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dc.contributor.author
Rechsteiner, Markus P.
dc.contributor.author
Floros, Xenofon
dc.contributor.author
Boehm, Bernhard O.
dc.contributor.author
Marselli, Lorella
dc.contributor.author
Marchetti, Piero
dc.contributor.author
Stoffel, Markus
dc.contributor.author
Moch, Holger
dc.contributor.author
Spinas, Giatgen A.
dc.date.accessioned
2020-04-30T09:17:34Z
dc.date.available
2018-08-09T14:03:28Z
dc.date.available
2020-03-09T12:53:05Z
dc.date.available
2020-03-09T12:55:32Z
dc.date.available
2020-03-09T12:58:59Z
dc.date.available
2020-03-09T13:16:57Z
dc.date.available
2020-04-30T09:04:30Z
dc.date.available
2020-04-30T09:08:44Z
dc.date.available
2020-04-30T09:11:27Z
dc.date.available
2020-04-30T09:17:34Z
dc.date.issued
2014-06-06
dc.identifier.issn
1932-6203
dc.identifier.other
10.1371/journal.pone.0098932
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/281612
dc.identifier.doi
10.3929/ethz-b-000164100
dc.description.abstract
In this study we aimed to establish an unbiased automatic quantification pipeline to assess islet specific features such as beta-cell area and density per islet based on immunofluorescence stainings. To determine these parameters, the in vivo protein expression levels of TMEM27 and BACE2 in pancreatic islets of 32 patients with type 2 diabetes (T2D) and in 28 non-diabetic individuals (ND) were used as input for the automated pipeline. The output of the automated pipeline was first compared to a previously developed manual area scoring system which takes into account the intensity of the staining as well as the percentage of cells which are stained within an islet. The median TMEM27 and BACE2 area scores of all islets investigated per patient correlated significantly with the manual scoring and with the median area score of insulin. Furthermore, the median area scores of TMEM27, BACE2 and insulin calculated from all T2D were significantly lower compared to the one of all ND. TMEM27, BACE2, and insulin area scores correlated as well in each individual tissue specimen. Moreover, islet size determined by costaining of glucagon and either TMEM27 or BACE2 and beta-cell density based either on TMEM27 or BACE2 positive cells correlated significantly. Finally, the TMEM27 area score showed a positive correlation with BMI in ND and an inverse pattern in T2D. In summary, automated quantification outperforms manual scoring by reducing time and individual bias. The simultaneous changes of TMEM27, BACE2, and insulin in the majority of the beta-cells suggest that these proteins reflect the total number of functional insulin producing beta-cells. Additionally, beta-cell subpopulations may be identified which are positive for TMEM27, BACE2 or insulin only. Thus, the cumulative assessment of all three markers may provide further information about the real beta-cell number per islet.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Public Library of Science
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.title
Automated Assessment of beta-Cell Area and Density per Islet and Patient Using TMEM27 and BACE2 Immunofluorescence Staining in Human Pancreatic beta-Cells
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
ethz.journal.title
PLoS ONE
ethz.journal.volume
9
en_US
ethz.journal.issue
6
en_US
ethz.journal.abbreviated
PLoS ONE
ethz.pages.start
e98932
en_US
ethz.size
10 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.identifier.nebis
006206116
ethz.publication.place
S.l.
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02030 - Dep. Biologie / Dep. of Biology::02539 - Institut für Molecular Health Sciences / Institute of Molecular Health Sciences::03739 - Stoffel, Markus / Stoffel, Markus
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02150 - Dep. Informatik / Dep. of Computer Science::02661 - Institut für Maschinelles Lernen / Institute for Machine Learning::03659 - Buhmann, Joachim M. / Buhmann, Joachim M.
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02030 - Dep. Biologie / Dep. of Biology::02539 - Institut für Molecular Health Sciences / Institute of Molecular Health Sciences::03739 - Stoffel, Markus / Stoffel, Markus
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02150 - Dep. Informatik / Dep. of Computer Science::02661 - Institut für Maschinelles Lernen / Institute for Machine Learning::03659 - Buhmann, Joachim M. / Buhmann, Joachim M.
ethz.relation.isReferencedBy
10.1371/journal.pone.0109467
ethz.date.deposited
2017-06-11T10:42:28Z
ethz.source
ECIT
ethz.identifier.importid
imp59365259a612041713
ethz.identifier.importid
imp593652142104d58622
ethz.ecitpid
pub:141693
ethz.ecitpid
pub:136054
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2020-04-30T09:17:46Z
ethz.rosetta.lastUpdated
2021-02-15T10:47:29Z
ethz.rosetta.versionExported
true
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/164100
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/86433
ethz.COinS
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