Speaker
Description
Parkinson's disease (PD) is a progressive neurodegenerative disorder whose underlying molecular mechanisms remain poorly understood, hindering development of targeted therapeutics. While 10% of cases are linked to mutations in over 20 genes, the majority are idiopathic, reflecting the disease's complexity. Recent approaches use multi-OMICS characterization of patient-derived neurons obtained through differentiation of induced pluripotent stem cells (iPSC), however, identifying underlying mechanisms of PD remains challenging. We addressed this challenge by a Non-negative Matrix Tri-Factorization (NMTF) framework that mechanistically integrates longitudinal RNASeq data from patient-derived dopaminergic neurons carrying PD-associated mutations in the PINK1 and SNCA genes. Single-cell RNA sequencing, proteomics, and metabolomics data — spanning iPSC differentiation across seven timepoints — are processed via a high-performance computing pipeline and enriched with biological knowledge from the BioGrid database. The NMTF decomposes data into genotype and phenotype embedding spaces, enabling comparison of genotype-phenotype coupling matrices across conditions to identify impaired regulatory pathways. Applying this pipeline to the iPSC derived neurons, reveals both common and mutation-specific disease mechanisms that extend our knowledge about PD development and progression beyond the classical differentially expressed gene analysis.
