Organellomics: AI-driven deep organellar phenotyping reveals novel ALS mechanisms in human neurons
Krispin, S.; van Zuiden, W.; Danino, Y. M.; Molitor, L.; Rudberg, N.; Bar, C.; Coyne, A.; Meimoun, T.; Waldron, F. M.; Gregory, J. M.; Fisher, T.; Nachshon, A.; Stern-Ginossar, N.; Yacovzada, N. S.; Hornstein, E.
Show abstract
Systematic assessment of organelle architectures, termed the organellome, offers valuable insights into cellular states and pathomechanisms, but remains largely uncharted. Here, we present a deep phenotypic learning based on vision transformers, resulting in the Neuronal Organellomics Vision Atlas (NOVA) model that studies confocal images of more than 30 markers of distinct membrane-bound and membraneless organelles in 11.5 million images of human neurons. Organellomics analysis quantifies perturbation-induced changes in organelle localization and morphology using a rigorous mixed-effects meta-analytic framework that accounts for sampling variance and experimental heterogeneity. Applying this approach, we delineate phenotypic alterations in neurons carrying ALS-associated mutations and uncover a physical and functional crosstalk between cytoplasmic mislocalized TDP-43, a hallmark of ALS, and processing bodies (P-bodies), membraneless organelles regulating mRNA stability. These findings are validated in patient-derived neurons and human neuropathology. NOVA establishes a scalable framework for quantitative mapping of subcellular phenotypes and provides a new avenue for investigating the neurocellular basis of disease.
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