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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月16日

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  • Srinivasan Sivanandan,
  • Bobby Leitmann,
  • Eric Lubeck,
  • Mohammad Muneeb Sultan,
  • Panagiotis Stanitsas,
  • Navpreet Ranu,
  • Alexis Ewer,
  • Jordan E. Mancuso,
  • Zachary F. Phillips,
  • Albert Kim,
  • John W. Bisognano,
  • John Cesarek,
  • Fiorella Ruggiu,
  • David Feldman,
  • Daphne Koller,
  • Eilon Sharon,
  • Ajamete Kaykas,
  • Max R. Salick,
  • Ci Chu

摘要

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.