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dc.contributor.authorYeo, Grace Hui Ting
dc.contributor.authorLin, Lin
dc.contributor.authorQi, Celine Yueyue
dc.contributor.authorCha, Minsun
dc.contributor.authorGifford, David K
dc.contributor.authorSherwood, Richard I.
dc.date.accessioned2022-07-05T15:50:23Z
dc.date.available2021-10-27T20:30:28Z
dc.date.available2022-07-05T15:50:23Z
dc.date.issued2020
dc.identifier.urihttps://hdl.handle.net/1721.1/136026.2
dc.description.abstract© 2020 Elsevier Inc. Empirical optimization of stem cell differentiation protocols is time consuming, is laborintensive, and typically does not comprehensively interrogate all relevant signaling pathways. Here we describe barcodelet single-cell RNA sequencing (barRNA-seq), which enables systematic exploration of cellular perturbations by tagging individual cells with RNA “barcodelets” to identify them on the basis of the treatments they receive. We apply barRNA-seq to simultaneously manipulate up to seven developmental pathways and study effects on embryonic stem cell (ESC) germ layer specification and mesodermal specification, uncovering combinatorial effects of signaling pathway activation on gene expression. We further develop a data-driven framework for identifying combinatorial signaling perturbations that drive cells toward specific fates, including several annotated in an existing scRNA-seq gastrulation atlas, and use this approach to guide ESC differentiation into a notochord-like population. We expect that barRNA-seq will have broad utility for investigating and understanding how cooperative signaling pathways drive cell fate acquisition.en_US
dc.language.isoen
dc.publisherElsevier BVen_US
dc.relation.isversionof10.1016/J.STEM.2020.04.020en_US
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivs Licenseen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.sourcePMCen_US
dc.titleA Multiplexed Barcodelet Single-Cell RNA-Seq Approach Elucidates Combinatorial Signaling Pathways that Drive ESC Differentiationen_US
dc.typeArticleen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.departmentMassachusetts Institute of Technology. Computational and Systems Biology Programen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Biological Engineeringen_US
dc.relation.journalCell Stem Cellen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2021-06-17T13:29:49Z
dspace.orderedauthorsYeo, GHT; Lin, L; Qi, CY; Cha, M; Gifford, DK; Sherwood, RIen_US
dspace.date.submission2021-06-17T13:29:51Z
mit.journal.volume26en_US
mit.journal.issue6en_US
mit.licensePUBLISHER_CC
mit.metadata.statusPublication Information Neededen_US


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