The DOI matches its Crossref record; 65 of the 65 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source doesn’t support the claim as written.
In the citing paper
“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]
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“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]
The cited source says what the claim attributes to it.
In the citing paper
“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]
The cited source says what the claim attributes to it.
In the citing paper
“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]
The cited source says what the claim attributes to it.
In the citing paper
“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]
The cited source says what the claim attributes to it.
In the citing paper
“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]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“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]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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
Cited by
Sorted by citations
Items 301–400 of 424
Diffusion Tensor Phenomapping of the Healthy and Pressure-Overloaded Human HeartPreprint
Rock, C. A., Chen, Y. I., Wang, R., Philip, A. L., Keil, B. R., Weiner, R. B., Elmariah, S., Mekkaoui, C., Nguyen, C. T., & Sosnovik, D. E. (2024) · medRxiv · doi:10.1101/2024.05.03.24306781 · Cited by 1
scKEPLM: Knowledge enhanced large-scale pre-trained language model for single-cell transcriptomicsPreprint
Li, Y., Qiao, G., & Wang, G. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.07.09.602633 · Cited by 1
Genetics of Cardiac Aging Implicate Organ-Specific VariationPreprint
Brundage, J., Barrios, J. P., Tison, G. H., & Pirruccello, J. P. (2024) · medRxiv · doi:10.1101/2024.08.02.24310874 · Cited by 1
LARP6 regulates the mRNA translation of fibrogenic genes in liver fibrosisPreprint
Kim, H. Y., Mizrahi, O., Lee, W., Rosenthal, S. B., Han, C., Yee, B. A., Blue, S. M., Diaz, J., Jonnalagadda, J., Hokutan, K., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.16.633226 · Cited by 1
Transformers Enhance the Predictive Power of Network MedicinePreprint
Spector, J. O., Aldana, A., Sebek, M., Ehlert, J., Frondeville, C. D., Ghiassian, S. D., & Barabási, A.-Ĺ. (2025) · medRxiv · doi:10.1101/2025.01.27.25321204 · Cited by 1
Predicting emergent phenotypes from single cell populations using CELLECTIONPreprint
Hu, H., Sanghi, S., & Quon, G. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.02.673886 · Cited by 1
Bleckwehl, T., Schumacher, D., Heymanns, C., Maryam, S., Andries, A.-S., Jurgens, S. J., Amrute, J., Hoeft, K., Wu, X., Liu, Y., et al. (2025) · medRxiv · doi:10.1101/2025.10.09.25337646 · Cited by 1
Self-citationHeartMap: An Integrated Cell Atlas of 2.4 million cells across 209 Individuals in Health and DiseasePreprint
Datar, Y., Chaffin, M. D., Simonson, B., Mandia, A., & Ellinor, P. T. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.06.686995 · Cited by 1
Self-citationGong, Z., Li, M., Ang, K. S., Zhao, X. H., Ramachandra, C. J. A., Wang, X., Hausenloy, D. J., Wang, Y., Chen, Y., & Chen, J. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.12.693831 · Cited by 1
Chun, B., Ngo, L., & Saucerman, J. J. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.03.17.435783
Single-cell profiles of CHD and CM
Huynh, K. (2022) · Nature Reviews Cardiology · doi:10.1038/s41569-022-00753-2
High-throughput methods to help understand heart disease
Wehrens, M., Timmer, L. T., & van Rooij, E. (2022) · Nature Cardiovascular Research · doi:10.1038/s44161-022-00126-5
The single-cell atlas of the heart reveals an unexpected erythrocyte-like cardiomyocytePreprint
Li, Y., Shen, J., Ma, L., & Hu, J. (2022) · Research Square · doi:10.21203/rs.3.rs-2048229/v1
Fibroseutvikling som årsak til hjertesvikt
Benestad, H. B. (2022) · Tidsskrift for Den norske legeforening · doi:10.4045/tidsskr.22.0606
Selección de lo mejor del año 2022 en cardiopatías familiares y genética cardiovascular
Jiménez‐Jáimez, J., Villacorta, E., Zorio, E., Ripoll‐Vera, T., Ruiz, R. M., Bermúdez-Jiménez, F. J., & Freire, J. L. (2022) · REC CardioClinics · doi:10.1016/j.rccl.2022.11.001
Will gazing into the single-cell multiome illuminate cardiovascular drug development?
Zhao, T. X., & Mallat, Z. (2023) · Nature Cardiovascular Research · doi:10.1038/s44161-022-00209-3
Minuteman – A versatile cloud computational platform for collaborative researchPreprint
Li, X. K., Charkaborty, J., Jameson, M., Moore, H., Laux‐Biehlmann, A., Hayat, S., & Jain, D. S. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.21.514916
Self-citationCardiovascular Disease Causes Proinflammatory Microvascular Changes in the Human Right AtriumPreprint
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. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4353420
DeGrave, A. N. (2023) · eDiss (Georg-August-Universität Göttingen) · doi:10.53846/goediss-9797
Katoh, M., Ko, T., Katagiri, M., Yamada, S., Ito, M., & Komuro, I. (2023) · Journal of Asian Pacific Society of Cardiology · doi:10.15420/japsc.2022.37
Yang, W., Zhu, Y., Tang, F., Zhao, J., & Yingbin, X. (2023) · European Journal of Clinical Investigation · doi:10.1111/eci.14051
Roles of Skeletal Muscle in Development: A Bioinformatics and Systems Biology Overview
Milanese, J., Marcotte, R., Costain, W. J., Kablar, B., & Drouin, S. (2023) · Advances in anatomy, embryology and cell biology · doi:10.1007/978-3-031-38215-4_2
Pisesky, A., Chen, C. K., Sun, M., Duaz, J., Stephenson, E. A., Coles, J. G., & Friedberg, M. K. (2023) · medRxiv · doi:10.1101/2023.12.11.23299838
Brooklyn plots to identify co-expression dysregulation in single cell sequencing
Patil, A. H., McCall, M. N., & Halushka, M. K. (2023) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqad112
Yu, H., Liu, X., Sun, M., Li, T., & Nie, Z. (2024) · Research Square · doi:10.21203/rs.3.rs-3924392/v1
Regulatory and Communication Shifts in Dilated Cardiomyopathy: A Multimodal Single-Cell AnalysisPreprint
Tang, Y., Zhang, J., Xu, X., Yu, Y., Ding, S., Li, B., Wang, G., Liu, X., Xu, Z., & Han, L. (2024) · Research Square · doi:10.21203/rs.3.rs-4180085/v1
Beyond the Granuloma: New Insights into Cardiac Sarcoidosis Using Spatial ProteomicsPreprint
Peyster, E. G., Smith, D. G., Bittermann, T., Bravo, P. E., & Margulies, K. B. (2024) · Research Square · doi:10.21203/rs.3.rs-4289663/v1
Self-citationLeveraging Human Tissue for Discovery in Heart Transplantation
Lavine, K. J. (2024) · Circulation · doi:10.1161/circulationaha.124.068884
Oxidative Stress and Heart Failure
Chen, Q. M., Morissey, S., & Alpert, J. S. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95488-4.00086-3
Exploring group-specific technical variation patterns of single-cell dataPreprint
Zhou, Y., Sheng, Q., & Jin, S. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.20.614043
Continual integration of single-cell multimodal data with MIRACLE
Zhou, J., Wang, J., Hu, S., Kan, T., Feng, C., Qiang, X., Dong, G., Shi, J., Liu, R., Bo, X., et al. (2026) · Nature Computational Science · doi:10.1038/s43588-026-01030-9
Kreutz, L., Gaab, A., Dona, M. S. I., Pinto, A. R., Tallquist, M. D., Groneberg, D., & Friebe, A. (2024) · iScience · doi:10.1016/j.isci.2024.111615
Anand, A., Punnen, J., Nagamalesh, U. M., Selvam, S., Bysani, M., Venkatesh, R., Nawin, K., Garg, S., Raghuraman, B., Shetty, V., et al. (2025) · Journal of Molecular and Cellular Cardiology Plus · doi:10.1016/j.jmccpl.2025.100282
The Brugada syndrome associated gene WT1 impacts on SCN5A expression and cardiac conductionPreprint
Marsman, E. M., Offerhaus, J. A., Stervinou, T., Mengarelli, I., Beekman, L., Wilde, L. M. W., Casini, S., Streef, T. J., van den Hoff, M. J. B., Goumans, M., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.16.633330
A cross-species multimodal database of gene expression in cardiac hypertrophyPreprint
Sandmann, C., Lanzer, J. D., Stein, F., Malovrh, E., Rüdinger, N., Frey, N., Sáez-Rodríguez, J., & Völkers, M. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.27.635074
Do, C., & Lähdesmäki, H. J. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.10.637389
Transcriptional Atlas of Cardiac DiseasePreprint
Meijers, W. C., Yousif, L. I. E., Sijtema, A. G., Mayr, M., Silljé, H. H. W., Thum, T., & de Boer, R. A. (2025) · Research Square · doi:10.21203/rs.3.rs-5943119/v1
Kolonay, D., Grueter, C. E., & Baskin, K. K. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5158818
Aguado‐Alvaro, L. P., Garitano, N., Esser‐Skala, W., Sayers, J., del Valle, C., Alameda, D., Mendieta-Esteban, J., Calleja-Cervantes, M. E., Goñi‐Salaverri, A., Zazpe, J., et al. (2025) · Qeios · doi:10.32388/2cc4zm
Cardiac Macrophage: Insights from Murine Models and Translational Potential for Human Studies
Liu, Y., Wang, T. T., Lu, Y., Riaz, M., & Qyang, Y. (2024) · Journal of Molecular and Cellular Cardiology · doi:10.1016/j.yjmcc.2025.05.001 · Cited by 1
Spatial Insights in Cardiovascular Aging
Dai, Z., Ding, H., Zhang, Q., Fu, L., & Tai, S. (2025) · Aging and Disease · doi:10.14336/ad.2025.0272
Zhu, Q., Liao, S., Zhou, Y., Zhou, R., & Ming, D. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.22.655661
The Role of Medical Therapy in Hypertrophic Cardiomyopathy
Lee, J.-W., Chun, K., Choi, E., & Seo, J. (2025) · Heart Failure Clinics · doi:10.1016/j.hfc.2025.06.005
Liu, Y., Zou, Q., Su, R., & Wei, L. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.04.668374 · Cited by 1
Whole-genome sequencing analysis of left ventricular structure and sphericity in 80,000 peoplePreprint
Pirruccello, J. P. (2025) · medRxiv · doi:10.1101/2025.08.22.25334019
Chun, B., Ngo, L., & Saucerman, J. J. (2025) · Journal of Molecular and Cellular Cardiology Plus · doi:10.1016/j.jmccpl.2025.100484
Warren, C. M., Ryba, D. M., Geist, G. E., Coronado, A. C., Wolska, B. M., Goldspink, P. H., & Solaro, R. J. (2025) · Journal of Molecular and Cellular Cardiology Plus · doi:10.1016/j.jmccpl.2025.100495
Chen, J., Schmidt, F., & Henao, R. (2026) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013701
A combined genomic arrhythmia propensity score delineates cumulative risk
Monroe, T. O., Puckelwartz, M. J., Pesce, L. L., Kearns, S. D., Page, P., Ibrahim, N., Weber, Z., Ugwor, E. I., Nóbrega, M. A., Kannankeril, P. J., et al. (2025) · Cell Reports Medicine · doi:10.1016/j.xcrm.2025.102455
Wang, Y., Wang, W., Mo, H., Hua, X., Cui, H., Chen, X., Zhang, Y., Liu, S., Zhao, Y., & Song, J. (2025) · Cardiovascular Pathology · doi:10.1016/j.carpath.2025.107808
Ferroptosis and autophagy-related genes contribute to hypertrophic cardiomyopathy progression
Zhang, A., Liu, W., Du, M., Li, F., Yi, M., Li, H., Yang, Z., & Li, F. (2025) · Scientific Reports · doi:10.1038/s41598-025-19206-0
Cao, M., Ke, D., Huang, D., Ni, J., Yuan, Y., Dai, X., & Zhou, H. (2025) · The FASEB Journal · doi:10.1096/fj.202502947rr
Capraz, T., Vöhringer, H. S., Serrano, K. S. K., Flores, R. O. R., Sáez-Rodríguez, J., & Huber, W. (2026) · Nature Communications · doi:10.1038/s41467-026-74694-6
Fu, M., Hua, X., Shu, S., Xu, X., Zhang, H., Peng, Z., Mo, H., Liu, Y., Chen, X., Yang, Y., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26659372
Fu, M., Hua, X., Shu, S., Xu, X., Zhang, H., Peng, Z., Mo, H., Liu, Y., Chen, X., Yang, Y., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26659366
Fu, M., Hua, X., Shu, S., Xu, X., Zhang, H., Peng, Z., Mo, H., Liu, Y., Chen, X., Yang, Y., et al. (2024) · Figshare · doi:10.6084/m9.figshare.24956003
Fu, M., Hua, X., Shu, S., Xu, X., Zhang, H., Peng, Z., Mo, H., Liu, Y., Chen, X., Yang, Y., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26659369
Fu, M., Hua, X., Shu, S., Xu, X., Zhang, H., Peng, Z., Mo, H., Liu, Y., Chen, X., Yang, Y., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26659375
Fu, M., Hua, X., Shu, S., Xu, X., Zhang, H., Peng, Z., Mo, H., Liu, Y., Chen, X., Yang, Y., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26659378
Fu, M., Hua, X., Shu, S., Xu, X., Zhang, H., Peng, Z., Mo, H., Liu, Y., Chen, X., Yang, Y., et al. (2024) · Figshare · doi:10.6084/m9.figshare.24956203
Han, W., Lan, Q., Zhang, R., Shen, Z., Zhong, J., & Hao, L. (2026) · European Journal of Pharmacology · doi:10.1016/j.ejphar.2026.178568
Schoger, E., Kim, R., Bleckwedel, F., Peralta, T. M., Priesmeier, L., Fischer, J., Stengel, L., Rocha, C., Santos, G. L., Lutz, S., et al. (2026) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-026-02593-9 · Cited by 1
Pozo, M. R., Pressler, M. P., Horvath, A., & Entcheva, E. G. (2026) · The Journal of Precision Medicine Health and Disease · doi:10.1016/j.premed.2026.100031
Immune Mechanisms in the Pathogenesis of Primary Cardiomyopathies
Uršić, D., & Kibel, A. (2026) · Cardiology and cardiovascular medicine. · doi:10.5772/intechopen.1014685
Deciphering the cardiac neuron landscape in heart failure patients
Zhuang, S., Yang, X., Zhang, N., Liu, J., Liu, K., Han, H., Zhai, S., Liu, M., Liang, H., Gu, Y., et al. (2026) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1014082
Tadros, H. J. J., Turaga, D., Zhao, Y., Tsai, C.-R., Wadhwa, L., Kearney, D. L., Adachi, I. A., Li, X., & Martin, J. F. (2026) · Circulation Heart Failure · doi:10.1161/circheartfailure.125.014092
FGF12 Alleviates Cardiac Hypertrophy by Inhibiting Phosphorylation of CaM/CaMKII/CREB1 Axis
Zhang, T., Yin, K., Li, T., Wang, S., & Zhou, Z. (2026) · Circulation Genomic and Precision Medicine · doi:10.1161/circgen.125.005362
Wang, P., Song, Y., Zhang, B., & Yang, J. (2026) · medRxiv · doi:10.64898/2026.04.20.26351344
Kuwahara, N., Nagao, M., Izawa, Y., Kaneshiro, K., Hashimura, H., Yoshida, T., Emoto, T., Toba, T., Tanaka, H., Ishida, T., et al. (2026) · Journal of the American Heart Association · doi:10.1161/jaha.125.047119
Gao, C., Shu, Y., Luo, L., Wang, N., Wang, T., Ren, Z., Shen, P., & Liu, Y. (2026) · Molecular and Cellular Biochemistry · doi:10.1007/s11010-026-05530-0
Cardiac GLP1R gene expression: A cross-species single-cell transcriptomic analysis
Dostal, C., Ernst, M., Reiner, J., Snelling, R., Hu, C., Pokreisz, P., Kiss, A., & Podesser, B. K. (2026) · Advances in Translational Research · doi:10.1556/1661.2026.00113
Tuomainen, T., Širinov, M. M., Rabiee, M., Schmauch, E., Koivumäki, J. T., Galani, K., Kellis, M., Kuusisto, J., Leigh, R. S., Naumenko, N., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.13.724801
Tang, Q., Li, X., Fan, X., Zheng, C., Ma, H., Luo, R., Tang, Y., Liu, S., Fan, C., Zhang, Y., et al. (2026) · BMC Medicine · doi:10.1186/s12916-026-04954-1
Kuznetsov, I. A., Li, K., Yang, Y., Zhao, W., Zhou, W., Zhu, W., Liang, J., Li, J., Edwards, J. J., & Arany, Z. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.01.729445
Re-evaluating the benefit of training scale in cell foundation modelsPreprint
Shakeel, M. H., Shen, M., & Mangiola, S. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.04.730272
Li, H., Liu, W. T., Huang, X., You, Z., & Huang, L. (2026) · Journal of the American Heart Association · doi:10.1161/jaha.125.047416
Ge, T., Hu, J., Lin, B., Yang, L., Ning, B., Bo, J., Ju, J., He, K., Li, G., Wang, C., et al. (2026) · Journal of Ethnopharmacology · doi:10.1016/j.jep.2026.122012
Ford, K., Abbay, H., Leonardson, A. S., Franks, J. M., & Chavkin, N. W. (2026) · Basic Research in Cardiology · doi:10.1007/s00395-026-01190-9
Mitochondrial protein OPA3 sustains cardiac function by regulating calcium handling in male mice
Geng, N., Chen, T., Li, H., Lu, S., Wang, Y., Gao, L., Bers, D. M., & Lu, X. (2026) · Nature Communications · doi:10.1038/s41467-026-73991-4
Cardiac immunity and heart repair: Mechanism, challenge, and future direction
Liu, Y., Ren, Q., Wang, H. T., Jiang, X., Zhao, M. H., Min, Q., Xie, Y., Cai, D., & Nie, Y. (2026) · Chinese Medical Journal · doi:10.1097/cm9.0000000000004078
Balakina-Vikulova, N. A., Katsnelson, L. B., & Solovyova, O. E. (2026) · Journal of Evolutionary Biochemistry and Physiology · doi:10.1134/s0022093026030087
K9HeartCircDB: a circRNA atlas of tachypacing-induced canine dilated cardiomyopathy
Chinmaya, C., Sinha, T., Nisini, N., Wang, T., Natarajaseenivasan, S. M., Dhanasekaran, R., Berretta, R. M., Kumar, A., Panda, A. C., Elrod, J. W., et al. (2026) · Journal of Translational Medicine · doi:10.1186/s12967-026-08866-9
Neprilysin mediated cleavage of phospholamban dysregulates SERCA in heart failurePreprint
Cunningham, J. D., Phillips, T. A., Mazzenga, A., Nagrani, K., Tuan, B. H., Edassery, S., Barefield, D. Y., & Robia, S. L. (2026) · bioRxiv · doi:10.64898/2026.06.23.732949
Engineered Heart Tissues Facilitate Noncoding Variant Studies in Cardiomyopathy
Weber, Z., Monroe, T. O., Holgren, C. W., Mitchell, R. M., Zhang, L., Thornburg, A. G., Salamone, I. M., Chychula, I. A., Karthik, F., Fullenkamp, D. E., et al. (2026) · Circulation Research · doi:10.1161/circresaha.125.327506
TGM2-mediated histone serotonylation is an epigenetic cardioprotective mechanism in HFpEFPreprint
Ogawara, R., Misaka, T., Suzuki, Y., Okochi, S., Ichimura, S., Miura, S., Yokokawa, T., Taira, S., Waguri, S., Oikawa, M., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.25.734596
Microprotein, macro-effect: DWORF as a therapeutic strategy in heart failure
Verry, J. P., & Makarewich, C. A. (2026) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2026.1864847
Palmer, I. M. A., & Collins, H. E. (2026) · Circulation Research · doi:10.1161/circresaha.126.328843
Noh, J., Shin, Y., Kim, M., & Oh, M. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag395
Voxel-scale diffusion tensor phenomapping of the healthy and pressure-overloaded human heart
Rock, C. A., Chen, Y. I., Wang, R., Philip, A. L., Keil, B. R., Weiner, R. B., Elmariah, S., Mekkaoui, C., Nguyen, C. T., & Sosnovik, D. E. (2026) · Nature Biomedical Engineering · doi:10.1038/s41551-026-01755-y
Li, Y. (2026) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2026.109287
Zhan, Y., Liang, G.-Y., Wang, J., Cen, X.-F., Zhang, N., Zhao, N., Xing, Y., Li, L.-L., Yu, T., Dong, W.-S., et al. (2026) · Research · doi:10.34133/research.1400
Jia, L.-Y., Hai, X.-X., Wu, Z., Li, J.-J., Yang, Y., Hou, M., Wu, L., Han, M.-Z., Zhang, Y., Xu, Y., et al. (2026) · Cell Death and Disease · doi:10.1038/s41419-026-09137-7
Reimagining VEGF Therapy: The Role of PLCγ2 in Pan-Organ Endothelial Networks
Abouhashem, A. S., Verma, S. S., Faden-McCormack, J., Moqidem, Y. A., Dutta, P., Sen, C. K., & Singh, K. (2026) · Advances in Wound Care · doi:10.1177/21621918261474733
Alakwaa, F., Elbaz, A., Azzam, S. A., & Alshebani, T. (2026) · BMC Bioinformatics · doi:10.1186/s12859-026-06632-8
Yoo, G., Kang, J.-Y., Park, M., Lee, J., Mun, D., Yun, N., & Joung, B. (2026) · Acta Biomaterialia · doi:10.1016/j.actbio.2026.08.013
Seo, M., Kim, Y.-A., Kim, R., Lee, H. G., & Kim, M. S. (2026) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2026.1814166
Xiao, S., Mo, C., Liu, Q., & Xu, L. (2026) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2026.1830967
Adipose-Myocardial Border in Atrial Fibrillation
Fitzgibbons, T. P., & Tran, K. (2026) · Circulation Research · doi:10.1161/circresaha.126.329119
APPLICATION OF MACHINE LEARNING METHODS IN EARLY DRUG DEVELOPMENT
Maksimov, A. S., & Дергачёва, Ж. М. (2026) · Vestnik Farmacii · doi:10.52540/2074-9457.2026.1.72
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review