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High-content phenotyping of Parkinson's disease patient stem cell-derived midbrain dopaminergic neurons using machine learning classification

Authors
  • Vuidel, Aurore
  • Cousin, Loïc
  • Weykopf, Beatrice
  • Haupt, Simone
  • Hanifehlou, Zahra
  • Wiest-Daesslé, Nicolas
  • Segschneider, Michaela
  • Lee, Joohyun
  • Kwon, Yong-Jun
  • Peitz, Michael
  • Ogier, Arnaud
  • Brino, Laurent
  • Brüstle, Oliver
  • Sommer, Peter
  • Wilbertz, Johannes H.
Type
Published Article
Journal
Stem Cell Reports
Publisher
Elsevier
Publication Date
Sep 29, 2022
Volume
17
Issue
10
Pages
2349–2364
Identifiers
DOI: 10.1016/j.stemcr.2022.09.001
PMID: 36179692
PMCID: PMC9561636
Source
PubMed Central
Keywords
Disciplines
  • Resource
License
Unknown

Abstract

In this article, Wilbertz and colleagues show that machine learning based on phenotypic features can distinguish Parkinson’s disease patient stem cell-derived dopaminergic neurons from isogenic controls. LRRK2 G2019S or SNCA triplication neurons were fluorescently stained, and quantitative phenotypic features were extracted. The resulting phenotypic profiles allowed classification algorithms to identify different genotypes and the detection of chemical compound effects.

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