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      Simultaneous single-cell profiling of lineages and cell types in the vertebrate brain

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          Abstract

          The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method, GESTALT, used CRISPR-Cas9 barcode editing for large-scale lineage tracing, but was restricted to early development and did not identify cell types. Here we present scGESTALT, which combines the lineage recording capabilities of GESTALT with cell-type identification by single-cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points, capturing lineage information from later stages of development. Sequencing of ~60,000 transcriptomes from the juvenile zebrafish brain identifies >100 cell types and marker genes. Using these data, we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types, brain regions, and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease.

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          Most cited references23

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          The Tol2kit: a multisite gateway-based construction kit for Tol2 transposon transgenesis constructs.

          Transgenesis is an important tool for assessing gene function. In zebrafish, transgenesis has suffered from three problems: the labor of building complex expression constructs using conventional subcloning; low transgenesis efficiency, leading to mosaicism in transient transgenics and infrequent germline incorporation; and difficulty in identifying germline integrations unless using a fluorescent marker transgene. The Tol2kit system uses site-specific recombination-based cloning (multisite Gateway technology) to allow quick, modular assembly of [promoter]-[coding sequence]-[3' tag] constructs in a Tol2 transposon backbone. It includes a destination vector with a cmlc2:EGFP (enhanced green fluorescent protein) transgenesis marker and a variety of widely useful entry clones, including hsp70 and beta-actin promoters; cytoplasmic, nuclear, and membrane-localized fluorescent proteins; and internal ribosome entry sequence-driven EGFP cassettes for bicistronic expression. The Tol2kit greatly facilitates zebrafish transgenesis, simplifies the sharing of clones, and enables large-scale projects testing the functions of libraries of regulatory or coding sequences. Copyright 2007 Wiley-Liss, Inc.
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            Seq-Well: A Portable, Low-Cost Platform for High-Throughput Single-Cell RNA-Seq of Low-Input Samples

            Single-cell RNA-Seq can precisely resolve cellular states but application to sparse samples is challenging. Here, we present Seq-Well, a portable, low-cost platform for massively-parallel single-cell RNA-Seq. Barcoded mRNA capture beads and single cells are sealed in an array of subnanoliter wells using a semi-permeable membrane, enabling efficient cell lysis and transcript capture. We characterize Seq-Well using species-mixing experiments and PBMCs, and profile thousands of primary human macrophages exposed to tuberculosis.
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              Single-Cell RNA-Seq with Waterfall Reveals Molecular Cascades underlying Adult Neurogenesis.

              Somatic stem cells contribute to tissue ontogenesis, homeostasis, and regeneration through sequential processes. Systematic molecular analysis of stem cell behavior is challenging because classic approaches cannot resolve cellular heterogeneity or capture developmental dynamics. Here we provide a comprehensive resource of single-cell transcriptomes of adult hippocampal quiescent neural stem cells (qNSCs) and their immediate progeny. We further developed Waterfall, a bioinformatic pipeline, to statistically quantify singe-cell gene expression along a de novo reconstructed continuous developmental trajectory. Our study reveals molecular signatures of adult qNSCs, characterized by active niche signaling integration and low protein translation capacity. Our analyses further delineate molecular cascades underlying qNSC activation and neurogenesis initiation, exemplified by decreased extrinsic signaling capacity, primed translational machinery, and regulatory switches in transcription factors, metabolism, and energy sources. Our study reveals the molecular continuum underlying adult neurogenesis and illustrates how Waterfall can be used for single-cell omics analyses of various continuous biological processes.
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                Author and article information

                Journal
                9604648
                20305
                Nat Biotechnol
                Nat. Biotechnol.
                Nature biotechnology
                1087-0156
                1546-1696
                18 February 2018
                28 March 2018
                June 2018
                28 September 2018
                : 36
                : 5
                : 442-450
                Affiliations
                [1 ]Department of Molecular and Cellular Biology, Harvard University, Cambridge, Massachusetts, USA
                [2 ]Allen Discovery Center for Cell Lineage Tracing, Seattle, Washington, USA
                [3 ]Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, USA
                [4 ]Department of Genome Sciences, University of Washington, Seattle, Washington, USA
                [5 ]Howard Hughes Medical Institute, Seattle, Washington, USA
                [6 ]Department of Biology, University of Utah, Salt Lake City, Utah, USA
                [7 ]Biozentrum, University of Basel, Switzerland
                [8 ]Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
                [9 ]Harvard Stem Cell Institute, Harvard University, Cambridge, Massachusetts, USA
                [10 ]Center for Brain Science, Harvard University, Cambridge, Massachusetts, USA
                Author notes
                [+ ]Co-corresponding authors. james.gagnon@ 123456gmail.com (J.A.G.); schier@ 123456fas.harvard.edu (A.F.S.)
                [*]

                These authors contributed equally to this work.

                Article
                NIHMS943813
                10.1038/nbt.4103
                5938111
                29608178
                e33a4c24-9639-4fd2-9774-946b4ff83430

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                Biotechnology

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