A pooled Cell Painting CRISPR screening platform enables de novo inference of gene function by self-supervised deep learning.

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

SUBMITTER: Sivanandan S 

PROVIDER: S-EPMC12770385 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature