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dc.contributor.authorChang, Jae-Byum
dc.contributor.authorChen, Fei
dc.contributor.authorYoon, Young-Gyu
dc.contributor.authorJung, Erica E
dc.contributor.authorBabcock, Hazen
dc.contributor.authorKang, Jeong Seuk
dc.contributor.authorAsano, Shoh
dc.contributor.authorSuk, Ho-Jun
dc.contributor.authorPak, Nikita
dc.contributor.authorTillberg, Paul W
dc.contributor.authorWassie, Asmamaw T
dc.contributor.authorCai, Dawen
dc.contributor.authorBoyden, Edward S
dc.date.accessioned2021-10-27T20:29:04Z
dc.date.available2021-10-27T20:29:04Z
dc.date.issued2017
dc.identifier.urihttps://hdl.handle.net/1721.1/135740
dc.description.abstract© 2017 Nature America, Inc. All rights reserved. We recently developed a method called expansion microscopy, in which preserved biological specimens are physically magnified by embedding them in a densely crosslinked polyelectrolyte gel, anchoring key labels or biomolecules to the gel, mechanically homogenizing the specimen, and then swelling the gel-specimen composite by ∼4.5× in linear dimension. Here we describe iterative expansion microscopy (iExM), in which a sample is expanded ∼20×. After preliminary expansion a second swellable polymer mesh is formed in the space newly opened up by the first expansion, and the sample is expanded again. iExM expands biological specimens ∼4.5 × 4.5, or ∼20×, and enables ∼25-nm-resolution imaging of cells and tissues on conventional microscopes. We used iExM to visualize synaptic proteins, as well as the detailed architecture of dendritic spines, in mouse brain circuitry.
dc.language.isoen
dc.publisherSpringer Nature
dc.relation.isversionof10.1038/NMETH.4261
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.
dc.sourcePMC
dc.titleIterative expansion microscopy
dc.typeArticle
dc.contributor.departmentMassachusetts Institute of Technology. Media Laboratory
dc.contributor.departmentMassachusetts Institute of Technology. Department of Biological Engineering
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.contributor.departmentHarvard University--MIT Division of Health Sciences and Technology
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mechanical Engineering
dc.contributor.departmentMcGovern Institute for Brain Research at MIT
dc.contributor.departmentMassachusetts Institute of Technology. Department of Brain and Cognitive Sciences
dc.relation.journalNature Methods
dc.eprint.versionAuthor's final manuscript
dc.type.urihttp://purl.org/eprint/type/JournalArticle
eprint.statushttp://purl.org/eprint/status/PeerReviewed
dc.date.updated2019-07-19T13:23:05Z
dspace.orderedauthorsChang, J-B; Chen, F; Yoon, Y-G; Jung, EE; Babcock, H; Kang, JS; Asano, S; Suk, H-J; Pak, N; Tillberg, PW; Wassie, AT; Cai, D; Boyden, ES
dspace.date.submission2019-07-19T13:23:07Z
mit.journal.volume14
mit.journal.issue6
mit.metadata.statusAuthority Work and Publication Information Needed


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