Abstract
Drug discovery for Parkinson’s disease (PD) is impeded by the lack of screenable phenotypes in scalable cell models. Here we present a novel unbiased phenotypic profiling platform that combines automation, Cell Painting, and deep learning. We applied this platform to primary fibroblasts from 91 PD patients and carefully matched healthy controls, generating the largest publicly available Cell Painting dataset to date. Using fixed weights from a convolutional deep neural network trained on ImageNet, we generated unbiased deep embeddings from each image, and applied these to train machine learning models to detect morphological disease phenotypes. Interestingly, our models captured individual variation by identifying specific cell lines within the cohort with high fidelity, even across different batches and plate layouts, demonstrating platform robustness and sensitivity. Importantly, our models were able to confidently separate LRRK2 and sporadic PD lines from healthy controls (ROC AUC 0.79 (0.08 standard deviation (SD))) supporting the capacity of this platform for PD modeling and drug screening applications.
Competing Interest Statement
B.M., D.C., J.H., E.T., B.F., C.J.H., S.D., S.J., P.F., G.B., J.G., R.O., L.A., A.D., K.R., N.G.S.C.A.T., L.B., R.S.A., E.S., D.P., S.A.N., F.J.M., Jr., and B.J. were employed by NYSCF. Y.C., M.F., S.A., A.G., S.V., A.N., Z.A., B.W., J.K., M.C., E.A.B., O.P., M.B., and S.J.Y. were employed by Google. M.F., A.G., S.V., A.N., Z.A., B.W., J.K., M.C., E.A.B., O.P., M.B., and S.J.Y. own Alphabet stock.
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