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dc.contributor.authorCleary, Brian Lowman
dc.contributor.authorLin, Stacie
dc.contributor.authorJaenisch, Rudolf
dc.contributor.authorRegev, Aviv
dc.contributor.authorLander, Eric Steven
dc.date.accessioned2020-03-26T18:55:47Z
dc.date.available2020-03-26T18:55:47Z
dc.date.issued2019-02
dc.identifier.issn0092-8674
dc.identifier.urihttps://hdl.handle.net/1721.1/124366
dc.description.abstractUnderstanding the molecular programs that guide differentiation during development is a major challenge. Here, we introduce Waddington-OT, an approach for studying developmental time courses to infer ancestor-descendant fates and model the regulatory programs that underlie them. We apply the method to reconstruct the landscape of reprogramming from 315,000 single-cell RNA sequencing (scRNA-seq) profiles, collected at half-day intervals across 18 days. The results reveal a wider range of developmental programs than previously characterized. Cells gradually adopt either a terminal stromal state or a mesenchymal-to-epithelial transition state. The latter gives rise to populations related to pluripotent, extra-embryonic, and neural cells, with each harboring multiple finer subpopulations. The analysis predicts transcription factors and paracrine signals that affect fates and experiments validate that the TF Obox6 and the cytokine GDF9 enhance reprogramming efficiency. Our approach sheds light on the process and outcome of reprogramming and provides a framework applicable to diverse temporal processes in biology.en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (grant HD045022)en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (grant R01 MH104610-15)en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (grant R01NS088538)en_US
dc.language.isoen
dc.publisherElsevier BVen_US
dc.relation.isversionof10.1016/j.cell.2019.01.006en_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.subjectGeneral Biochemistry, Genetics and Molecular Biologyen_US
dc.titleOptimal-Transport Analysis of Single-Cell Gene Expression Identifies Developmental Trajectories in Reprogrammingen_US
dc.typeArticleen_US
dc.identifier.citationSchiebinger, Geoffrey et al. "Optimal-Transport Analysis of Single-Cell Gene Expression Identifies Developmental Trajectories in Reprogramming." Cell 176 (2019): 928-943 © 2019 The Author(s)en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Biologyen_US
dc.contributor.departmentMassachusetts Institute of Technology. Computational and Systems Biology Programen_US
dc.relation.journalCellen_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.updated2020-02-19T18:47:49Z
dspace.date.submission2020-02-19T18:47:51Z
mit.journal.volume176en_US
mit.journal.issue4en_US
mit.licensePUBLISHER_CC
mit.metadata.statusComplete


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