Single cell transcriptomics (SCT) entails the profiling of all messenger RNAs present in a single cell and constitutes the most widely-used sc profiling technology. Unlike bulk RNA-seq profiling where sequencing libraries are generated from thousands of cells, scRNA-seq technologies isolate single cells and generate cell-specific sequencing libraries to mark RNA content with a cell-specific molecular barcode. Analysis of this data generates gene expression estimates at the single cell level. SCT enables the measurement of the transcriptomic information for a range of thousands up to millions of single cells in a single experiment.
Separating tissues into cells has been around for a long time, for example cell sorting and other techniques that have been around for some time are used to look at differences between cells or cell types. However analysis at the expression level is a new approach. Single cell transcriptomics has appeared in publications starting in 2009, so it is a very recent technology. In their overview published in 2017 (Svensson, Valentine, et al. “Power analysis of single-cell RNA-sequencing experiments.” Nature methods 14.4 (2017): 381.), Svensson et. al. compiled a list of SCT protocols that developed from simple and manual to more and more automated, showing how this technology evolved and continues to diversify.
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