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dc.contributor.authorUeda, Hiroki R
dc.contributor.authorErtürk, Ali
dc.contributor.authorChung, Kwanghun
dc.contributor.authorGradinaru, Viviana
dc.contributor.authorChédotal, Alain
dc.contributor.authorTomancak, Pavel
dc.contributor.authorKeller, Philipp J
dc.date.accessioned2021-10-27T20:05:45Z
dc.date.available2021-10-27T20:05:45Z
dc.date.issued2020
dc.identifier.urihttps://hdl.handle.net/1721.1/134611
dc.description.abstract© 2020, Springer Nature Limited. State-of-the-art tissue-clearing methods provide subcellular-level optical access to intact tissues from individual organs and even to some entire mammals. When combined with light-sheet microscopy and automated approaches to image analysis, existing tissue-clearing methods can speed up and may reduce the cost of conventional histology by several orders of magnitude. In addition, tissue-clearing chemistry allows whole-organ antibody labelling, which can be applied even to thick human tissues. By combining the most powerful labelling, clearing, imaging and data-analysis tools, scientists are extracting structural and functional cellular and subcellular information on complex mammalian bodies and large human specimens at an accelerated pace. The rapid generation of terabyte-scale imaging data furthermore creates a high demand for efficient computational approaches that tackle challenges in large-scale data analysis and management. In this Review, we discuss how tissue-clearing methods could provide an unbiased, system-level view of mammalian bodies and human specimens and discuss future opportunities for the use of these methods in human neuroscience.
dc.language.isoen
dc.publisherSpringer Science and Business Media LLC
dc.relation.isversionof10.1038/S41583-019-0250-1
dc.rightsCreative Commons Attribution-Noncommercial-Share Alike
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/
dc.sourcePMC
dc.titleTissue clearing and its applications in neuroscience
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.relation.journalNature Reviews Neuroscience
dc.eprint.versionAuthor's final manuscript
dc.type.urihttp://purl.org/eprint/type/JournalArticle
eprint.statushttp://purl.org/eprint/status/PeerReviewed
dc.date.updated2021-06-08T18:24:35Z
dspace.orderedauthorsUeda, HR; Ertürk, A; Chung, K; Gradinaru, V; Chédotal, A; Tomancak, P; Keller, PJ
dspace.date.submission2021-06-08T18:24:36Z
mit.journal.volume21
mit.journal.issue2
mit.licenseOPEN_ACCESS_POLICY
mit.metadata.statusAuthority Work and Publication Information Needed


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