Organellomics: AI-driven deep organellar phenotyping of human neurons

Molitor, L.; Krispin, S.; Van-Zuiden, W.; Danino, Y. M.; Rudberg, N.; Bar, C.; Amzallag, E.; Lubliner, J.; Siany, A.; Eitan, C.; Cohen, Y.; Yacovzada, N. S.; Hornstein, E.

doi:10.1101/2024.01.31.572110

bioRxiv · 10.1101/2024.01.31.572110

Organellomics: AI-driven deep organellar phenotyping of human neurons

Molitor, L.·Krispin, S.·Van-Zuiden, W.·Danino, Y. M.·Rudberg, N.·Bar, C.·Amzallag, E.·Lubliner, J.·Siany, A.·Eitan, C.·Cohen, Y.·Yacovzada, N. S.·Hornstein, E.

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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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Molitor, L., Krispin, S., Van-Zuiden, W., Danino, Y. M., Rudberg, N., Bar, C., Amzallag, E., Lubliner, J., Siany, A., Eitan, C., Cohen, Y., Yacovzada, N. S., Hornstein, E.. 2024-01-31. Organellomics: AI-driven deep organellar phenotyping of human neurons. https://doi.org/10.1101/2024.01.31.572110

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