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High-content phenotyping of Parkinson's disease patient stem cell-derived midbrain dopaminergic neurons using machine learning classification
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 u...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9561636/ https://www.ncbi.nlm.nih.gov/pubmed/36179692 http://dx.doi.org/10.1016/j.stemcr.2022.09.001 |
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author | 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. |
author_facet | 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. |
author_sort | Vuidel, Aurore |
collection | PubMed |
description | 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 α-synuclein (αSyn) 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 according to their genotype. Additionally, we show that chemical compound treatments, targeting LRRK2 kinase activity or αSyn 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 αSyn. This phenotyping and classification strategy improves the practical exploitability of mDANs for disease modeling and the identification of novel LRRK2-associated drug targets. |
format | Online Article Text |
id | pubmed-9561636 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-95616362022-10-15 High-content phenotyping of Parkinson's disease patient stem cell-derived midbrain dopaminergic neurons using machine learning classification 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. Stem Cell Reports Resource 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 α-synuclein (αSyn) 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 according to their genotype. Additionally, we show that chemical compound treatments, targeting LRRK2 kinase activity or αSyn 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 αSyn. This phenotyping and classification strategy improves the practical exploitability of mDANs for disease modeling and the identification of novel LRRK2-associated drug targets. Elsevier 2022-09-29 /pmc/articles/PMC9561636/ /pubmed/36179692 http://dx.doi.org/10.1016/j.stemcr.2022.09.001 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Resource 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. High-content phenotyping of Parkinson's disease patient stem cell-derived midbrain dopaminergic neurons using machine learning classification |
title | High-content phenotyping of Parkinson's disease patient stem cell-derived midbrain dopaminergic neurons using machine learning classification |
title_full | High-content phenotyping of Parkinson's disease patient stem cell-derived midbrain dopaminergic neurons using machine learning classification |
title_fullStr | High-content phenotyping of Parkinson's disease patient stem cell-derived midbrain dopaminergic neurons using machine learning classification |
title_full_unstemmed | High-content phenotyping of Parkinson's disease patient stem cell-derived midbrain dopaminergic neurons using machine learning classification |
title_short | High-content phenotyping of Parkinson's disease patient stem cell-derived midbrain dopaminergic neurons using machine learning classification |
title_sort | high-content phenotyping of parkinson's disease patient stem cell-derived midbrain dopaminergic neurons using machine learning classification |
topic | Resource |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9561636/ https://www.ncbi.nlm.nih.gov/pubmed/36179692 http://dx.doi.org/10.1016/j.stemcr.2022.09.001 |
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