Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2019 Mar 31;42(3):189-199.
doi: 10.14348/molcells.2019.2446. Epub 2019 Feb 12.

Dissecting Cellular Heterogeneity Using Single-Cell RNA Sequencing

Yoon Ha Choi  1 , Jong Kyoung Kim  1
Affiliations
Review

Dissecting Cellular Heterogeneity Using Single-Cell RNA Sequencing

Yoon Ha Choi et al. Mol Cells. .

Abstract

Cell-to-cell variability in gene expression exists even in a homogeneous population of cells. Dissecting such cellular heterogeneity within a biological system is a prerequisite for understanding how a biological system is developed, homeo-statically regulated, and responds to external perturbations. Single-cell RNA sequencing (scRNA-seq) allows the quantitative and unbiased characterization of cellular heterogeneity by providing genome-wide molecular profiles from tens of thousands of individual cells. A major question in analyzing scRNA-seq data is how to account for the observed cell-to-cell variability. In this review, we provide an overview of scRNA-seq protocols, computational approaches for dissecting cellular heterogeneity, and future directions of single-cell transcriptomic analysis.

Keywords: RNA sequencing; cellular heterogeneity; single-cell; single-cell genomics; single-cell transcriptomics.

PubMed Disclaimer

Figures

Fig. 1
Fig. 1
Computational workflow for analyzing scRNA-seq data.
See this image and copyright information in PMC

References

    1. Aibar S., Gonzalez-Blas C.B., Moerman T., Huynh-Thu V.A., Imrichova H., Hulselmans G., Rambow F., Marine J.C., Geurts P., Aerts J., et al. SCENIC: single-cell regulatory network inference and clustering. Nat Methods. 2017;14:1083–1086. - PMC - PubMed
    1. Alavi A., Ruffalo M., Parvangada A., Huang Z., Bar-Joseph Z. A web server for comparative analysis of single-cell RNA-seq data. Nat Commun. 2018;9:4768. - PMC - PubMed
    1. Alquicira-Hernandez J., Nguyen Q., Powell J.E. scPred: scPred: Cell type prediction at single-cell resolution. bioRxiv. 2018:369538.
    1. Amir E.D., Davis K.L., Tadmor M.D., Simonds E.F., Levine J.H., Bendall S.C., Shenfeld D.K., Krishnaswamy S., Nolan G.P., Pe’er D. viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nat Biotechnol. 2013;31:545–552. - PMC - PubMed
    1. Anders S., Huber W. Differential expression analysis for sequence count data. Genome Biol. 2010;11:R106. - PMC - PubMed

LinkOut - more resources