Integrating deep learning and unbiased automated high-content screening to identify complex disease signatures in human fibroblasts

Drug discovery for diseases such as Parkinson9s disease (PD) are impeded by the lack of screenable cellular phenotypes. We present a novel, unbiased phenotypic profiling platform that combines automated cell culture, high-content imaging, Cell Painting, and deep learning. We applied this platform to primary fibroblasts from 91 PD patients and matched healthy controls, creating the largest publicly available Cell Painting image dataset to date at 48 terabytes. Using fixed weights from a convolutional deep neural network trained on ImageNet, we generated deep embeddings from each image and trained machine learning models to detect morphological disease phenotypes. Our platform9s robustness and sensitivity allowed the detection of individual-specific variation with high fidelity, across batches and plate layouts. Lastly, our models confidently separated LRRK2 and sporadic PD lines from healthy controls (ROC AUC 0.79 (0.08 standard deviation (SD))) supporting the capacity of this platform for complex disease modeling and drug screening applications.