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dc.contributor.author
Allalou, Amin
dc.contributor.author
Wu, Yuelong
dc.contributor.author
Ghannad-Rezaie, Mostafa
dc.contributor.author
Eimon, Peter M.
dc.contributor.author
Yanik, Mehmet Fatih
dc.date.accessioned
2018-08-14T10:27:47Z
dc.date.available
2017-06-12T20:51:59Z
dc.date.available
2018-08-14T10:27:47Z
dc.date.issued
2017
dc.identifier.other
10.7554/eLife.23379
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/130432
dc.identifier.doi
10.3929/ethz-b-000130432
dc.description.abstract
Here, we describe an automated platform suitable for large-scale deep-phenotyping of zebrafish mutant lines, which uses optical projection tomography to rapidly image brain-specific gene expression patterns in 3D at cellular resolution. Registration algorithms and correlation analysis are then used to compare 3D expression patterns, to automatically detect all statistically significant alterations in mutants, and to map them onto a brain atlas. Automated deep-phenotyping of a mutation in the master transcriptional regulator fezf2 not only detects all known phenotypes but also uncovers important novel neural deficits that were overlooked in previous studies. In the telencephalon, we show for the first time that fezf2 mutant zebrafish have significant patterning deficits, particularly in glutamatergic populations. Our findings reveal unexpected parallels between fezf2 function in zebrafish and mice, where mutations cause deficits in glutamatergic neurons of the telencephalon-derived neocortex.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
eLife Sciences Publications
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.title
Automated deep-phenotyping of the vertebrate brain
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2017-04-13
ethz.journal.title
eLife
ethz.journal.volume
6
en_US
ethz.pages.start
e23379
en_US
ethz.size
26 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.identifier.nebis
007613147
ethz.publication.place
Cambridge
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02533 - Institut für Neuroinformatik / Institute of Neuroinformatics::09474 - Yanik, Mehmet Fatih / Yanik, Mehmet Fatih
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02533 - Institut für Neuroinformatik / Institute of Neuroinformatics::09474 - Yanik, Mehmet Fatih / Yanik, Mehmet Fatih
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02533 - Institut für Neuroinformatik / Institute of Neuroinformatics::09474 - Yanik, Mehmet Fatih / Yanik, Mehmet Fatih
ethz.date.deposited
2017-06-12T20:53:11Z
ethz.source
ECIT
ethz.identifier.importid
imp593655685260d26243
ethz.ecitpid
pub:193438
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2018-08-14T10:27:52Z
ethz.rosetta.lastUpdated
2018-11-07T23:21:40Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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