Evidence map›Paper›PMID 32393832›Full record

ArticleNature methods2020

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

Emily C Wheeler, Anthony Q Vu, Jaclyn M Einstein, Matthew DiSalvo, Noorsher Ahmed, Eric L Van Nostrand, Alexander A Shishkin, Wenhao Jin, Nancy L Allbritton, Gene W Yeo

Open access · greenAbstract read
In one paragraph

Article in Nature methods, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers.

0numbers the graph read from it
0cells of the map it votes in
64citing papers in PubMed
4.9field-weighted citation impact, top 4% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

64 citing papers in PubMed, 91 citations in OpenAlex.

  1. Review
  2. A pooled image-based CRISPR screen identifies EAF1 as abioRxiv : the preprint server for biology · 2026
    Article
  3. Review
  4. Article
  5. Review
  6. TSC2 is a stress granule suppressor.bioRxiv : the preprint server for biology · 2026
    Article
  7. Review
  8. Article
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4 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 2 institutions in 1 country.

Emily C Wheeler *Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA.
Anthony Q Vu *Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA.ORCID http://orcid.org/0000-0001-8922-6409
Jaclyn M EinsteinDepartment of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA.
Matthew DiSalvoJoint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill and Raleigh, NC, USA.
Noorsher AhmedDepartment of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA.ORCID http://orcid.org/0000-0003-1701-3994
Eric L Van NostrandDepartment of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA.
Alexander A ShishkinDepartment of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA.
Wenhao JinDepartment of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA.
Nancy L AllbrittonJoint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill and Raleigh, NC, USA.
Gene W YeoDepartment of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA. geneyeo@ucsd.edu.
University of California San Diego · USUniversity of North Carolina at Chapel Hill · US

Funding

SYNTHETIC SYSTEMSP50GM085764 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI WINZELER, ELIZABETH A · 2010 to 2018
$23.3M
Functional RNA elements in the human genomeR01HG004659 · NHGRI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Kevin Daniel Corbett, Eugene Wei-Ming Yeo · 2008 to 2026
$12.4M
Using Networks to Seed Hierarchical Whole-cell Models of CancerU54CA209891 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI KROGAN, NEVAN J · 2017 to 2021
$10.9M
Training Program in Basic Clinical GeneticsT32GM008666 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI HAMILTON, BRUCE A · 1998 to 2021
$8.6M
Therapeutic strategies for microsatellite expansion diseases using RNA-targeting CRISPR/CasR01NS103172 · NINDS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI MAURICE SCOTT SWANSON, Eugene Wei-Ming Yeo · 2017 to 2026
$5.6M
Generation of a Gene-Targeted Human iPS Cell Library for Macular DegenerationR01EY024556 · NEI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI ALLBRITTON, NANCY L. · 2014 to 2018
$2.9M
UC San Diego Genetics Training ProgramT32GM145427 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI BRUCE A HAMILTON · 2022 to 2026
$2.6M
Robust, high-throughput identification of RNA processing regulators and regulatory networks genome-wideK99HG009530 · NHGRI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI VAN NOSTRAND, ERIC LYMAN · 2017 to 2018
$250k
Identification of RNA binding proteins that control Myc-dependent survival in cancer.F31CA217173 · NCI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI EINSTEIN, JACLYN M · 2018 to 2019
$76k
High-Throughput Generation of Pancreatic Organoids with Controlled Stromal Milieus using Microraft-Based Cell SortingF31CA206233 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI DISALVO, MATTHEW · 2016 to 2017
$67k
NCI NIH HHS F31 CA206233NCI NIH HHS F31 CA217173NCI NIH HHS U54 CA209891NEI NIH HHS R01 EY024556NHGRI NIH HHS K99 HG009530NHGRI NIH HHS R01 HG004659NIGMS NIH HHS P50 GM085764NIGMS NIH HHS T32 GM008666NIGMS NIH HHS T32 GM145427NINDS NIH HHS R01 NS103172
6 · The paper itself

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.

Indexed as

Clustered Regularly Interspaced Short Palindromic RepeatsCRISPR-Cas SystemsCytoplasmHumansMachine LearningMicroscopy, ConfocalOxidative StressProtein AggregatesRNA-Binding ProteinsRNA, Guide, CRISPR-Cas SystemsTissue Array AnalysisProtein AggregatesRNA-Binding ProteinsRNA, Guide, CRISPR-Cas Systems

Identifiers

PMID32393832
PMCPMC7357298
OpenAlexW3023985995

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.