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dc.contributor.authorAlbanese, Alexandre
dc.contributor.authorSwaney, Justin M
dc.contributor.authorYun, Dae Hee
dc.contributor.authorEvans, Nicholas B
dc.contributor.authorAntonucci, Jenna M
dc.contributor.authorVelasco, Silvia
dc.contributor.authorSohn, Chang Ho
dc.contributor.authorArlotta, Paola
dc.contributor.authorGehrke, Lee
dc.contributor.authorChung, Kwanghun
dc.date.accessioned2021-10-27T20:30:51Z
dc.date.available2021-10-27T20:30:51Z
dc.date.issued2020
dc.identifier.urihttps://hdl.handle.net/1721.1/136109
dc.description.abstractBrain organoids grown from human pluripotent stem cells self-organize into cytoarchitectures resembling the developing human brain. These three-dimensional models offer an unprecedented opportunity to study human brain development and dysfunction. Characterization currently sacrifices spatial information for single-cell or histological analysis leaving whole-tissue analysis mostly unexplored. Here, we present the SCOUT pipeline for automated multiscale comparative analysis of intact cerebral organoids. Our integrated technology platform can rapidly clear, label, and image intact organoids. Algorithmic- and convolutional neural network-based image analysis extract hundreds of features characterizing molecular, cellular, spatial, cytoarchitectural, and organoid-wide properties from fluorescence microscopy datasets. Comprehensive analysis of 46 intact organoids and ~ 100 million cells reveals quantitative multiscale “phenotypes" for organoid development, culture protocols and Zika virus infection. SCOUT provides a much-needed framework for comparative analysis of emerging 3D in vitro models using fluorescence microscopy.
dc.language.isoen
dc.publisherSpringer Science and Business Media LLC
dc.relation.isversionof10.1038/s41598-020-78130-7
dc.rightsCreative Commons Attribution 4.0 International license
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourceScientific Reports
dc.titleMultiscale 3D phenotyping of human cerebral organoids
dc.typeArticle
dc.contributor.departmentMassachusetts Institute of Technology. Institute for Medical Engineering & Science
dc.contributor.departmentPicower Institute for Learning and Memory
dc.contributor.departmentMassachusetts Institute of Technology. Department of Chemical Engineering
dc.contributor.departmentMassachusetts Institute of Technology. Department of Brain and Cognitive Sciences
dc.contributor.departmentHarvard University--MIT Division of Health Sciences and Technology
dc.relation.journalScientific Reports
dc.eprint.versionFinal published version
dc.type.urihttp://purl.org/eprint/type/JournalArticle
eprint.statushttp://purl.org/eprint/status/PeerReviewed
dc.date.updated2021-06-08T18:16:30Z
dspace.orderedauthorsAlbanese, A; Swaney, JM; Yun, DH; Evans, NB; Antonucci, JM; Velasco, S; Sohn, CH; Arlotta, P; Gehrke, L; Chung, K
dspace.date.submission2021-06-08T18:16:34Z
mit.journal.volume10
mit.journal.issue1
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work and Publication Information Needed


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