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      scDual-Seq: mapping the gene regulatory program of Salmonella infection by host and pathogen single-cell RNA-sequencing

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          Abstract

          The interaction between a pathogen and a host is a highly dynamic process in which both agents activate complex programs. Here, we introduce a single-cell RNA-sequencing method, scDual-Seq, that simultaneously captures both host and pathogen transcriptomes. We use it to study the process of infection of individual mouse macrophages with the intracellular pathogen Salmonella typhimurium. Among the infected macrophages, we find three subpopulations and we show evidence for a linear progression through these subpopulations, supporting a model in which these three states correspond to consecutive stages of infection.

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          The online version of this article (doi:10.1186/s13059-017-1340-x) contains supplementary material, which is available to authorized users.

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          Most cited references 18

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          The technology and biology of single-cell RNA sequencing.

          The differences between individual cells can have profound functional consequences, in both unicellular and multicellular organisms. Recently developed single-cell mRNA-sequencing methods enable unbiased, high-throughput, and high-resolution transcriptomic analysis of individual cells. This provides an additional dimension to transcriptomic information relative to traditional methods that profile bulk populations of cells. Already, single-cell RNA-sequencing methods have revealed new biology in terms of the composition of tissues, the dynamics of transcription, and the regulatory relationships between genes. Rapid technological developments at the level of cell capture, phenotyping, molecular biology, and bioinformatics promise an exciting future with numerous biological and medical applications.
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            Single cell RNA Seq reveals dynamic paracrine control of cellular variation

            High-throughput single-cell transcriptomics offers an unbiased approach for understanding the extent, basis, and function of gene expression variation between seemingly identical cells. Here, we sequence single-cell RNA-Seq libraries prepared from over 1,700 primary mouse bone marrow derived dendritic cells (DCs) spanning several experimental conditions. We find substantial variation between identically stimulated DCs, in both the fraction of cells detectably expressing a given mRNA and the transcript’s level within expressing cells. Distinct gene modules are characterized by different temporal heterogeneity profiles. In particular, a “core” module of antiviral genes is expressed very early by a few “precocious” cells, but is later activated in all cells. By stimulating cells individually in sealed microfluidic chambers, analyzing DCs from knockout mice, and modulating secretion and extracellular signaling, we show that this response is coordinated via interferon-mediated paracrine signaling. Surprisingly, preventing cell-to-cell communication also substantially reduces variability in the expression of an early-induced “peaked” inflammatory module, suggesting that paracrine signaling additionally represses part of the inflammatory program. Our study highlights the importance of cell-to-cell communication in controlling cellular heterogeneity and reveals general strategies that multicellular populations use to establish complex dynamic responses.
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              Single-cell RNA-seq: advances and future challenges

              Phenotypically identical cells can dramatically vary with respect to behavior during their lifespan and this variation is reflected in their molecular composition such as the transcriptomic landscape. Single-cell transcriptomics using next-generation transcript sequencing (RNA-seq) is now emerging as a powerful tool to profile cell-to-cell variability on a genomic scale. Its application has already greatly impacted our conceptual understanding of diverse biological processes with broad implications for both basic and clinical research. Different single-cell RNA-seq protocols have been introduced and are reviewed here—each one with its own strengths and current limitations. We further provide an overview of the biological questions single-cell RNA-seq has been used to address, the major findings obtained from such studies, and current challenges and expected future developments in this booming field.
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                Author and article information

                Contributors
                Itai.Yanai@nyumc.org
                Journal
                Genome Biol
                Genome Biol
                Genome Biology
                BioMed Central (London )
                1474-7596
                1474-760X
                27 October 2017
                27 October 2017
                2017
                : 18
                Affiliations
                [1 ]ISNI 0000 0004 1936 8753, GRID grid.137628.9, Institute for Computational Medicine and Department of Biochemistry and Molecular Pharmacology, , New York University School of Medicine, ; New York, NY 10016 USA
                [2 ]ISNI 0000000121102151, GRID grid.6451.6, Department of Biology, , Technion – Israel Institute of Technology, ; Haifa, 32000 Israel
                [3 ]GRID grid.66859.34, Broad Institute of Harvard and MIT, ; Cambridge, MA 02142 USA
                [4 ]ISNI 0000 0004 0386 9924, GRID grid.32224.35, Department of Molecular Biology and Center for Computational and Integrative Biology, , Massachusetts General Hospital, ; Boston, MA 02114 USA
                [5 ]ISNI 000000041936754X, GRID grid.38142.3c, Department of Genetics, , Harvard Medical School, ; Boston, MA 02115 USA
                [6 ]ISNI 0000 0004 0604 7563, GRID grid.13992.30, Present address: Department of Biological Regulation, , Weizmann Institute of Science, ; Rehovot, Israel
                Article
                1340
                10.1186/s13059-017-1340-x
                5658913
                29073931
                © The Author(s). 2017

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                Funding
                Funded by: ISF-Broad
                Categories
                Method
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                © The Author(s) 2017

                Genetics

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