Image-based pooled whole-genome CRISPRi screening for subcellular phenotypes

Gil Kanfer, Shireen A. Sarraf, Yaakov Maman, Heather Baldwin, Eunice Dominguez-Martin, Kory R. Johnson, Michael E. Ward, Martin Kampmann, Jennifer Lippincott-Schwartz, Richard J. Youle

Research output: Contribution to journalArticlepeer-review

39 Scopus citations

Abstract

Genome-wide CRISPR screens have transformed our ability to systematically interrogate human gene function, but are currently limited to a subset of cellular phenotypes. We report a novel pooled screening approach for a wider range of cellular and subtle subcellular phenotypes. Machine learning and convolutional neural network models are trained on the subcellular phenotype to be queried. Genome-wide screening then utilizes cells stably expressing dCas9-KRAB (CRISPRi), photoactivatable fluorescent protein (PA-mCherry), and a lentiviral guide RNA (gRNA) pool. Cells are screened by using microscopy and classified by artificial intelligence (AI) algorithms, which precisely identify the genetically altered phenotype. Cells with the phenotype of interest are photoactivated and isolated via flow cytometry, and the gRNAs are identified by sequencing. A proof-of-concept screen accurately identified PINK1 as essential for Parkin recruitment to mitochondria. A genome-wide screen identified factors mediating TFEB relocation from the nucleus to the cytosol upon prolonged starvation. Twenty-one of the 64 hits called by the neural network model were independently validated, revealing new effectors of TFEB subcellular localization. This approach, AI-photoswitchable screening (AI-PS), offers a novel screening platform capable of classifying a broad range of mammalian subcellular morphologies, an approach largely unattainable with current methodologies at genome-wide scale.

Original languageEnglish
Article numbere202006180
JournalJournal of Cell Biology
Volume220
Issue number2
DOIs
StatePublished - 1 Feb 2021

Bibliographical note

Publisher Copyright:
© 2021 Kanfer et al. This article is distributed under the terms of an Attribution-Noncommercial-Share Alike-No Mirror Sites license for the first six months after the publication date (see http://www.rupress.org/terms/). After six months it is available under a Creative Commons License (Attribution-Noncommercial-Share Alike 4.0 International license, as described at https://creativecommons.org/licenses/by-nc-sa/4.0/).

Funding

We thank Nico Tjandra for intellectual contributions. We thank Nick Ader, Eric Bunker, Elyssa Hawk, and Sue Smith for helping with cloning, cell lines, and Lentivirus production. We thank Catherine Nezich, Hetal Shah, Jose Norbert Vargas, and Benoit Kornmann for comments on the manuscript, and all the Youle laboratory and the Lippincott-Schwartz laboratory members for critical comments. We thank Talya Chooly for supporting the project. Flow cytometry cell sorting and sample isolation was performed at the Flow Cytometry Core, National Heart, Lung, and Blood Institute. Next-generation deep sequencing was performed at the CCR Genomics Core, National Cancer Institute. We thank the National Institutes of Health (NIH)-based Nikon team for helping with imaging and integration of our external codes. This work used the computational resources of the NIH HPC Biowulf cluster (https://hpc.nih.gov). This work was supported by the National Institute of Neurological Disorders and Stroke intramural program, and by National Institutes of Health, National Institute of General Medical Sciences grant DP2 GM119139 (to M. Kampmann). This work was supported by the National Institute of Neurological Disorders and Stroke intramural program, and by National Institutes of Health, National Institute of General Medical Sciences grant DP2 GM119139 (to M. Kampmann).

FundersFunder number
National Institutes of Health
National Heart, Lung, and Blood Institute
National Cancer Institute
National Institute of General Medical SciencesDP2 GM119139
National Institute of Neurological Disorders and StrokeZIANS003155

    Fingerprint

    Dive into the research topics of 'Image-based pooled whole-genome CRISPRi screening for subcellular phenotypes'. Together they form a unique fingerprint.

    Cite this