![]() Perhaps one of the most compelling reasons for doing so is that scRNA-seq can describe RNA molecules in individual cells with high resolution and on a genomic scale. Since the first scRNA-seq study was published in 2009, there has been increasing interest in conducting such studies. Nevertheless, the averaging that occurs in pooling large numbers of cells does not allow detailed assessment of the fundamental biological unit-the cell-or the individual nuclei that package the genome. Taking just one clinically relevant example-RNA-seq was recently performed on haematopoietic stem cells to stratify acute myeloid leukaemia patients into cohorts requiring differing treatment regimens. RNA-seq on pooled cells has yielded a vast amount of information that continues to fuel discovery and innovation in biomedicine. Transcriptomics was initially conducted on ensembles of millions of cells, firstly with hybridization-based microarrays, and later with next-generation sequencing (NGS) techniques referred to as RNA-seq. However, it remains challenging to examine simultaneously the entire complement of the thousands of proteins (known as the ‘proteome’) expressed by the genome that exist in a single cell.Īs a proxy for studying the proteome, many researchers have turned to protein-encoding, mRNA molecules (collectively termed the ‘transcriptome’), whose expression correlates well with cellular traits and changes in cellular state. More recently, mass cytometry (Box 1), which involves cell staining with antibodies labelled with heavy metal ions and quantitative measurements using time-of-flight detectors, has increased the number of proteins that can be assessed by five- to tenfold and has started to reveal previously unappreciated levels of heterogeneity and complexity among apparently homogeneous cell populations, for example among immune cells. For protein expression studies, the application of multi-colour flow cytometry and fluorescently conjugated monoclonal antibodies has made the simultaneous assessment of small numbers of proteins on vast numbers of single cells commonplace in experimental and clinical research. To understand cellular responses, assessments of gene expression or protein expression are needed. This approach has yielded much useful molecular information, for example in genome-wide association studies (GWASs), where genomic DNA assessments have identified single-nucleotide polymorphisms (SNPs) in the genomes of individual humans that have been associated with particular biological traits and disease susceptibilities. Given that the absolute quantity of any of these molecules is very small in a single living cell, for practical reasons many of these molecules have been assessed in ensembles of thousands to billions of cells. To generate a molecular understanding of cells, the cells can be assessed in a variety of ways, for example through analyses of genomic DNA sequences, chromatin structure, messenger RNA (mRNA) sequences, non-protein-coding RNA, protein expression, protein modifications and metabolites. Medicine now exists in a cellular and molecular era, where experimental biologists and clinicians seek to understand and modify cell behaviour through targeted molecular approaches. 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. 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. 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, this has hindered direct assessment of the fundamental unit of biology-the cell. For practical reasons, the technique is usually conducted on samples comprising thousands to millions of cells. RNA-seq has fueled much discovery and innovation in medicine over recent years. 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.
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