Neural networks reveal novel gene signatures in Parkinson disease from single-nuclei transcriptomes.

ABSTRACT: Parkinson disease (PD) is a progressive neurodegenerative disease with an incompletely understood genetic architecture that necessitates novel discovery methods. We introduce an explainable machine learning framework that uses single-cell/nuclei RNA sequencing (sc/snRNAseq) to identify molecular markers of diseased cells and nominate candidate genes for targeted genomic analysis. Application to four snRNAseq datasets characterizing the post-mortem midbrain identified cell type-specific gene sets that consistently distinguished PD from healthy cells across all datasets (mean balanced accuracy = 0.92) and highlighted ten novel candidate genes in PD. Among these, GPC6 was identified as a marker of PD dopaminergic neurons and a member of the heparan sulfate proteoglycan family, implicated in t

SUBMITTER: Fiorini MR 

PROVIDER: S-EPMC12540932 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature