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Department of Surgery, Second Affiliated Hospital of Zhejiang University, Hangzhou, Zhejiang, ChinaLiangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, Zhejiang, China
Department of Surgery, Second Affiliated Hospital of Zhejiang University, Hangzhou, Zhejiang, ChinaLiangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, Zhejiang, China
Department of Surgery, Second Affiliated Hospital of Zhejiang University, Hangzhou, Zhejiang, ChinaLiangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, Zhejiang, China
Department of Surgery, Second Affiliated Hospital of Zhejiang University, Hangzhou, Zhejiang, ChinaLiangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, Zhejiang, China
Department of Surgery, Second Affiliated Hospital of Zhejiang University, Hangzhou, Zhejiang, ChinaLiangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, Zhejiang, China
Department of Otolaryngology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
⁎⁎ Ruibin Xi and Weihua Gong, contributed to this study.
Affiliations
Department of Surgery, Second Affiliated Hospital of Zhejiang University, Hangzhou, Zhejiang, ChinaLiangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, Zhejiang, China
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.
Although heart transplantation has been successfully used for treating end-stage heart disease, allograft rejection remains a major barrier to long-term graft survival.
Acute cellular rejection (ACR), one of the most common complications, is characterized by the infiltration of mononuclear cells in the perivasculature and interstitium.
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,
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.
Donor macrophages express unique transcriptional profiles compared to their recipient counterparts that are capable of persisting for several years after renal transplantation.
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.
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).
Fibroblasts undergo phenotypic and functional transitions, ranging from proinflammatory and anti-inflammatory to pro-scar producing profiles, for effective scar formation.
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.
CXCR3 chemokines are potent chemoattractants for various immune cells, such as macrophages, dendritic cells, type I T helper cells and natural killer (NK) cells.
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.
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 (Figure 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 (Figure 1B, Figure 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) (Figure 1C-E, Figure 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 (Figure 1F). After the first day, almost all immune cells were from the recipient, while most of the nonimmune cells were from the donor (Figure S3A, B). These results indicated that donor antigen-reactive immune cells might be involved in the tissue damage of the graft.
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 (Figure 1C). The right panel is split based on the 4 different time points (Figure 1D). (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) (Figure 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 Figure 2C and 2D, respectively. Increased proportions of CTLs and NKT-like cells were observed at day 5.
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). () 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 to 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 (Figure 2E). Next, we investigated the expanded clones among different T cell subclusters from day 0 to day 5 (Figure 2F, Figure 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 Figure 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 (Figure 2H). Finally, we compared the expression levels of expanded and nonexpanded CTL cells (Figure 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 (Figure 3A, B). The genes correlated with the pseudotime inferred by Monocle2
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 (Figure 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 (Figure 3C). Furthermore, the clusters identified by the unsupervised clustering analysis were similar to those seen by the regulon-based clustering analysis (Figure S5C). The proportion of CFB subclusters at different time points and the proportion of the 4 time points in each CFB subcluster are shown (Figure 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 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. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
To further explore the potential functional heterogeneity of CFBs, we identified differentially expressed genes (DEGs) for each CFB subcluster (Figure 3F) and performed pathway enrichment analysis of these DEGs (Figure 3G). Notably, CFB5, the most abundant fibroblast subtype on day 5 (>30%), showed elevated expression of genes mainly involved in immunomodulation (Figure 3G) and interferon-responsive transcription factors (Gtf3a, Irf2, Stat2 and Irf8) (Figure 3H, Figure 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 (Figure 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
) (Figure 4C) and found that the number of CXCL10+Gbp2+ CFBs was indeed significantly increased at day 5 (Figure 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) (Figure 4A, B), suggesting that CFB5 might have proinflammatory and antigen presentation properties (Supplemental table).
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. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
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 (Supplemental table). 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 (Figure 4D, E). Under the stimulation of IFN-γ, primary mouse CFBs highly expressed MHC I and MHC II and secreted more CXCL9 and CXCL10 (Figure S6C, D). We next explored whether CFB5 might also play a role in other heart disease models and analysed single-cell data from 2 myocardial infarction studies and a normal heart study.
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
(Figure 4G, Figure. 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
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.
We revealed a dense communication network among CFBs and immune cells at different time points posttransplantation (Figure 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 (Figure S8A), suggesting the important roles of CFB5 in the late stage of transplant rejection.
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 (Figure 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.
CXCL9-CXCR3 and CXCL10-CXCR3 were in the top 5 chemokine-related ligand‒receptor pairs at day 5 but not at the other days (Figure 5C, Figure 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 (Figure 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 (Figure. 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) (Figure 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.
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.
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.
CFB1 cells expressed genes encoding ECM proteins, including Eln, Cilp, Smoc2 and Sparcl1, and were recently termed matrix fibrocytes and late-response fibroblasts
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.
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.
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.
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.
Moreover, in mouse myocardial infarction, progenitor-like CFBs showed remarkable plasticity and altered their cytokine production profiles under different microenvironments.
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.
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 (Figure. S7E), expression of CFB5 marker genes (Figure. S7F) and trajectory inference (Figure S7G). Additionally, we noted that some velocity vectors from other CFBs pointed towards CFB5 (Figure 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.
Consistently, CXCL9/CXCL10-CXCR3 was also revealed to be significant in the recruitment of CD4+ and CD8+ T cells, especially proinflammatory Th1 cells.
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 2 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.
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.
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
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/.
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.
Disclosure statement
The authors declare that they have no competing interests.
This month on JHLT: The Podcast, the JHLT Digital Media Editors review two studies from the February issue of The Journal of Heart and Lung Transplantation—and bring in a couple of experts to help them make sense of some new technology.
Single cell technologies are emerging as non-biased techniques to discover novel biological pathways in a variety of pre-clinical models and in human tissue both in health and disease. To help the editors—and you!—understand single cell approaches and this study, JHLT editors Ben Kopecky, MD, PhD and Kory Lavine, MD, PhD, from the Washington University St. Louis, appear in the episode to explore the methodology and what the study tells us.
When considering differences in pathophysiology of the subtypes of PAH, changes in biomarkers in angiogenesis and inflammation may provide useful insights and potential therapeutic targets. The authors of this study prospectively looked at 33 biomarkers of angiogenesis and inflammation in a cohort of patients across various PAH etiologies, and made correlations to clinical outcomes.
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