GSE21180
Characterization of the single-cell transcriptional landscape by highly multiplex RNA-Seq
Our understanding of the development and maintenance of tissues has been greatly aided by large-scale gene expression analysis. However, tissues are invariably complex, and expression analysis of a tissue confounds the true expression patterns of its constitutent cell types. Here we propose a novel strategy to access such complex samples. Hundreds of single-cell RNA-Seq expression profiles were generated, and clustered to form a two-dimensional cell map onto which expression data was projected. The resulting cell map integrates three levels of organization: the whole population of cells, the functionally distinct subpopulations it contains, and the single cells themselves—all without need for known markers to classify cell types. The feasibility of the strategy is demonstrated by analyzing the complete transcriptomes of 436 single cells of three distinct types. We believe this strategy will enable the unbiased discovery and analysis of naturally occurring cell types during development, adult physiology and disease. Overall design: Comparison of single-cell mRNA expression in 436 single cells from three cell types