A pooled Cell Painting CRISPR screening platform enables de novo inference of gene function by self-supervised deep learning
Srinivasan Sivanandan, Bobby Leitmann, Eric Lubeck, Mohammad M. Sultan, Panagiotis Stanitsas, Navpreet Ranu, Alexis Ewer, Jordan E. Mancuso, Zachary F. Phillips, Albert M. Kim, John W. Bisognano, John Cesarek, Fiorella Ruggiu, David Feldman, Daphne Koller, Eilon Sharon, Ajamete Kaykas, Max R. Salick, Ci Chu
Abstract
Pooled CRISPR screening enables large-scale interrogation of gene functions but typically measures simple phenotypes such as fitness. High-content methods like Perturb-seq extend dimensionality to transcriptomics but are costly and limited in scope. Optical pooled screening (OPS) combines pooled CRISPR screening with imaging to yield scalable, information-rich readouts, yet existing implementations remain pathway-specific. Here we describe an OPS-compatible Cell Painting platform that enables hypothesis-free reverse genetic screening through multiplexed morphological profiling. We validate this technique using a well-defined morphological gene set, compare classical image analysis to self-supervised learning methods using a mechanism-of-action library, and perform discovery screening with a druggable genome library. By combining rich morphological data with deep learning, gene networks emerge without the need for target-specific biomarkers, leading to unbiased discovery of gene functions. Sivanandan, Leitmann, and colleagues present the CellPaint-POSH platform, which combines pooled CRISPR screening with Cell Painting. Using self-supervised deep learning on cell images, the method enables discovery of gene function and biological networks.