Derivation, Characterization, and Neural Differentiation of Integration-Free Induced Pluripotent Stem Cell Lines from Parkinson's Disease Patients Carrying SNCA, LRRK2, PARK2, and GBA Mutations.

• Main Authors: Olga Momcilovic, Renuka Sivapatham, Tal Ronnen Oron, Morten Meyer, Sean Mooney, Mahendra S Rao, Xianmin Zeng
• Format: Article
• Language: English
• Published: Public Library of Science (PLoS) 2016-01-01
• Series: PLoS ONE
• Online Access: http://europepmc.org/articles/PMC4871453?pdf=render

We report generation of induced pluripotent stem cell (iPSC) lines from ten Parkinson's disease (PD) patients carrying SNCA, PARK2, LRRK2, and GBA mutations, and one age-matched control. After validation of pluripotency, long-term genome stability, and integration-free reprogramming, eight of these lines (one of each SNCA, LRRK2 and GBA, four PARK2 lines, and the control) were differentiated into neural stem cells (NSC) and subsequently to dopaminergic cultures. We did not observe significant differences in the timeline of neural induction and NSC derivation between the patient and control line, nor amongst the patient lines, although we report considerable variability in the efficiency of dopaminergic differentiation among patient lines. We performed whole genome expression analyses of the lines at each stage of differentiation (fibroblast, iPSC, NSC, and dopaminergic culture) in an attempt to identify alterations by large-scale evaluation. While gene expression profiling clearly distinguished cells at different stages of differentiation, no mutation-specific clustering or difference was observed, though consistent changes in patient lines were detected in genes associated mitochondrial biology. We further examined gene expression in a stress model (MPTP-induced dopaminergic neuronal death) using two clones from the SNCA triplication line, and detected changes in genes associated with mitophagy. Our data suggested that even a well-characterized line of a monogenic disease may not be sufficient to determine the cause or mechanism of the disease, and highlights the need to use more focused strategies for large-scale data analysis.