Massively parallel single-cell RNA-Seq for dissecting cell type and cell state compositions
In multi-cellular organisms, biological function emerges when cells of heterogeneous types and states are combined into complex tissues. Nevertheless unbiased dissection of tissues into coherent cell subpopulations is currently lacking. We introduce an automated, massively parallel single cell RNA sequencing method for intuitively analyzing in-vivo transcriptional states in thousands of single cells. Combined with unsupervised classification algorithms, it facilitates ab initio and marker-free characterization of classical hematopoietic cell types from splenic tissues. Importantly, modeling single cells transcriptional states in dendritic cells subpopulations, where a cell type hierarchy is difficult to define with marker-based approaches, uncovers complex combinatorial activity of multiple gene modules and capture cell-to-cell variability in steady state conditions and following pathogen activation. Massively parallel single cell RNA-seq thereby emerges as an effective tool for unbiased dissection of complex tissues. Overall design: CD11c+ enriched splenocyte mRNA profiles from single cells were generated by deep sequencing of thousands of single cells, sequenced in several batches in an Illumina Hiseq 2000 The ''umitab.txt'' processed data file contains the mRNA counts (post-filtering RMT counts) of a gene per each well (columns) The ''experimental_design.txt'' contains a detailed information regarding each well. The ''readme0421.txt'' was provided with details about each supplementary file.
External Link: /pubmed:24531970