mRNA-Seq whole-transcriptome analysis of a single cell
Tang F et al. (2009) Nature Methods 6, 377–382. DOI: 10.1038/nmeth.1315
Abstract from the paper
Next-generation sequencing technology is a powerful tool for transcriptome analysis. However, under certain conditions, only a small amount of material is available, which requires more sensitive techniques that can preferably be used at the single-cell level.
Here we describe a single-cell digital gene expression profiling assay. Using our mRNA-Seq assay with only a single mouse blastomere, we detected the expression of 75% (5,270) more genes than microarray techniques and identified 1,753 previously unknown splice junctions called by at least 5 reads. Moreover, 8–19% of the genes with multiple known transcript isoforms expressed at least two isoforms in the same blastomere or oocyte, which unambiguously demonstrated the complexity of the transcript variants at whole-genome scale in individual cells.
Finally, for Dicer1-/- and Ago2-/- (Eif2c2-/-) oocytes, we found that 1,696 and 1,553 genes, respectively, were abnormally upregulated compared to wild-type controls, with 619 genes in common.
++++++++++++
That seminal publication from the Gurdon Institute was followed by an explosion in gene expression studies, including the Human Cell Atlas project at the Wellcome Sanger Institute.
The place of this technology in the burgeoning field of transcriptomics is clearly illustrated in this Perspective article from Sarah Teichmann and colleagues last year, tracing from the first two papers by Tang et al. up to the present day, where 100,000 cells can be analysed in parallel.
Exponential scaling of single-cell RNA-seq in the past decade
Svensson V et al. (2018) Nature Protocols 13: 599–604. DOI: 10.1038/nprot.2017.149.
Fig. 1. Advances in experimental technologies empower the Human Cell Atlas project. (a) Technologies for single-cell genomics. (b) Timeline and scale of single-cell RNA-Seq (grey circles) and single-cell ATAC-Seq (black circles). (C) Timeline and scale of methods for highly multiplexed spatial analysis of intact tissue, including the measurement type (protein, RNA) and tissue area.