Pooled CRISPR screens with imaging on microraft arrays reveals stress granule-regulatory factors

• Published in: Nature methods Vol. 17; no. 6; pp. 636 - 642
• Main Authors: Wheeler, Emily C., Vu, Anthony Q., Einstein, Jaclyn M., DiSalvo, Matthew, Ahmed, Noorsher, Van Nostrand, Eric L., Shishkin, Alexander A., Jin, Wenhao, Allbritton, Nancy L., Yeo, Gene W.
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.06.2020; Nature Publishing Group
• Subjects: 631/208; 631/61; 631/61/191; 631/80; 631/80/2373; Arrays; Automation; Binding proteins; Bioinformatics; Biological Microscopy; Biological Techniques; Biomedical and Life Sciences; Biomedical Engineering/Biotechnology; Clustered Regularly Interspaced Short Palindromic Repeats - genetics; Crafts; CRISPR; CRISPR-Cas Systems - genetics; Cytology; Cytoplasm - metabolism; Genetic screening; Granular materials; gRNA; Humans; Imaging; Learning algorithms; Life Sciences; Machine Learning; Mechanization; Microscopy, Confocal - methods; Morphology; Oxidative Stress - genetics; Phenotypes; Phenotyping; Protein Aggregates - genetics; Protein binding; Proteins; Proteomics; Ribonucleic acid; RNA; RNA, Guide, CRISPR-Cas Systems - genetics; RNA-binding protein; RNA-Binding Proteins - genetics; Tissue Array Analysis - methods
• ISSN: 1548-7091; 1548-7105; 1548-7105
• DOI: 10.1038/s41592-020-0826-8

Genetic screens using pooled CRISPR-based approaches are scalable and inexpensive, but restricted to standard readouts, including survival, proliferation and sortable markers. However, many biologically relevant cell states involve cellular and subcellular changes that are only accessible by microscopic visualization, and are currently impossible to screen with pooled methods. Here we combine pooled CRISPR–Cas9 screening with microraft array technology and high-content imaging to screen image-based phenotypes (CRaft-ID; CRISPR-based microRaft followed by guide RNA identification). By isolating microrafts that contain genetic clones harboring individual guide RNAs (gRNA), we identify RNA-binding proteins (RBPs) that influence the formation of stress granules, the punctate protein–RNA assemblies that form during stress. To automate hit identification, we developed a machine-learning model trained on nuclear morphology to remove unhealthy cells or imaging artifacts. In doing so, we identified and validated previously uncharacterized RBPs that modulate stress granule abundance, highlighting the applicability of our approach to facilitate image-based pooled CRISPR screens. CRISPR-based microraft followed by guide RNA identification (CRaft-ID) combines microraft arrays, microscopy and CRISPR–Cas9 technology for high-content image-based phenotyping. CRaft-ID was used to identify proteins involved in stress granule formation.