Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Single-cell RNA sequencing in cardiovascular development, disease and medicine

Abstract

Advances in single-cell RNA sequencing (scRNA-seq) technologies in the past 10 years have had a transformative effect on biomedical research, enabling the profiling and analysis of the transcriptomes of single cells at unprecedented resolution and throughput. Specifically, scRNA-seq has facilitated the identification of novel or rare cell types, the analysis of single-cell trajectory construction and stem or progenitor cell differentiation, and the comparison of healthy and disease-related tissues at single-cell resolution. These applications have been critical in advances in cardiovascular research in the past decade as evidenced by the generation of cell atlases of mammalian heart and blood vessels and the elucidation of mechanisms involved in cardiovascular development and stem or progenitor cell differentiation. In this Review, we summarize the currently available scRNA-seq technologies and analytical tools and discuss the latest findings using scRNA-seq that have substantially improved our knowledge on the development of the cardiovascular system and the mechanisms underlying cardiovascular diseases. Furthermore, we examine emerging strategies that integrate multimodal single-cell platforms, focusing on future applications in cardiovascular precision medicine that use single-cell omics approaches to characterize cell-specific responses to drugs or environmental stimuli and to develop effective patient-specific therapeutics.

Key points

  • The advent of single-cell RNA sequencing (scRNA-seq) technologies has facilitated the profiling and analysis of the transcriptomes of single cells at unprecedented resolution and throughput.

  • scRNA-seq allows the identification of rare subpopulations of cells as well as the cellular trajectory analysis of each cell’s transcriptome, helping to identify cell-state transitions during development and progenitor or stem cell differentiation.

  • In addition to the characterization of specific tissues or organ systems, scRNA-seq has also been performed on a larger scale to establish comprehensive cell atlases of various major organs, including the heart.

  • Multimodal single-cell platforms can be integrated and used to evaluate cell population heterogeneity and its contributions to patient-specific drug responses and adverse effects.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Fig. 1: Workflow of single-cell RNA sequencing.
Fig. 2: Applications of scRNA-seq in cardiovascular research.
Fig. 3: Comparison of cell population clustering methods.
Fig. 4: Single-cell characterization of the human adult heart.
Fig. 5: Single-cell multiomics approaches for cardiovascular precision medicine.

References

  1. Heid, C. A., Stevens, J., Livak, K. J. & Williams, P. M. Real time quantitative PCR. Genome Res. 6, 986–994 (1996).

    CAS  PubMed  Google Scholar 

  2. Trapnell, C. Defining cell types and states with single-cell genomics. Genome Res. 25, 1491–1498 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Islam, S. et al. Quantitative single-cell RNA-seq with unique molecular identifiers. Nat. Methods 11, 163–166 (2014).

    CAS  PubMed  Google Scholar 

  4. Shapiro, E., Biezuner, T. & Linnarsson, S. Single-cell sequencing-based technologies will revolutionize whole-organism science. Nat. Rev. Genet. 14, 618–630 (2013).

    CAS  PubMed  Google Scholar 

  5. Potter, S. S. Single-cell RNA sequencing for the study of development, physiology and disease. Nat. Rev. Nephrol. 14, 479–492 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. The Tabula Muris Consortium et al. Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris. Nature 562, 367–372 (2018).

    CAS  Google Scholar 

  7. Han, X. et al. Mapping the Mouse Cell Atlas by Microwell-Seq. Cell 172, 1091–1107.e17 (2018).

    CAS  PubMed  Google Scholar 

  8. Cusanovich, D. A. et al. A single-cell atlas of in vivo mammalian chromatin accessibility. Cell 174, 1309–1324.e18 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Gao, S. et al. Tracing the temporal-spatial transcriptome landscapes of the human fetal digestive tract using single-cell RNA-sequencing. Nat. Cell Biol. 20, 721–734 (2018).

    CAS  PubMed  Google Scholar 

  10. Wang, P. et al. Dissecting the global dynamic molecular profiles of human fetal kidney development by single-cell RNA sequencing. Cell Rep. 24, 3554–3567.e3 (2018).

    CAS  PubMed  Google Scholar 

  11. Zhong, S. et al. A single-cell RNA-seq survey of the developmental landscape of the human prefrontal cortex. Nature 555, 524–528 (2018).

    CAS  PubMed  Google Scholar 

  12. Cui, Y. et al. Single-cell transcriptome analysis maps the developmental track of the human heart. Cell Rep. 26, 1934–1950.e5 (2019).

    CAS  PubMed  Google Scholar 

  13. Ramilowski, J. A. et al. A draft network of ligand-receptor-mediated multicellular signalling in human. Nat. Commun. 6, 7866 (2015).

    CAS  PubMed  Google Scholar 

  14. Ståhl, P. L. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78–82 (2016).

    PubMed  Google Scholar 

  15. Jemt, A. et al. An automated approach to prepare tissue-derived spatially barcoded RNA-sequencing libraries. Sci. Rep. 6, 37137 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Asp, M. et al. Spatial detection of fetal marker genes expressed at low level in adult human heart tissue. Sci. Rep. 7, 12941 (2017).

    PubMed  PubMed Central  Google Scholar 

  17. Berglund, E. et al. Spatial maps of prostate cancer transcriptomes reveal an unexplored landscape of heterogeneity. Nat. Commun. 9, 2419 (2018).

    PubMed  PubMed Central  Google Scholar 

  18. Asp, M. et al. A spatiotemporal organ-wide gene expression and cell atlas of the developing human heart. Cell 179, 1647–1660.e19 (2019).

    CAS  PubMed  Google Scholar 

  19. Svensson, V., Vento-Tormo, R. & Teichmann, S. A. Exponential scaling of single-cell RNA-seq in the past decade. Nat. Protoc. 13, 599–604 (2018).

    CAS  PubMed  Google Scholar 

  20. Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Gladka, M. M. et al. Single-cell sequencing of the healthy and diseased heart reveals cytoskeleton-associated protein 4 as a new modulator of fibroblasts activation. Circulation 138, 166–180 (2018).

    CAS  PubMed  Google Scholar 

  22. Ramsköld, D. et al. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat. Biotechnol. 30, 777–782 (2012).

    PubMed  PubMed Central  Google Scholar 

  23. Picelli, S. et al. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9, 171–181 (2014).

    CAS  PubMed  Google Scholar 

  24. Picelli, S. Full-length single-cell RNA sequencing with Smart-seq2. Methods Mol. Biol. 1979, 25–44 (2019).

    CAS  PubMed  Google Scholar 

  25. Hwang, B., Lee, J. H. & Bang, D. Single-cell RNA sequencing technologies and bioinformatics pipelines. Exp. Mol. Med. 50, 96 (2018).

    PubMed Central  Google Scholar 

  26. Hashimshony, T., Wagner, F., Sher, N. & Yanai, I. CEL-seq: single-cell RNA-seq by multiplexed linear amplification. Cell Rep. 2, 666–673 (2012).

    CAS  PubMed  Google Scholar 

  27. Hashimshony, T. et al. CEL-seq2: sensitive highly-multiplexed single-cell RNA-seq. Genome Biol. 17, 77 (2016).

    PubMed  PubMed Central  Google Scholar 

  28. Zheng, G. X. Y. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Papalexi, E. & Satija, R. Single-cell RNA sequencing to explore immune cell heterogeneity. Nat. Rev. Immunol. 18, 35–45 (2018).

    CAS  PubMed  Google Scholar 

  30. Zappia, L., Phipson, B. & Oshlack, A. Exploring the single-cell RNA-seq analysis landscape with the scRNA-tools database. PLoS Comput. Biol. 14, e1006245 (2018).

    PubMed  PubMed Central  Google Scholar 

  31. Nguyen, Q. H., Pervolarakis, N., Nee, K. & Kessenbrock, K. Experimental considerations for single-cell RNA sequencing approaches. Front. Cell Dev. Biol. 6, 108 (2018).

    PubMed  PubMed Central  Google Scholar 

  32. Vieira Braga, F. A. & Miragaia, R. J. in Single Cell Methods: Sequencing and Proteomics (ed. Proserpio, V.) 9–21 (Springer, 2019).

  33. See, K. et al. Single cardiomyocyte nuclear transcriptomes reveal a lincRNA-regulated de-differentiation and cell cycle stress-response in vivo. Nat. Commun. 8, 225 (2017).

    PubMed  PubMed Central  Google Scholar 

  34. Hu, P. et al. Single-nucleus transcriptomic survey of cell diversity and functional maturation in postnatal mammalian hearts. Genes. Dev. 32, 1344–1357 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Linscheid, N. et al. Quantitative proteomics and single-nucleus transcriptomics of the sinus node elucidates the foundation of cardiac pacemaking. Nat. Commun. 10, 2889 (2019).

    PubMed  PubMed Central  Google Scholar 

  36. Kannan, S. et al. Large particle fluorescence-activated cell sorting enables high-quality single-cell RNA sequencing and functional analysis of adult cardiomyocytes. Circ. Res. 125, 567–569 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. DeLuca, D. S. et al. RNA-SeQC: RNA-seq metrics for quality control and process optimization. Bioinformatics 28, 1530–1532 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Bacher, R. & Kendziorski, C. Design and computational analysis of single-cell RNA-sequencing experiments. Genome Biol. 17, 63 (2016).

    PubMed  PubMed Central  Google Scholar 

  39. Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).

    PubMed  PubMed Central  Google Scholar 

  41. McCarthy, D. J., Campbell, K. R., Lun, A. T. L. & Wills, Q. F. Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R. Bioinformatics 33, 1179–1186 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Diaz, A. et al. SCell: integrated analysis of single-cell RNA-seq data. Bioinformatics 32, 2219–2220 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Guo, M., Wang, H., Potter, S. S., Whitsett, J. A. & Xu, Y. SINCERA: a pipeline for single-cell RNA-seq profiling analysis. PLoS Comput. Biol. 11, e1004575 (2015).

    PubMed  PubMed Central  Google Scholar 

  45. Stegle, O., Teichmann, S. A. & Marioni, J. C. Computational and analytical challenges in single-cell transcriptomics. Nat. Rev. Genet. 16, 133–145 (2015).

    CAS  PubMed  Google Scholar 

  46. Kiselev, V. Y., Andrews, T. S. & Hemberg, M. Challenges in unsupervised clustering of single-cell RNA-seq data. Nat. Rev. Genet. 20, 273–282 (2019).

    CAS  PubMed  Google Scholar 

  47. van der Maaten, L. J. P. & Hinton, G. E. Visualizing high-dimensional data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).

    Google Scholar 

  48. Becht, E. et al. Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol. 37, 38–44 (2019).

    CAS  Google Scholar 

  49. Crow, M., Paul, A., Ballouz, S., Huang, Z. J. & Gillis, J. Characterizing the replicability of cell types defined by single cell RNA-sequencing data using MetaNeighbor. Nat. Commun. 9, 884 (2018).

    PubMed  PubMed Central  Google Scholar 

  50. Andrews, T. S. & Hemberg, M. Identifying cell populations with scRNASeq. Mol. Asp. Med. 59, 114–122 (2018).

    CAS  Google Scholar 

  51. Lin, P., Troup, M. & Ho, J. W. K. CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data. Genome Biol. 18, 59 (2017).

    PubMed  PubMed Central  Google Scholar 

  52. Žurauskienė, J. & Yau, C. pcaReduce: hierarchical clustering of single cell transcriptional profiles. BMC Bioinformatics 17, 140 (2016).

    PubMed  PubMed Central  Google Scholar 

  53. Levine, J. H. et al. Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Cell 162, 184–197 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Raykov, Y. P., Boukouvalas, A., Baig, F. & Little, M. A. What to do when K-means clustering fails: a simple yet principled alternative algorithm. PLoS One 11, e0162259 (2016).

    PubMed  PubMed Central  Google Scholar 

  55. Tanay, A. & Regev, A. Scaling single-cell genomics from phenomenology to mechanism. Nature 541, 331–338 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Kester, L. & van Oudenaarden, A. Single-cell transcriptomics meets lineage tracing. Cell Stem Cell 23, 166–179 (2018).

    CAS  PubMed  Google Scholar 

  57. Su, T. et al. Single-cell analysis of early progenitor cells that build coronary arteries. Nature 559, 356–362 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Wang, W. et al. A single-cell transcriptional roadmap for cardiopharyngeal fate diversification. Nat. Cell Biol. 21, 674–686 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Ludwig, L. S. et al. Lineage tracing in humans enabled by mitochondrial mutations and single-cell genomics. Cell 176, 1325–1339.e22 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Xu, J. et al. Single-cell lineage tracing by endogenous mutations enriched in transposase accessible mitochondrial DNA. eLife 8, e45105 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Skelly, D. A. et al. Single-cell transcriptional profiling reveals cellular diversity and intercommunication in the mouse heart. Cell Rep. 22, 600–610 (2018).

    CAS  PubMed  Google Scholar 

  62. Li, G. et al. Single cell expression analysis reveals anatomical and cell cycle-dependent transcriptional shifts during heart development. Development https://doi.org/10.1242/dev.173476 (2019).

  63. Paik, D. T. et al. Large-scale single-cell RNA-seq reveals molecular signatures of heterogeneous populations of human induced pluripotent stem cell-derived endothelial cells. Circ. Res. 123, 443–450 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Li, G. et al. Transcriptomic profiling maps anatomically patterned subpopulations among single embryonic cardiac cells. Dev. Cell 39, 491–507 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. DeLaughter, D. M. et al. Single-cell resolution of temporal gene expression during heart development. Dev. Cell 39, 480–490 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Lescroart, F. et al. Defining the earliest step of cardiovascular lineage segregation by single-cell RNA-seq. Science 359, 1177–1181 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. Devine, W. P., Wythe, J. D., George, M., Koshiba-Takeuchi, K. & Bruneau, B. G. Early patterning and specification of cardiac progenitors in gastrulating mesoderm. eLife 3, e03848 (2014).

    PubMed Central  Google Scholar 

  68. Jia, G. et al. Single cell RNA-seq and ATAC-seq analysis of cardiac progenitor cell transition states and lineage settlement. Nat. Commun. 9, 4877 (2018).

    PubMed  PubMed Central  Google Scholar 

  69. Xiong, H. et al. Single-cell transcriptomics reveals chemotaxis-mediated intraorgan crosstalk during cardiogenesis. Circ. Res. 125, 398–410 (2019).

    CAS  PubMed  Google Scholar 

  70. de Soysa, T. Y. et al. Single-cell analysis of cardiogenesis reveals basis for organ-level developmental defects. Nature 572, 120–124 (2019).

    PubMed  PubMed Central  Google Scholar 

  71. Muraro, M. J. et al. A single-cell transcriptome atlas of the human pancreas. Cell Syst. 3, 385–394.e3 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. Ackers-Johnson, M., Tan, W. L. W. & Foo, R. S.-Y. Following hearts, one cell at a time: recent applications of single-cell RNA sequencing to the understanding of heart disease. Nat. Commun. 9, 4434 (2018).

    PubMed  PubMed Central  Google Scholar 

  73. Coppini, R. et al. Isolation and functional characterization of human ventricular cardiomyocytes from fresh surgical samples. J. Vis. Exp. https://doi.org/10.3791/51116 (2014).

  74. Nomura, S. et al. Cardiomyocyte gene programs encoding morphological and functional signatures in cardiac hypertrophy and failure. Nat. Commun. 9, 4435 (2018).

    PubMed  PubMed Central  Google Scholar 

  75. Honkoop, H. et al. Single-cell analysis uncovers that metabolic reprogramming by ErbB2 signaling is essential for cardiomyocyte proliferation in the regenerating heart. eLife 8, e50163 (2019).

    PubMed  PubMed Central  Google Scholar 

  76. Li, Z. et al. Single-cell transcriptome analyses reveal novel targets modulating cardiac neovascularization by resident endothelial cells following myocardial infarction. Eur. Heart J. 40, 2507–2520 (2019).

    PubMed  PubMed Central  Google Scholar 

  77. Paik, D. T. et al. Wnt10b gain-of-function improves cardiac repair by arteriole formation and attenuation of fibrosis. Circ. Res. 117, 804–816 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. Farbehi, N. et al. Single-cell expression profiling reveals dynamic flux of cardiac stromal, vascular and immune cells in health and injury. eLife 8, e43882 (2019).

    PubMed  PubMed Central  Google Scholar 

  79. Zeisberg, E. M. et al. Endothelial-to-mesenchymal transition contributes to cardiac fibrosis. Nat. Med. 13, 952–961 (2007).

    CAS  PubMed  Google Scholar 

  80. Aisagbonhi, O. et al. Experimental myocardial infarction triggers canonical Wnt signaling and endothelial-to-mesenchymal transition. Dis. Model. Mech. 4, 469–483 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  81. Ubil, E. et al. Mesenchymal-endothelial transition contributes to cardiac neovascularization. Nature 514, 585–590 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  82. Tucker, N. R. et al. Transcriptional and cellular diversity of the human heart. Preprint at bioRxiv https://doi.org/10.1101/2020.01.06.896076 (2020).

    Article  Google Scholar 

  83. Red-Horse, K., Ueno, H., Weissman, I. L. & Krasnow, M. Coronary arteries form by developmental reprogramming of venous cells. Nature 464, 549–553 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  84. Sharma, B., Chang, A. & Red-Horse, K. Coronary artery development: progenitor cells and differentiation pathways. Annu. Rev. Physiol. 79, 1–19 (2017).

    CAS  PubMed  Google Scholar 

  85. Kalluri, A. S. et al. Single cell analysis of the normal mouse aorta reveals functionally distinct endothelial cell populations. Circulation 140, 147–163 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  86. McDonald, A. I. et al. Endothelial regeneration of large vessels is a biphasic process driven by local cells with distinct proliferative capacities. Cell Stem Cell 23, 210–225.e6 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  87. Kuznetsova, T., Prange, K. H. M., Glass, C. K. & de Winther, M. P. J. Transcriptional and epigenetic regulation of macrophages in atherosclerosis. Nat. Rev. Cardiol. https://doi.org/10.1038/s41569-019-0265-3 (2019).

  88. Souilhol, C. et al. Endothelial responses to shear stress in atherosclerosis: a novel role for developmental genes. Nat. Rev. Cardiol. 17, 52–63 (2020).

    PubMed  Google Scholar 

  89. Basatemur, G. L., Jørgensen, H. F., Clarke, M. C. H., Bennett, M. R. & Mallat, Z. Vascular smooth muscle cells in atherosclerosis. Nat. Rev. Cardiol. 16, 727–744 (2019).

    PubMed  Google Scholar 

  90. Libby, P. et al. Atherosclerosis. Nat. Rev. Dis. Prim. 5, 56 (2019).

    PubMed  Google Scholar 

  91. Wirka, R. C. et al. Atheroprotective roles of smooth muscle cell phenotypic modulation and the TCF21 disease gene as revealed by single-cell analysis. Nat. Med. 25, 1280–1289 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  92. Yao, F. et al. Histone variant H2A.Z is required for the maintenance of smooth muscle cell identity as revealed by single-cell transcriptomics. Circulation 138, 2274–2288 (2018).

    CAS  PubMed  Google Scholar 

  93. Cochain, C. et al. Single-cell RNA-seq reveals the transcriptional landscape and heterogeneity of aortic macrophages in murine atherosclerosis. Circ. Res. 122, 1661–1674 (2018).

    CAS  PubMed  Google Scholar 

  94. Winkels, H. et al. Atlas of the immune cell repertoire in mouse atherosclerosis defined by single-cell RNA-sequencing and mass cytometry. Circ. Res. 122, 1675–1688 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  95. Lukowski, S. W. et al. Single-cell transcriptional profiling of aortic endothelium identifies a hierarchy from endovascular progenitors to differentiated cells. Cell Rep. 27, 2748–2758.e3 (2019).

    CAS  PubMed  Google Scholar 

  96. Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566, 496–502 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  97. Regev, A. et al. The Human Cell Atlas. eLife 6, e27041 (2017).

    PubMed  PubMed Central  Google Scholar 

  98. HuBMAP Consortium. The human body at cellular resolution: the NIH human biomolecular atlas program. Nature 574, 187–192 (2019).

    CAS  Google Scholar 

  99. Franzén, O., Gan, L.-M. & Björkegren, J. L. M. PanglaoDB: a web server for exploration of mouse and human single-cell RNA sequencing data. Database 2019, baz046 (2019).

    PubMed  PubMed Central  Google Scholar 

  100. Shi, Y., Inoue, H., Wu, J. C. & Yamanaka, S. Induced pluripotent stem cell technology: a decade of progress. Nat. Rev. Drug. Discov. 16, 115–130 (2017).

    CAS  PubMed  Google Scholar 

  101. Paik, D. T., Chandy, M. & Wu, J. C. Patient and disease-specific induced pluripotent stem cells for discovery of personalized cardiovascular drugs and therapeutics. Pharmacol. Rev. 72, 320–342 (2020).

    PubMed  PubMed Central  Google Scholar 

  102. Bedada, F. B., Wheelwright, M. & Metzger, J. M. Maturation status of sarcomere structure and function in human iPSC-derived cardiac myocytes. Biochim. Biophys. Acta 1863, 1829–1838 (2016).

    CAS  PubMed  Google Scholar 

  103. Friedman, C. E. et al. Single-cell transcriptomic analysis of cardiac differentiation from human PSCs reveals HOPX-dependent cardiomyocyte maturation. Cell Stem Cell 23, 586–598.e8 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  104. Churko, J. M. et al. Defining human cardiac transcription factor hierarchies using integrated single-cell heterogeneity analysis. Nat. Commun. 9, 4906 (2018).

    PubMed  PubMed Central  Google Scholar 

  105. Gu, M. et al. Patient-specific iPSC-derived endothelial cells uncover pathways that protect against pulmonary hypertension in BMPR2 mutation carriers. Cell Stem Cell 20, 490–504.e5 (2017).

    CAS  PubMed  Google Scholar 

  106. McCracken, I. R. et al. Transcriptional dynamics of pluripotent stem cell-derived endothelial cell differentiation revealed by single-cell RNA sequencing. Eur. Heart J. 41, 1024–1036 (2020).

    PubMed  Google Scholar 

  107. Williams, I. M. & Wu, J. C. Generation of endothelial cells from human pluripotent stem cells. Arterioscler. Thromb. Vasc. Biol. 39, 1317–1329 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  108. Cao, J. et al. Comprehensive single-cell transcriptional profiling of a multicellular organism. Science 357, 661–667 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  109. Rosenberg, A. B. et al. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science 360, 176–182 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  110. Efremova, M. & Teichmann, S. A. Computational methods for single-cell omics across modalities. Nat. Methods 17, 14–17 (2020).

    CAS  PubMed  Google Scholar 

  111. Buenrostro, J. D. et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486–490 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  112. Satpathy, A. T. et al. Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion. Nat. Biotechnol. 37, 925–936 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  113. Lareau, C. A. et al. Droplet-based combinatorial indexing for massive-scale single-cell chromatin accessibility. Nat. Biotechnol. 37, 916–924 (2019).

    CAS  PubMed  Google Scholar 

  114. Cusanovich, D. A. et al. The cis-regulatory dynamics of embryonic development at single-cell resolution. Nature 555, 538–542 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  115. Corces, M. R. et al. Lineage-specific and single-cell chromatin accessibility charts human hematopoiesis and leukemia evolution. Nat. Genet. 48, 1193–1203 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  116. Satpathy, A. T. et al. Transcript-indexed ATAC-seq for precision immune profiling. Nat. Med. 24, 580–590 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  117. Cho, S., Irianto, J. & Discher, D. E. Mechanosensing by the nucleus: from pathways to scaling relationships. J. Cell Biol. 216, 305–315 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  118. Cho, S. et al. Mechanosensing by the lamina protects against nuclear rupture, DNA damage, and cell-cycle arrest. Dev. Cell 49, 920–935.e5 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  119. Cao, J. et al. Joint profiling of chromatin accessibility and gene expression in thousands of single cells. Science 361, 1380–1385 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  120. Karemaker, I. D. & Vermeulen, M. Single-cell DNA methylation profiling: technologies and biological applications. Trends Biotechnol. 36, 952–965 (2018).

    CAS  PubMed  Google Scholar 

  121. Angermueller, C. et al. Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity. Nat. Methods 13, 229–232 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  122. Cheow, L. F. et al. Single-cell multimodal profiling reveals cellular epigenetic heterogeneity. Nat. Methods 13, 833–836 (2016).

    CAS  PubMed  Google Scholar 

  123. Pott, S. Simultaneous measurement of chromatin accessibility, DNA methylation, and nucleosome phasing in single cells. eLife 6, 23203 (2017).

    Google Scholar 

  124. Clark, S. J. et al. scNMT-seq enables joint profiling of chromatin accessibility DNA methylation and transcription in single cells. Nat. Commun. 9, 781 (2018).

    PubMed  PubMed Central  Google Scholar 

  125. Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods. 14, 865–868 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  126. Specht, H. et al. Single-cell mass-spectrometry quantifies the emergence of macrophage heterogeneity. Preprint at bioRxiv https://doi.org/10.1101/665307 (2019).

    Article  Google Scholar 

  127. Swaminathan, J. et al. Highly parallel single-molecule identification of proteins in zeptomole-scale mixtures. Nat. Biotechnol. 36, 1076–1082 (2018).

    CAS  Google Scholar 

  128. Salmén, F. et al. Barcoded solid-phase RNA capture for spatial transcriptomics profiling in mammalian tissue sections. Nat. Protoc. 13, 2501–2534 (2018).

    PubMed  Google Scholar 

  129. Rodriques, S. G. et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 363, 1463–1467 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  130. Shalek, A. K. & Benson, M. Single-cell analyses to tailor treatments. Sci. Transl Med. 9, aan4730 (2017).

    Google Scholar 

  131. Levitin, H. M., Yuan, J. & Sims, P. A. Single-cell transcriptomic analysis of tumor heterogeneity. Trends Cancer 4, 264–268 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  132. Kim, K.-T. et al. Application of single-cell RNA sequencing in optimizing a combinatorial therapeutic strategy in metastatic renal cell carcinoma. Genome Biol. 17, 80 (2016).

    PubMed  PubMed Central  Google Scholar 

  133. Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  134. Lavin, Y. et al. Innate immune landscape in early lung adenocarcinoma by paired single-cell analyses. Cell 169, 750–765.e17 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  135. Kolandaivelu, K., Leiden, B. B., O’Gara, P. T. & Bhatt, D. L. Non-adherence to cardiovascular medications. Eur. Heart J. 35, 3267–3276 (2014).

    CAS  PubMed  Google Scholar 

  136. Matsa, E. et al. Transcriptome profiling of patient-specific human iPSC-cardiomyocytes predicts individual drug safety and efficacy responses in vitro. Cell Stem Cell 19, 311–325 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  137. Lam, C. K. et al. Identifying the transcriptome signatures of calcium channel blockers in human induced pluripotent stem cell-derived cardiomyocytes. Circ. Res. 125, 212–222 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  138. Strzelecka, P. M., Ranzoni, A. M. & Cvejic, A. Dissecting human disease with single-cell omics: application in model systems and in the clinic. Dis. Models Mech. 11, 036525 (2018).

    Google Scholar 

  139. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  140. Jaitin, D. A. et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 343, 776–779 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  141. Bagnoli, J. W. et al. Sensitive and powerful single-cell RNA sequencing using mcSCRB-seq. Nat. Commun. 9, 2937 (2018).

    PubMed  PubMed Central  Google Scholar 

  142. Goldstein, L. D. et al. Massively parallel nanowell-based single-cell gene expression profiling. BMC Genomics 18, 519 (2017).

    PubMed  PubMed Central  Google Scholar 

  143. Goodyer, W. R. et al. Transcriptomic profiling of the developing cardiac conduction system at single-cell resolution. Circ. Res. 125, 379–397 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  144. Tang, J. et al. Arterial Sca1+ vascular stem cells generate de novo smooth muscle for artery repair and regeneration. Cell Stem Cell 26, 81–96.e4 (2020).

    CAS  PubMed  Google Scholar 

  145. Martini, E. et al. Single-cell sequencing of mouse heart immune infiltrate in pressure overload-driven heart failure reveals extent of immune activation. Circulation 140, 2089–2107 (2019).

    CAS  PubMed  Google Scholar 

  146. Francesconi, M. et al. Single cell RNA-seq identifies the origins of heterogeneity in efficient cell transdifferentiation and reprogramming. eLife 8, 41627 (2019).

    CAS  Google Scholar 

  147. Nguyen, Q. H. et al. Single-cell RNA-seq of human induced pluripotent stem cells reveals cellular heterogeneity and cell state transitions between subpopulations. Genome Res. 28, 1053–1066 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  148. Ohta, R. et al. Laminin-guided highly efficient endothelial commitment from human pluripotent stem cells. Sci. Rep. 6, 35680 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors are supported by grants from the NIH: K99 HL150216 (D.T.P.), RM1 HG007735 (H.Y.C.), R01 HL130020 (J.C.W.), R01 HL145676 (J.C.W.) and P01 HL141084 (J.C.W.), and from the Leducq Foundation: 18CVD05 (J.C.W.). The authors thank B. C. Wu (Stanford University, USA), J. X. Zhang (Stanford University, USA) and H. Lee (Stanford University, USA) for critical reading of the manuscript.

Author information

Authors and Affiliations

Authors

Contributions

D.T.P. and L.T. researched data for the article, D.T.P., H.Y.C. and J.C.W. substantially contributed to the discussion of its content, and D.T.P., S.C. and L.T. wrote, reviewed and edited the manuscript before submission.

Corresponding authors

Correspondence to David T. Paik or Joseph C. Wu.

Ethics declarations

Competing interests

H.Y.C. is a co-founder of Accent Therapeutics and Boundless Bio, and an adviser to 10x Genomics, Arsenal Biosciences and Spring Discovery. J.C.W. is a co-founder of Khloris Biosciences, but has no competing interests, as the work presented here is completely independent. The other authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Related links

Human Cell Atlas: https://www.humancellatlas.org/

PanglaoDB: https://panglaodb.se/

scRNASeqDB: https://bioinfo.uth.edu/scrnaseqdb/

Single Cell Portal: https://singlecell.broadinstitute.org/single_cell

Tabula Muris: https://tabula-muris.ds.czbiohub.org/

The Human BioMolecular Atlas Program: https://commonfund.nih.gov/hubmap

Glossary

Quantitative PCR

A polymerase chain reaction (PCR) that records product expression in real time.

Microarray

A chip containing thousands of wells with a bound DNA of known sequence, which can be used to bind and measure the expression of transcriptome mRNA.

Bulk RNA sequencing

Bulk resolution, next-generation sequencing, which reveals RNA presence and quantity in a sample of cells during time of measurement.

Transcriptome

All RNA molecules expressed in a cell or cell population.

Cellular trajectory analysis

Computational analysis technique used to track and group cells based on their course through a dynamic process such as cell differentiation or the cell cycle.

Fluorescence-activated cell sorting

(FACS). Technique in which target cell types in suspension are separated and sorted by flow cytometry based on fluorescence information.

R packages

Single-cell gene-expression analysis software package written in R that can be run in integrated development environments, such as RStudio.

Python packages

Single-cell gene-expression analysis software package written in Python that can be run in integrated development environments such as Sublime Text or Visual Studio.

Principal component analysis

(PCA). A linear statistical technique that reduces the number of experimental variables to the minimum amount.

t-Distributed stochastic neighbour embedding

(tSNA). Nonlinear variable reduction method that displays high-dimension data points, such as cell transcriptome data, on 2D or 3D graphs, primarily separating points on the basis of (dis)similarity to each other.

Uniform manifold approximation and projection

(UMAP). Nonlinear variable reduction method that displays high-dimension data points on 2D or 3D distance-dependent graphs, which can be used to reveal information such as cell differentiation trajectory and cell state.

Euclidean distance

A measurement of difference or dissimilarity between a pair of samples in an n-dimensional feature space.

Induced pluripotent stem cells

(iPSCs). Pluripotent stem cells reprogrammed from adult somatic cells.

Immunohistochemistry

Antibody-based detection method of protein in samples of tissue.

In situ hybridization

Labelling technique that uses the hybridization of labelled cDNA to locate specific nucleic acid sequences in tissue sections.

Enzyme-linked immunosorbent assay

(ELISA). Plate-based antibody detection assay for biomolecules in which enzyme–antibody conjugates attach to specific antigens anchored to a surface and subsequent incubation in a substrate reveals the presence of antigens.

Mass cytometry

(CyTOF). Variant of flow cytometry using metal ion-labelled antibodies and readout using time-of-flight mass spectrometry.

Combinatorial indexing

Single-cell RNA sequencing method using transposase nuclei barcoding, fluorescence-activated nuclei sorting and PCR to index subpopulations of cells from tissues or organs.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Paik, D.T., Cho, S., Tian, L. et al. Single-cell RNA sequencing in cardiovascular development, disease and medicine. Nat Rev Cardiol 17, 457–473 (2020). https://doi.org/10.1038/s41569-020-0359-y

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41569-020-0359-y

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing