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dc.contributor.authorFrosch, Matthew P.
dc.contributor.authorWedeen, Van J.
dc.contributor.authorSeung, H. Sebastian
dc.contributor.authorMurray, Evan
dc.contributor.authorCho, Jae Hun
dc.contributor.authorKu, Taeyun
dc.contributor.authorSwaney, Justin Mark
dc.contributor.authorKim, Sung-Yon
dc.contributor.authorChoi, Heejin
dc.contributor.authorPark, Young-Gyun
dc.contributor.authorPark, Jeong-Yoon
dc.contributor.authorHubbert, Austin W.
dc.contributor.authorMcCue, Margaret Grace
dc.contributor.authorLing, Sara Lynn
dc.contributor.authorBakh, Naveed Ali
dc.contributor.authorChung, Kwanghun
dc.date.accessioned2017-03-21T19:45:57Z
dc.date.available2017-03-21T19:45:57Z
dc.date.issued2015-12
dc.date.submitted2015-10
dc.identifier.issn0092-8674
dc.identifier.issn1097-4172
dc.identifier.urihttp://hdl.handle.net/1721.1/107624
dc.description.abstractCombined measurement of diverse molecular and anatomical traits that span multiple levels remains a major challenge in biology. Here, we introduce a simple method that enables proteomic imaging for scalable, integrated, high-dimensional phenotyping of both animal tissues and human clinical samples. This method, termed SWITCH, uniformly secures tissue architecture, native biomolecules, and antigenicity across an entire system by synchronizing the tissue preservation reaction. The heat- and chemical-resistant nature of the resulting framework permits multiple rounds (>20) of relabeling. We have performed 22 rounds of labeling of a single tissue with precise co-registration of multiple datasets. Furthermore, SWITCH synchronizes labeling reactions to improve probe penetration depth and uniformity of staining. With SWITCH, we performed combinatorial protein expression profiling of the human cortex and also interrogated the geometric structure of the fiber pathways in mouse brains. Such integrated high-dimensional information may accelerate our understanding of biological systems at multiple levels.en_US
dc.description.sponsorshipSimons Foundation. Postdoctoral Fellowshipen_US
dc.description.sponsorshipLife Sciences Research Foundationen_US
dc.description.sponsorshipBurroughs Wellcome Fund (Career Award at the Scientific Interface)en_US
dc.description.sponsorshipSearle Scholars Programen_US
dc.description.sponsorshipMichael J. Fox Foundation for Parkinson's Researchen_US
dc.description.sponsorshipUnited States. Defense Advanced Research Projects Agencyen_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (1-U01-NS090473-01)en_US
dc.language.isoen_US
dc.publisherElsevieren_US
dc.relation.isversionofhttp://dx.doi.org/10.1016/j.cell.2015.11.025en_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.titleSimple, Scalable Proteomic Imaging for High-Dimensional Profiling of Intact Systemsen_US
dc.typeArticleen_US
dc.identifier.citationMurray, Evan et al. “Simple, Scalable Proteomic Imaging for High-Dimensional Profiling of Intact Systems.” Cell 163.6 (2015): 1500–1514.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Institute for Medical Engineering & Scienceen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Brain and Cognitive Sciencesen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Chemical Engineeringen_US
dc.contributor.departmentPicower Institute for Learning and Memoryen_US
dc.contributor.mitauthorMurray, Evan
dc.contributor.mitauthorCho, Jae Hun
dc.contributor.mitauthorKu, Taeyun
dc.contributor.mitauthorSwaney, Justin Mark
dc.contributor.mitauthorKim, Sung-Yon
dc.contributor.mitauthorChoi, Heejin
dc.contributor.mitauthorPark, Young-Gyun
dc.contributor.mitauthorPark, Jeong-Yoon
dc.contributor.mitauthorHubbert, Austin W.
dc.contributor.mitauthorMcCue, Margaret Grace
dc.contributor.mitauthorLing, Sara Lynn
dc.contributor.mitauthorBakh, Naveed Ali
dc.contributor.mitauthorChung, Kwanghun
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
dspace.orderedauthorsMurray, Evan; Cho, Jae Hun; Goodwin, Daniel; Ku, Taeyun; Swaney, Justin; Kim, Sung-Yon; Choi, Heejin; Park, Young-Gyun; Park, Jeong-Yoon; Hubbert, Austin; McCue, Margaret; Vassallo, Sara; Bakh, Naveed; Frosch, Matthew P.; Wedeen, Van J.; Seung, H. Sebastian; Chung, Kwanghunen_US
dspace.embargo.termsNen_US
dc.identifier.orcidhttps://orcid.org/0000-0003-2927-7321
dc.identifier.orcidhttps://orcid.org/0000-0003-2880-349X
dc.identifier.orcidhttps://orcid.org/0000-0001-9447-7579
dc.identifier.orcidhttps://orcid.org/0000-0002-4830-9133
dc.identifier.orcidhttps://orcid.org/0000-0003-3681-7410
dc.identifier.orcidhttps://orcid.org/0000-0001-6376-1323
dc.identifier.orcidhttps://orcid.org/0000-0002-2287-9998
dc.identifier.orcidhttps://orcid.org/0000-0002-0907-6736
dc.identifier.orcidhttps://orcid.org/0000-0002-8167-3340
mit.licensePUBLISHER_CCen_US
mit.metadata.statusComplete


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