Machine learning-based image analysis of Parkinson's disease iPS-derived neurons predicts genotype and reveals mitochondria-lysosome abnormalities
Li, Y.; Powell, M.; Chedid, J.; Sutharsan, R.; Garrido, A. L.; Abu-Bonsrah, D.; Pavan, C.; Fraser, T.; Ovchinnikov, D.; Zhong, M.; Davis, R.; Strbenac, D.; Johnston, J. A.; Thompson, L. H.; Kirik, D.; Parish, C. L.; Halliday, G. M.; Sue, C. M.; Dzamko, N.; Wali, G.
Show abstract
Mitochondrial and lysosomal dysfunction are central features of Parkinsons disease (PD) across major genetic forms including PRKN, SNCA, and LRRK2. We applied cell morphomics, a machine-learning-based framework combining high-content imaging with quantitative feature extraction, to analyse mitochondrial and lysosomal morphology at single-cell resolution in iPS cell-derived cortical neurons from PD patients and healthy controls (13 lines total). Supervised machine-learning models distinguished PD neurons from controls with high accuracy (AUC = 0.87) and reliably separated individual genotypes. Feature importance and attribution analysis revealed genotype-specific organelle biases, with mitochondrial features dominating classification in PRKN neurons, balanced mitochondrial and lysosomal contributions in SNCA neurons, and a greater lysosomal contribution in LRRK2 neurons. Multi-class models retained strong performance, and findings were reproduced across two independent laboratories using different dyes and imaging conditions. These results demonstrate that morphomics provides a robust and scalable framework to quantify genotype-specific organelle abnormalities in PD neurons and supports its application for cellular stratification and biomarker discovery.
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