A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications

RNA sequencing (RNA-seq) is a genomic approach for the detection and quantitative analysis of messenger RNA molecules in a biological sample and is useful for studying cellular responses. RNA-seq has fueled much discovery and innovation in medicine over recent years. For practical reasons, the technique is usually conducted on samples comprising thousands to millions of cells. However, this has hindered direct assessment of the fundamental unit of biology-the cell. Since the first single-cell RNA-sequencing (scRNA-seq) study was published in 2009, many more have been conducted, mostly by specialist laboratories with unique skills in wet-lab single-cell genomics, bioinformatics, and computation. However, with the increasing commercial availability of scRNA-seq platforms, and the rapid ongoing maturation of bioinformatics approaches, a point has been reached where any biomedical researcher or clinician can use scRNA-seq to make exciting discoveries. Researchers from the QIMR Berghofer Medical Research Institute present a practical guide to help researchers design their first scRNA-seq studies, including introductory information on experimental hardware, protocol choice, quality control, data analysis and biological interpretation.

 General workflow of single-cell RNA-sequencing (scRNA-seq) experiments


A typical scRNA-seq workflow includes most of the following steps: 1) isolation of single cells, 2) cell lysis while preserving mRNA, 3) mRNA capture, 4) reverse transcription of primed RNA into complementary DNA (cDNA), 5) cDNA amplification, 6) preparation of cDNA sequencing library, 7) pooling of sequence libraries, 8) use of bio-informatic tools to assess quality and variability, and 9) use of specialized tools to analyse and present the data. t-SNE t-distributed stochastic neighbour embedding

Haque A, Engel J, Teichmann SA, Lönnberg T. (2017) A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications. Genome Med 9(1):75. [article]

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