Vuidel, Aurore, Cousin, Loic, Weykopf, Beatrice, Haupt, Simone, Hanifehlou, Zahra, Wiest-Daessle, Nicolas, Segschneider, Michaela, Lee, Joohyun, Kwon, Yong-Jun, Peitz, Michael, Ogier, Arnaud, Brino, Laurent, Bruestle, Oliver, Sommer, Peter ORCID: 0000-0001-9241-5710 and Wilbertz, Johannes H. (2022). High-content phenotyping of Parkinson's disease patient stem cell-derived midbrain dopaminergic neurons using machine learning classification. Stem Cell Rep., 17 (10). S. 2349 - 2365. CAMBRIDGE: CELL PRESS. ISSN 2213-6711

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Abstract

Combining multiple Parkinson's disease (PD) relevant cellular phenotypes might increase the accuracy of midbrain dopaminergic neuron (mDAN) in vitro models. We differentiated patient-derived induced pluripotent stem cells (iPSCs) with a LRRK2 G2019S mutation, isogenic control, and genetically unrelated iPSCs into mDANs. Using automated fluorescence microscopy in 384-well-plate format, we identified elevated levels of a-synuclein (aSyn) and serine 129 phosphorylation, reduced dendritic complexity, and mitochondrial dysfunction. Next, we measured additional image-based phenotypes and used machine learning (ML) to accurately classify mDANs ac-cording to their genotype. Additionally, we show that chemical compound treatments, targeting LRRK2 kinase activity or aSyn levels, are detectable when using ML classification based on multiple image-based phenotypes. We validated our approach using a second isogenic patient-derived SNCA gene triplication mDAN model which overexpresses aSyn. This phenotyping and classification strategy improves the practical exploitability of mDANs for disease modeling and the identification of novel LRRK2-associated drug targets.

Item Type: Journal Article
Creators:
CreatorsEmailORCIDORCID Put Code
Vuidel, AuroreUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Cousin, LoicUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Weykopf, BeatriceUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Haupt, SimoneUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Hanifehlou, ZahraUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Wiest-Daessle, NicolasUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Segschneider, MichaelaUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Lee, JoohyunUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Kwon, Yong-JunUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Peitz, MichaelUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Ogier, ArnaudUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Brino, LaurentUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Bruestle, OliverUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Sommer, PeterUNSPECIFIEDorcid.org/0000-0001-9241-5710UNSPECIFIED
Wilbertz, Johannes H.UNSPECIFIEDUNSPECIFIEDUNSPECIFIED
URN: urn:nbn:de:hbz:38-686669
DOI: 10.1016/j.stemcr.2022.09.001
Journal or Publication Title: Stem Cell Rep.
Volume: 17
Number: 10
Page Range: S. 2349 - 2365
Date: 2022
Publisher: CELL PRESS
Place of Publication: CAMBRIDGE
ISSN: 2213-6711
Language: English
Faculty: Unspecified
Divisions: Unspecified
Subjects: no entry
Uncontrolled Keywords:
KeywordsLanguage
PROTEIN-KINASE-C; ALPHA; LRRK2; DELTAMultiple languages
Cell & Tissue Engineering; Cell BiologyMultiple languages
URI: http://kups.ub.uni-koeln.de/id/eprint/68666

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