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Title:
Massively Parallel Single-Cell RNA-Seq for Marker-Free Decomposition of Tissues into Cell Types
Authors:
Jaitin, Diego Adhemar; Kenigsberg, Ephraim; Keren-Shaul, Hadas; Elefant, Naama; Paul, Franziska; Zaretsky, Irina; Mildner, Alexander; Cohen, Nadav; Jung, Steffen; Tanay, Amos; Amit, Ido
Affiliation:
AA(Department of Immunology, Weizmann Institute, Rehovot 76100, Israel), AB(Department of Computer Science and Applied Mathematics, Weizmann Institute, Rehovot 76100, Israel.; Department of Biological Regulation, Weizmann Institute, Rehovot 76100, Israel), AC(Department of Immunology, Weizmann Institute, Rehovot 76100, Israel), AD(Department of Immunology, Weizmann Institute, Rehovot 76100, Israel), AE(Department of Immunology, Weizmann Institute, Rehovot 76100, Israel), AF(Department of Immunology, Weizmann Institute, Rehovot 76100, Israel), AG(Department of Immunology, Weizmann Institute, Rehovot 76100, Israel), AH(Department of Computer Science and Applied Mathematics, Weizmann Institute, Rehovot 76100, Israel.; Department of Biological Regulation, Weizmann Institute, Rehovot 76100, Israel), AI(Department of Immunology, Weizmann Institute, Rehovot 76100, Israel), AJ(Department of Computer Science and Applied Mathematics, Weizmann Institute, Rehovot 76100, Israel.; Department of Biological Regulation, Weizmann Institute, Rehovot 76100, Israel), AK(Department of Immunology, Weizmann Institute, Rehovot 76100, Israel)
Publication:
Science, Volume 343, Issue 6172, pp. 776-779 (2014). (Sci Homepage)
Publication Date:
02/2014
Category:
GENETICS Molecular-Biology, Genetics, Sociology
Origin:
SCIENCE
Abstract Copyright:
(c) 2014: Science
DOI:
10.1126/science.1247651
Bibliographic Code:
2014Sci...343..776J

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.
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