Pooled CRISPR Screens With Imaging on Microraft Arrays Reveals Stress Granule-Regulatory Factors

Authors

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

Publication Date

6-1-2020

Journal

Nature Methods

DOI

10.1038/s41592-020-0826-8

PMID

32393832

PMCID

PMC7357298

PubMedCentral® Posted Date

11-11-2020

PubMedCentral® Full Text Version

Author MSS

Published Open-Access

yes

Keywords

CRISPR-Cas Systems, Clustered Regularly Interspaced Short Palindromic Repeats, Cytoplasm, Humans, Machine Learning, Microscopy, Confocal, Oxidative Stress, Protein Aggregates, RNA, Guide, CRISPR-Cas Systems, RNA-Binding Proteins, Tissue Array Analysis

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.

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