Journal
NATURE METHODS
Volume 17, Issue 6, Pages 636-+Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41592-020-0826-8
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Funding
- National Science Foundation
- Ruth L. Kirschstein Institutional National Research Award from the National Institute for General Medical Sciences [T32 GM008666]
- Ruth L. Kirschstein F31 National Research Service Award [F31 CA206233, F31 CA217173]
- Cancer Systems Biology Training Program [P50 GM085764, U54 CA209891]
- National Human Genome Research Institute [K99HG009530]
- NIH [EY024556, NS103172, HG004659]
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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. 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.
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