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

Abstract

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.

Probing Plasmodium falciparum sexual commitment at the single-cell level

“…Next, we compared the transcription levels per gene across single cells with those from the same time points from a population-level RNA-seq experiment 16 . The comparison demonstrated that overall expression levels per gene are significantly, though weakly, correlated between single-cell DGE and population-level RNA-seq (F-test, p<2.2×10 -16 ) Notably, the weak correlation we observe between the two transcriptional datasets highlights the known gene “drop-out” effect of single-cell sequencing 20, 21 . Altogether, these experiments demonstrate that the DGE platform presented here is able to capture mRNA profiles of single P. falciparum parasites at sufficient depth to i) detect transcriptional differences between 4-hour time points in the cell cycle and ii) recapitulate overall transcriptional profiles from population-level RNA-seq experiments.…”

Characterisation of particles in solution – a perspective on light scattering and comparative technologies

“…In contrast, single-particle analysis techniques, such as PTA, or single-particle inductively coupled plasma mass spectrometry or quantitative-TEM accurately measure individual particle. These single particle, or analyte, techniques are becoming increasingly utilised in a number of other analytical areas, with techniques such as single-cell mRNA sequencing operating under a similar principle, compared to the ensemble measurements via bulk RNA sequencing [26,27], since they allow for the analysis of the true variability within a sample or population.…”

Transcriptome profiles of isolated murine Achilles tendon proper‐ and peritenon‐derived progenitor cells

“…Hence, we see value in future studies where cell populations are further parsed by surface marker presentation, adherence qualities, and/or colony size ahead of transcriptomics. By combining FACS from a subset of markers with current single cell RNA‐sequencing technology, instead of considering pooled regional progenitor differences, much more precise comparisons could be made, perhaps even down to the combined cell type and cell state level for any one progenitor . Additionally, analyses of protein levels of these markers were not performed as this study represented an analysis of transcript abundance.…”