High-resolution single-cell transcriptomic survey of cardiomyocytes from patients with hypertrophic cardiomyopathy
Received date: 02 Jun 2023
Revised date: 28 Aug 2023
Accepted date: 15 Sep 2023
Copyright
Hypertrophic cardiomyopathy (HCM) is a common inherited cardiovascular disease, which can cause heart failure and lead to death. In this study, we performed high-resolution single-cell RNA-sequencing of 2115 individual cardiomyocytes obtained from HCM patients and normal controls. Signature up- and down-regulated genes in HCM were identified by integrative analysis across 37 patients and 41 controls from our data and published human single-cell and single-nucleus RNA-seq datasets, which were further classified into gene modules by single-cell co-expression analysis. Using our high-resolution dataset, we also investigated the heterogeneity among individual cardiomyocytes and revealed five distinct clusters within HCM cardiomyocytes. Interestingly, we showed that some extracellular matrix (ECM) genes were up-regulated in the HCM cardiomyocytes, suggesting that they play a role in cardiac remodelling. Taken together, our study comprehensively profiled the transcriptomic programs of HCM cardiomyocytes and provided insights into molecular mechanisms underlying the pathogenesis of HCM.
Jiansen Lu , Jie Ren , Jie Liu , Minjie Lu , Yueli Cui , Yuhan Liao , Yuan Zhou , Yun Gao , Fuchou Tang , Jizheng Wang , Shuiyun Wang , Lu Wen , Lei Song . High-resolution single-cell transcriptomic survey of cardiomyocytes from patients with hypertrophic cardiomyopathy[J]. Cell Proliferation, 2024 , 57(3) : e13557 . DOI: 10.1111/cpr.13557
1 |
Maron BJ, Maron MS, Semsarian C. Genetics of hypertrophic cardiomyopathy after 20 years: clinical perspectives. J Am Coll Cardiol. 2012;60:705-715.
|
2 |
Authors/Task Force members, Elliott PM, Anastasakis A, et al. 2014 ESC guidelines on diagnosis and management of hypertrophic cardiomyopathy: the Task Force for the Diagnosis and Management of Hypertrophic Cardiomyopathy of the European Society of Cardiology (ESC). Eur Heart J. 2014;35:2733-2779.
|
3 |
Ren XW, Zhang L, Zhang Y, Li Z, Siemers N, Zhang Z. Insights gained from single-cell analysis of immune cells in the tumor microenvironment. Annu Rev Immunol. 2021;39:583-609.
|
4 |
Paik DT, Cho S, Tian L, Chang HY, Wu JC. Single-cell RNA sequencing in cardiovascular development, disease and medicine. Nat Rev Cardiol. 2020;17:457-473.
|
5 |
See K, Tan WLW, Lim EH, et al. Single cardiomyocyte nuclear transcriptomes reveal a lincRNA-regulated de-differentiation and cell cycle stress-response in vivo. Nat Commun. 2017;8:225.
|
6 |
Nomura S, Satoh M, Fujita T, et al. Cardiomyocyte gene programs encoding morphological and functional signatures in cardiac hypertrophy and failure. Nat Commun. 2018;9:4435.
|
7 |
McLellan MA, Skelly DA, Dona MSI, et al. High-resolution transcriptomic profiling of the heart during chronic stress reveals cellular drivers of cardiac fibrosis and hypertrophy. Circulation. 2020;142:1448-1463.
|
8 |
Chaffin M, Papangeli I, Simonson B, et al. Single-nucleus profiling of human dilated and hypertrophic cardiomyopathy. Nature. 2022;608:174-180.
|
9 |
Liu X, Yin K, Chen L, et al. Lineage-specific regulatory changes in hypertrophic cardiomyopathy unraveled by single-nucleus RNA-seq and spatial transcriptomics. Cell Discov. 2023;9:6.
|
10 |
Derks W, Bergmann O. Polyploidy in cardiomyocytes: roadblock to heart regeneration? Circ Res. 2020;126:552-565.
|
11 |
Wehrens M, de Leeuw AE, Wright-Clark M, et al. Single-cell transcriptomics provides insights into hypertrophic cardiomyopathy. Cell Rep. 2022;39:110809.
|
12 |
Li L, Dong J, Yan L, et al. Single-cell RNA-Seq analysis maps development of human germline cells and gonadal niche interactions. Cell Stem Cell. 2017;20:858-873.e854.
|
13 |
Cui Y, Zheng Y, Liu X, et al. Single-cell transcriptome analysis maps the developmental track of the human heart. Cell Rep. 2019;26:1934-1950.e5.
|
14 |
Tucker NR, Chaffin M, Bedi JrKC, et al. Myocyte specific upregulation of ACE2 in cardiovascular disease: implications for SARS-CoV-2 mediated myocarditis. Circulation. 2020;142:708-710.
|
15 |
Cantuti-Castelvetri L, Ojha R, Pedro LD, et al. Neuropilin-1 facilitates SARS-CoV-2 cell entry and infectivity. Science. 2020;370:856-860.
|
16 |
Hudak A, Letoha A, Szilak L, Letoha T. Contribution of Syndecans to the cellular entry of SARS-CoV-2. Int J Mol Sci. 2021;22:5336.
|
17 |
Vasak M, Meloni G. Chemistry and biology of mammalian metallothioneins. J Biol Inorg Chem. 2011;16:1067-1078.
|
18 |
Wang J, Song Y, Elsherif L, et al. Cardiac metallothionein induction plays the major role in the prevention of diabetic cardiomyopathy by zinc supplementation. Circulation. 2006;113:544-554.
|
19 |
Aibar S, González-Blas CB, Moerman T, et al. SCENIC: single-cell regulatory network inference and clustering. Nat Methods. 2017;14:1083-1086.
|
20 |
Van de Sande B, Flerin C, Davie K, et al. A scalable SCENIC workflow for single-cell gene regulatory network analysis. Nat Protoc. 2020;15:2247-2276.
|
21 |
Palioura D, Lazou A, Drosatos K. Kruppel-like factor (KLF)5: an emerging foe of cardiovascular health. J Mol Cell Cardiol. 2022;163:56-66.
|
22 |
Zhang XZ, Liu FY, Wang QQ, Geng YX. Overexpressed microRNA-506 and microRNA-124 alleviate H2O2-induced human cardiomyocyte dysfunction by targeting kruppel-like factor 4/5. Mol Med Rep. 2017;16:5363-5369.
|
23 |
Yamaguchi N. Cardiac pressure overload decreases ETV1 expression in the left atrium, contributing to atrial electrical and structural remodeling (vol 143, pg 805, 2021). Circulation. 2021;143:e745.
|
24 |
Rommel C, Rösner S, Lother A, et al. The transcription factor ETV1 induces atrial remodeling and arrhythmia. Circ Res. 2018;123:550-563.
|
25 |
Aydin S, Ugur K, Aydin S, Sahin I, Yardim M. Biomarkers in acute myocardial infarction: current perspectives. Vasc Health Risk Manag. 2019;15:1-10.
|
26 |
Henderson NC, Rieder F, Wynn TA. Fibrosis: from mechanisms to medicines. Nature. 2020;587:555-566.
|
27 |
Ho CY, López B, Coelho-Filho OR, et al. Myocardial fibrosis as an early manifestation of hypertrophic cardiomyopathy. N Engl J Med. 2010;363:552-563.
|
28 |
Ren CW, Liu JJ, Li JH, Li JW, Dai J, Lai YQ. RNAseq profiling of mRNA associated with hypertrophic cardiomyopathy. Mol Med Rep. 2016;14:5573-5586.
|
29 |
Denisenko E, Guo BB, Jones M, et al. Systematic assessment of tissue dissociation and storage biases in single-cell and single-nucleus RNA-seq workflows. Genome Biol. 2020;21:130.
|
30 |
Thrupp N, Sala Frigerio C, Wolfs L, et al. Single-nucleus RNA-Seq is not suitable for detection of microglial activation genes in humans. Cell Rep. 2020;32:108189.
|
31 |
Gimeno JR, Olivotto I, Rodríguez AI, et al. Impact of SARS-Cov-2 infection in patients with hypertrophic cardiomyopathy: results of an international multicentre registry. ESC Heart Fail. 2022;9:2189-2198.
|
32 |
Kim JB, Porreca GJ, Song L, et al. Polony multiplex analysis of gene expression (PMAGE) in mouse hypertrophic cardiomyopathy. Science. 2007;316:1481-1484.
|
33 |
Liu X, Ma Y, Yin K, et al. Long non-coding and coding RNA profiling using strand-specific RNA-seq in human hypertrophic cardiomyopathy. Sci Data. 2019;6:90.
|
34 |
Koshman YE, Patel N, Chu M, et al. Regulation of connective tissue growth factor gene expression and fibrosis in human heart failure. J Card Fail. 2013;19:283-294.
|
35 |
Accornero F, van Berlo JH, Correll RN, et al. Genetic analysis of connective tissue growth factor as an effector of transforming growth factor beta signaling and cardiac remodeling. Mol Cell Biol. 2015;35:2154-2164.
|
36 |
Fontes MS, Kessler EL, van Stuijvenberg L, et al. CTGF knockout does not affect cardiac hypertrophy and fibrosis formation upon chronic pressure overload. J Mol Cell Cardiol. 2015;88:82-90.
|
37 |
Dorn LE, Petrosino JM, Wright P, Accornero F. CTGF/CCN2 is an autocrine regulator of cardiac fibrosis. J Mol Cell Cardiol. 2018;121:205-211.
|
38 |
Ochoa CD, Wu RF, Terada LS. ROS signaling and ER stress in cardiovascular disease. Mol Aspects Med. 2018;63:18-29.
|
39 |
Islam S, Kjällquist U, Moliner A, et al. Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. Genome Res. 2011;21:1160-1167.
|
40 |
Smith T, Heger A, Sudbery I. UMI-tools: modeling sequencing errors in unique molecular identifiers to improve quantification accuracy. Genome Res. 2017;27:491-499.
|
41 |
Dobin A, Davis CA, Schlesinger F, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29:15-21.
|
42 |
Liao Y, Smyth GK, Shi W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2014;30:923-930.
|
43 |
Stuart T, Butler A, Hoffman P, et al. Comprehensive integration of single-cell data. Cell. 2019;177:1888-1902.e21.
|
44 |
Korsunsky I, Millard N, Fan J, et al. Fast, sensitive and accurate integration of single-cell data with harmony. Nat Methods. 2019;16:1289-1296.
|
45 |
Butler A, Hoffman P, Smibert P, Papalexi E, Satija R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol. 2018;36:411-420.
|
46 |
Zhou Y, Zhou B, Pache L, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun. 2019;10:1-10.
|
47 |
Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics. 2012;16:284-287.
|
48 |
Efremova M, Vento-Tormo M, Teichmann SA, Vento-Tormo R. Cell-PhoneDB: inferring cell-cell communication from combined expression of multi-subunit ligand-receptor complexes. Nat Protoc. 2020;15:1484-1506.
|
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