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<p>Mitochondrial and lysosomal dysfunction are central features of Parkinson’s disease (PD) across major genetic forms including <i>PRKN</i>, <i>SNCA</i>, and <i>LRRK2</i>. We applied <i>cell morphomics</i>, 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 in the relative contribution of mitochondrial and lysosomal features, with mitochondrial features dominating classification in <i>PRKN</i> neurons, balanced mitochondrial and lysosomal contributions in <i>SNCA</i> neurons, and a greater lysosomal contribution in <i>LRRK2</i> 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.</p>

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Machine learning image analysis predicts Parkinson disease genotype and mitochondrial lysosomal abnormalities in iPS neurons

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月1日

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  • Yan Li,
  • Maximilian Powell,
  • Jessica Chedid,
  • Ratneswary Sutharsan,
  • Adahir Labrador-Garrido,
  • Dad Abu-Bonsrah,
  • Chiara Pavan,
  • Tyra Fraser,
  • Dmitry Ovchinnikov,
  • Melanie Zhong,
  • Ryan Davis,
  • Dario Strbenac,
  • Jennifer A. Johnston,
  • Lachlan H. Thompson,
  • Deniz Kirik,
  • Clare L. Parish,
  • Glenda M. Halliday,
  • Carolyn M. Sue,
  • Nicolas Dzamko,
  • Gautam Wali

摘要

Mitochondrial and lysosomal dysfunction are central features of Parkinson’s 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 in the relative contribution of mitochondrial and lysosomal features, 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.