Abstract
Cardiac fibroblasts (CFs) are key mediators of heart repair following myocardial infarction (MI). A specific CF subpopulation, termed Reparative Cardiac Fibroblasts (RCFs), has been shown to orchestrate scar formation and prevent ventricular rupture after MI. However, the timing of RCF appearance and the molecular events underlying this transition remain largely undefined. Here, we present a multi-modal dataset capturing the transcriptional dynamics of CFs during the early phase post-MI. Our integrative dataset combines bulk RNA sequencing, RNAscope in situ hybridization, and spatial transcriptomics to anatomically and temporally map the gene expression changes associated with the transition into RCFs. The dataset provides resources to characterize the distinct molecular programs that guide the emergence of RCFs from Periostin (Postn)+ activated CFs. This dataset provides a valuable resource for investigating CF heterogeneity and reparative pathways following MI. All raw and processed data, along with detailed metadata and annotations, are made available to facilitate reuse by the cardiovascular and single-cell biology communities.
Similar content being viewed by others
Code availability
All script for the analysis used to generate the processed data and the visualizations/figures in the present manuscript are available on https://github.com/lsudupe/Cardio_paper/.
References
Thannickal, V. J., Zhou, Y., Gaggar, A. & Duncan, S. R. Fibrosis: ultimate and proximate causes. J Clin Invest 124, 4673–4677, https://doi.org/10.1172/JCI74368 (2014).
Janbandhu, V. et al. Novel Mouse Model for Selective Tagging, Purification, and Manipulation of Cardiac Myofibroblasts. Circulation 149, 1931–1934, https://doi.org/10.1161/CIRCULATIONAHA.123.067754 (2024).
Tsukui, T., Wolters, P. J. & Sheppard, D. Alveolar fibroblast lineage orchestrates lung inflammation and fibrosis. Nature, https://doi.org/10.1038/s41586-024-07660-1 (2024).
Kuppe, C. et al. Decoding myofibroblast origins in human kidney fibrosis. Nature 589, 281–286, https://doi.org/10.1038/s41586-020-2941-1 (2021).
Kuppe, C. et al. Spatial multi-omic map of human myocardial infarction. Nature 608, 766–777, https://doi.org/10.1038/s41586-022-05060-x (2022).
Ruiz-Villalba, A. et al. Single-Cell RNA Sequencing Analysis Reveals a Crucial Role for CTHRC1 (Collagen Triple Helix Repeat Containing 1) Cardiac Fibroblasts After Myocardial Infarction. Circulation 142, 1831–1847, https://doi.org/10.1161/CIRCULATIONAHA.119.044557 (2020).
Driskell, R. R. et al. Distinct fibroblast lineages determine dermal architecture in skin development and repair. Nature 504, 277–281, https://doi.org/10.1038/nature12783 (2013).
Yang, W. et al. Single-Cell Transcriptomic Analysis Reveals a Hepatic Stellate Cell-Activation Roadmap and Myofibroblast Origin During Liver Fibrosis in Mice. Hepatology 74, 2774–2790, https://doi.org/10.1002/hep.31987 (2021).
Amrute, J. M. et al. Targeting immune-fibroblast cell communication in heart failure. Nature 635, 423–433, https://doi.org/10.1038/s41586-024-08008-5 (2024).
Kleinbongard, P. et al. Cardiac fibroblasts: answering the call. Am J Physiol Heart Circ Physiol 327, H681–H686, https://doi.org/10.1152/ajpheart.00478.2024 (2024).
Hilgendorf, I., Frantz, S. & Frangogiannis, N. G. Repair of the Infarcted Heart: Cellular Effectors, Molecular Mechanisms and Therapeutic Opportunities. Circ Res 134, 1718–1751, https://doi.org/10.1161/CIRCRESAHA.124.323658 (2024).
Rieder, F. et al. Fibrosis: cross-organ biology and pathways to development of innovative drugs. Nat Rev Drug Discov https://doi.org/10.1038/s41573-025-01158-9 (2025).
Konkimalla, A. et al. Transitional cell states sculpt tissue topology during lung regeneration. Cell Stem Cell 30, 1486–1502 e1489, https://doi.org/10.1016/j.stem.2023.10.001 (2023).
Li, J. et al. Autocrine CTHRC1 activates hepatic stellate cells and promotes liver fibrosis by activating TGF-beta signaling. EBioMedicine 40, 43–55, https://doi.org/10.1016/j.ebiom.2019.01.009 (2019).
Buechler, M. B. et al. Cross-tissue organization of the fibroblast lineage. Nature 593, 575–579, https://doi.org/10.1038/s41586-021-03549-5 (2021).
NCBI Gene Expression Omnibus https://identifiers.org/geo/GSE132146 (2020).
Yata, Y. et al. DNase I-hypersensitive sites enhance alpha1(I) collagen gene expression in hepatic stellate cells. Hepatology 37, 267–276, https://doi.org/10.1053/jhep.2003.50067 (2003).
Ruiz-Villalba, A. et al. Interacting resident epicardium-derived fibroblasts and recruited bone marrow cells form myocardial infarction scar. J Am Coll Cardiol 65, 2057–2066, https://doi.org/10.1016/j.jacc.2015.03.520 (2015).
Jaitin, D. A. et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 343, 776–779, https://doi.org/10.1126/science.1247651 (2014).
Lavin, Y. et al. Innate Immune Landscape in Early Lung Adenocarcinoma by Paired Single-Cell Analyses. Cell 169, 750–765.e717, https://doi.org/10.1016/j.cell.2017.04.014 (2017).
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21, https://doi.org/10.1093/bioinformatics/bts635 (2013).
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, Austria, 2021).
Liao, Y., Smyth, G. K. & Shi, W. The R package Rsubread is easier, faster, cheaper and better for alignment and quantification of RNA sequencing reads. Nucleic Acids Res 47, e47, https://doi.org/10.1093/nar/gkz114 (2019).
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq. 2. Genome Biol 15, 550, https://doi.org/10.1186/s13059-014-0550-8 (2014).
Hanzelmann, S., Castelo, R. & Guinney, J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 14, 7, https://doi.org/10.1186/1471-2105-14-7 (2013).
Hafemeister, C. & Satija, R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol 20, 296, https://doi.org/10.1186/s13059-019-1874-1 (2019).
Stuart, T. et al. Comprehensive Integration of Single-Cell Data. Cell 177, 1888–1902 e1821, https://doi.org/10.1016/j.cell.2019.05.031 (2019).
Becht, E. et al. Dimensionality reduction for visualizing single-cell data using UMAP. Nat Biotechnol https://doi.org/10.1038/nbt.4314 (2018).
Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nat Methods 14, 1083–1086, https://doi.org/10.1038/nmeth.4463 (2017).
Finak, G. et al. MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biol 16, 278, https://doi.org/10.1186/s13059-015-0844-5 (2015).
La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498, https://doi.org/10.1038/s41586-018-0414-6 (2018).
Bergen, V., Lange, M., Peidli, S., Wolf, F. A. & Theis, F. J. Generalizing RNA velocity to transient cell states through dynamical modeling. Nat Biotechnol 38, 1408–1414, https://doi.org/10.1038/s41587-020-0591-3 (2020).
Calcagno, D. M. et al. Single-cell and spatial transcriptomics of the infarcted heart define the dynamic onset of the border zone in response to mechanical destabilization. Nature Cardiovascular Research 1, 1039–1055, https://doi.org/10.1038/s44161-022-00160-3 (2022).
NCBI Gene Expression Omnibus https://identifiers.org/geo/GSE261428 (2025).
NCBI Gene Expression Omnibus https://identifiers.org/geo/GSE267256 (2025).
NCBI Gene Expression Omnibus https://identifiers.org/geo/GSE265828 (2025).
Huang, C. et al. Asporin, an extracellular matrix protein, is a beneficial regulator of cardiac remodeling. Matrix Biol 110, 40–59, https://doi.org/10.1016/j.matbio.2022.04.005 (2022).
Acknowledgements
This work was supported by Instituto de Salud Carlos III and Fondo Europeo de Desarrollo Regional funds (PI16/00129, CPII15/00017, PI19/00501), Red de Terapia Celular RD16/0011/0005 and Ministerio de Economía y Empresa (Program RETOS Cardiomesh), ERANET II (Nanoreheart), and the Horizon 2020 Program BRAVE. Dr Ruiz-Villalba is supported by Fondo Social Europeo/Ministerio de Economía, Industria y Competitividad–Agencia Estatal de Investigación/ IJCI-2016-30254, the Spanish Ministerio de Ciencia, Innovación y Universidades (MICIU)/Agencia estatal de investigación (AEI) (RTI2018-095410-BI00, PID2020-119430RJ-I00, RYC21-034611-I; and CNS2022-135973), European Social Fund Plus (RYC21-034611-I); and European Union NextGenerationEU / Plan de Recuperación, Transformación y Resiliencia (PRTR) (CNS2022-135973). Nuria Planell was supported by grant RYC2021‐032197‐I, funded by MICIU/AEI/10.13039/501100011033 and European Union NextGeneration EU/PRTR.
Author information
Authors and Affiliations
Contributions
S.C.H., A.R.V. conceived the study, designed and performed experiments, analyzed and interpreted the data. S.C.H., A.R.V., D.G.C. wrote the manuscript. M.A., L.S., J.P.R., L.D.M., J.C.F., A.L.P. performed data analysis. N.P. helped with data analysis, provided relevant intellectual input, and edited the manuscript. A.V.Z., P.S.M.U. performed some experiments and provided relevant intellectual input. M.L.M., S.S., G.A., P.R.C. performed some experiments. M.W., S.J. procured human samples. E.M.L., V.L., J.T. reviewed and edited the manuscript. J.M.P.P., S.J., F.P., D.G.C. provided relevant intellectual input and edited the manuscript. F.P., A.R.V., D.G.C. obtained funding, and supervised the whole project. All authors approved the final manuscript.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
41597_2025_6533_MOESM1_ESM.xlsx
Supplementary Excel file containing the lists of genes within Dynamic1 and Dynamic 2 determined by their roles in dynamic transcriptional shifts revealed by RNA velocity analysis and a ranking strategy.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Hernández, S.C., Ainciburu, M., Sudupe, L. et al. Single-cell and spatial transcriptomic profiling of cardiac fibroblasts following myocardial infarction. Sci Data (2026). https://doi.org/10.1038/s41597-025-06533-0
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41597-025-06533-0
