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

By A. Haque, Jessica A. Engel, Sarah A. Teichmann, T. Lönnberg. Genome Medicine, 2017

957 citations recorded by the source corpus.

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. In this review, we 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.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/11e864ad6776dc95506e0824a5d8891299a7c2ef

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cancer Research

In Molecular Biology

Citations in the corpus

An introduction to spatial transcriptomics for biomedical research (2022, background): “This triggered an explosion in efforts to study heterogeneity and dynamic change in eukaryotic cells using single-cell transcriptomics, more commonly referred to as single-cell RNA-seq (scRNA-seq) [2].”
Joint probabilistic modeling of single-cell multi-omic data with totalVI (2021, background): “By extending single-cell RNA-sequencing (scRNA-seq) to simultaneously measure the abundance of proteins on the cell surface, CITE-seq [6,7] presents the opportunity to connect the information that can be gleaned from the transcriptome [8, 9] to the functional information contained in proteins [10, 1”
The protein expression profile of ACE2 in human tissues (2020, neutral): “It may also lead to the “drop out” phenomenon, where a gene showing high expression levels in a certain cell lacks expression in other cells corresponding to the same cell type (Haque et al , 2017).”
FlyBase: a guided tour of highlighted features (2022, background): “Over the past few years, single-cell RNA sequencing (scRNA-seq) (Haque et al. 2017) has proved an invaluable tool in biomedical research.”