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Massively Parallel Single-Cell RNA-Seq for Marker-Free Decomposition of Tissues into Cell Types

Science  14 Feb 2014:
Vol. 343, Issue 6172, pp. 776-779
DOI: 10.1126/science.1247651

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Abstract

In multicellular organisms, biological function emerges when heterogeneous cell types form complex organs. Nevertheless, dissection of tissues into mixtures of cellular subpopulations is currently challenging. We introduce an automated massively parallel single-cell RNA sequencing (RNA-seq) approach for analyzing in vivo transcriptional states in thousands of single cells. Combined with unsupervised classification algorithms, this facilitates ab initio cell-type characterization of splenic tissues. Modeling single-cell transcriptional states in dendritic cells and additional hematopoietic cell types uncovers rich cell-type heterogeneity and gene-modules activity in steady state and after pathogen activation. Cellular diversity is thereby approached through inference of variable and dynamic pathway activity rather than a fixed preprogrammed cell-type hierarchy. These data demonstrate single-cell RNA-seq as an effective tool for comprehensive cellular decomposition of complex tissues.

Introducing MARS-Seq

Immune cells are typically differentiated by surface markers; however, this designation is somewhat crude and does not allow for fine distinctions that might be characterized by their RNA transcripts. Jaitin et al. (p. 776) used massively parallel single-cell RNA-sequencing (MARS-Seq) analysis to explore cellular heterogeneity within the immune system by assembling an automated experimental platform that enables RNA profiling of cells sorted from tissues using flow cytometry. More than 1000 cells could be sequenced, and unsupervised clustering analysis of the RNA profiles revealed distinct cellular groupings that corresponded to B cells, macrophages, and dendritic cells. This approach provides the ability to perform a bottom-up characterization of in vivo cell-type landscapes independent of cell markers or prior knowledge.

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