ArticleNature communications2025
A pooled Cell Painting CRISPR screening platform enables de novo inference of gene function by self-supervised deep learning.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- High-Content CRISPR Screening: Methods and Applications.MedComm · 2026Review
- Artificial intelligence in drug discovery - what it is, where we stand and the path forward.Nature reviews. Drug discovery · 2026Review
- Image-based, pooled phenotyping reveals multidimensional, disease-specific variant effects.Cell · 2026Article
- Brieflow: an integrated computational pipeline for high-throughput analysis of optical pooled screening data.Nature communications · 2026Article
- Progress and new challenges in image-based profiling.Molecular systems biology · 2026Review
- A self-supervised machine learning pipeline for extracting information from live cell images at multiple doses and timepoints.Scientific reports · 2026Article
- VIP-OT: Dissecting Single-Cell Biochemical State Dynamics under Perturbation via Vibrational Painting and Optimal Transport.bioRxiv : the preprint server for biology · 2025Article
- Cell-DINO: Self-supervised image-based embeddings for cell fluorescent microscopy.PLoS computational biology · 2025Article
- Article
- NIS-Seq enables cell-type-agnostic optical perturbation screening.Nature biotechnology · 2025Article
- Perturbomics: CRISPR-Cas screening-based functional genomics approach for drug target discovery.Experimental & molecular medicine · 2025Review
- CellPHIE: Integrating Pathway Discovery With Pooled Profiling of Perturbations Uncovers Pathways of Huntington's Disease, Including Genetic Modifiers of Neuronal Development and Morphology.bioRxiv : the preprint server for biology · 2025Article
- Article
- Self-supervision advances morphological profiling by unlocking powerful image representations.Scientific reports · 2025Article
- Integrating Biological Knowledge for Robust Microscopy Image Profiling onProceedings. IEEE International Conference on Computer Vision · 2025Article
- Spatial omics advances for in situ RNA biology.Molecular cell · 2024Review
- Building, benchmarking, and exploring perturbative maps of transcriptional and morphological data.PLoS computational biology · 2024Article
- Answering open questions in biology using spatial genomics and structured methods.BMC bioinformatics · 2024Review
- Mantis: High-throughput 4D imaging and analysis of the molecular and physical architecture of cells.PNAS nexus · 2024Article
- Mantis: high-throughput 4D imaging and analysis of the molecular and physical architecture of cells.bioRxiv : the preprint server for biology · 2024Article
Corrections and comments
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Authors and funding
19 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.