Abstract
Inflammation and tissue fibrosis co-exist and are causally linked to organ dysfunction1,2. However, the molecular mechanisms driving immune–fibroblast cell communication in human cardiac disease remain unexplored and there are at present no approved treatments that directly target cardiac fibrosis3,4. Here we performed multiomic single-cell gene expression, epitope mapping and chromatin accessibility profiling in 45 healthy donor, acutely infarcted and chronically failing human hearts. We identified a disease-associated fibroblast trajectory that diverged into distinct populations reminiscent of myofibroblasts and matrifibrocytes, the latter expressing fibroblast activator protein (FAP) and periostin (POSTN). Genetic lineage tracing of FAP+ fibroblasts in vivo showed that they contribute to the POSTN lineage but not the myofibroblast lineage. We assessed the applicability of experimental systems to model cardiac fibroblasts and demonstrated that three different in vivo mouse models of cardiac injury were superior compared with cultured human heart and dermal fibroblasts in recapitulating the human disease phenotype. Ligand–receptor analysis and spatial transcriptomics predicted that interactions between C-C chemokine receptor type 2 (CCR2) macrophages and fibroblasts mediated by interleukin-1β (IL-1β) signalling drove the emergence of FAP/POSTN fibroblasts within spatially defined niches. In vivo, we deleted the IL-1 receptor on fibroblasts and the IL-1β ligand in CCR2+ monocytes and macrophages, and inhibited IL-1β signalling using a monoclonal antibody, and showed reduced FAP/POSTN fibroblasts, diminished myocardial fibrosis and improved cardiac function. These findings highlight the broader therapeutic potential of targeting inflammation to treat tissue fibrosis and preserve organ function.
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Data availability
Raw and processed sequencing files can be found on the Gene Expression Omnibus super series (GSE218392). Source data are provided with this paper.
Code availability
Scripts used for analysis in this manuscript can be found at https://github.com/jamrute/2024_Nature_IL1B_ImmuneFibroblast.
Change history
28 October 2024
Some Extended Data Figures originally displayed incorrectly as low-resolution images; the image quality has now been corrected.
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Acknowledgements
K.J.L. is supported by the Washington University in St. Louis Rheumatic Diseases Research Resource-Based Center (grant no. NIH P30AR073752), the National Institutes of Health (grant nos. R01 HL138466, R01 HL139714, R01 HL151078, R01 HL161185, R35 HL161185), the Leducq Foundation Network (grant no. 20CVD02), the Burroughs Welcome Fund (grant no. 1014782), sponsored research agreement from Amgen, the Children’s Discovery Institute of Washington University and St. Louis Children’s Hospital (grant nos. CH-II-2015-462, CH-II-2017-628, PM-LI-2019-829), the Foundation of Barnes-Jewish Hospital (grant no. 8038-88) and generous gifts from Washington University School of Medicine. J.M.A. is supported by the American Heart Association Predoctoral Fellowship (grant no. 826325), the Washington University School of Medicine Medical Scientist Training Program and the Leducq Foundation Network Seed Grant (grant no. 20CVD02). Y.L. is supported by the National Institutes of Health (grant nos. R35HL145212, P41EB025815) and the Leducq Foundation Network (grant no. 20CVD02). Study design schematics used in Figs. 1a, 2h,l, 3a,c,e–h, 4e,f,j and 5a,f and Extended Data Figs. 4f,g, 6e, 7e, 9j and 10d were created using BioRender (https://BioRender.com). We thank the Genome Technology Access Center at the McDonnell Genome Institute at Washington University School of Medicine for help with genomic analysis. The Center is partially supported by NCI Cancer Center Support Grant no. P30 CA91842 to the Siteman Cancer Center. This publication is solely the responsibility of the authors and does not necessarily represent the official views of the NCRR or the NIH.
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J.M.A., B.A. and K.J.L. conceived the study and interpreted the data. J.M.A. made all the figures. J.M.A. and K.J.L. drafted the manuscript with direction from B.A. J.M.A. and A.B. isolated cells and prepared cDNA from human CITE-seq samples. T.Y. made all CITE-seq and Multiome libraries for sequencing. A.P. and J.M.A. performed all Multiome experiments. P.M., A.B. and A.K. performed all snRNA-seq. C.J. collected all clinical sample data. J.M.A. and X.L. performed all computational analysis. J.M.A. and X.Z. performed all Multiome analysis. J.M.A., X.L., C.K. and T.B. analysed spatial transcriptomics data with direction from S.H., R.K. and K.J.L. A.F. and S.Y.S. performed human in vitro experiments. J.M.A. performed in vivo mouse surgeries and A.K. conducted all echocardiography. J.M.A., I.-H.J., B.K., S.Y., V.P., C.K. and F.F.K. performed immunohistochemistry and analysed and processed images. S.Y. performed in vivo FAP lineage tracing experiments. P.L. and N.O.S. conducted the mendelian randomization analysis. Y.L., G.S.H. and R.V. performed all PET imaging experiments. C.B. and N.A.R. generated reagents. D.K., R.K., M.F., Y.T., P.L., N.O.S. and S.J. provided input for the manuscript. All authors contributed to the experimental design, data analysis and interpretation as well as manuscript production. K.J.L. is responsible for all aspects of this manuscript including experimental design, data analysis and manuscript production. All authors approved the final version of the manuscript.
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X.L., T.Y., S.Y.S., A.F., M.F., X.Z., S.J., C.-M.L. and B.A. are or were employed by Amgen. The other authors declare no competing interests.
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Extended data figures and tables
Extended Data Fig. 1 CITE-seq multi-modal clustering overview.
(a) UMAP embedding construction using RNA, protein, or weighted nearest neighbor analysis. (b) CITE-seq protein assay weights used for WNN clustering. (c) Heatmap of top marker genes for each cell type in global UMAP. (d) Integrated global UMAP colored by patient sample. (e) Cell type composition split by each sample. (f) Protein expression as a density plot for top CITE-seq protein markers in different cell types.
Extended Data Fig. 2 Multiome (RNA + ATAC) multi-modal clustering and gene signatures.
(a) Heatmap of top differentially expressed genes from snRNA-seq atlas. (b) DotPlot and (c) UMAP embedding of z-scores for top marker genes for cell types in (a). (d) UMAP embedding plot of Multiome data clustered by RNA, ATAC, joint, and joint clustering embedding with RNA annotations overlaid. (e) Correlation heatmap of RNA versus ATAC and (f) RNA versus joint RNA/ATAC clustering.
Extended Data Fig. 3 Fibroblast cell state transcriptional and molecular signatures.
(a) Heatmap of top marker genes for fibroblast cell states. (b) DotPlot of key marker genes for fibroblast cell states. (c) Gene set z-scores for fibroblast cell states plotted in UMAP embedding. (d) THY1 RNA, protein, and protein density plot in fibroblast UMAP space. (e) Density plots for differentially expressed protein markers in fibroblast UMAP space. (f) GO analysis from clusterProfiler for fibroblast cell states. (g) DoRothEA Transcription factor enrichment analysis across fibroblast cell states. (h) Density plot of fibroblast cell states by condition.
Extended Data Fig. 4 Cross-tissue and spatial integration of human HF fibroblast cell states.
(a) Gene set signature kernel density embedding plot for donor (top) and HF (bottom) – genes are derived from pseudobulk differential expression analysis between donor and HF. Statistically significant genes (adjusted p-value < 0.05, log2FC > 0.58, and base mean expression > 500). (b) Gene expression density plot in UMAP embedding for POSTN, RUNX1, EDNRA, and MEOX1 (markers distinguishing F2 and F9 lineages). (c) Integrated UMAP of perturbated pathological fibroblasts and (d) split by disease category. (e) Dotplot of marker genes for clusters in (c). (f) Fibroblast cell state mapping in control, infarcted, fibrotic, border zone, and remote zone LV Visium spatial transcriptomics sections (n = 28) with cell state gene signature grouped by section. (g) MISTy fibroblast cell state predictive modeling with predictor cell on y-axis and target cell on x-axis across 12 samples. (h) Cell state co-localization analysis using CellTrek SColoc. Schematics in f, g were created using BioRender (https://biorender.com).
Extended Data Fig. 5 Epigenetic regulation of fibroblast cell state transition.
(a) Palantir derived entropy with terminal cell states noted and marker genes for terminal states in addition to FAP plotted in FDL embedding highlighting FAP derived fibroblasts diverge into myofibroblats and PFs. (b) Heatmap of pseudobulk differentially expressed genes between donor and HF and terminal state marker genes over pseudotime. (c) CellOracle gene regulatory network betweenness centrality score for RUNX1 by HF etiology – a higher score indicates that the TF has a greater influence on informational flow in the gene regulatory network. (d) Average expression of RUNX1 per HF patient versus POSTN in fibroblasts from the same patient. R2 indicates the regression coefficient and the p-value tests whether the slope is significantly non-zero. (e) Differential accessibility expression analysis between donor and heart failure in fibroblasts.
Extended Data Fig. 6 In vivo mouse to human integration across cardiac injury models.
Reference mapping scores for mouse (a) MI and (c) TAC fibroblasts onto human heart CITE-seq fibroblast UMAP embedding. (b) Reference mapped data split by MI time point in human space. (d) Reference mapped data split by sham, TAC, TAC + JQ1 treatment, and TAC + JQ1 withdrawn in human space. (e) Experimental design for Ang II/PE 28-day pumps for sequencing. (f) QC metrics post filtering. (g) Integrated global UMAP split by sham and Ang II/PE at day 28. (h) Heatmap of top marker genes for clusters from (g) (Supplementary Table 21). (i) Reference mapping scores for mouse Ang II/PE and sham d28 fibroblasts onto human heart CITE-seq fibroblast UMAP embedding. Schematics in e were created using BioRender (https://biorender.com).
Extended Data Fig. 7 In vitro models of fibroblast activation quality control, clustering, and mapping.
(a) UMAP embedding with separate clustering in each cell line with density plots split by experimental condition (Supplementary Table 22). (b) Composition stack graphs for NHCF, NHDF, and iHCF grouped by biological and technical replicates for identified clusters. (c) Heatmap of marker genes for each cell line for clusters in (a). (d) Reference mapping scores for in vitro fibroblasts onto human heart CITE-seq fibroblast UMAP embedding. (e) Heatmap of mapping scores with in vitro clusters on x-axis and human heart CITE-seq fibroblasts on y-axis. Schematics in e were created using BioRender (https://biorender.com).
Extended Data Fig. 8 Myeloid cell state characterization post-MI.
(a) UMAP embedding plot of myeloid cells with annotated cell states (Supplementary Table 23). (b) Myeloid cell state composition across four groups. (c) Gaussian kernel density estimation of cells across four groups (left) and split by MI time in AMI patients with corresponding cell state composition. (d) Dot Plot of marker genes for macrophages cell states (y-axis) and grouped by cell type (x-axis). (e) Inflammation gene set score split across 4 groups and (e) grouped from time post-MI. (f) CCR2 and FOLR2 (protein) expression in UMAP embedding. (g) Spatial transcriptomic AMI (2-day post-MI) infarct zone (IZ) sample with label transferred annotations from snRNA-seq reference map. (h) Monocyte gene set score mapped into space (left) and inflammation score (right).
Extended Data Fig. 9 Spatial co-localization analysis of immune-stromal states in human MI.
(a) SPOTlight derived cell spot deconvolution Pearson correlation coefficients for cell neighborhoods in donor, AMI and ICM spatial transcriptomic sections. (b) Integration of spatial spots from 28 Visium spatial transcriptomics samples (Supplementary Table 24). (c) FAP, POSTN, CD68, and CD68/FAP expression density plot from integrated spatial spots.(d) Heatmap of spatial niche marker genes. (e) UMAP embedding plot of spatial clusters with three distinct niches and corresponding spatial location of clusters (Supplementary Table 25). (f) Heatmap of top differentially expressed genes across spatial niches. (g) Dot plot of marker genes and gene set scores for spatial niches. (h) Spatial clusters from (g) overlaid in space z-score for marker genes in F9 (POSTN, COMP, FAP, COL1A1, THBS4, COL3A1) and F2 (ACTA2, TAGN), and imputed NF-kB pathway score. (i) IL-1R expression in global UMAP embedding (left) and IL-1R expression DotPlot in fibroblast cell states (right). (j) qPCR for IL-1R expression in sorted fibroblasts and macrophages in Ang II/PE infused mice at d7. Unpaired t-test and samples are from independent biological animals. Error bars are +/− SEM.
Extended Data Fig. 10 In vivo fibroblast state maturation with anti-IL-1β mAb treatment.
(a) Integrated UMAP for sham, isotype and anti- IL-1β mAb treated mice fibroblasts with annotated clusters (left) and colored by condition (right) (Supplementary Table 26). (b) scRNA-seq QC metrics for study design in Fig. 5f. (c) Differentially expressed marker genes for clustering in (a). (d) Reference mapping of fibroblasts in (a) to human CITE-seq fibroblast cell states UMAP embedding (left) and heatmap of average cell prediction score of human fibroblast cell states (y-axis) in annotated mouse fibroblasts (x-axis) (right). (e) Cell state composition stack plot of mapped cell states with F9 highlighted (top) and clustering from (a) with Postn+ state highlighted. (f) Experimental workflow for cardiac injury experiment with treatment with an anti-IL-1β mAb or isotype control for FAP activation (left) and scRNA-seq QC metrics (right). (g) Integrated UMAP for isotype and anti-IL-1β mAb treated mice FAP + /FAP- fibroblasts with annotated clusters and density plot of Fap/Postn co-localization with area of maximal expression highlighted (Supplementary Table 27). (h) Heatmap of marker genes for clustering in (g). (i) Gaussian kernel density plots of 4 conditions in integrated UMAP embedding. (j) Heatmap of key fibroblast cell state genes grouped by four conditions with rows clustered by similarity. (k) Immunofluorescence of POSTN in a representative sham, Ang II/PE + Isotype, and Ang II/PE + anti- IL-1β mAb heart (left) with quantification (right); scale bar = 500 um. (l) RT-PCR of Postn in mouse hearts split by 3 groups at day 7 post Ang II/PE infusion. (m) Representative trichrome staining images from Ang II/PE day 28 hearts from isotype and anti- IL-1β mAb treated mice; scale bar = 100 px. For (k) and (l) ordinary one-way ANOVA with Turkey multiple testing correction from independent biological animals. Error bars are +/− SEM.
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Amrute, J.M., Luo, X., Penna, V. et al. Targeting immune–fibroblast cell communication in heart failure. Nature 635, 423–433 (2024). https://doi.org/10.1038/s41586-024-08008-5
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DOI: https://doi.org/10.1038/s41586-024-08008-5
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