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dc.contributor.author
Pfeiffer, Michael
dc.contributor.author
Betizeau, Marion
dc.contributor.author
Waltispurger, Julie
dc.contributor.author
Pfister, Sabina Sara
dc.contributor.author
Douglas, Rodney J.
dc.contributor.author
Kennedy, Henry
dc.contributor.author
Dehay, Colette
dc.date.accessioned
2019-12-04T12:55:16Z
dc.date.available
2017-06-11T23:10:40Z
dc.date.available
2019-12-04T12:55:16Z
dc.date.issued
2016-02-15
dc.identifier.issn
0021-9967
dc.identifier.issn
1096-9861
dc.identifier.issn
0092-7317
dc.identifier.other
10.1002/cne.23820
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/110491
dc.identifier.doi
10.3929/ethz-b-000110491
dc.description.abstract
Generation of the primate cortex is characterized by the diversity of cortical precursors and the complexity of their lineage relationships. Recent studies have reported miscellaneous precursor types based on observer classification of cell biology features including morphology, stemness, and proliferative behavior. Here we use an unsupervised machine learning method for Hidden Markov Trees (HMTs), which can be applied to large datasets to classify precursors on the basis of morphology, cell‐cycle length, and behavior during mitosis. The unbiased lineage analysis automatically identifies cell types by applying a lineage‐based clustering and model‐learning algorithm to a macaque corticogenesis dataset. The algorithmic results validate previously reported observer classification of precursor types and show numerous advantages: It predicts a higher diversity of progenitors and numerous potential transitions between precursor types. The HMT model can be initialized to learn a user‐defined number of distinct classes of precursors. This makes it possible to 1) reveal as yet undetected precursor types in view of exploring the significant features of precursors with respect to specific cellular processes; and 2) explore specific lineage features. For example, most precursors in the experimental dataset exhibit bidirectional transitions. Constraining the directionality in the HMT model leads to a reduction in precursor diversity following multiple divisions, thereby suggesting that one impact of bidirectionality in corticogenesis is to maintain precursor diversity. In this way we show that unsupervised lineage analysis provides a valuable methodology for investigating fundamental features of corticogenesis.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Wiley
en_US
dc.rights.uri
http://creativecommons.org/licenses/by-nc/4.0/
dc.subject
Cell lineages
en_US
dc.subject
Clustering
en_US
dc.subject
Corticogenesis
en_US
dc.subject
Hidden Markov Trees
en_US
dc.subject
Primate cortex
en_US
dc.title
Unsupervised lineage‐based characterization of primate precursors reveals high proliferative and morphological diversity in the OSVZ
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution-NonCommercial 4.0 International
dc.date.published
2015-06-06
ethz.journal.title
The journal of comparative neurology
ethz.journal.volume
524
en_US
ethz.journal.issue
3
en_US
ethz.journal.abbreviated
J Comp Neurol
ethz.pages.start
535
en_US
ethz.pages.end
563
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.identifier.nebis
000986174
ethz.publication.place
Hoboken, NJ
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
03453 - Douglas, Rodney J.
en_US
ethz.leitzahl.certified
03453 - Douglas, Rodney J.
ethz.date.deposited
2017-06-11T23:11:43Z
ethz.source
ECIT
ethz.identifier.importid
imp593653f511f7e57978
ethz.ecitpid
pub:171729
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2017-07-15T08:25:13Z
ethz.rosetta.lastUpdated
2019-12-04T12:55:32Z
ethz.rosetta.versionExported
true
ethz.COinS
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