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Single-nucleus profiling of human dilated and hypertrophic cardiomyopathy
Open accessAbstract
Heart failure encompasses a heterogeneous set of clinical features that converge on impaired cardiac contractile function 1,2 and presents a growing public health concern. Previous work has highlighted changes in both transcription and protein expression in failing hearts 3,4, but may overlook molecular changes in less prevalent cell types. Here we identify extensive molecular alterations in failing hearts at single-cell resolution by performing single-nucleus RNA sequencing of nearly 600,000 nuclei in left ventricle samples from 11 hearts with dilated cardiomyopathy and 15 hearts with hypertrophic cardiomyopathy as well as 16 non-failing hearts. The transcriptional profiles of dilated or hypertrophic cardiomyopathy hearts broadly converged at the tissue and cell-type level. Further, a subset of hearts from patients with cardiomyopathy harbour a unique population of activated fibroblasts that is almost entirely absent from non-failing samples. We performed a CRISPR-knockout screen in primary human cardiac fibroblasts to evaluate this fibrotic cell state transition; knockout of genes associated with fibroblast transition resulted in a reduction of myofibroblast cell-state transition upon TGFβ1 stimulation for a subset of genes. Our results provide insights into the transcriptional diversity of the human heart in health and disease as well as new potential therapeutic targets and biomarkers for heart failure.
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“However, they did not identify human cardiac fibroblast populations that drive fibrosis.”
From Targeting the Immune-Fibrosis Axis in Myocardial Infarction and Heart Failure (2022) · cites this paper as [20]
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“However, these studies rely on nuclear RNA information and lack cellular transcriptomic, epigenetic and proteomic profiling information necessary to obtain a granular picture of the precise immune and fibroblast populations that emerge in the failing human heart and collaborate to drive cardiac fibrosis.”
From Targeting immune–fibroblast cell communication in heart failure (2024) · cites this paper as [19]
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“By contrast, cultured human primary cardiac and dermal fibroblasts and immortalized cardiac fibroblasts were relatively poor models of human cardiac fibroblast diversity under resting or stimulated (TGFβ (refs. 19,39,40), IL-1β (ref. 41)) conditions.”
From Targeting immune–fibroblast cell communication in heart failure (2024) · cites this paper as [19]
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“While numerous studies have implicated a role for TGFβ signaling in maladaptive cardiac remodeling and scar formation in HF 20,33”
From Targeting the Immune-Fibrosis Axis in Myocardial Infarction and Heart Failure (2022) · cites this paper as [20]
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“Numerous single-nucleus RNA sequencing (snRNA-seq) studies have investigated the healthy and failing human heart18–22.”
From Targeting immune–fibroblast cell communication in heart failure (2024) · cites this paper as [19]
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“Numerous single-nucleus (snRNA-seq) and single-cell RNA sequencing (scRNA-seq) studies have profiled the healthy human and failing human heart 19-23 and observed expansion of immune cells 21 and fibroblast diversification in the failing heart 20,23 .”
From Targeting the Immune-Fibrosis Axis in Myocardial Infarction and Heart Failure (2022) · cites this paper as [20]
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“These human cell atlases have provided new insights into the cellular landscape of HF and identified unexpected cell diversity within the stromal compartment.”
From Targeting the Immune-Fibrosis Axis in Myocardial Infarction and Heart Failure (2022) · cites this paper as [20]
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“In contrast, primary human cardiac fibroblasts, primary human dermal fibroblasts, and immortalized human cardiac fibroblasts were relatively poor models of human cardiac fibroblast diversity under resting or stimulated (TGFβ 20,33,34 , IL-1 β 35 ) conditions.”
From Targeting the Immune-Fibrosis Axis in Myocardial Infarction and Heart Failure (2022) · cites this paper as [20]
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“First, we fine-tuned Geneformer to distinguish cardiomyocytes35 from non-failing hearts (n=9) or hearts affected by hypertrophic (n=11) or dilated (n=9) cardiomyopathy with an overall out-of-sample accuracy of 90% (Fig. 6b, Extended Data Fig. 10a).”
From Transfer learning enables predictions in network biology (2023) · cites this paper as [35]
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“An example of this issue could arise for cardiomyocytes, which are very large cells that generate a significantly greater number of RNA-Seq reads than other cell types in the heart [44, 45].”
From Benchmarking of computational demultiplexing methods for single-nucleus RNA sequencing data (2025) · cites this paper as [44]
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Citations
Cites
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106
Fiji: an open-source platform for biological-image analysis
Schindelin, J., Arganda-Carreras, I., Frise, E., Kaynig, V., Longair, M., Pietzsch, T., Preibisch, S., Rueden, C., Saalfeld, S., Schmid, B., et al. (2012) · Nature Methods · doi:10.1038/nmeth.2019 · Cited by 66,890
Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206
Cutadapt removes adapter sequences from high-throughput sequencing reads
Martin, M. (2011) · EMBnet.journal · doi:10.14806/ej.17.1.200 · Cited by 33,889
Salmon provides fast and bias-aware quantification of transcript expression
Patro, R., Duggal, G., Love, M. I., Irizarry, R. A., & Kingsford, C. (2017) · Nature Methods · doi:10.1038/nmeth.4197 · Cited by 12,700
Aligning sequence reads, clone sequences and assembly contigs with BWA-MEMPreprint
Li, H. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1303.3997 · Cited by 10,846
Fast, sensitive and accurate integration of single-cell data with Harmony
Korsunsky, I., Millard, N., Fan, J., Slowikowski, K., Zhang, F., Wei, K., Baglaenko, Y., Brenner, M., Loh, P.-R., & Raychaudhuri, S. (2019) · Nature Methods · doi:10.1038/s41592-019-0619-0 · Cited by 10,488
SCANPY: large-scale single-cell gene expression data analysis
Wolf, F. A., Angerer, P., & Theis, F. J. (2018) · Genome Biology · doi:10.1186/s13059-017-1382-0 · Cited by 8,881
The Ensembl Variant Effect Predictor
McLaren, W., Gil, L., Hunt, S. E., Riat, H. S., Ritchie, G. R. S., Thormann, A., Flicek, P., & Cunningham, F. (2016) · Genome Biology · doi:10.1186/s13059-016-0974-4 · Cited by 8,099
voom: precision weights unlock linear model analysis tools for RNA-seq read counts
Law, C. W., Chen, Y., Shi, W., & Smyth, G. K. (2014) · Genome Biology · doi:10.1186/gb-2014-15-2-r29 · Cited by 6,155
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Martin, S. S., Aday, A. W., Almarzooq, Z. I., Anderson, C. A. M., Arora, P., Avery, C. L., Baker-Smith, C. M., Barone Gibbs, B., Beaton, A. Z., Boehme, A. K., et al. (2024) · Circulation · doi:10.1161/cir.0000000000001209 · Cited by 2,800
Transfer learning enables predictions in network biology
Theodoris, C. V., Xiao, L., Chopra, A., Chaffin, M. D., Al Sayed, Z. R., Hill, M. C., Mantineo, H., Brydon, E. M., Zeng, Z., Liu, X. S., et al. (2023) · Nature · doi:10.1038/s41586-023-06139-9 · Cited by 1,193
Self-citationMartin, S. S., Aday, A. W., Allen, N. B., Almarzooq, Z., Anderson, C. A. M., Arora, P. K., Avery, C. L., Baker-Smith, C. M., Bansal, N., Beaton, A., et al. (2025) · Circulation · doi:10.1161/cir.0000000000001303 · Cited by 1,284
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Self-citationOmmen, S. R., Ho, C. Y., Asif, I. M., Balaji, S., Burke, M. A., Day, S. M., Dearani, J. A., Epps, K. C., Evanovich, L., Ferrari, V. A., et al. (2024) · Circulation · doi:10.1161/cir.0000000000001250 · Cited by 807
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Kanemaru, K., Cranley, J., Muraro, D., Miranda, A. M. A., Ho, S. Y., Wilbrey-Clark, A., Patrick Pett, J., Polanski, K., Richardson, L., Litvinukova, M., et al. (2023) · Nature · doi:10.1038/s41586-023-06311-1 · Cited by 425
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Ommen, S. R., Ho, C. Y., Asif, I. M., Balaji, S., Burke, M. A., Day, S. M., Dearani, J. A., Epps, K., Evanovich, L., Ferrari, V. A., et al. (2024) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2024.02.014 · Cited by 425
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Chen, R., Zhang, H., Tang, B., Luo, Y., Yang, Y., Zhong, X., Chen, S., Xu, X., Huang, S., & Liu, C. (2024) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-024-01840-1 · Cited by 418
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Amrute, J. M., Luo, X., Penna, V., Yang, S., Yamawaki, T., Hayat, S., Bredemeyer, A., Jung, I.-H., Kadyrov, F. F., Heo, G. S., et al. (2024) · Nature · doi:10.1038/s41586-024-08008-5 · Cited by 339
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Rood, J. E., Maartens, A., Hupalowska, A., Teichmann, S. A., & Regev, A. (2022) · Nature Medicine · doi:10.1038/s41591-022-02104-7 · Cited by 222
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Farah, E. N., Hu, R., Kern, C., Zhang, Q., Lu, T.-Y., Ma, Q., Tran, S., Zhang, B., Carlin, D. E., Monell, A., et al. (2024) · Nature · doi:10.1038/s41586-024-07171-z · Cited by 213
Fleming, S. J., Chaffin, M. D., Arduini, A., Akkad, A.-D., Banks, E., Marioni, J. C., Philippakis, A. A., Ellinor, P. T., & Babadi, M. (2019) · doi:10.1101/791699 · Cited by 164
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Wang, R., Maron, B. A., & Loscalzo, J. (2023) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.122.318731 · Cited by 125
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Simonson, B., Chaffin, M. D., Hill, M. C., Atwa, O., Guedira, Y., Bhasin, H., Hall, A. W., Hayat, S., Baumgart, S. J., Bedi, K. C., et al. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.112086 · Cited by 108
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Chen, Y. T., & Zou, J. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.16.562533 · Cited by 106
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Shi, J., Yang, C., Zhang, J., Zhao, K., Li, P., Kong, C., Wu, X., Sun, H., Zheng, R., Sun, W. Y., et al. (2023) · Circulation Research · doi:10.1161/circresaha.122.322244 · Cited by 93
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Patrick, R., Janbandhu, V. C., Tallapragada, V. J., Tan, S. S. M., McKinna, E. E., Contreras, O., Ghazanfar, S., Humphreys, D. T., Murray, N. J., Tran, T. H. Y., et al. (2024) · Science Advances · doi:10.1126/sciadv.adk8501 · Cited by 96
Defining cardiac functional recovery in end-stage heart failure at single-cell resolution
Amrute, J. M., Lai, L., Ma, P., Koenig, A. L., Kamimoto, K., Bredemeyer, A., Shankar, T. S., Kuppe, C., Kadyrov, F. F., Schulte, L. J., et al. (2023) · Nature Cardiovascular Research · doi:10.1038/s44161-023-00260-8 · Cited by 79
Fibroblast activation and heterogeneity in fibrotic disease
Zhang, X., Zhang, Y., & Liu, Y. (2025) · Nature Reviews Nephrology · doi:10.1038/s41581-025-00969-8 · Cited by 86
Tadros, R., Zheng, S. L., Grace, C., Jordà, P., Francis, C., West, D. M., Jurgens, S. J., Thomson, K. L., Harper, A. R., Ormondroyd, E., et al. (2025) · Nature Genetics · doi:10.1038/s41588-025-02087-4 · Cited by 82
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Schlittler, M., Pramstaller, P. P., Rossini, A., & Bortoli, M. D. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms241914845 · Cited by 81
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Yuan, L., Xu, Z., Meng, B., & Ye, L. (2025) · BMC Genomics · doi:10.1186/s12864-025-11511-2 · Cited by 73
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Fayyaz, A. U., Eltony, M., Prokop, L. J., Koepp, K. E., Borlaug, B. A., Dasari, S., Bois, M. C., Margulies, K. B., Maleszewski, J. J., Wang, Y., et al. (2024) · Nature Reviews Cardiology · doi:10.1038/s41569-024-01067-1 · Cited by 75
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Yang, S. H., Penna, V. R., & Lavine, K. J. (2025) · Nature Reviews Cardiology · doi:10.1038/s41569-024-01109-8 · Cited by 67
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Chen, Y. T., & Zou, J. (2024) · Nature Biomedical Engineering · doi:10.1038/s41551-024-01284-6 · Cited by 205
Henry, A., Mo, X., Finan, C., Chaffin, M. D., Speed, D., Issa, H. G., Denaxas, S., Ware, J. S., Zheng, S. L., Mälarstig, A., et al. (2025) · Nature Genetics · doi:10.1038/s41588-024-02064-3 · Cited by 71
Self-citationErgothioneine controls mitochondrial function and exercise performance via direct activation of MPST
Sprenger, H., Mittenbühler, M. J., Sun, Y., Vranken, J. G. V., Schindler, S., Jayaraj, A., Khetarpal, S. A., Smythers, A. L., Vargas‐Castillo, A., Puszynska, A. M., et al. (2025) · Cell Metabolism · doi:10.1016/j.cmet.2025.01.024 · Cited by 64
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Cadosch, N., Gil‐Cruz, C., Perez‐Shibayama, C., & Ludewig, B. (2024) · Circulation Research · doi:10.1161/circresaha.124.323892 · Cited by 54
Jurgens, S. J., Rämö, J. T., Kramarenko, D. R., Wijdeveld, L. F. J. M., Haas, J., Chaffin, M. D., Garnier, S., Gaziano, L., Weng, L.-C. C., Lipov, A., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01975-5 · Cited by 59
Self-citationMast cells: a novel therapeutic avenue for cardiovascular diseases?
Poto, R., Marone, G., Galli, S. J., & Varricchi, G. (2024) · Cardiovascular Research · doi:10.1093/cvr/cvae066 · Cited by 54
Fibroblasts and immune cells: at the crossroad of organ inflammation and fibrosis
Smolgovsky, S. A., Theall, B. P., Wagner, N., & Alcaide, P. (2023) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.00545.2023 · Cited by 49
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Palmer, J. A., Rosenthal, N. A., Teichmann, S. A., & Litviňuková, M. (2024) · Circulation Research · doi:10.1161/circresaha.124.323672 · Cited by 48
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Lother, A., & Kohl, P. K. (2023) · Basic Research in Cardiology · doi:10.1007/s00395-023-01000-6 · Cited by 46
Multicellular factor analysis of single-cell data for a tissue-centric understanding of disease
Flores, R. O. R., Lanzer, J. D., Dimitrov, D., Velten, B., & Sáez-Rodríguez, J. (2023) · eLife · doi:10.7554/elife.93161 · Cited by 43
Horitani, K., Chavkin, N. W., Arai, Y., Wang, Y., Ogawa, H., Yura, Y., Evans, M. A., Cochran, J. D., Thel, M. C., Polizio, A. H., et al. (2024) · Nature Cardiovascular Research · doi:10.1038/s44161-024-00441-z · Cited by 44
Kattih, B., Boeckling, F., Shumliakivska, M., Tombor, L., Rasper, T., Schmitz, K., Hoffmann, J., Nicin, L., Abplanalp, W. T., Carstens, D. C., et al. (2023) · Cardiovascular Research · doi:10.1093/cvr/cvad140 · Cited by 42
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Li, H., Chen, X., Wang, J. J., Shen, J., Abuduwufuer, K., Zhang, Z., Dong, Z., Wen, Z., He, J., Chen, S., et al. (2025) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-025-02143-9 · Cited by 40
Yıldırım, Z., Swanson, K., Wu, X., Zou, J., & Wu, J. C. (2024) · The Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-022724-095035 · Cited by 40
Wang, Y., Qiu, X., Li, Q., Qin, J., Ye, L., Zhang, X., Huang, X., Wen, X., Wang, Z., He, W., et al. (2025) · Journal of Translational Medicine · doi:10.1186/s12967-025-06103-3 · Cited by 37
Perturbomics: CRISPR–Cas screening-based functional genomics approach for drug target discovery
Park, B.-S., Lee, M., Kim, J. Y., & Kim, T. (2025) · Experimental & Molecular Medicine · doi:10.1038/s12276-025-01487-0 · Cited by 41
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Melas, M., Beltsios, E. T., Adamou, A., Koumarelas, K. E., & McBride, K. L. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm12010225 · Cited by 37
Xu, X., Hua, X., Han, M., Hu, S., & Song, J. (2023) · Basic Research in Cardiology · doi:10.1007/s00395-022-00972-1 · Cited by 38
Perez-Shibayama, C., Gil-Cruz, C., Cadosch, N., Lütge, M., Cheng, H.-W., De Martin, A., Frischmann, K., Joachimbauer, A., Onder, L., Papadopoulou, I., et al. (2024) · Nature Cardiovascular Research · doi:10.1038/s44161-024-00432-0 · Cited by 35
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Long, X., Yuan, X., & Du, J. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.04.007 · Cited by 39
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Reitz, C. J., Kuzmanov, U., & Gramolini, A. O. (2023) · PROTEOMICS · doi:10.1002/pmic.202200289 · Cited by 35
Decoding organ fibrosis: mechanistic insights and emerging therapeutic strategies
Chen, X., Zhang, J., Guo, L., Wu, C., Zhou, J., Xu, M., Mo, L., Li, Y., & He, J. (2026) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-025-02532-0 · Cited by 40
Zhang, X., Gao, Y., Zhou, Y., Liu, Z., & Liu, R. (2023) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2023.114413 · Cited by 38
Chelko, S. P., Penna, V. R., Engel, M., Shiel, E. A., Centner, A. M., Farra, W., Cannon, E. N., Landim-Vieira, M., Schaible, N., Lavine, K., et al. (2024) · Journal of Clinical Investigation · doi:10.1172/jci172014 · Cited by 32
Embeddings from language models are good learners for single-cell data analysis
Liu, T., Chen, T., Zheng, W., Luo, X., Chen, Y. T., & Zhao, H. (2026) · Patterns · doi:10.1016/j.patter.2025.101431 · Cited by 37
Wertheim, B. M., Wang, R., Guillermier, C., Hütter, C. V. R., Oldham, W. M., Menche, J., Steinhauser, M. L., & Maron, B. A. (2023) · JCI Insight · doi:10.1172/jci.insight.163932 · Cited by 29
Zhou, J., Tian, G., Quan, Y., Kong, Q., Huang, F., Li, J., Wu, W.-C., Tang, Y., Zhou, Z., & Liu, X. (2023) · JCI Insight · doi:10.1172/jci.insight.160745 · Cited by 28
Linna-Kuosmanen, S., Schmauch, E., Galani, K., Ojanen, J., Boix, C. A., Örd, T., Toropainen, A., Singha, P., Moreau, P. R., Harju, K., et al. (2024) · Cell Reports Medicine · doi:10.1016/j.xcrm.2024.101556 · Cited by 29
Cardiac fibroblasts in myocardial injury and heart failure
Linthout, S. V., Matz, I., González, A., & Davis, J. (2025) · European Heart Journal · doi:10.1093/eurheartj/ehaf902 · Cited by 38
Fu, M., Hua, X., Shu, S., Xu, X., Zhang, H., Peng, Z., Mo, H., Liu, Y., Chen, X., Yang, Y., et al. (2024) · BMC Medicine · doi:10.1186/s12916-023-03232-8 · Cited by 27
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Zaveri, S., Srivastava, U., Qu, Y. S., Chahine, M., & Boutjdir, M. (2023) · Frontiers in Physiology · doi:10.3389/fphys.2023.1144069 · Cited by 26
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Liu, T., Chen, T., Zheng, W., Luo, X., Chen, Y., & Zhao, H. (2023) · doi:10.1101/2023.12.07.569910 · Cited by 30
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Wang, X., Du, W., Li, Y., Yang, H., Zhang, Y., Akbar, R., Morgan, H., Peng, T., Chen, J., Sadayappan, S., et al. (2024) · JCI Insight · doi:10.1172/jci.insight.173832 · Cited by 24
She, G., Hai, X.-X., Jia, L.-Y., Zhang, Y., Ren, Y., Pang, Z., Wu, L., Han, M.-Z., Zhang, Y., Li, J., et al. (2025) · Redox Biology · doi:10.1016/j.redox.2025.103597 · Cited by 25
Lysozyme 1 Inflamed CCR2 + Macrophages Promote Obesity-Induced Cardiac Dysfunction
Zhang, L., Han, H., Xu, A., Sathe, A. A., Fu, S., Zhao, J., Cai, W., Yang, Y., Liu, J., Bai, H., et al. (2024) · Circulation Research · doi:10.1161/circresaha.124.324106 · Cited by 23
Cardiac fibroblast BAG3 regulates TGFBR2 signaling and fibrosis in dilated cardiomyopathy
Wang, B. Z., Morsink, M. A. J., Kim, S. W., Luo, L. J., Zhang, X., Soni, R. K., Lock, R. I., Rao, J., Kim, Y., Zhang, A., et al. (2025) · Journal of Clinical Investigation · doi:10.1172/jci181630 · Cited by 22
Transcriptional profile of the rat cardiovascular system at single-cell resolution
Arduini, A., Fleming, S. J., Xiao, L., Hall, A. W., Akkad, A.-D., Chaffin, M. D., Bendinelli, K. J., Tucker, N. R., Papangeli, I., Mantineo, H., et al. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.115091 · Cited by 24
Self-citationMultiomic Spatial Mapping of Myocardial Infarction and Implications for Personalized Therapy
Schumacher, D., & Kramann, R. (2022) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.122.318333 · Cited by 24
Sultan, I., Ramste, M. O., Peletier, P., Hemanthakumar, K. A., Ramanujam, D. P., Tirronen, A., von Wright, Y., Antila, S., Saharinen, P., Eklund, L., et al. (2024) · Circulation Research · doi:10.1161/circresaha.123.324136 · Cited by 22
Fibroblast Diversity and Epigenetic Regulation in Cardiac Fibrosis
Aguado‐Alvaro, L. P., Garitano, N., & Pelacho, B. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25116004 · Cited by 25
Read, D. F., Booth, G. T., Daza, R. M., Jackson, D. L., Gladden, R. G., Srivatsan, S. R., Ewing, B., Franks, J. M., Spurrell, C. H., Gomes, A. R., et al. (2024) · Communications Biology · doi:10.1038/s42003-024-06582-y · Cited by 22
Current and future perspectives of single-cell multi-omics technologies in cardiovascular research
Tan, W. L. W., Seow, W. Q., Zhang, A., Rhee, S., Wong, W. H., Greenleaf, W. J., & Wu, J. C. (2023) · Nature Cardiovascular Research · doi:10.1038/s44161-022-00205-7 · Cited by 22
Xue, S., Luo, Z., Li, A., Feng, M. S., Li, S., Zhou, S., Xu, K., Wang, H., Zhang, J., Ouyang, F., et al. (2025) · Advanced Materials · doi:10.1002/adma.202503474 · Cited by 21
Deficiency of Transcription Factor Sp1 Contributes to Hypertrophic Cardiomyopathy
Zhang, F., Zhou, H., Xue, J., Zhang, Y., Zhou, L., Leng, J., Fang, G., Liu, Y., Wang, Y., Liu, H., et al. (2024) · Circulation Research · doi:10.1161/circresaha.123.323272 · Cited by 21
Long, X., Yan, B., & Mo, Z. (2023) · Medicine Advances · doi:10.1002/med4.34 · Cited by 21
Spatial transcriptional landscape of human heart failure
Lee, S. E., Joo, J. H., Hwang, H. S., Chen, S., Evans, D., Lee, K. Y., Kim, K., Hyun, J., Kim, M., Jung, S., et al. (2025) · European Heart Journal · doi:10.1093/eurheartj/ehaf272 · Cited by 29
Bian, R., Wang, Y., Li, Z., & Xu, X. (2023) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2023.1154920 · Cited by 20
Xiong, G., Bekiranov, S., & Zhang, A. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad493 · Cited by 20
Large-scale single-nuclei profiling identifies role for ATRNL1 in atrial fibrillation
Hill, M. C., Simonson, B., Roselli, C., Xiao, L., Herndon, C. N., Chaffin, M. D., Mantineo, H., Atwa, O., Bhasin, H., Guedira, Y., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-54296-w · Cited by 23
Self-citationImmune cells and related cytokines in dilated cardiomyopathy
Zhang, J., Cheng, L., Li, Z., Li, H., Liu, Y., Zhan, H., Xu, H., Huang, Y., Feng, F., & Li, Y. (2024) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2024.116159 · Cited by 20
A high-resolution view of the heterogeneous aging endothelium
Dobner, S., Tóth, F., & de Rooij, L. P. M. H. (2024) · Angiogenesis · doi:10.1007/s10456-023-09904-6 · Cited by 23
Lian, J., Lin, H., Zhong, Z., Song, Y., Shao, X., Zhou, J., Xu, L., Sun, Z., Yang, Y., Chi, J., et al. (2025) · Journal of Cellular and Molecular Medicine · doi:10.1111/jcmm.70358 · Cited by 17
ARID5A orchestrates cardiac aging and inflammation through MAVS mRNA stabilization
Fan, Y., Zheng, Y., Zhang, Y., Xu, G., Liu, C., Hu, J., Ji, Q., Zhang, S., Fang, S., Lei, J., et al. (2025) · Nature Cardiovascular Research · doi:10.1038/s44161-025-00635-z · Cited by 20
Wang, Y., Singh, K., Lokman, A. B., Deng, S., Sunitha, B., Lima, J. C., Jr., Beglov, J., Kelly, M., Blease, A., Fung, J. C. K., et al. (2025) · Science Translational Medicine · doi:10.1126/scitranslmed.adq3516 · Cited by 18
Guo, Z., Ataran, A., Ma, P., Yu, W., Hajirezaei, H., Ripoll, C. V., Pedersen, L. N., Rashidi, O., Chan, M. M., Shen, M., et al. (2025) · Circulation · doi:10.1161/circulationaha.124.071217 · Cited by 18
Self-citationLu, J., Ren, J., Liu, J., Lu, M., Cui, Y., Liao, Y., Zhou, Y., Gao, Y., Tang, F., Wang, J., et al. (2023) · Cell Proliferation · doi:10.1111/cpr.13557 · Cited by 18
Gokulnath, P., Spanos, M., Lehmann, H. I., Sheng, Q., Rodosthenous, R. S., Chaffin, M. D., Varrias, D., Chatterjee, E., Hutchins, E. D., Li, G., et al. (2024) · Circulation · doi:10.1161/circulationaha.123.065513 · Cited by 24
Self-citationLanzer, J. D., Flores, R. O. R., Liñares-Blanco, J., Steier, M., Rangrez, A. Y., Frey, N., & Sáez-Rodríguez, J. (2025) · Nature Communications · doi:10.1038/s41467-025-62219-6 · Cited by 20
Decellularized heart extracellular matrix alleviates activation of hiPSC-derived cardiac fibroblasts
Kerr, C. M., Silver, S. E., Choi, Y. S., Floy, M. E., Bradshaw, A. D., Cho, S., Palecek, S. P., & Mei, Y. (2023) · Bioactive Materials · doi:10.1016/j.bioactmat.2023.08.023 · Cited by 18
Sheng, Y., Wang, Y., Yuan, C., Ye, D., Wu, L., Kang, H., Zhang, X., Cheng, X. J., Li, B., Zhu, D., et al. (2023) · Journal of Advanced Research · doi:10.1016/j.jare.2023.09.012 · Cited by 21
Genome-wide enhancer-associated tandem repeats are expanded in cardiomyopathy
Mitina, A., Khan, M., Lesurf, R., Yin, Y., Engchuan, W., Hamdan, O., Pellecchia, G., Trost, B., Backstrom, I., Guo, K., et al. (2024) · EBioMedicine · doi:10.1016/j.ebiom.2024.105027 · Cited by 16
From Cell to Gene: Deciphering the Mechanism of Heart Failure With Single‐Cell Sequencing
Zhang, D., Wen, Q., Zhang, R., Kou, K., Lin, M., Zhang, S., Yang, J., Shi, H., Yang, Y., Tan, X., et al. (2024) · Advanced Science · doi:10.1002/advs.202308900 · Cited by 17
Arai, Y., Chavkin, N. W., Arai, Y., Halvardson, J., Bjurling, J., Doviak, H., Cochran, J. D., Evans, M. A., Horitani, K., Yura, Y., et al. (2025) · Science Translational Medicine · doi:10.1126/scitranslmed.adv4071 · Cited by 16
PBX/Knotted 1 homeobox-2 (PKNOX2) is a novel regulator of myocardial fibrosis
Chen, L., Li, H., Liu, X., Zhang, N., Wang, K., Shi, A., Gao, H., Akdiş, D., Saguner, A. M., Xu, X., et al. (2024) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-024-01804-5 · Cited by 16
Tong, Z., Yan, X., Chen, T., Song, W., Su, D., Wang, N., Zhang, Y., Kong, Y., Wang, D., Wang, P., et al. (2025) · Circulation · doi:10.1161/circulationaha.125.073836 · Cited by 17
Luo, S., Ye, D., Zhang, Y., Wang, Y., Zhou, M., Lv, M., Wang, X., Zhong, K., Zhang, Y., Hu, L., et al. (2025) · Circulation · doi:10.1161/circulationaha.125.075903 · Cited by 18
Metadata
- Authors
- Chaffin, M., Papangeli, I., Simonson, B., Akkad, A.-D., Hill, M. C., Arduini, A., Fleming, S. J., Melanson, M., Hayat, S., Kost-Alimova, M., Atwa, O., Ye, J., Bedi, K. C., Nahrendorf, M., Kaushik, V. K., Stegmann, C. M., Margulies, K. B., Tucker, N. R., & Ellinor, P. T.
- Affiliations
- Broad Institute (US)
- University of Pennsylvania (US)
- Massachusetts General Hospital (US)
- Masonic Medical Research Laboratory (US)
- Corresponding author
- Ellinor, Patrick T.
- ORCID
- Venue
- Nature
- Published
- 2022-06-22
- Volume
- 608
- Issue
- 7921
- Pages
- 174-180
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC12591363
- OpenAlex
- W4283257569
- Cited by
- 418
- References
- 61
- Citations by year
- 2026: 110 · 2025: 120 · 2024: 100 · 2023: 67 · 2022: 19
- FWCI
- 54.31
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- American Heart Association
- National Institutes of Health (1s10od026839-01)
- National Heart, Lung, and Blood Institute (K01HL140187, R01HL128914, R01HL105993, K24HL105780, R01HL092577, T32HL007208)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac Fibrosis and Remodeling
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiomyopathy and Myosin Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Keywords
- dilated cardiomyopathy, hypertrophic cardiomyopathy, heart failure, single-nucleus RNA sequencing, cardiac fibroblasts, myofibroblast differentiation, myocardial fibrosis, TGF-β1 signaling, CRISPR knockout screen, cardiac transcriptome, left ventricle, biomarkers, therapeutic target
- MeSH terms
- RNA-Seq, Cardiomyopathy, Dilated, Cardiomyopathy, Hypertrophic, Cell Nucleus, Cells, Cultured, Heart Failure, Heart Ventricles, Humans, Myocardium, Transcription, Genetic, Case-Control Studies, Gene Expression Profiling, Transforming Growth Factor beta1, Gene Knockout Techniques, Myofibroblasts, Single-Cell Analysis, CRISPR-Cas Systems
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