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Massively parallel single-nucleus RNA-seq with DroNc-seq

dc.contributor.authorAvraham-Davidi, Inbal
dc.contributor.authorBurks, Tyler
dc.contributor.authorShekhar, Karthik
dc.contributor.authorHofree, Matan
dc.contributor.authorAguet, François
dc.contributor.authorGelfand, Ellen
dc.contributor.authorArdlie, Kristin
dc.contributor.authorWeitz, David A
dc.contributor.authorRozenblatt-Rosen, Orit
dc.contributor.authorZhang, Feng
dc.contributor.authorHabib, Naomi
dc.contributor.authorChoudhury, Sourav
dc.contributor.authorRegev, Aviv
dc.contributor.authorBasu, Anindita, 1978-
dc.date.accessioned2018-03-21T18:49:11Z
dc.date.available2018-03-21T18:49:11Z
dc.date.issued2017-08
dc.date.submitted2017-03
dc.identifier.issn1548-7091
dc.identifier.issn1548-7105
dc.identifier.urihttp://hdl.handle.net/1721.1/114253
dc.description.abstractSingle-nucleus RNA sequencing (sNuc-seq) profiles RNA from tissues that are preserved or cannot be dissociated, but it does not provide high throughput. Here, we develop DroNc-seq: massively parallel sNuc-seq with droplet technology. We profile 39,111 nuclei from mouse and human archived brain samples to demonstrate sensitive, efficient, and unbiased classification of cell types, paving the way for systematic charting of cell atlases. Keywords: Cellular neuroscience; Gene expression; Gene expression analysis; RNA sequencingen_US
dc.language.isoen_US
dc.publisherNature Publishing Groupen_US
dc.relation.isversionofhttp://dx.doi.org/10.1038/nmeth.4407en_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceProf. Zhang via Courtney Crummetten_US
dc.titleMassively parallel single-nucleus RNA-seq with DroNc-seqen_US
dc.typeArticleen_US
dc.identifier.citationHabib, Naomi et al. “Massively Parallel Single-Nucleus RNA-Seq with DroNc-Seq.” Nature Methods 14, 10 (August 2017): 955–958 © 2017 Nature Americaen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Biologyen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Brain and Cognitive Sciencesen_US
dc.contributor.departmentMcGovern Institute for Brain Research at MITen_US
dc.contributor.departmentKoch Institute for Integrative Cancer Research at MITen_US
dc.contributor.approverZhang, Fengen_US
dc.contributor.mitauthorHabib, Naomi
dc.contributor.mitauthorChoudhury, Sourav
dc.contributor.mitauthorRegev, Aviv
dc.relation.journalNature Methodsen_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
dspace.orderedauthorsHabib, Naomi; Avraham-Davidi, Inbal; Basu, Anindita; Burks, Tyler; Shekhar, Karthik; Hofree, Matan; Choudhury, Sourav R; Aguet, François; Gelfand, Ellen; Ardlie, Kristin; Weitz, David A; Rozenblatt-Rosen, Orit; Zhang, Feng; Regev, Aviven_US
dspace.embargo.termsNen_US
dc.identifier.orcidhttps://orcid.org/0000-0001-8567-2049
mit.licensePUBLISHER_POLICYen_US


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