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Review

Single-Cell RNA Sequencing (scRNA-seq) in Cardiac Tissue: Applications and Limitations

ORCID Icon, , , ORCID Icon & ORCID Icon
Pages 641-657 | Published online: 02 Oct 2021

References

  • Mortality GBD, Causes of Death C. Global, regional, and national age-sex specific all-cause and cause-specific mortality for 240 causes of death, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet. 2015;385(9963):117–171. doi:10.1016/S0140-6736(14)61682-2
  • Mortality GBD, Causes of Death C. Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet. 2016;388(10053):1459–1544.
  • Thomas H, Diamond J, Vieco A, et al. Global atlas of cardiovascular disease 2000–2016: the path to prevention and control. Glob Heart. 2018;13(3):143–163. doi:10.1016/j.gheart.2018.09.511
  • Leon-Mimila P, Wang J, Huertas-Vazquez A. Relevance of multi-omics studies in cardiovascular diseases. Front Cardiovasc Med. 2019;6:91. doi:10.3389/fcvm.2019.00091
  • Leopold JA, Loscalzo J. Emerging role of precision medicine in cardiovascular disease. Circ Res. 2018;122(9):1302–1315. doi:10.1161/CIRCRESAHA.117.310782
  • Neeland IJ, Poirier P, Despres JP. Cardiovascular and metabolic heterogeneity of obesity: clinical challenges and implications for management. Circulation. 2018;137(13):1391–1406.
  • McCormick ME, Manduchi E, Witschey WR, et al. Integrated Regional Cardiac Hemodynamic Imaging and RNA Sequencing Reveal Corresponding Heterogeneity of Ventricular Wall Shear Stress and Endocardial Transcriptome. J Am Heart Assoc. 2016;5(4):e003170. doi:10.1161/JAHA.115.003170
  • Paik DT, Tian L, Williams IM, et al. Single-cell RNA sequencing unveils unique transcriptomic signatures of organ-specific endothelial cells. Circulation. 2020;142(19):1848–1862. doi:10.1161/CIRCULATIONAHA.119.041433
  • Ruan H, Liao Y, Ren Z, et al. Single-cell reconstruction of differentiation trajectory reveals a critical role of ETS1 in human cardiac lineage commitment. BMC Biol. 2019;17(1):89. doi:10.1186/s12915-019-0709-6
  • Tucker NR, Chaffin M, Fleming SJ, et al. Transcriptional and cellular diversity of the human heart. Circulation. 2020;142(5):466–482. doi:10.1161/CIRCULATIONAHA.119.045401
  • Litvinukova M, Talavera-Lopez C, Maatz H, et al. Cells of the adult human heart. Nature. 2020;588(7838):466–472. doi:10.1038/s41586-020-2797-4
  • Kamdar F, Das S, Gong W, et al. Stem cell-derived cardiomyocytes and beta-adrenergic receptor blockade in Duchenne muscular dystrophy cardiomyopathy. J Am Coll Cardiol. 2020;75(10):1159–1174. doi:10.1016/j.jacc.2019.12.066
  • Kulkarni A, Anderson AG, Merullo DP, Konopka G. Beyond bulk: a review of single cell transcriptomics methodologies and applications. Curr Opin Biotechnol. 2019;58:129–136. doi:10.1016/j.copbio.2019.03.001
  • Pollen AA, Nowakowski TJ, Shuga J, et al. Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex. Nat Biotechnol. 2014;32(10):1053–1058. doi:10.1038/nbt.2967
  • Picelli S, Bjorklund AK, Faridani OR, Sagasser S, Winberg G, Sandberg R. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat Methods. 2013;10(11):1096–1098. doi:10.1038/nmeth.2639
  • Sheng K, Cao W, Niu Y, Deng Q, Zong C. Effective detection of variation in single-cell transcriptomes using MATQ-seq. Nat Methods. 2017;14(3):267–270. doi:10.1038/nmeth.4145
  • Jaitin DA, Kenigsberg E, Keren-Shaul H, et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science. 2014;343(6172):776–779. doi:10.1126/science.1247651
  • Hashimshony T, Wagner F, Sher N, Yanai I. CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. Cell Rep. 2012;2(3):666–673. doi:10.1016/j.celrep.2012.08.003
  • Macosko EZ, Basu A, Satija R, et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell. 2015;161(5):1202–1214. doi:10.1016/j.cell.2015.05.002
  • Bagnoli JW, Ziegenhain C, Janjic A, et al. Sensitive and powerful single-cell RNA sequencing using mcSCRB-seq. Nat Commun. 2018;9(1):2937. doi:10.1038/s41467-018-05347-6
  • Zheng GX, Terry JM, Belgrader P, et al. Massively parallel digital transcriptional profiling of single cells. Nat Commun. 2017;8:14049. doi:10.1038/ncomms14049
  • Gierahn TM, Wadsworth MH 2nd, Hughes TK, et al. Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput. Nat Methods. 2017;14(4):395–398. doi:10.1038/nmeth.4179
  • Rosenberg AB, Roco CM, Muscat RA, et al. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science. 2018;360(6385):176–182. doi:10.1126/science.aam8999
  • Goldstein LD, Chen YJ, Dunne J, et al. Massively parallel nanowell-based single-cell gene expression profiling. BMC Genomics. 2017;18(1):519. doi:10.1186/s12864-017-3893-1
  • Chaudhry F, Isherwood J, Bawa T, et al. Single-cell RNA sequencing of the cardiovascular system: new looks for old diseases. Front Cardiovasc Med. 2019;6:173. doi:10.3389/fcvm.2019.00173
  • Luecken MD, Theis FJ. Current best practices in single-cell RNA-seq analysis: a tutorial. Mol Syst Biol. 2019;15(6):e8746. doi:10.15252/msb.20188746
  • Andrews TS, Kiselev VY, McCarthy D, Hemberg M. Tutorial: guidelines for the computational analysis of single-cell RNA sequencing data. Nat Protoc. 2021;16(1):1–9. doi:10.1038/s41596-020-00409-w
  • Svensson V, Vento-Tormo R, Teichmann SA. Exponential scaling of single-cell RNA-seq in the past decade. Nat Protoc. 2018;13(4):599–604. doi:10.1038/nprot.2017.149
  • Potter SS. Single-cell RNA sequencing for the study of development, physiology and disease. Nat Rev Nephrol. 2018;14(8):479–492. doi:10.1038/s41581-018-0021-7
  • Santoro F, Chien KR, Sahara M. Isolation of human ESC-derived cardiac derivatives and embryonic heart cells for population and single-cell RNA-seq analysis. STAR Protoc. 2021;2(1):100339. doi:10.1016/j.xpro.2021.100339
  • Yamada S, Nomura S. Review of single-cell RNA sequencing in the heart. Int J Mol Sci. 2020;21:21. doi:10.3390/ijms21218345
  • Gladka MM, Molenaar B, de Ruiter H, et al. Single-cell sequencing of the healthy and diseased heart reveals cytoskeleton-associated protein 4 as a new modulator of fibroblasts activation. Circulation. 2018;138(2):166–180. doi:10.1161/CIRCULATIONAHA.117.030742
  • Lacar B, Linker SB, Jaeger BN, et al. Nuclear RNA-seq of single neurons reveals molecular signatures of activation. Nat Commun. 2016;7:11022. doi:10.1038/ncomms11022
  • Xin Y, Kim J, Ni M, et al. Use of the Fluidigm C1 platform for RNA sequencing of single mouse pancreatic islet cells. Proc Natl Acad Sci U S A. 2016;113(12):3293–3298. doi:10.1073/pnas.1602306113
  • Nayak R, Hasija Y. A hitchhiker’s guide to single-cell transcriptomics and data analysis pipelines. Genomics. 2021;113(2):606–619. doi:10.1016/j.ygeno.2021.01.007
  • McGinnis CS, Murrow LM, Gartner ZJ. DoubletFinder: doublet detection in single-cell RNA sequencing data using artificial nearest neighbors. Cell Syst. 2019;8(4):329–337 e324. doi:10.1016/j.cels.2019.03.003
  • Picelli S, Faridani OR, Bjorklund AK, Winberg G, Sagasser S, Sandberg R. Full-length RNA-seq from single cells using Smart-seq2. Nat Protoc. 2014;9(1):171–181. doi:10.1038/nprot.2014.006
  • Picelli S. Full-Length single-cell RNA sequencing with Smart-seq2. Methods Mol Biol. 2019;1979:25–44.
  • Ziegenhain C, Vieth B, Parekh S, et al. Comparative Analysis of single-cell RNA sequencing methods. Mol Cell. 2017;65(4):631–643 e634. doi:10.1016/j.molcel.2017.01.023
  • Salomon R, Kaczorowski D, Valdes-Mora F, et al. Droplet-based single cell RNAseq tools: a practical guide. Lab Chip. 2019;19(10):1706–1727. doi:10.1039/C8LC01239C
  • Lareau CA, Ma S, Duarte FM, Buenrostro JD. Inference and effects of barcode multiplets in droplet-based single-cell assays. Nat Commun. 2020;11(1):866. doi:10.1038/s41467-020-14667-5
  • Liu SJ, Nowakowski TJ, Pollen AA, et al. Single-cell analysis of long non-coding RNAs in the developing human neocortex. Genome Biol. 2016;17:67. doi:10.1186/s13059-016-0932-1
  • Asp M, Giacomello S, Larsson L, et al. A spatiotemporal organ-wide gene expression and cell atlas of the developing human heart. Cell. 2019;179(7):1647–1660 e1619. doi:10.1016/j.cell.2019.11.025
  • Xu D, Ma M, Xu Y, et al. Single-cell transcriptome analysis indicates new potential regulation mechanism of ACE2 and NPs signaling among heart failure patients infected with SARS-CoV-2. medRxiv. 2020;8:103.
  • Li Z, Solomonidis EG, Meloni M, et al. Single-cell transcriptome analyses reveal novel targets modulating cardiac neovascularization by resident endothelial cells following myocardial infarction. Eur Heart J. 2019;40(30):2507–2520. doi:10.1093/eurheartj/ehz305
  • Wang L, Yu P, Zhou B, et al. Single-cell reconstruction of the adult human heart during heart failure and recovery reveals the cellular landscape underlying cardiac function. Nat Cell Biol. 2020;22(1):108–119. doi:10.1038/s41556-019-0446-7
  • Saelens W, Cannoodt R, Todorov H, Saeys Y. A comparison of single-cell trajectory inference methods. Nat Biotechnol. 2019;37(5):547–554. doi:10.1038/s41587-019-0071-9
  • Song D, Li JJ. PseudotimeDE: inference of differential gene expression along cell pseudotime with well-calibrated p-values from single-cell RNA sequencing data. Genome Biol. 2021;22(1):124. doi:10.1186/s13059-021-02341-y
  • Phansalkar RS, Krieger J, Zhao M, et al. Coronary blood vessels from distinct origins converge to equivalent states during mouse and human development. bioRxiv. 2021;2:584.
  • Ren Z, Yu P, Li D, et al. Single-cell reconstruction of progression trajectory reveals intervention principles in pathological cardiac hypertrophy. Circulation. 2020;141(21):1704–1719. doi:10.1161/CIRCULATIONAHA.119.043053
  • Zhang Q, Carlin D, Zhu F, et al. Unveiling complexity and multipotentiality of early heart fields. Circ Res. 2021;129(4):474–487. doi:10.1161/CIRCRESAHA.121.318943
  • La Manno G, Soldatov R, Zeisel A, et al. RNA velocity of single cells. Nature. 2018;560(7719):494–498. doi:10.1038/s41586-018-0414-6
  • Bergen V, Lange M, Peidli S, Wolf FA, Theis FJ. Generalizing RNA velocity to transient cell states through dynamical modeling. Nat Biotechnol. 2020;38(12):1408–1414. doi:10.1038/s41587-020-0591-3
  • Wolfien M, Galow AM, Muller P, et al. Single-nucleus sequencing of an entire mammalian heart: cell type composition and velocity. Cells. 2020;9:2.
  • Liu X, Chen W, Li W, et al. Single-cell RNA-Seq of the developing cardiac outflow tract reveals convergent development of the vascular smooth muscle cells. Cell Rep. 2019;28(5):1346–1361 e1344. doi:10.1016/j.celrep.2019.06.092
  • Hulsmans M, Clauss S, Xiao L, et al. Macrophages facilitate electrical conduction in the heart. Cell. 2017;169(3):510–522 e520. doi:10.1016/j.cell.2017.03.050
  • King KR, Aguirre AD, Ye YX, et al. IRF3 and type I interferons fuel a fatal response to myocardial infarction. Nat Med. 2017;23(12):1481–1487. doi:10.1038/nm.4428
  • Ma Y, Mouton AJ, Lindsey ML. Cardiac macrophage biology in the steady-state heart, the aging heart, and following myocardial infarction. Transl Res. 2018;191:15–28. doi:10.1016/j.trsl.2017.10.001
  • Efremova M, Vento-Tormo M, Teichmann SA, Vento-Tormo R. CellPhoneDB: inferring cell-cell communication from combined expression of multi-subunit ligand-receptor complexes. Nat Protoc. 2020;15(4):1484–1506. doi:10.1038/s41596-020-0292-x
  • Jin S, Guerrero-Juarez CF, Zhang L, et al. Inference and analysis of cell-cell communication using CellChat. Nat Commun. 2021;12(1):1088. doi:10.1038/s41467-021-21246-9
  • Cabello-Aguilar S, Alame M, Kon-Sun-Tack F, Fau C, Lacroix M, Colinge J. SingleCellSignalR: inference of intercellular networks from single-cell transcriptomics. Nucleic Acids Res. 2020;48(10):e55. doi:10.1093/nar/gkaa183
  • Wang B, Zhu J, Pierson E, Ramazzotti D, Batzoglou S. Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning. Nat Methods. 2017;14(4):414–416. doi:10.1038/nmeth.4207
  • Korsunsky I, Millard N, Fan J, et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat Methods. 2019;16(12):1289–1296. doi:10.1038/s41592-019-0619-0
  • Stuart T, Butler A, Hoffman P, et al. Comprehensive Integration of Single-Cell Data. Cell. 2019;177(7):1888–1902 e1821. doi:10.1016/j.cell.2019.05.031
  • Kiselev VY, Kirschner K, Schaub MT, et al. SC3: consensus clustering of single-cell RNA-seq data. Nat Methods. 2017;14(5):483–486. doi:10.1038/nmeth.4236
  • Galow AM, Wolfien M, Muller P, et al. Integrative cluster analysis of whole hearts reveals proliferative cardiomyocytes in adult mice. Cells. 2020;9:5. doi:10.3390/cells9051144
  • Kuppe C, Flores RO, Li Z, et al. Spatial multi-omic map of human myocardial infarction. BioRxiv. 2020. doi:10.1101/2020.12.08.411686
  • Vu TN, Nguyen HN, Calza S, Kalari KR, Wang L, Pawitan Y. Cell-level somatic mutation detection from single-cell RNA sequencing. Bioinformatics. 2019;35(22):4679–4687. doi:10.1093/bioinformatics/btz288
  • Linnarsson S, Teichmann SA. Single-cell genomics: coming of age. Genome Biol. 2016;17:97. doi:10.1186/s13059-016-0960-x
  • Asp M, Bergenstrahle J, Lundeberg J. Spatially resolved transcriptomes-next generation tools for tissue exploration. Bioessays. 2020;42(10):e1900221. doi:10.1002/bies.201900221
  • Moffitt JR, Zhuang X. RNA imaging with multiplexed error-robust fluorescence in situ hybridization (MERFISH). Methods Enzymol. 2016;572:1–49.
  • Gelali E, Custodio J, Girelli G, Wernersson E, Crosetto N, Bienko M. An application-directed, versatile DNA FISH Platform for Research and Diagnostics. Methods Mol Biol. 2018;1766:303–333.
  • Gelali E, Girelli G, Matsumoto M, et al. iFISH is a publically available resource enabling versatile DNA FISH to study genome architecture. Nat Commun. 2019;10(1):1636. doi:10.1038/s41467-019-09616-w
  • Mohenska M, Tan NM, Tokolyi A, et al. 3D-Cardiomics: a spatial transcriptional atlas of the mammalian heart. bioRxiv. 2019:792002. doi:10.1101/792002
  • Lahnemann D, Koster J, Szczurek E, et al. Eleven grand challenges in single-cell data science. Genome Biol. 2020;21(1):31.
  • Islam S, Zeisel A, Joost S, et al. Quantitative single-cell RNA-seq with unique molecular identifiers. Nat Methods. 2014;11(2):163–166. doi:10.1038/nmeth.2772
  • Deng Q, Ramskold D, Reinius B, Sandberg R. Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells. Science. 2014;343(6167):193–196. doi:10.1126/science.1245316
  • Derrien T, Johnson R, Bussotti G, et al. The GENCODE v7 catalog of human long noncoding RNAs: analysis of their gene structure, evolution, and expression. Genome Res. 2012;22(9):1775–1789. doi:10.1101/gr.132159.111
  • Sun W, Dong H, Balaz M, et al. snRNA-seq reveals a subpopulation of adipocytes that regulates thermogenesis. Nature. 2020;587(7832):98–102. doi:10.1038/s41586-020-2856-x
  • Kiselev VY, Andrews TS, Hemberg M. Challenges in unsupervised clustering of single-cell RNA-seq data. Nat Rev Genet. 2019;20(5):273–282. doi:10.1038/s41576-018-0088-9
  • Cui Y, Zhang S, Liang Y, Wang X, Ferraro TN, Chen Y. Consensus clustering of single-cell RNA-seq data by enhancing network affinity. Brief Bioinform. 2021. doi:10.1093/bib/bbab236
  • Wagner DE, Klein AM. Lineage tracing meets single-cell omics: opportunities and challenges. Nat Rev Genet. 2020;21(7):410–427. doi:10.1038/s41576-020-0223-2
  • Waylen LN, Nim HT, Martelotto LG, Ramialison M. From whole-mount to single-cell spatial assessment of gene expression in 3D. Commun Biol. 2020;3(1):602. doi:10.1038/s42003-020-01341-1
  • Roth R, Kim S, Kim J, Rhee S. Single-cell and spatial transcriptomics approaches of cardiovascular development and disease. BMB Rep. 2020;53(8):393–399. doi:10.5483/BMBRep.2020.53.8.130
  • Singh M, Al-Eryani G, Carswell S, et al. High-throughput targeted long-read single cell sequencing reveals the clonal and transcriptional landscape of lymphocytes. Nat Commun. 2019;10(1):3120. doi:10.1038/s41467-019-11049-4
  • Rozenblatt-Rosen O, Stubbington MJT, Regev A, Teichmann SA. The Human Cell Atlas: from vision to reality. Nature. 2017;550(7677):451–453. doi:10.1038/550451a
  • Suryawanshi H, Clancy R, Morozov P, Halushka MK, Buyon JP, Tuschl T. Cell atlas of the foetal human heart and implications for autoimmune-mediated congenital heart block. Cardiovasc Res. 2020;116(8):1446–1457. doi:10.1093/cvr/cvz257
  • Hu Z, Liu W, Hua X, et al. Single-cell transcriptomic atlas of different human cardiac arteries identifies cell types associated with vascular physiology. Arterioscler Thromb Vasc Biol. 2021;41(4):1408–1427. doi:10.1161/ATVBAHA.120.315373
  • Miao Y, Tian L, Martin M, et al. Intrinsic endocardial defects contribute to hypoplastic left heart syndrome. Cell Stem Cell. 2020;27(4):574–589 e578. doi:10.1016/j.stem.2020.07.015
  • Cao J, O’Day DR, Pliner HA, et al. A human cell atlas of fetal gene expression. Science. 2020;370:6518. doi:10.1126/science.aba7721
  • Han X, Zhou Z, Fei L, et al. Construction of a human cell landscape at single-cell level. Nature. 2020;581(7808):303–309. doi:10.1038/s41586-020-2157-4
  • Friedman CE, Nguyen Q, Lukowski SW, et al. Single-Cell Transcriptomic Analysis of Cardiac Differentiation from Human PSCs Reveals HOPX-Dependent Cardiomyocyte Maturation. Cell Stem Cell. 2018;23(4):586–598 e588. doi:10.1016/j.stem.2018.09.009
  • Churko JM, Garg P, Treutlein B, et al. Defining human cardiac transcription factor hierarchies using integrated single-cell heterogeneity analysis. Nat Commun. 2018;9(1):4906. doi:10.1038/s41467-018-07333-4
  • Cui Y, Zheng Y, Liu X, et al. Single-cell transcriptome analysis maps the developmental track of the human heart. Cell Rep. 2019;26(7):1934–1950 e1935. doi:10.1016/j.celrep.2019.01.079
  • Sahara M, Santoro F, Sohlmer J, et al. Population and single-cell analysis of human cardiogenesis reveals unique lgr5 ventricular progenitors in embryonic outflow tract. Dev Cell. 2019;48(4):475–490 e477. doi:10.1016/j.devcel.2019.01.005
  • Gambardella L, McManus SA, Moignard V, et al. BNC1 regulates cell heterogeneity in human pluripotent stem cell-derived epicardium. Development. 2019;146:24.
  • Zhou Y, Liu Z, Welch JD, et al. Single-cell transcriptomic analyses of cell fate transitions during human cardiac reprogramming. Cell Stem Cell. 2019;25(1):149–164 e149. doi:10.1016/j.stem.2019.05.020
  • 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(1):4435. doi:10.1038/s41467-018-06639-7
  • 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(1):225. doi:10.1038/s41467-017-00319-8
  • Tabula Muris C. A single-cell transcriptomic atlas characterizes ageing tissues in the mouse. Nature. 2020;583(7817):590–595. doi:10.1038/s41586-020-2496-1
  • de Soysa TY, Ranade SS, Okawa S, et al. Single-cell analysis of cardiogenesis reveals basis for organ-level developmental defects. Nature. 2019;572(7767):120–124. doi:10.1038/s41586-019-1414-x
  • Goodyer WR, Beyersdorf BM, Paik DT, et al. Transcriptomic profiling of the developing cardiac conduction system at single-cell resolution. Circ Res. 2019;125(4):379–397. doi:10.1161/CIRCRESAHA.118.314578
  • Vidal R, Wagner JUG, Braeuning C, et al. Transcriptional heterogeneity of fibroblasts is a hallmark of the aging heart. JCI Insight. 2019;4:22. doi:10.1172/jci.insight.131092
  • Zhang Y, Gago-Lopez N, Li N, et al. Single-cell imaging and transcriptomic analyses of endogenous cardiomyocyte dedifferentiation and cycling. Cell Discov. 2019;5:30. doi:10.1038/s41421-019-0095-9
  • Linscheid N, Logantha S, Poulsen PC, et al. Quantitative proteomics and single-nucleus transcriptomics of the sinus node elucidates the foundation of cardiac pacemaking. Nat Commun. 2019;10(1):2889. doi:10.1038/s41467-019-10709-9
  • Farbehi N, Patrick R, Dorison A, et al. Single-cell expression profiling reveals dynamic flux of cardiac stromal, vascular and immune cells in health and injury. Elife. 2019;1:8.
  • Hu P, Liu J, Zhao J, et al. Single-nucleus transcriptomic survey of cell diversity and functional maturation in postnatal mammalian hearts. Genes Dev. 2018;32(19–20):1344–1357. doi:10.1101/gad.316802.118
  • Tabula Muris C, Overall C, Logistical C, et al. Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris. Nature. 2018;562(7727):367–372.
  • Wang Y, Yao F, Wang L, et al. Single-cell analysis of murine fibroblasts identifies neonatal to adult switching that regulates cardiomyocyte maturation. Nat Commun. 2020;11(1):2585. doi:10.1038/s41467-020-16204-w
  • Han X, Zhang J, Liu Y, et al. The lncRNA Hand2os1/Uph locus orchestrates heart development through regulation of precise expression of Hand2. Development. 2019;146:13. doi:10.1242/dev.176198
  • Hulin A, Hortells L, Gomez-Stallons MV, et al. Maturation of heart valve cell populations during postnatal remodeling. Development. 2019;146:12.
  • Yekelchyk M, Guenther S, Preussner J, Braun T. Mono- and multi-nucleated ventricular cardiomyocytes constitute a transcriptionally homogenous cell population. Basic Res Cardiol. 2019;114(5):36. doi:10.1007/s00395-019-0744-z
  • Xiong H, Luo Y, Yue Y, et al. Single-cell transcriptomics reveals chemotaxis-mediated intraorgan crosstalk during cardiogenesis. Circ Res. 2019;125(4):398–410. doi:10.1161/CIRCRESAHA.119.315243
  • Dong J, Hu Y, Fan X, et al. Single-cell RNA-seq analysis unveils a prevalent epithelial/mesenchymal hybrid state during mouse organogenesis. Genome Biol. 2018;19(1):31. doi:10.1186/s13059-018-1416-2
  • Kretzschmar K, Post Y, Bannier-Helaouet M, et al. Profiling proliferative cells and their progeny in damaged murine hearts. Proc Natl Acad Sci U S A. 2018;115(52):E12245–E12254. doi:10.1073/pnas.1805829115
  • Li G, Xu A, Sim S, et al. Transcriptomic Profiling maps anatomically patterned subpopulations among single embryonic cardiac cells. Dev Cell. 2016;39(4):491–507. doi:10.1016/j.devcel.2016.10.014
  • DeLaughter DM, Bick AG, Wakimoto H, et al. Single-cell resolution of temporal gene expression during heart development. Dev Cell. 2016;39(4):480–490. doi:10.1016/j.devcel.2016.10.001
  • Lescroart F, Wang X, Lin X, et al. Defining the earliest step of cardiovascular lineage segregation by single-cell RNA-seq. Science. 2018;359(6380):1177–1181. doi:10.1126/science.aao4174
  • Weinberger M, Simoes FC, Patient R, Sauka-Spengler T, Riley PR. Functional heterogeneity within the developing Zebrafish Epicardium. Dev Cell. 2020;52(5):574–590 e576. doi:10.1016/j.devcel.2020.01.023
  • Holowiecki A, Linstrum K, Ravisankar P, Chetal K, Salomonis N, Waxman JS. Pbx4 limits heart size and fosters arch artery formation by partitioning second heart field progenitors and restricting proliferation. Development. 2020;147:5.
  • Honkoop H, de Bakker DE, Aharonov A, et al. Single-cell analysis uncovers that metabolic reprogramming by ErbB2 signaling is essential for cardiomyocyte proliferation in the regenerating heart. Elife. 2019;1:8.
  • Chestnut B, Casie Chetty S, Koenig AL, Sumanas S. Single-cell transcriptomic analysis identifies the conversion of zebrafish Etv2-deficient vascular progenitors into skeletal muscle. Nat Commun. 2020;11(1):2796. doi:10.1038/s41467-020-16515-y
  • Zhang W, Zhang S, Yan P, et al. A single-cell transcriptomic landscape of primate arterial aging. Nat Commun. 2020;11(1):2202. doi:10.1038/s41467-020-15997-0
  • Ma S, Sun S, Li J, et al. Single-cell transcriptomic atlas of primate cardiopulmonary aging. Cell Res. 2021;31(4):415–432. doi:10.1038/s41422-020-00412-6