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

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 microsc...

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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
Online Access	Get full text
ISSN	1548-7091 1548-7105 1548-7105
DOI	10.1038/s41592-020-0826-8

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Abstract	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.
AbstractList	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. 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.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. 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. 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 gRNA Identification). By isolating microRafts that contain genetic clones harboring individual guide RNAs, we identify RNA binding proteins (RBPs) that influence the formation of stress granules, 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. 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.
Audience	Academic
Author	Allbritton, Nancy L. Einstein, Jaclyn M. Yeo, Gene W. Shishkin, Alexander A. Wheeler, Emily C. DiSalvo, Matthew Ahmed, Noorsher Vu, Anthony Q. Van Nostrand, Eric L. Jin, Wenhao
AuthorAffiliation	2 Institute for Genomic Medicine and UCSD Stem Cell Program, University of California San Diego, La Jolla, California 4 Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 5 Current affiliation: Eclipse BioInnovations, San Diego, California 7 These authors contributed equally 6 Current affiliation: Department of Bioengineering, University of Washington, Seattle, WA 3 Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill and Raleigh, North Carolina 1 Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, California
AuthorAffiliation_xml	– name: 3 Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill and Raleigh, North Carolina – name: 5 Current affiliation: Eclipse BioInnovations, San Diego, California – name: 1 Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, California – name: 6 Current affiliation: Department of Bioengineering, University of Washington, Seattle, WA – name: 4 Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina – name: 7 These authors contributed equally – name: 2 Institute for Genomic Medicine and UCSD Stem Cell Program, University of California San Diego, La Jolla, California
Author_xml	– sequence: 1 givenname: Emily C. surname: Wheeler fullname: Wheeler, Emily C. organization: Department of Cellular and Molecular Medicine, University of California San Diego, Institute for Genomic Medicine and UCSD Stem Cell Program, University of California San Diego – sequence: 2 givenname: Anthony Q. orcidid: 0000-0001-8922-6409 surname: Vu fullname: Vu, Anthony Q. organization: Department of Cellular and Molecular Medicine, University of California San Diego, Institute for Genomic Medicine and UCSD Stem Cell Program, University of California San Diego – sequence: 3 givenname: Jaclyn M. surname: Einstein fullname: Einstein, Jaclyn M. organization: Department of Cellular and Molecular Medicine, University of California San Diego, Institute for Genomic Medicine and UCSD Stem Cell Program, University of California San Diego – sequence: 4 givenname: Matthew surname: DiSalvo fullname: DiSalvo, Matthew organization: Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University – sequence: 5 givenname: Noorsher orcidid: 0000-0003-1701-3994 surname: Ahmed fullname: Ahmed, Noorsher organization: Department of Cellular and Molecular Medicine, University of California San Diego, Institute for Genomic Medicine and UCSD Stem Cell Program, University of California San Diego – sequence: 6 givenname: Eric L. surname: Van Nostrand fullname: Van Nostrand, Eric L. organization: Department of Cellular and Molecular Medicine, University of California San Diego, Institute for Genomic Medicine and UCSD Stem Cell Program, University of California San Diego – sequence: 7 givenname: Alexander A. surname: Shishkin fullname: Shishkin, Alexander A. organization: Department of Cellular and Molecular Medicine, University of California San Diego, Institute for Genomic Medicine and UCSD Stem Cell Program, University of California San Diego, Eclipse BioInnovations – sequence: 8 givenname: Wenhao surname: Jin fullname: Jin, Wenhao organization: Department of Cellular and Molecular Medicine, University of California San Diego, Institute for Genomic Medicine and UCSD Stem Cell Program, University of California San Diego – sequence: 9 givenname: Nancy L. surname: Allbritton fullname: Allbritton, Nancy L. organization: Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Department of Chemistry, University of North Carolina at Chapel Hill, Department of Bioengineering, University of Washington – sequence: 10 givenname: Gene W. surname: Yeo fullname: Yeo, Gene W. email: geneyeo@ucsd.edu organization: Department of Cellular and Molecular Medicine, University of California San Diego, Institute for Genomic Medicine and UCSD Stem Cell Program, University of California San Diego
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/32393832$$D View this record in MEDLINE/PubMed
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Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 E.C.W., A.Q.V., and G.W.Y conceptualized the project; E.L.V. designed the CRISPR library; J.M.E. cloned the CRISPR library and performed viral infections; A.Q.V. optimized cell plating on microRaft arrays; E.C.W. wrote analysis software and performed targeted library prep; M.D. assisted with confocal imaging and fabricated microRaft arrays; A.A.S. and E.L.V. designed the bulk CRISPR library prep method; N.A. and A.Q.V. implemented neural network analysis; W.J. performed PPI analyses; A.Q.V. and E.C.W performed validation experiments; E.C.W, A.Q.V. and G.W.Y. wrote the manuscript; N.L.A. and G.W.Y supervised the project. Author Contributions
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PublicationTitleAbbrev	Nat Methods
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PublicationYear	2020
Publisher	Nature Publishing Group US Nature Publishing Group
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Snippet	Genetic screens using pooled CRISPR-based approaches are scalable and inexpensive, but restricted to standard readouts, including survival, proliferation and... Genetic screens using pooled CRISPR-based approaches are scalable and inexpensive, but restricted to standard readouts including survival, proliferation and...
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SubjectTerms	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
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Title	Pooled CRISPR screens with imaging on microraft arrays reveals stress granule-regulatory factors
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