Abstract
Matrix-derived biophysical cues are known to regulate the activation of fibroblasts and their subsequent transdifferentiation into myofibroblasts1,2,3,4,5,6, but whether modulation of these signals can suppress fibrosis in intact tissues remains unclear, particularly in the cardiovascular system7,8,9,10. Here we demonstrate across multiple scales that inhibition of matrix mechanosensing in persistently activated cardiac fibroblasts potentiates—in concert with soluble regulators of the TGFβ pathway—a robust transcriptomic, morphological and metabolic shift towards quiescence. By conducting a meta-analysis of public human and mouse single-cell sequencing datasets, we identify the focal-adhesion-associated tyrosine kinase SRC as a fibroblast-enriched mechanosensor that can be targeted selectively in stromal cells to mimic the effects of matrix softening in vivo. Pharmacological inhibition of SRC by saracatinib, coupled with TGFβ suppression, induces synergistic repression of key profibrotic gene programs in fibroblasts, characterized by a marked inhibition of the MRTF–SRF pathway, which is not seen after treatment with either drug alone. Importantly, the dual treatment alleviates contractile dysfunction in fibrotic engineered heart tissues and in a mouse model of heart failure. Our findings point to joint inhibition of SRC-mediated stromal mechanosensing and TGFβ signalling as a potential mechanotherapeutic strategy for treating cardiovascular fibrosis.
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Data availability
Raw and processed sc/snRNA-seq data have been deposited in the NCBI Gene Expression Omnibus (GEO) under accession number GSE291370. Proteomics data generated from this study have been uploaded to ProteomeXchange and MassIVE under accession numbers PXD061188 and MSV000097212, respectively. Public datasets reanalysed in this study are cited in the references and are also available at NCBI GEO (GSE120064)39, the European Genome–phenome Archive (EGA; EGAS00001006374)33, the Human Cell Atlas (HCA) Data Coordination Platform (DCP; ERP123138)36, the Broad Institute’s Single Cell Portal (SCP498)37, the ArrayExpress database at EMBL-EBI (E-MTAB-7376 and E-MTAB-7365)38, ProteomeXchange (PXD016492)41, ProteomeXchange (PXD007171) and the NCBI National Library of Medicine (genome assemblies GRCh38, GCF_000001405.26 (human); and GRCm38, GCF_000001635.20 (mouse)). Source data are provided with this paper.
Code availability
No custom code or mathematical algorithms were developed for the study. All scRNA-seq/snRNA-seq data processing and analyses were performed using standard pipelines with Seurat (v.5.1.0) and open source R packages cited in the Methods and Reporting Summary, under the default settings provided in the corresponding vignettes.
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Acknowledgements
We thank the staff at the Vincent Coates Foundation Mass Spectrometry Laboratory, Stanford University Mass Spectrometry (SUMS, RRID: SCR_017801) and the Stanford Cancer Institute Proteomics/Mass Spectrometry Shared Resource for use of the Bruker timsTOF Ultra and the nanoElute 2 system (RRID: SCR_025639); and the staff at the Stanford Cardiovascular Institute for providing seed funding. This work was also supported by National Institutes of Health (NIH) grants F32 HL152483 and K99 HL166695 (to S.C.); F32 HL173968 (to A.C.); K99 HL163443 (to D.T.); and R01 HL113006, R01 HL130020, R01 HL141371, R01 HL141851, R01 HL150693, R01 HL145676, R01 HL146690 and R01 HL163680 (to J.C.W.), and by American Heart Association (AHA) 17MERIT33610009 (to J.C.W.).
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Contributions
S.C. and J.C.W. conceptualized and designed the study. S.C., S.R. and J.C.W. wrote the manuscript. S.R. performed the animal experiments and helped to analyse data. C.M.M. performed the dynamic matrix softening experiments. A.C. performed the coIP–MS experiments and assisted in data analysis. H.K. and D.T. assisted with the EHT experiments. S.C. and A.K performed and analysed the FRAP experiments. A.M. performed the cardiomyocyte contractility assays. J.W.J. performed the Seahorse XF Assay experiments. H.S.S., D.T.P. and M.W. assisted with running, initial processing and integration of the scRNA-seq data. X.W. and P.N.T. performed the animal surgeries. M.M. performed the endothelial cell tube formation assays. N.M.B. assisted with RNA velocity analysis. J.L. and S.M. performed the virtual screen and MD simulations. V.D.W. provided the human foetal tissue samples. Y.J.W., H.M.B. and J.C.W. supervised the study and provided funding support.
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J.C.W. is a co-founder and member of the scientific advisory board of Greenstone Biosciences. H.M.B. is a cofounder and member of the scientific advisory board of Epirium Bio. The other authors declare no competing interests.
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Extended data figures and tables
Extended Data Fig. 1 iPSC-CFs undergo spontaneous activation on stiff environments or at low cell densities unless mechanosensing is inhibited.
(A) Representative immunofluorescence images of αSMA (ACTA2) and YAP1 staining in iPSC-CFs cultured for ~10 passages on rigid tissue culture plastic (~ GPa) at varying cell densities (initial seeding at 2.5–40k cells/well). Images are representative of experiments repeated twice independently with similar results. Top row scale bar = 100 µm; bottom row scale bar = 10 µm. (B) % αSMA+ cells (left) and YAP1 N/C ratio (right) plotted versus cell seeding density: 2.5k, 5k, 10k, 20k, 40k cells/well: % αSMA+ cells (n = 13, 15, 15, 15, 20 randomly selected fields of view, respectively, from n = 3 biological replicates/group) and YAP1 N/C (n = 20, 18, 21, 26, 27 cells, respectively, from n = 3 biological replicates/group). (C) % αSMA+ cells plotted versus YAP1 N/C ratio (n-numbers equivalent to those of panel B). (D) αSMA immunoblot of iPSC-CFs undergoing spontaneous CF-to-MyoFb transition on hydrogels of varying stiffness for ~10 passages. Blots are representative of n = 2 independently performed experiments with similar results. (E) Reported stiffness ranges for embryonic, healthy adult, myocardial infarction (MI), heart failure with preserved ejection fraction (HFpEF), and transverse aortic constriction (TAC) hearts15,65. Normalized 2D projected cell area (top) and % of Ki67+ cells (bottom) versus stiffness: 2, 8, 16, 64 kPa and rigid plastic. Normalized cell area (n = 21, 18, 22, 13, 11 cells, respectively, from n = 3 biological replicates/group) and Ki67+ (n = 9, 15, 7, 5, 5, 8 randomly selected fields of view, respectively, from n = 3 biological replicates/group). (F) Half-maximal effective stiffness (“ES50â€, analogous to EC50 for a drug dose-response, in units of kPa) for each group, obtained from curve-fitting (data in main Fig. 1d, four-parameter dose-response stimulation with variable slope on GraphPad Prism v6.0). ES50 is defined as the gel stiffness at which the half-maximal response is produced, which in this case is the % of αSMA+ cells. ES50 for each group is plotted as individual bars, and the asymptotic standard error obtained from the best-fit curves (“Std. Error†values) are presented as error bars. P-values were calculated based on ordinary one-way ANOVA with Dunnett’s test (*’s) versus 2.5k/well (panel B), versus 2 kPa (panel E), or versus DMSO on plastic (panel E); and the two-sided Student’s t-test (#’s). Data are mean ± SEM (with the exception of panel F, for which individual bars ± error bars represent ES50 ± Std. Error).
Extended Data Fig. 2 iPSC-CFs resemble primary human foetal CFs at the transcriptomic level.
(A) UMAP plot of quiescent iPSC-CFs mapped onto a reference dataset of primary fibroblasts (FBs) from various human foetal tissues (heart, lung, skin, kidney, and liver). (B) Feature plots showing expression of TCF21 and PDGFRA in iPSC-CFs and primary CFs. (C) Correlation plot of iPSC-CFs, FBs from various foetal tissues (lung, skin, kidney, liver), and major cardiac cell types (CFs, SMCs, CMs, and ECs). (D-F) Representative feature plots for: (D) pan-fibroblast genes, (E) CM marker genes, and (F) EC marker genes. (G) The 25 most variable genes along pseudotime inferred by Slingshot-based trajectory analysis of TGFβ-activated iPSC-CFs. Notable ECM genes (purple) and SMC/MyoFb-associated genes (orange) are highlighted. (H) Gene network plot (“cnetplot†generated by clusterProfiler) for the MyoFb1 cluster.
Extended Data Fig. 3 Combination treatment drives morphological remodelling and population redistribution at the single-cell transcriptomic level without altering cell cycle.
(A) Additional representative time-lapse images of SM22α (TAGLN)-CFP reporter iPSC-derived MyoFbs subjected to light-induced matrix softening over 38 hrs, either with or without TGFβi. Scale bar = 100 µm. (B) Normalized 2D cell projected area and cell aspect ratio (length/width; L/W) before and 38 hrs after matrix softening. Stiff control (n = 10), TGFβi (n = 12), Softened (n = 11), Softened+ TGFβi (n = 10 cells). Data from the two separate experiments were normalized and pooled. (C) Individual UMAP plots (top row) and corresponding cell density plots (bottom row) for all four treatment groups. (D) UMAP plot generated based on the cell cycle phase of each cell, scored using canonical markers. (E) Cell cycle distribution of the total population in each of the four treatment conditions. (F) Cell cycle distribution by cluster in the four treatment conditions. (G) Representative immunofluorescence images of Ki67 and DAPI-stained MyoFbs. Bottom: quantitation of % Ki67+ cells in the four treatment conditions: Stiff control (n = 11), TGFβi (n = 5), Soft (n = 6), Soft+TGFβi (n = 5 randomly selected fields of view, from n = 3 biological replicates/group). (H) (i) RNA velocity plots of Stiff control (purple) and Soft+TGFβi (orange), and (ii) ‘ΔRNA velocity’ generated by subtracting individual velocity vectors (\(\vec{{\rm{S}}{\rm{o}}{\rm{f}}{\rm{t}}+{\rm{T}}{\rm{G}}{\rm{F}}{\rm{\beta }}{\rm{i}}}-\vec{{\rm{S}}{\rm{t}}{\rm{i}}{\rm{f}}{\rm{f}}}\)) on the coordinate grid. Images in panels A and G are each representative of experiments repeated twice independently with similar results. P-values were calculated based on ordinary one-way ANOVA with Dunnett’s test (*’s) versus Stiff baseline control; and the two-sided Student’s t-test (#’s). Data are mean ± SEM.
Extended Data Fig. 4 Combination treatment downregulates pro-fibrotic pathways and synergistically inhibits MRTF-A retention in the MyoFb nucleus.
(A) Heatmap of the top 25 TFs whose activity levels were most variable across the treatment conditions, for all identified clusters, based on activity fingerprinting by DoRothEA. (B) Violin plots of key downstream target genes of YAP/TAZ-TEAD. (C) Representative αSMA immunofluorescence images (left) and corresponding histogram of anti-αSMA intensity in single MyoFbs. Inset: bar graph of % αSMA+ cells (threshold set at 5-fold increase relative to baseline). Stiff (n = 49), TGFβi (n = 41), Stiff→Soft (n = 22), Stiff→Soft+ TGFβi (n = 30 randomly selected fields of view). Images are representative of experiments repeated n = 3 times independently with similar results. Scale bar = 100 µm. (D) Gel contraction assay of undifferentiated iPSC-CFs and MyoFbs after culturing (‘priming’) for 4 days on soft or stiff substrates, with or without TGFβi treatment. Primed cells were trypsinized and mixed with a commercially available gel solution for the contraction assay. 2D gel areas were measured after 12 h (n = 3 biological replicates per group). (E) Dot plot showing the expression of major fibrosis-associated ECM proteins, ECM cross-linkers, and various MMP isoforms (collagenases and gelatinases) in the four groups after 2 days of treatment. (F) Schematic of experimental design illustrating TGFβ perturbations in a soft matrix background (orange), or stiff matrix culture in the presence of TGFβi. (G-H) Representative immunofluorescence images and quantitation of MRTF-A N/C ratio in the five treatment groups: Soft (n = 11), Stiff (n = 12), Stiff+LatB (latrunculin-B; n = 12), Soft+TGFβ (n = 11), Soft+TGFβ + SB (n = 11 cells). (I) Subcellular localization of enriched MRTF-A interactors in the CoIP-MS data. (J-K) Representative immunofluorescence images and quantitation of MRTF-A and SORBS2 co-localization in the nucleus and cytoplasm under the different treatment conditions: Undiff CF (n = 8), MyoFb (n = 37), TGFβi (n = 18), Soft (n = 12), Soft+TGFβi (n = 11 cells). Individual cells are shown as opaque data points in the background in panel K. Images in panels G and J are representative of experiments repeated twice independently with similar results. P-values were calculated based on ordinary one-way ANOVA with Dunnett’s test (*’s) versus Soft control (panel H); and the two-sided Student’s t-test (#’s) versus MyoFb (panel K). Data are mean ± SEM.
Extended Data Fig. 5 SRC is unique among major cellular mechanosensors in its enrichment in cardiac stromal cells.
(A) Nebulosa feature plots for a broad range of major mechanosensors, including mechano-responsive TFs (e.g., YAP/TAZ), focal adhesion components (e.g., FAK (PTK2)), mechanically-gated ion channels (e.g., PIEZO2), integrins (e.g., ITGA1), and nuclear envelope proteins (e.g., LMNA) in adult mouse heart. SRC’s unique enrichment in CFs and mural cell populations is highlighted with arrowheads (magenta). (B-F) Representative UMAP plots and Nebulosa feature plots illustrating SRC expression in scRNA-seq datasets of: (B) human adult heart36, (C) FBs isolated from various human fetal tissues, (D) mouse model of myocardial infarction (MI)38, (E) additional mouse data set for TAC hearts39, and (F) TGFβ-simulated iPSC-CFs. (G) Violin plot of SRC expression across all clusters in TGFβ-simulated iPSC-CFs.
Extended Data Fig. 6 Saracatinib recapitulates the effects of matrix softening in vitro.
(A) Connectivity Map (CMap42) analysis showing the top 20 genes for which overexpression (OE) or knockdown (KD) results in similar perturbation signatures as saracatinib treatment. (B-C) Schematic of the experiment timeline (B) and representative immunofluorescence images (C) of cells treated with various drug combinations: “SAR†= saracatinib; “SB†= SB431542; “PFD†= pirfenidone; Scale bar = 150 µm; inset = 50 µm. (D) % αSMA-positive cells (defined as ≥ 5X versus baseline control) over the course of the experiment. For days 2, 4, and 9: DMSO (n = 4, 11, 13), SAR (n = 2, 8, 10), SB (n = 3, 7, 8), PFD (n = 2, 8, 6), SAR + SB (n = 3, 6, 8), SAR + PFD (n = 3, 11, 14 randomly selected frames of view, respectively, from n = 3 biological replicates/group). (E-G) Cell aspect ratio (elongation) versus normalized 2D cell area (E), and nuclear/cytoplasmic ratio of (F) YAP1 and (G) MRTF-A at day 9. DMSO (n = 58), SAR (n = 44), SB (n = 41), PFD (n = 38), SAR + SB (n = 31), SAR + PFD (n = 44 cells). (H) Representative images of f-actin and DNA in cells treated with TGFβi, either alone or with three different SRC inhibitors (“DAS†= dasatinib; “BOS†= bosutinib). Scale bar = 150 µm. (I) % cells with actomyosin stress fibres in the different treatment groups: CF, MyoFb, TGFβi, TGFβi + DAS, TGFβi + SAR, TGFβi + BOS (n = 5, 8, 6, 5, 5, 5 randomly selected fields of view, respectively, from n = 3 biological replicates/group). (J) Scatter plot of cell aspect ratio (length/width; L/W) versus 2D area. CF (n = 35), MyoFb (n = 42), TGFβi (n = 69), TGFβi + DAS (n = 25), TGFβi + SAR (n = 35), TGFβi + BOS (n = 77 cells). (K) Immunoblots and corresponding densitometry quantitation of pYAP1, αSMA, collagen-I, and POSTN. Blots are representative of n = 2 independently performed experiments with similar results (for collagen-I and POSTN, data from two additional experiments were normalized and pooled). Images shown are from two blots derived from the same samples from the same experiment, processed in parallel. For groups with n = 2, the error bars indicate the range between the two data points. (L) Subcellular localization of enriched MRTF-A interactors in the CoIP-MS data. (M) PCA analysis of CoIP-MS data from Fig. 3f, now including the SAR and SAR+TGFβi groups. (N) Heatmap of the anti-MRTF-A CoIP-MS data, showing all seven experimental groups (including those from Fig. 3f–h). The top 10 differentially enriched MRTF-A protein interactors in the MyoFb group (purple) are shown on the right. (O) Anti-SORBS2 immunoblot of the CoIP-MS input. The blot was not repeated due to sample availability after running the CoIP-MS. (P) Representative images (left) and quantitation (right) of MRTF-A and SORBS2 co-localization in the nucleus and cytoplasm across the different treatment groups (including those from Extended Data Fig. 4j,k. Undiff CF (n = 9), MyoFb (n = 37), TGFβi (n = 19), Soft (n = 12), Soft+TGFβi (n = 11), SAR (n = 11), SAR+TGFβi (n = 9 cells). Images in panels C, H, and P are representative of experiments repeated twice independently with similar results. P-values were calculated based on ordinary one-way ANOVA with Dunnett’s test (*’s) versus DMSO (panels D-G) or Undiff CF control (panel I); and the two-sided Student’s t-test (#’s) versus MyoFb (panels J, P). Data are mean ± SEM.
Extended Data Fig. 7 Saracatinib potentiates reversal of metabolic states in MyoFbs without affecting the viability and function of cardiomyocytes and endothelial cells.
(A) Seahorse XF Assay measurements of: (i) oxygen consumption rate (OCR), (ii) extracellular acidification rate (ECAR), and (iii) OCR versus ECAR after 2 days of drug treatments (n = 4 replicates/condition). (B-i) PrestoBlue viability assay for varying concentrations of a direct YAP/TAZ inhibitor verteporfin (‘VP’, top) and the SRC inhibitor saracatinib (‘SAR’, bottom) in iPSC-CMs, iPSC-ECs, iPSC-CFs, and iPSC-MyoFbs (n = 3 replicates per group). (B-ii) Half-maximal inhibitory concentration (IC50) values for VP and SAR, for each cell type. IC50 values were obtained from curve-fitting (data from panel B-ii, four-parameter dose-response inhibition with variable slope on GraphPad Prism v6.0). IC50 for each group is plotted as individual bars, and the asymptotic standard error obtained from the best-fit curves (“Std. Error†values) are presented as error bars. (C) Representative images (left) and quantitation (right) of EC tube formation assay after treatment with VP or SAR (1 µM each). n = 6 randomly selected fields of view, from n = 3 biological replicates/group. Images are representative of experiments repeated twice independently with similar results. (D) Heatmap (top) and line graph (bottom) illustrating the sharp decrease in SRC expression relative to cardiac genes (TTN, MYH7, and TNNT2) throughout the course of iPSC-CM differentiation (bulk RNA-seq, n = 3 technical replicates per time point). (E-G) Beating area, contraction velocity, and relaxation velocity measurements in VP- and SAR-treated iPSC-CMs at differentiation day 90 (D90): DMSO (n = 19), VP (n = 22), SAR (n = 24 randomly selected fields of view, from n = 3 biological replicates/group). (H) Western blot of phosphorylated SRC (Y416) and total SRC in MyoFbs under TGFβ drug perturbations. SAR was included as a positive control. Blots are representative of n = 3 experiments repeated with similar results. Images are from two blots from the same samples from the same experiment, processed in parallel. P-values were calculated based on ordinary one-way ANOVA with Dunnett’s test (*’s) versus DMSO control; and the two-sided Student’s t-test (#’s). Data are mean ± SEM (with the exception of panel B-ii, for which individual bars ± error bars represent IC50 ± Std. Error).
Extended Data Fig. 8 Combination treatment alleviates contractile dysfunction in fibrotic engineered heart tissues.
(A) Schematic of EHT formation and fibrotic compaction of EHTs upon TGFβ stimulation. Right inset: snapshots of relaxed (diastole; cyan) and contracted (systole; magenta) states. (B) Representative images of EHTs for each condition in their relaxed states (diastole). The distance between the centre of the two silicon posts were used to measure the diastolic tissue length, Ld (yellow dotted line). Images are representative of experiments repeated four times with similar results. Scale bar = 1 mm. (C-F) Fractional shortening (FS%) and diastolic tissue length (Ld) measured over time for each drug treatment group (C,E) and FS% and Ld after 2 days of treatment with the different drugs, normalized to DMSO control (D,F). Ctrl (n = 13), TGFβ (n = 13), S (n = 10), P (n = 11), S + P (n = 12 EHTs). (G) Elastic modulus (stiffness) of EHTs 4 days after the drug treatments: Ctrl (n = 5), TGFβ (n = 7), S (n = 4), P (n = 3), S + P (n = 7). For panels C and E (between days 0 and 6) and for panels D and G, data from four separate experiments were normalized and pooled. P-values were calculated based on ordinary one-way ANOVA with Dunnett’s test (*’s) versus control EHTs; and the two-sided Student’s t-test (#’s). Data are mean ± SEM.
Extended Data Fig. 9 Combination treatment in MI and TAC models of cardiac fibrosis.
(A) Schematic summary of the MI experiment. SAR + PFD (‘S + P’) dual treatment was started at three different time points: Sham (n = 6), MI (n = 14), and S + P starting on day 1 (n = 8), day 4 (n = 10), and day 7 (n = 4 animals) post-MI surgery. (B) Survival curves for the five treatment groups. (C) Representative images of Masson’s trichrome-stained tissue sections and (D) M-mode echocardiography scans for each group at day 21 post-MI. Images are representative of at least four animals per group, from one MI cohort. (E-I) Time-course of left ventricular ejection fraction, LVEF (%, (E)), and endpoint echocardiography measurements of LVEF (%) (F), fractional shortening (FS%, (G)), left ventricular posterior wall thickness in diastole (LPWd, (H)), and left ventricular internal diameter (LVIDd, (I)). At endpoint, Sham (n = 6), MI (n = 9), ‘S + P (d4-)’ (n = 8), and ‘S + P (d7-)’ (n = 4 animals). (J) Schematic summary of the TAC experiment, copied from Fig. 5b. (K-M) Time-course measurements of FS (K, left; for days 18, 24, and 42, data from three separate experiments were normalized and pooled), and week 9 measurements of: FS (K, right), left ventricular internal diameter (LVIDd, (L)), and heart weight/body weight (mg/g) ratio (M). Sham, TAC, S + P, S + P withdrawn, S, and P (n = 10, 12, 12, 6, 7, 7 animals, respectively). P-values were calculated based on ordinary one-way ANOVA with Dunnett’s test (*’s) versus Sham; and the two-sided Student’s t-test (#’s) versus MI (panels F-I) or versus TAC (panels K-M). Data are mean ± SEM.
Extended Data Fig. 10 Combination treatment induces transcriptional and protein-level changes in TAC hearts without significantly affecting inflammation or immune signalling.
(A) Left: Western blot of phosphorylated YAP (S127) and total YAP in whole heart lysates from the different drug treatment groups: Sham, Sham S + P, TAC 4 wk, TAC 6 wk, S + P, S, P (n = 5, 5, 10, 9, 9, 8, 9 hearts per lysate, respectively). Right: snRNA-seq violin plot displaying module scores for major YAP/TAZ target genes (Ccn2 (CTGF), Ankrd1, Amotl2, Birc5, and Igfbp2) specifically in CFs. (B) Violin plots showing module scores for common genes associated with oxygen consumption rate (OCR, left) and extracellular acidification rate (ECAR, right). (C) Normalized mass spectrometry intensity values for SORBS2 (ArgBP2) in two published datasets of mouse TAC hearts41,66: Kuzmanov et al and Rudebusch et al. (n = 5 and 4 tissue samples, respectively). (D) Violin plot of Sorbs2 expression in the five groups. Horizontal bars indicate mean. Cells with >0 Sorbs2 expression are shown. (E) Gene Ontology enrichment analysis for SAR + PFD versus TAC hearts. Fisher’s exact test (with Benjamini-Hochberg adjustment; one-sided). (F) Dot plot of major pro-inflammatory cytokine genes in the heart. (G-I) CellChat-based inference of cell-cell communication mechanisms, showing changes in modes of signalling from immune cells to CF populations (immune→CF) in the SAR + PFD group relative to TAC (H,I). A permutation test (randomization; one-sided) was used to compute communication probability strength of ligand-receptor pairs. (J-K) Immunoblots and corresponding quantitation of SRC phosphorylation and key fibrosis-associated proteins αSMA and POSTN for each group: Sham, Sham S + P, TAC (4 wk), TAC (6 wk), S + P, S, P (n = 5, 5, 10, 9, 9, 8, 9 hearts per lysate, respectively). Immunoblots in panels A and K are representative of n = 3 experiments performed independently with similar results. Images are from two blots from the same samples from the same experiment, processed under identical conditions. For panel K, data from an additional experiment was normalized and pooled. P-values were calculated based on ordinary one-way ANOVA with Dunnett’s test (*’s) versus Sham; and the two-sided Student’s t-test (#’s). Data are mean ± SEM.
Supplementary information
Supplementary Fig. 1
Immunoblot source data. Original uncropped immunoblot images for the main figures (pages 1–2) and extended data figures (pages 3–4). The red dotted boxes and text indicate the exact cropped regions that were included in the figures and the corresponding probed protein, respectively. The black text labels to the right of each blot indicate proteins that were probed simultaneously (for example, for GAPDH normalization) but not included in the figures. Uncropped images for: Fig. 3e (4 samples, from left to right: soft, soft + TGFβi, stiff (Ctrl), stiff + TGFβi), three separate immunoblots of the same samples run under identical conditions are shown (a); Fig. 4f (3 samples, from left to right: sham, TAC (4 weeks), TAC (6 weeks)) (b); Extended Data Fig. 1d (4 samples, from left to right: 2, 8, 16 and 64 kPa) (c); Extended Data Fig. 6k (6 samples, from left to right: Undiff CF, MyoFb, SAR + SB, SAR + PFD, SB, PFD), two separate immunoblots of the same samples run in parallel are shown (d); Extended Data Fig. 6o (7 samples, from left to right: Undiff CF, MyoFb, SAR, TGFβi, SAR + TGFβi, soft, soft + TGFβi) (e); Extended Data Fig. 7h (4 samples, from left to right: DMSO, SB (TGFβi), PFD (TGFβi), SAR; the bottom blot is identical to the one shown on page 1 (second row, top left) with two shared samples from the same experimental conditions) (f); and Extended Data Fig. 10a and 10j, respectively (6 samples, from left to right: sham, sham S + P, TAC (4 weeks), TAC (6 weeks), S + P, S, P) (g and h). The rightward arrows (labelled with strip and re-blot) indicate that the blot (left) was first stripped then reprobed using a different antibody resulting in a new blot (right) of the same samples from the same experiment.
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Cho, S., Rhee, S., Madl, C.M. et al. Selective inhibition of stromal mechanosensing suppresses cardiac fibrosis. Nature 642, 766–775 (2025). https://doi.org/10.1038/s41586-025-08945-9
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DOI: https://doi.org/10.1038/s41586-025-08945-9
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