Prediction of mechanistic subtypes of Parkinson's using patient-derived stem cell models.

ABSTRACT: Parkinson's disease is a common, incurable neurodegenerative disorder that is clinically heterogeneous: it is likely that different cellular mechanisms drive the pathology in different individuals. So far it has not been possible to define the cellular mechanism underlying the neurodegenerative disease in life. We generated a machine learning-based model that can simultaneously predict the presence of disease and its primary mechanistic subtype in human neurons. We used stem cell technology to derive control or patient-derived neurons, and generated different disease subtypes through chemical induction or the presence of mutation. Multidimensional fluorescent labelling of organelles was performed in healthy control neurons and in four different disease subtypes, and both the quantitative s

SUBMITTER: D'Sa K 

PROVIDER: S-EPMC10442231 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature