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

Srinivasan Sivanandan¹, Bobby Leitmann¹, Eric Lubeck¹, Mohammad Muneeb Sultan¹, Panagiotis Stanitsas¹, Navpreet Ranu¹, Alexis Ewer¹, Jordan E Mancuso¹, Zachary F Phillips¹, Albert Kim¹, John W Bisognano¹, John Cesarek¹, Fiorella Ruggiu¹, David Feldman¹, Daphne Koller², Eilon Sharon³, Ajamete Kaykas⁴, Max R Salick⁵, Ci Chu^6,7

¹Insitro Inc, South San Francisco, CA, USA.

²Insitro Inc, South San Francisco, CA, USA. daphne@insitro.com.

³Insitro Inc, South San Francisco, CA, USA. eilon@insitro.com.

⁴Insitro Inc, South San Francisco, CA, USA. akaykas@insitro.com.

⁵Insitro Inc, South San Francisco, CA, USA. max@insitro.com.

⁶Insitro Inc, South San Francisco, CA, USA. chuci393@gmail.com.

⁷Xaira Therapeutics, South San Francisco, CA, USA. chuci393@gmail.com.

PubMed PMC DOI PDF

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 implementation…

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