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

Nature Reviews Cardiology volume 17, pages 457–473 (2020)Cite this article

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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.

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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.

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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.

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Authors and Affiliations

  1. Stanford Cardiovascular Institute, Stanford, CA, USA

    David T. Paik, Sangkyun Cho, Lei Tian & Joseph C. Wu

  2. Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA

    David T. Paik, Sangkyun Cho, Lei Tian & Joseph C. Wu

  3. Center for Personal Dynamic Regulomes, Stanford University School of Medicine, Stanford, CA, USA

    Howard Y. Chang

  4. Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA

    Howard Y. Chang

  5. Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA

    Joseph C. Wu

Authors
  1. David T. Paik
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  2. Sangkyun Cho
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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.

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

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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.

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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.

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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

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