Speaker
Description
The lack of screenable phenotypes in scalable cell models has limited progress in drug discovery and early diagnostics for complex diseases. Here we present a novel unbiased phenotypic profiling platform that combines high-throughput cell culture automation, Cell Painting, and deep learning. We built various models to extract meaningful features at single cell level, including deep learning embeddings, and fixed measurements extracted using our in-house tool ScaleFExSM. Using these features, we leveraged different aggregation levels to highlight phenotypes hidden by cell and donor variation as well as other known confounders. Cells were then characterized and phenotyped to deliver interpretable outputs. We applied our platform to primary fibroblasts and iPSC-derived neurons from large cohorts of disease-affected donors and carefully matched controls. The pipeline was also used to characterize drug shifts and their effects on diseases of interest to see whether they had a beneficial effect on the affected cells. Combined with the large cell line repository available at NYSCF, the presented platform holds great potential to uncover morphological signatures of different diseases and conditions to advance precision drug discovery.
| Authors | Bianca Migliori*,Jeff Winchell, Gabriel Comolet, Neeloy Bose, Alyssa Duren-Lubanski,Tomasz Rusielewicz, Frederick Monsma,Daniel Paull |
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| Keywords | High Content Imaging, cell imaging at scale, CellPainting, Artificial Intelligence, Computer Vision, fluorescence tags, drug discovery, disease profiling |
