. 3b), suggesting that CFBs may be associated with late graft rejection. To elucidate the posttransplant roles of CFBs, we performed unsupervised clustering of all CFBs and identified 7 subclusters, CFB0 through CFB6 (Fig. 3c). Furthermore, the clusters identified by the unsupervised clustering analysis were similar to those seen by the regulon-based clustering analysis (Fig. S5c). The proportion of CFB subclusters at different time points and the proportion of the 4 time points in each CFB subcluster are shown (Fig. 3d, e). Although, overall, the number of CFBs had a large decrease at day 5, the number and proportion of the CFB5 subcluster significantly increased at day 5 (Table 1).
      Figure 3
      Figure 3Heterogeneity of CFB transcription and potential functions. a. Monocle trajectories of CFB cells coloured by different pseudotimes. Each dot represents a cell. b. Heatmap showing 5 different groups of pseudotime-dependent genes; green indicates low levels of expression, and purple indicates high levels of expression (right). The top enriched GO terms for each temporal cluster are listed (left). c. UMAP plot showing colour-coded CFB subclusters in heart grafts (left). The right panel is split based on the time point (right). d. Bar chart showing the proportion of each of the 4 time points in each CFB subcluster. e. Bar chart showing the proportion of different CFB subclusters at different time points. f. Heatmap displaying the average expression levels of the top 3 DEGs among CFB subclusters. g. Pathway enrichment analysis revealing active cellular pathways in CFB subclusters 0, 1, 2, 3, 4, 5 & 6. The node size represents the number of genes in a pathway. Each black circle represents the most highly corresponding pathways as labelled in Cytoscape (Version 3.8.2). The light blue lines show the intrapathway relationships, which also represent the number of shared genes between each pathway. h. Dot plot displaying the RSS of selected regulons for each CFB subcluster. The numbers in parentheses indicate the number of genes in the regulon. The dot colour reflects the regulon activity.
      Table 1
      The number of different CFB subclusters at different time points
      Day0Day1Day3Day5
      CFB015482132273826
      CFB11813205628246
      CFB2805446510156
      CFB38958524951
      CFB4147112337107
      CFB5556776334
      CFB6194342442
      The number of different T cell subclusters at different time points
      Day0Day1Day3Day5
      CTL135457352
      Igfbp4+76313102178
      Mki67+28328268
      NKT-like096126
      Th1729853127
      Tnfrsf4+187764421
      Treg92811102
      The number of different cell types at different time points
      Day0Day1Day3Day5
      Bcell832972120
      endo1181086992
      fib545730814562962
      glial5725156
      granulocytes138710972913161
      mye2550646744268055
      NKcell7042753481
      pericytes37254014
      SMC30264829
      Tcell1476492991474
      To further explore the potential functional heterogeneity of CFBs, we identified differentially expressed genes (DEGs) for each CFB subcluster (Fig. 3f) and performed pathway enrichment analysis of these DEGs (Fig. 3g). Notably, CFB5, the most abundant fibroblast subtype on day 5 (>30%), showed elevated expression of genes mainly involved in immunomodulation (Fig. 3g) and interferon-responsive transcription factors (Gtf3a, Irf2, Stat2 and Irf8) (Fig. 3h, Fig. S5d). These results showed that CFBs had a high level of gene expression heterogeneity, leading to potential functional heterogeneity.

      CFB5 showed a strong interferon response and antigen-presenting capability

      CFB5, which could be characterized by the high expression of CXCL10 and Gbp2 (Fig. 4a), had an unexpectedly significant increase in its number and proportion at day 5 posttransplantation. To confirm this, we performed multiplexed immunofluorescence staining of CXCL10, Gbp2 and vimentin (a best-defined marker of CFBs
      • Souders C.A.
      • Bowers S.L.
      • Baudino T.A.
      Cardiac fibroblast: the renaissance cell.
      ) (Fig. 4c) and found that the number of CXCL10+Gbp2+ CFBs was indeed significantly increased at day 5 (Fig. 4e). Compared with other subtypes of fibroblasts, CFB5 might have distinct roles in ACR. CFB5 highly expressed chemotaxis-associated genes (CXCL9 and CXCL10) and antigen presentation-related genes (such as MHC molecules H2-Q6 and H2-Q7) (Fig. 4a, b), suggesting that CFB5 might have proinflammatory and antigen presentation properties (Table 2).
      Figure 4
      Figure 4CFB5 showed a strong interferon response and antigen-presenting capability. a. Violin plots showing the expression of selected CFB5 marker genes in different CFB subclusters. b. Feature plots and related violin plots showing the response to the IFN-γ signature, MHC Ⅰ signature, MHC Ⅱ signature and chemokine signature. c. Representative immunofluorescence images from grafts at days 0, 1, 3 and 5 post transplantation: vimentin (red), CXCL10 (green), Gbp2 (pink), and DAPI (blue), bar=100 μm. d. Representative immunofluorescence images from grafts at day 5 posttransplantation anti-IFN-γ treatment: vimentin (red), CXCL10 (green), Gbp2 (pink), DAPI (blue), bar=100 μm. e. Quantitative analysis of CXCL10+ Gbp2+ CFBs. Data are shown as the mean±SEM. The Mann–Whitney two-tailed test was used for statistical analysis. f. Feature plots showing the expression of the CFB5 signature genes Cxcl10, Gbp2, Iigp1, Bst2, Gbp6, Igtp, Psmb8, Ili47, Isg15, Gbp3, Ifit3, Gbp7, Irf7, Ifi203 and Phf11d in CFBs from two cases of MI. h. Feature plots showing the expression of the CFB5 signature in CFBs from one case of a normal heart.
      Table 2
      The signatures used in our study
      GeneGene set
      Actg1GO_response_to_interferon-gamma
      Aqp4GO_response_to_interferon-gamma
      Arg1GO_response_to_interferon-gamma
      CiitaGO_response_to_interferon-gamma
      CapgGO_response_to_interferon-gamma
      Casp1GO_response_to_interferon-gamma
      Cdc37GO_response_to_interferon-gamma
      Cdc42GO_response_to_interferon-gamma
      Socs1GO_response_to_interferon-gamma
      Cited1GO_response_to_interferon-gamma
      Cyp27b1GO_response_to_interferon-gamma
      Dapk3GO_response_to_interferon-gamma
      EvlGO_response_to_interferon-gamma
      GapdhGO_response_to_interferon-gamma
      Gbp2bGO_response_to_interferon-gamma
      Gbp2GO_response_to_interferon-gamma
      Gch1GO_response_to_interferon-gamma
      H2-AaGO_response_to_interferon-gamma
      H2-Ab1GO_response_to_interferon-gamma
      H2-Eb1GO_response_to_interferon-gamma
      H2-Q7GO_response_to_interferon-gamma
      HpxGO_response_to_interferon-gamma
      Irf8GO_response_to_interferon-gamma
      Irgm1GO_response_to_interferon-gamma
      IfngGO_response_to_interferon-gamma
      Il12bGO_response_to_interferon-gamma
      Il12rb1GO_response_to_interferon-gamma
      Irf1GO_response_to_interferon-gamma
      Acod1GO_response_to_interferon-gamma
      Cd47GO_response_to_interferon-gamma
      Jak2GO_response_to_interferon-gamma
      Kif16bGO_response_to_interferon-gamma
      Kif5bGO_response_to_interferon-gamma
      Xcl1GO_response_to_interferon-gamma
      Gbp4GO_response_to_interferon-gamma
      Mrc1GO_response_to_interferon-gamma
      Myo1cGO_response_to_interferon-gamma
      Nos2GO_response_to_interferon-gamma
      Slc11a1GO_response_to_interferon-gamma
      Ccl21aGO_response_to_interferon-gamma
      Med1GO_response_to_interferon-gamma
      PpargGO_response_to_interferon-gamma
      Ptpn2GO_response_to_interferon-gamma
      SirpaGO_response_to_interferon-gamma
      Rab12GO_response_to_interferon-gamma
      Rab20GO_response_to_interferon-gamma
      Ccl1GO_response_to_interferon-gamma
      Ccl11GO_response_to_interferon-gamma
      Ccl12GO_response_to_interferon-gamma
      Ccl17GO_response_to_interferon-gamma
      Ccl2GO_response_to_interferon-gamma
      Ccl20GO_response_to_interferon-gamma
      Ccl22GO_response_to_interferon-gamma
      Ccl25GO_response_to_interferon-gamma
      Ccl3GO_response_to_interferon-gamma
      Ccl4GO_response_to_interferon-gamma
      Ccl5GO_response_to_interferon-gamma
      Ccl6GO_response_to_interferon-gamma
      Ccl7GO_response_to_interferon-gamma
      Ccl8GO_response_to_interferon-gamma
      Ccl9GO_response_to_interferon-gamma
      Cx3cl1GO_response_to_interferon-gamma
      SncaGO_response_to_interferon-gamma
      Trim21GO_response_to_interferon-gamma
      Stat1GO_response_to_interferon-gamma
      Stx4aGO_response_to_interferon-gamma
      Stxbp1GO_response_to_interferon-gamma
      Stxbp2GO_response_to_interferon-gamma
      Stxbp3GO_response_to_interferon-gamma
      Stxbp4GO_response_to_interferon-gamma
      Tdgf1GO_response_to_interferon-gamma
      Tgtp1GO_response_to_interferon-gamma
      Tlr4GO_response_to_interferon-gamma
      Otop1GO_response_to_interferon-gamma
      Trp53GO_response_to_interferon-gamma
      Rpl13aGO_response_to_interferon-gamma
      TxkGO_response_to_interferon-gamma
      Vamp3GO_response_to_interferon-gamma
      Vamp8GO_response_to_interferon-gamma
      VimGO_response_to_interferon-gamma
      WasGO_response_to_interferon-gamma
      ZyxGO_response_to_interferon-gamma
      Ccl19GO_response_to_interferon-gamma
      Tlr2GO_response_to_interferon-gamma
      UbdGO_response_to_interferon-gamma
      Rab11fip5GO_response_to_interferon-gamma
      Vamp4GO_response_to_interferon-gamma
      Irgm2GO_response_to_interferon-gamma
      MefvGO_response_to_interferon-gamma
      Gbp3GO_response_to_interferon-gamma
      Stx8GO_response_to_interferon-gamma
      Ccl24GO_response_to_interferon-gamma
      SyncripGO_response_to_interferon-gamma
      Cdc42ep4GO_response_to_interferon-gamma
      NmiGO_response_to_interferon-gamma
      Ccl21cGO_response_to_interferon-gamma
      Cxcl16GO_response_to_interferon-gamma
      Ifitm3GO_response_to_interferon-gamma
      Actr2GO_response_to_interferon-gamma
      Ifitm1GO_response_to_interferon-gamma
      Vps26bGO_response_to_interferon-gamma
      Bst2GO_response_to_interferon-gamma
      Dapk1GO_response_to_interferon-gamma
      Rab43GO_response_to_interferon-gamma
      KynuGO_response_to_interferon-gamma
      Rps6kb1GO_response_to_interferon-gamma
      Actr3GO_response_to_interferon-gamma
      Ifitm7GO_response_to_interferon-gamma
      Stx11GO_response_to_interferon-gamma
      Gbp8GO_response_to_interferon-gamma
      Parp9GO_response_to_interferon-gamma
      Ifitm2GO_response_to_interferon-gamma
      Dnaja3GO_response_to_interferon-gamma
      Gbp6GO_response_to_interferon-gamma
      Cdc42ep2GO_response_to_interferon-gamma
      EprsGO_response_to_interferon-gamma
      Slc26a6GO_response_to_interferon-gamma
      Il23rGO_response_to_interferon-gamma
      Pde12GO_response_to_interferon-gamma
      Ifitm6GO_response_to_interferon-gamma
      Rab7bGO_response_to_interferon-gamma
      GsnGO_response_to_interferon-gamma
      Gbp5GO_response_to_interferon-gamma
      Gbp7GO_response_to_interferon-gamma
      Gbp9GO_response_to_interferon-gamma
      FlnbGO_response_to_interferon-gamma
      ShflGO_response_to_interferon-gamma
      Myo18aGO_response_to_interferon-gamma
      Nlrc5GO_response_to_interferon-gamma
      Ccl26GO_response_to_interferon-gamma
      Parp14GO_response_to_interferon-gamma
      Gbp10GO_response_to_interferon-gamma
      Ccl21bGO_response_to_interferon-gamma
      2610528A11RikChemokines
      Ccl1Chemokines
      Ccl2Chemokines
      Ccl3Chemokines
      Ccl4Chemokines
      Ccl5Chemokines
      Ccl6Chemokines
      Ccl7Chemokines
      Ccl8Chemokines
      Ccl9Chemokines
      Ccl11Chemokines
      Ccl12Chemokines
      Ccl17Chemokines
      Ccl19Chemokines
      Ccl20Chemokines
      Ccl21aChemokines
      Ccl21bChemokines
      Ccl21cChemokines
      Ccl22Chemokines
      Ccl24Chemokines
      Ccl25Chemokines
      Ccl26Chemokines
      Ccl27aChemokines
      Ccl28Chemokines
      CklfChemokines
      Cx3cl1Chemokines
      Cxcl1Chemokines
      Cxcl2Chemokines
      Cxcl3Chemokines
      Cxcl5Chemokines
      Cxcl9Chemokines
      Cxcl10Chemokines
      Cxcl11Chemokines
      Cxcl12Chemokines
      Cxcl13Chemokines
      Cxcl14Chemokines
      Cxcl15Chemokines
      Cxcl16Chemokines
      Cxcl17Chemokines
      Gm1987Chemokines
      Gm2564Chemokines
      Pf4Chemokines
      PpbpChemokines
      Xcl1Chemokines
      Ackr1Chemokine receptors
      Ackr2Chemokine receptors
      Ackr3Chemokine receptors
      Ackr4Chemokine receptors
      Ccrl2Chemokine receptors
      Pitpnm3Chemokine receptors
      Ccr1Chemokine receptors
      Ccr2Chemokine receptors
      Ccr3Chemokine receptors
      Ccr4Chemokine receptors
      Ccr5Chemokine receptors
      Ccr6Chemokine receptors
      Ccr7Chemokine receptors
      Ccr8Chemokine receptors
      Ccr9Chemokine receptors
      Ccr10Chemokine receptors
      Xcr1Chemokine receptors
      Cx3cr1Chemokine receptors
      Cxcr1Chemokine receptors
      Cxcr2Chemokine receptors
      Cxcr3Chemokine receptors
      Cxcr4Chemokine receptors
      Cxcr5Chemokine receptors
      Cxcr6Chemokine receptors
      6030468B19RikCytokines
      Aimp1Cytokines
      AregCytokines
      Bmp1Cytokines
      Bmp2Cytokines
      Bmp3Cytokines
      Bmp4Cytokines
      Bmp5Cytokines
      Bmp6Cytokines
      Bmp7Cytokines
      Bmp8aCytokines
      Bmp8bCytokines
      Bmp10Cytokines
      Bmp15Cytokines
      C1qtnf4Cytokines
      Cd40lgCytokines
      Cd70Cytokines
      Cer1Cytokines
      Clcf1Cytokines
      Cmtm2aCytokines
      Cmtm2bCytokines
      Cmtm3Cytokines
      Cmtm5Cytokines
      Cmtm7Cytokines
      Cmtm8Cytokines
      CntfCytokines
      Crlf1Cytokines
      Crlf2Cytokines
      Csf1Cytokines
      Csf2Cytokines
      Csf3Cytokines
      Ctf1Cytokines
      Ctf2Cytokines
      Ebi3Cytokines
      Edn1Cytokines
      EpoCytokines
      Fam3bCytokines
      FaslCytokines
      Fgf2Cytokines
      Flt3lCytokines
      Gdf1Cytokines
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      Gdf6Cytokines
      Gdf7Cytokines
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      Gdf10Cytokines
      Gdf11Cytokines
      Gdf15Cytokines
      Gm13271Cytokines
      Gm13272Cytokines
      Gm13275Cytokines
      Gm13276Cytokines
      Gm13277Cytokines
      Gm13283Cytokines
      Gpi1Cytokines
      Grem1Cytokines
      Grem2Cytokines
      GrnCytokines
      Hmgb1Cytokines
      Ifna1Cytokines
      Ifna2Cytokines
      Ifna4Cytokines
      Ifna5Cytokines
      Ifna6Cytokines
      Ifna7Cytokines
      Ifna9Cytokines
      Ifna11Cytokines
      Ifna12Cytokines
      Ifna13Cytokines
      Ifna14Cytokines
      Ifna15Cytokines
      Ifna16Cytokines
      IfnabCytokines
      Ifnb1Cytokines
      IfneCytokines
      IfngCytokines
      IfnkCytokines
      Ifnl2Cytokines
      Ifnl3Cytokines
      IfnzCytokines
      Il1aCytokines
      Il1bCytokines
      Il1f5Cytokines
      Il1f6Cytokines
      Il1f8Cytokines
      Il1f9Cytokines
      Il1f10Cytokines
      Il1rnCytokines
      Il2Cytokines
      Il3Cytokines
      Il4Cytokines
      Il5Cytokines
      Il6Cytokines
      Il7Cytokines
      Il9Cytokines
      Il10Cytokines
      Il11Cytokines
      Il12aCytokines
      Il12bCytokines
      Il13Cytokines
      Il15Cytokines
      Il16Cytokines
      Il17aCytokines
      Il17bCytokines
      Il17cCytokines
      Il17dCytokines
      Il17fCytokines
      Il18Cytokines
      Il19Cytokines
      Il20Cytokines
      Il21Cytokines
      Il22Cytokines
      Il23aCytokines
      Il24Cytokines
      Il25Cytokines
      Il27Cytokines
      Il31Cytokines
      Il33Cytokines
      Il34Cytokines
      IltifbCytokines
      InhaCytokines
      InhbaCytokines
      InhbbCytokines
      InhbcCytokines
      InhbeCytokines
      KitlCytokines
      Lefty1Cytokines
      Lefty2Cytokines
      LifCytokines
      LtaCytokines
      LtbCytokines
      MifCytokines
      MstnCytokines
      NamptCytokines
      NdpCytokines
      NodalCytokines
      OsmCytokines
      Pglyrp1Cytokines
      Prl7d1Cytokines
      Scg2Cytokines
      Scgb3a1Cytokines
      Sectm1aCytokines
      Sectm1bCytokines
      Slurp1Cytokines
      Spp1Cytokines
      Tafa5Cytokines
      Tgfb1Cytokines
      Tgfb2Cytokines
      Tgfb3Cytokines
      ThpoCytokines
      Timp1Cytokines
      TnfCytokines
      Tnfsf4Cytokines
      Tnfsf8Cytokines
      Tnfsf9Cytokines
      Tnfsf10Cytokines
      Tnfsf11Cytokines
      Tnfsf12Cytokines
      Tnfsf13Cytokines
      Tnfsf13bCytokines
      Tnfsf14Cytokines
      Tnfsf15Cytokines
      Tnfsf18Cytokines
      TslpCytokines
      VegfaCytokines
      Wnt1Cytokines
      Wnt2Cytokines
      Wnt5aCytokines
      Wnt7aCytokines
      H2-Ke1MHCI score
      TapbpMHCI score
      H2-Ke2MHCI score
      H2-K2MHCI score
      H2-K1MHCI score
      H2-Ke6MHCI score
      H2-Ke5MHCI score
      H2-D1MHCI score
      H2-D2MHCI score
      H2-D3MHCI score
      H2-D4MHCI score
      H2-LMHCI score
      H2-QMHCI score
      H2-Q1MHCI score
      H2-Q2MHCI score
      H2-Q3MHCI score
      H2-Q4MHCI score
      H2-Q5MHCI score
      H2-Q6MHCI score
      H2-Q7MHCI score
      H2-Q8MHCI score
      H2-Q9MHCI score
      H2-Q10MHCI score
      H2-Q11MHCI score
      H2-Q12MHCI score
      H2-Q13MHCI score
      H2-Q14MHCI score
      H2-Q15MHCI score
      H2-TMHCI score
      H2-T24MHCI score
      H2-T23MHCI score
      H2-T22MHCI score
      H2-T21MHCI score
      H2-T20MHCI score
      H2-T19MHCI score
      H2-T18MHCI score
      H2-T17MHCI score
      H2-T16MHCI score
      H2-T15MHCI score
      H2-T14MHCI score
      H2-T13MHCI score
      H2-T12MHCI score
      H2-T11-psMHCI score
      H2-T10MHCI score
      H2-T9MHCI score
      H2-T8MHCI score
      H2-T7MHCI score
      H2-T6MHCI score
      H2-T5MHCI score
      H2-T4MHCI score
      H2-T3MHCI score
      H2-T2MHCI score
      H2-T1MHCI score
      H2-T18rsMHCI score
      H2-M10-ps1MHCI score
      H2-M10.1MHCI score
      H2-M10.2MHCI score
      H2-M10.4MHCI score
      H2-M10-ps2MHCI score
      H2-M1MHCI score
      H2-M7-psMHCI score
      H2-M8-psMHCI score
      H2-M9MHCI score
      H2-M10-ps3MHCI score
      H2-M10.3MHCI score
      H2-M11MHCI score
      H2-M10.5MHCI score
      H2-M6-psMHCI score
      H2-M4-psMHCI score
      H2-M5MHCI score
      H2-M3MHCI score
      H2-M2MHCI score
      H2-PaMHCII score
      H2-PbMHCII score
      H2-Ab1MHCII score
      H2-AaMHCII score
      H2-Eb1MHCII score
      H2-Eb2MHCII score
      H2-Ea-psMHCII score
      H2-OaMHCII score
      H2-ObMHCII score
      H2-DMaMHCII score
      H2-DMb1MHCII score
      H2-DMb2MHCII score
      Socs1M1 genes
      NfkbizM1 genes
      Irf5M1 genes
      Irf1M1 genes
      Il1bM1 genes
      TnfM1 genes
      Il6M1 genes
      Il8M1 genes
      Il27M1 genes
      Il23aM1 genes
      Il12aM1 genes
      Il12bM1 genes
      Ccl5M1 genes
      Cxcl9M1 genes
      Cxcl10M1 genes
      Cxcl11M1 genes
      MarcoM1 genes
      Mmp9M1 genes
      Nos2M1 genes
      Ido1M1 genes
      Mrc1M2 genes
      Arg1M2 genes
      RetnlaM2 genes
      Chil3M2 genes
      Alox15M2 genes
      Il4raM2 genes
      Nfil3M2 genes
      F13a1M2 genes
      Sbno2M2 genes
      Socs2M2 genes
      Socs3M2 genes
      Irf4M2 genes
      Il10M2 genes
      Ccl1M2 genes
      Ccl4M2 genes
      Ccl13M2 genes
      Ccl17M2 genes
      Ccl18M2 genes
      Ccl20M2 genes
      Ccl22M2 genes
      Ccl24M2 genes
      Cxcl13M2 genes
      Adam19Core cDC genes
      Amica1Core cDC genes
      Ap1s3Core cDC genes
      Ass1Core cDC genes
      Bcl11aCore cDC genes
      BtlaCore cDC genes
      Ccr7Core cDC genes
      Flt3Core cDC genes
      Gpr114Core cDC genes
      Gpr132Core cDC genes
      Gpr68Core cDC genes
      Gpr82Core cDC genes
      H2-Eb2Core cDC genes
      Hmgn3Core cDC genes
      KitCore cDC genes
      Klri1Core cDC genes
      KmoCore cDC genes
      P2ry10Core cDC genes
      Pvrl1Core cDC genes
      Rab30Core cDC genes
      6-SepCore cDC genes
      Slamf7Core cDC genes
      Traf1Core cDC genes
      Zbtb46Core cDC genes
      PecrCore macrophage genes
      AgmoCore macrophage genes
      1810011H11RikCore macrophage genes
      FerCore macrophage genes
      Tlr4Core macrophage genes
      Pon3Core macrophage genes
      Mr1Core macrophage genes
      ArsgCore macrophage genes
      Fcgr1Core macrophage genes
      Camk1Core macrophage genes
      Fgd4Core macrophage genes
      SqrdlCore macrophage genes
      Csf3rCore macrophage genes
      Plod1Core macrophage genes
      Tom1Core macrophage genes
      Myo7aCore macrophage genes
      Pld3Core macrophage genes
      Tpp1Core macrophage genes
      CtsdCore macrophage genes
      Pla2g15Core macrophage genes
      Lamp2Core macrophage genes
      Pla2g4aCore macrophage genes
      MertkCore macrophage genes
      Tlr7Core macrophage genes
      Cd14Core macrophage genes
      Tbxas1Core macrophage genes
      Fcgr3Core macrophage genes
      Sepp1Core macrophage genes
      GlulCore macrophage genes
      Cd164Core macrophage genes
      Tcn2Core macrophage genes
      Dok3Core macrophage genes
      CtslCore macrophage genes
      Tspan14Core macrophage genes
      ComtCore macrophage genes
      Dram2Core macrophage genes
      Abca1Core macrophage genes
      Cxcl10CFB5
      Gbp2CFB5
      Iigp1CFB5
      Bst2CFB5
      Gbp6CFB5
      IgtpCFB5
      Psmb8CFB5
      Ifi47CFB5
      Isg15CFB5
      Gbp3CFB5
      Ifit3CFB5
      Gbp7CFB5
      Irf7CFB5
      Ifi203CFB5
      Phf11dCFB5
      CFB5 also highly expressed genes (lfit1, lfit3, lfit3b, ligp1, Ifi47 and Isg15) that were closely associated with the cellular response to interferon-gamma (IFN-γ) and had high overall expression of IFN-γ response genes (Table 2). We hypothesized that the significant increase in CFB5 cells at day 5 may be associated with the IFN-γ-associated signalling pathway. Thus, we used anti-IFN-γ at day 5 posttransplantation and observed a decrease in CFB5 cells in the grafts compared with the control group (Fig. 4d, e). Under the stimulation of IFN-γ, primary mouse CFBs highly expressed MHC I and MHC II and secreted more CXCL9 and CXCL10 (Fig. S6c, d). We next explored whether CFB5 might also play a role in other heart disease models and analysed single-cell data from two myocardial infarction studies and a normal heart study
      • Hesse J.
      • et al.
      Single-cell transcriptomics defines heterogeneity of epicardial cells and fibroblasts within the infarcted murine heart.
      ,
      • Farbehi N.
      • et al.
      Single-cell expression profiling reveals dynamic flux of cardiac stromal, vascular and immune cells in health and injury.
      ,
      • Skelly D.A.
      • et al.
      Single-Cell Transcriptional Profiling Reveals Cellular Diversity and Intercommunication in the Mouse Heart.
      . In the two myocardial infarction datasets, we observed that the marker genes of CFB5, IFN-γ response genes and Ly6a were expressed in a portion of fibroblast cells, especially in the aCSC-8 cluster
      • Hesse J.
      • et al.
      Single-cell transcriptomics defines heterogeneity of epicardial cells and fibroblasts within the infarcted murine heart.
      and cluster 721 (Fig. 4f, Fig. S7a, c and d). Moreover, the CFB5 marker genes were also expressed in a small number of normal heart CFB cells
      • Skelly D.A.
      • et al.
      Single-Cell Transcriptional Profiling Reveals Cellular Diversity and Intercommunication in the Mouse Heart.
      (Fig. 4g, Fig. S7b). These results indicated that the number of CFB5-like fibroblasts was increased in response to pathological cardiac stimuli. On the other hand, unlike CFB5, the aCSC-8 cluster
      • Hesse J.
      • et al.
      Single-cell transcriptomics defines heterogeneity of epicardial cells and fibroblasts within the infarcted murine heart.
      and cluster 721 did not highly express chemokines and antigen presentation-related genes, indicating that CFB5-like fibroblasts from different disease models might have their own unique transcription features.

      Intercellular network analysis revealed the important roles of the CXCL9/CXCL10-CXCR3 axis in transplant rejection

      To systematically explore the interaction between CFB subclusters and immune cells in acute rejection, we performed cell‒cell communication analysis using CellChat
      • Jin S.
      • et al.
      Inference and analysis of cell-cell communication using CellChat.
      . We revealed a dense communication network among CFBs and immune cells at different time points posttransplantation (Fig. 5a). Overall, the immune cells interacting with CFBs mainly included myeloid cells, NK cells, and T cells. The interactions of CFB0 with immune cells were weakened at day 5 due to its decreased cell number. Consistent with previous analyses, the interaction between CFB5 and T cells increased over time and was the most abundant on day 5 (Fig. S8a), suggesting the important roles of CFB5 in the late stage of transplant rejection.
      Figure 5
      Figure 5Intercellular network analysis revealed important roles of the CXCL9/CXCL10-CXCR3 axis in transplant rejection. a. Heatmap showing the number of total potential ligand‒receptor pairs between different cell types at each time point obtained with CellChat. The bar plot represents the sum of a column or row. b. Dot plot showing the selected 24 ligand‒receptor pairs between CFBs (CFB0, CFB5) and immune cells (myeloid cells, NK cells, T cells) at different time points. The dot colour indicates the communication probability. c. Histogram showing the relative contribution of the different chemokine ligand‒receptor pairs to the overall CXCL signalling pathway at day 5 posttransplantation. d. The survival time of the allografts treated with AMG 487 was significantly longer than that of the untreated allografts (control group) (13.17±2.04 vs. 9.33±2.07, p=0.013 in Mantel‒Cox test).
      We identified ligand‒receptor pairs between CFB0/CFB5 and myeloid/NK/T cells (CFB cells expressing ligand signals and immune cells expressing receptors). The significantly altered signals included ligand‒receptor pairs related to antigen presentation, such as H2-k1 signalling, H3-m3 signalling, and H2-t22 signalling, and pairs related to chemokines, such as CCR2 signalling, CCR5 signalling, and CXCR3 signalling (Fig. 5b). The interactions between CXCR3 and its ligands, CXCL9 and CXCL10, were markedly increased in CFB5 cells at day 5 posttransplantation. Similarly, the signals related to antigen presentation also underwent significant changes in CFB5 at day 5. Thus, CFB5 cells may affect the activation of immune cells after heart transplantation through alterations in ligand‒receptor interactions of antigen presentation-related signals.
      Chemokines and inflammatory stimuli induce circulating peripheral leukocytes to infiltrate the graft and to trigger transplant rejection
      • Colvin B.L.
      • Thomson A.W.
      Chemokines, their receptors, and transplant outcome.
      . CXCL9-CXCR3 and CXCL10-CXCR3 were in the top 5 chemokine-related ligand‒receptor pairs at day 5 but not at the other days (Fig. 5c, Fig. S8b), suggesting that they might play important roles in chemokine-mediated immune cell infiltration at day 5 posttransplantation. During the process of transplant rejection, the expression levels of CXCL9, CXCL10 and IFN-γ were increased (Fig. S9a). In addition, we observed that the number of CXCR3+CD8+ T cells and CXCR3+CD4+ T cells was significantly increased in grafts at day 5 post transplantation (Fig. S10a). To further verify the roles of the CXCL9/CXCL10-CXCR3 axis in regulating the early response to transplant rejection, we used CXCR3 antagonism, a small-molecule inhibitor AMG 487, in mouse allogeneic heterotopic heart transplants and found that AMG 487 treatment significantly improved allograft survival (mean survival time, MST=13.17±2.04 vs. 9.33±2.07 days, p=0.013 via the Mantel‒Cox test) (Fig. 5d). Taken together, these results indicate that CXCR3 may be a promising therapeutic target to prevent acute transplant rejection.

      Discussion

      In this study, we used scRNA-seq to profile the cellular landscape of heart allografts. This analysis unmasked the complexity of immune cells within allografts and identified the development and responses of T cells during the progression of transplant rejection. Analysis of TCR repertoires demonstrated that ongoing disease progression was able to engender increasingly enhanced capacities for an adaptive T cell immune response and elicited greater destruction for grafts. Importantly, we revealed heterogeneous subtypes of CFBs and their pivotal regulatory pathways and found that the CFB subtype CFB5 might be associated with acute graft rejection. In addition, cell‒cell communication analyses suggested that potential crosstalk among CFBs, macrophages, and T cells contributes to acute graft rejection and demonstrated the role of the CXCL9/CXCL10-CXCR3 axis. These findings together present a valuable comprehensive cell atlas of heart allografts, which will help us better understand the occurrence and development of acute rejection.
      T cells have long been considered central players in adaptive immune response-mediated transplant rejection. Our scRNA-seq and scTCR-seq analyses showed multiple T cell subpopulations with clonal expansion in the allografts, in which CTLs remained one of the most robust populations. Highly clonally expanded CD8+ T cells have been considered a marker of infectious disease severity, and their association with better outcomes in patients with COVID-19 has been reported
      • Liao M.
      • et al.
      Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19.
      . However, expanded CTLs lead to more severe destruction for grafts in transplant rejection. Our data showed that the clonally expanded capacity of T cells was stronger at the late stage of allograft formation.
      CFBs are considered the principal players in extracellular matrix production in the heart
      • Tallquist M.D.
      Developmental Pathways of Cardiac Fibroblasts.
      . They play important functional roles in immune regulation and cellular response to stimuli
      • Wang L.
      • et al.
      Single cell dual-omics reveals the transcriptomic and epigenomic diversity of cardiac non-myocytes.
      . The role of CFBs in acute transplant rejection remains largely undefined. Our novel data have identified diverse CFB subclusters. CFB0 highly expressed genes involved in proinflammation and marker genes of dendritic cells and phagocytic cells (Cd52, C1qb, Lyz2 and Actb), suggesting that CFB0 is a myeloid-like myofibroblast
      • Forte E.
      • et al.
      Dynamic Interstitial Cell Response during Myocardial Infarction Predicts Resilience to Rupture in Genetically Diverse Mice.
      . CFB1 cells expressed genes encoding ECM proteins, including Eln, Cilp, Smoc2 and Sparcl1, and were recently termed matrix fibrocytes and late-response fibroblasts
      • Forte E.
      • et al.
      Dynamic Interstitial Cell Response during Myocardial Infarction Predicts Resilience to Rupture in Genetically Diverse Mice.
      or activated fibroblasts
      • Farbehi N.
      • et al.
      Single-cell expression profiling reveals dynamic flux of cardiac stromal, vascular and immune cells in health and injury.
      . CFB2 was found to express genes associated with ribosome biogenesis, suggesting that there is high protein synthesis activity in CFB2. A CFB subtype similar to CFB2 was observed in a myocardial infarction study
      • Hesse J.
      • et al.
      Single-cell transcriptomics defines heterogeneity of epicardial cells and fibroblasts within the infarcted murine heart.
      . CFB3 was characterized by the expression of cell chemotaxis-associated genes (Spp1, Lox, CCL8 and CCL14). CFB4 cells, with upregulated Col8a1 and Cilp expression, resembled the reported late-resolution fibroblasts
      • Zhang C.L.
      • et al.
      Cartilage intermediate layer protein-1 alleviates pressure overload-induced cardiac fibrosis via interfering TGF-beta1 signaling.
      . Col8a1 is an extracellular matrix component that can confer tensile strength and support vessel integrity and structure. Cilp is a matricellular protein involved in the regulation of matrix remodelling. CFB6 highly expressed Wnt pathway-related genes (Wif1 and Dkk3), which are referred to as Wnt-expressing or endocardial-derived fibroblasts
      • Hesse J.
      • et al.
      Single-cell transcriptomics defines heterogeneity of epicardial cells and fibroblasts within the infarcted murine heart.
      ,
      • Farbehi N.
      • et al.
      Single-cell expression profiling reveals dynamic flux of cardiac stromal, vascular and immune cells in health and injury.
      ,
      • Forte E.
      • et al.
      Dynamic Interstitial Cell Response during Myocardial Infarction Predicts Resilience to Rupture in Genetically Diverse Mice.
      . In addition, CFB5 highly expressed the Ly6a gene, which encodes Sca1 and is a marker for putative cardiac progenitor cells
      • Tang J.
      • et al.
      Fate Mapping of Sca1(+) Cardiac Progenitor Cells in the Adult Mouse Heart.
      . Progenitor-like CFBs are known to have restricted cardiovascular differentiation potential and extensive proliferation capacity
      • Zhang Y.
      • et al.
      Expandable Cardiovascular Progenitor Cells Reprogrammed from Fibroblasts.
      . This population of progenitor-like CFBs could be converted into cardiomyocytes, smooth muscle cells and endothelial cells in vitro or when transplanted into immunodeficient mice with infarcted hearts
      • Zhang Y.
      • et al.
      Expandable Cardiovascular Progenitor Cells Reprogrammed from Fibroblasts.
      ,
      • Xu J.
      • et al.
      Therapeutic effects of CXCR4(+) subpopulation of transgene-free induced cardiosphere-derived cells on experimental myocardial infarction.
      . Moreover, in mouse myocardial infarction, progenitor-like CFBs showed remarkable plasticity and altered their cytokine production profiles under different microenvironments
      • Chen G.
      • et al.
      Sca-1(+) cardiac fibroblasts promote development of heart failure.
      . These data suggest that CFB5 might have mixed characteristics of the reported interferon-responsive/interferon-stimulated fibroblasts
      • Hesse J.
      • et al.
      Single-cell transcriptomics defines heterogeneity of epicardial cells and fibroblasts within the infarcted murine heart.
      ,
      • Forte E.
      • et al.
      Dynamic Interstitial Cell Response during Myocardial Infarction Predicts Resilience to Rupture in Genetically Diverse Mice.
      and the Sca1-high/progenitor-like fibroblasts
      • Farbehi N.
      • et al.
      Single-cell expression profiling reveals dynamic flux of cardiac stromal, vascular and immune cells in health and injury.
      , conferring it with its possible multipotent differentiation potential and high proliferation capacity upon interferon stimulation. Many of these subclusters have been reported in myocardial infarction, suggesting that CFBs may share common features across different disease conditions.
      Endothelial cells exposed to IFN-γ are characterized by hypermigration, proliferation, apoptosis resistance and inflammatory activation
      • Gairhe S.
      • et al.
      Type I interferon activation and endothelial dysfunction in caveolin-1 insufficiency-associated pulmonary arterial hypertension.
      . In addition, endothelial cells can express mesenchymal genes and acquire metabolic adaptation during the early stages post myocardial infarction
      • Tombor L.S.
      • et al.
      Single cell sequencing reveals endothelial plasticity with transient mesenchymal activation after myocardial infarction.
      . Therefore, we putatively hypothesized that endothelial-mesenchymal transition (EndMT) might occur in the heart transplantation model. A possible role was revealed for EndMT in the increased number of CFB5 cells through transcriptional correlation analysis (Fig. S7e), expression of CFB5 marker genes (Fig. S7f) and trajectory inference (Fig. S7g). Additionally, we noted that some velocity vectors from other CFBs pointed towards CFB5 (Fig. S7g), which might indicate that CFB5 cells were converted from other CFBs. CFB5 cells highly expressed chemotaxis-associated and antigen presentation-related genes and had intense interactions with myeloid cells and T cells. A recent study demonstrated that intervening in the CXCL9/CXCL10-CXCR3 axis could reduce CD4+ T cell infiltration and prevent adverse cardiac remodelling
      • Ngwenyama N.
      • et al.
      CXCR3 regulates CD4+ T cell cardiotropism in pressure overload-induced cardiac dysfunction.
      . Consistently, CXCL9/CXCL10-CXCR3 was also revealed to be significant in the recruitment of CD4+ and CD8+ T cells, especially proinflammatory Th1 cells
      • Karin N.
      CXCR3 Ligands in Cancer and Autoimmunity, Chemoattraction of Effector T Cells, and Beyond.
      . In our dataset, CXCL9 and CXCL10 were highly expressed in CFB5, again implying roles of CFB5 cells in regulating the recruitment and infiltration of immune cells. In summary, CFB5 may play critical roles in the acute rejection of heart transplants through potential crosstalk with myeloid cells and T cells. Our in vivo intervention experiments further demonstrated CXCL9/CXCL10-CXCR3 as potential targets to improve heart transplant survival.
      In addition to novel findings, there are limitations to our study. First, cardiac rejection is one of the most important factors affecting graft survival, and this event is usually divided into two main types: ACR and antibody-mediated rejection (AMR). AMR is characterized by the presence of specific antibodies produced by B cells detected in the myocardium
      • Khachatoorian Y.
      • et al.
      Noninvasive biomarkers for prediction and diagnosis of heart transplantation rejection.
      ,
      • Novak J.
      • Machackova T.
      • Krejci J.
      • Bienertova-Vasku J.
      • Slaby O.
      MicroRNAs as theranostic markers in cardiac allograft transplantation: from murine models to clinical practice.
      . However, our scRNA-seq analysis mainly focused on T cell-mediated ACR and did not cover AMR. AMR in the heart is not easily recognizable by histological diagnosis, and the transcriptional expression profile of B cells needs to be further analysed. Second, this paper is based on single-cell analysis of a mouse model. Due to the difficulty of obtaining transplanted heart specimens in the clinic, the findings of this paper have not been validated in humans. Third, the identity of cardiac fibroblasts is susceptible to false positive findings due to the insufficient specificity of vimentin as a marker. Another limitation of this study is the lack of an immunosuppressive treatment group. Immunosuppressants are routinely adopted to use for transplanted patients. However, the investigation did not evaluate the impacts of the immunosuppressants on CFBs. Therefore, understanding the role of immunosuppressants in altering transcriptional profile of CFBs will be of great significance in the future.
      In summary, our study reveals for the first time the transcriptional landscape of heart allografts at single-cell resolution and provides a comprehensive and in-depth understanding of heart allografts. The findings in this paper may help to develop more effective therapeutic strategies to improve transplant survival in the future.

      Disclosure statement

      The authors declare that they have no competing interests. This work was supported by the National Key Basic Research Program of China (2020YFE0204000), the National Natural Science Foundation of China (No. 81870306, 11971039), and the Fundamental Research Funds for the Central Universities (2015XZZX004-21). The authors sincerely thank Bo Hong and Qin Ye, two pathologists from Zhejiang University, for the histological evaluation of rejection.

      Author Contributions Statement

      All authors have directly participated in the planning, execution, or analysis of this study. W.G. and R.X. supervised the project. W.G., R.X., D.K., Z.S. and J.C. designed the study. S.H. and Y.S. analysed the data; X.M., Y.J., X.Y., Z.W. and H.L. performed the experiments; D.K., S.H. and R.X. wrote the paper; J.L. and M.X. revised the paper.

      Data and materials availability

      The raw sequence data reported in this paper have been deposited in the Genome Sequence Archive
      • Chen T.
      • et al.
      The Genome Sequence Archive Family: Toward Explosive Data Growth and Diverse Data Types.
      in the National Genomics Data Center
      • Members C.-N.
      Partners. Database Resources of the National Genomics Data Center, China National Center for Bioinformation in 2021.
      , China National Center for Bioinformation/Beijing Institute of Genomics, Chinese Academy of Sciences (GSA: CRA005239), which is publicly accessible at https://ngdc.cncb.ac.cn/gsa. Exploration and visualization of the scRNA-seq datasets in this study can also be performed at http://39.104.203.4:3838/HT_shinyapp_v0.1/.

      Figure design

      Figure 1a was created with BioRender.com.

      Appendix. Supplementary materials

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