Single-cell transcriptomics

Single-cell transcriptomics examines the gene expression level of individual cells in a given population by simultaneously measuring the messenger RNA (mRNA) concentration of hundreds to thousands of genes. The unraveling of heterogenous cell populations, reconstruction of cellular developmental trajectories and modelling of transcriptional dynamics all previously masked in bulk transcriptome measurements is made possible through analysis of this transcriptomic data.

Gene expression analysis has become routine through the development of high-throughput RNA sequencing (RNA-seq) and microarrays. RNA analysis that was previously limited to tracing individual transcripts by Northern blots or quantitative PCR is now used frequently to characterize the expression profiles of populations of thousands of cells.The data produced from the bulk based assays has led to the identification of genes that are differentially expressed in distinct cell populations and biomarker discovery.

These genomic studies are limited as they provide measurements for whole tissues and as a result show an average expression profile for all the constituent cells. In multicellular organisms different cell types within the same population can have distinct roles and form subpopulations with different transcriptional profiles. Correlations in the gene expression of the subpopulations can often be missed due to the lack of subpopulation identification. Moreover, bulk assays fail to identify if a change in the expression profile is due to a change in regulation or composition, in which one cell type arises to dominate the population. Lastly, when examining cellular progression through differentiation, average expression profiles are only able to order cells by time rather than their stage of development and are consequently unable to show trends in gene expression levels specific to certain stages.

Recent advances in biotechnology allow the measurement of gene expression in hundreds to thousands of individual cells simultaneously. Whilst these technological breakthroughs have enabled the generation of single-cell transcriptomic data there are new computational and analytical challenges presented by the data produced. Techniques used for analysing RNA-seq data from bulk cell populations can be used for single-cell data but many new computational approaches have been designed for this data type to facilitate a complete and detailed study of single-cell expression profiles.

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