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
. 2018 Apr;122(1):e57.
doi: 10.1002/cpmb.57.

Introduction to Single-Cell RNA Sequencing

Affiliations

Introduction to Single-Cell RNA Sequencing

Thale Kristin Olsen et al. Curr Protoc Mol Biol. 2018 Apr.

Abstract

During the last decade, high-throughput sequencing methods have revolutionized the entire field of biology. The opportunity to study entire transcriptomes in great detail using RNA sequencing (RNA-seq) has fueled many important discoveries and is now a routine method in biomedical research. However, RNA-seq is typically performed in "bulk," and the data represent an average of gene expression patterns across thousands to millions of cells; this might obscure biologically relevant differences between cells. Single-cell RNA-seq (scRNA-seq) represents an approach to overcome this problem. By isolating single cells, capturing their transcripts, and generating sequencing libraries in which the transcripts are mapped to individual cells, scRNA-seq allows assessment of fundamental biological properties of cell populations and biological systems at unprecedented resolution. Here, we present the most common scRNA-seq protocols in use today and the basics of data analysis and discuss factors that are important to consider before planning and designing an scRNA-seq project. © 2018 by John Wiley & Sons, Inc.

Keywords: RNA sequencing; gene expression profiling; single-cell analysis.

PubMed Disclaimer

References

Literature Cited

    1. Anders, S., Pyl, P. T., & Huber, W. (2015). HTSeq-a Python framework to work with high-throughput sequencing data. Bioinformatics, 31, 166-169. doi: 10.1093/bioinformatics/btu638.
    1. Bacher, R., & Kendziorski, C. (2016). Design and computational analysis of single-cell RNA-sequencing experiments. Genome Biology, 17, 63. doi: 10.1186/s13059-016-0927-y.
    1. Brehm-Stecher, B. F., & Johnson, E. A. (2004). Single-cell microbiology: Tools, technologies, and applications. Microbiology and Molecular Biology Reviews: MMBR, 68, 538-559. doi: 10.1128/MMBR.68.3.538-559.2004.
    1. van den Brink, S. C., Sage, F., Vértesy, Á., Spanjaard, B., Peterson-Maduro, J., Baron, C. S., … van Oudenaarden, A. (2017). Single-cell sequencing reveals dissociation-induced gene expression in tissue subpopulations. Nature Methods, 14, 935-936. doi: 10.1038/nmeth.4437.
    1. Burrell, R. A., McGranahan, N., Bartek, J., & Swanton, C. (2013). The causes and consequences of genetic heterogeneity in cancer evolution. Nature, 501, 338-345. doi: 10.1038/nature12625.

Publication types

Substances

LinkOut - more resources