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  • Primer
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High-content CRISPR screening

Nature Reviews Methods Primers volume 2, Article number: 8 (2022) Cite this article

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Abstract

CRISPR screens are a powerful source of biological discovery, enabling the unbiased interrogation of gene function in a wide range of applications and species. In pooled CRISPR screens, various genetically encoded perturbations are introduced into pools of cells. The targeted cells proliferate under a biological challenge such as cell competition, drug treatment or viral infection. Subsequently, the perturbation-induced effects are evaluated by sequencing-based counting of the guide RNAs that specify each perturbation. The typical results of such screens are ranked lists of genes that confer sensitivity or resistance to the biological challenge of interest. Contributing to the broad utility of CRISPR screens, adaptations of the core CRISPR technology make it possible to activate, silence or otherwise manipulate the target genes. Moreover, high-content read-outs such as single-cell RNA sequencing and spatial imaging help characterize screened cells with unprecedented detail. Dedicated software tools facilitate bioinformatic analysis and enhance reproducibility. CRISPR screening has unravelled various molecular mechanisms in basic biology, medical genetics, cancer research, immunology, infectious diseases, microbiology and other fields. This Primer describes the basic and advanced concepts of CRISPR screening and its application as a flexible and reliable method for biological discovery, biomedical research and drug development — with a special emphasis on high-content methods that make it possible to obtain detailed biological insights directly as part of the screen.

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Fig. 1: Experimental design for CRISPR screening.
Fig. 2: Preparation of CRISPR gRNA libraries.
Fig. 3: CRISPR-mediated perturbation of cells.
Fig. 4: CRISPR screening with high-content read-out.
Fig. 5: Bioinformatic analysis of CRISPR screening data.
Fig. 6: Applications of CRISPR screening.
Fig. 7: Applications of CRISPR screening in diverse microorganisms.

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Acknowledgements

The authors thank D. Seruggia for critical reading of the manuscript. C.B. is supported by a European Research Council (ERC) Starting Grant (no. 679146) and Consolidator Grant (no. 101001971) of the European Union’s Horizon 2020 Research and Innovation Programme. G.S. and L.S.Q. are supported by the Li Ka Shing Foundation, the US NIH Common Fund 4D Nucleome Program (U01 EB021240) and a US National Science Foundation CAREER award (award no. 2046650; L.S.Q.). R.S.S. is supported by a Canadian Institutes of Health Research (CIHR) Project Grant (PJT 162195). J.S., J.S.W. and X.Z. are Howard Hughes Medical Institute investigators.

Author information

Authors and Affiliations

  1. CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria

    Christoph Bock & Paul Datlinger

  2. Institute of Artificial Intelligence, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria

    Christoph Bock

  3. Department of Genome Sciences, University of Washington, Seattle, WA, USA

    Florence Chardon & Jay Shendure

  4. Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK

    Matthew A. Coelho & Mathew Garnett

  5. Department of Genetics, Yale University School of Medicine, New Haven, CT, USA

    Matthew B. Dong & Sidi Chen

  6. Systems Biology Institute, Yale University, West Haven, CT, USA

    Matthew B. Dong & Sidi Chen

  7. Center for Cancer Systems Biology, Yale University, West Haven, CT, USA

    Matthew B. Dong & Sidi Chen

  8. Donnelly Centre, University of Toronto, Toronto, Ontario, Canada

    Keith A. Lawson & Jason Moffat

  9. Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada

    Keith A. Lawson & Jason Moffat

  10. Howard Hughes Medical Institute, Harvard University, Cambridge, MA, USA

    Tian Lu & Xiaowei Zhuang

  11. Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA

    Tian Lu & Xiaowei Zhuang

  12. Department of Physics, Harvard University, Cambridge, MA, USA

    Tian Lu & Xiaowei Zhuang

  13. Department of Molecular and Cellular Biology, University of Guelph, Guelph, Ontario, Canada

    Laetitia Maroc & Rebecca S. Shapiro

  14. Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA

    Thomas M. Norman & Jonathan S. Weissman

  15. Howard Hughes Medical Institute, University of California, San Francisco, CA, USA

    Thomas M. Norman & Jonathan S. Weissman

  16. Program for Computational and Systems Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Thomas M. Norman

  17. Center for Genetic Medicine Research, Children’s National Hospital, Washington, DC, USA

    Bicna Song & Wei Li

  18. Department of Genomics and Precision Medicine, George Washington University, Washington, DC, USA

    Bicna Song & Wei Li

  19. Department of Bioengineering, Stanford University, Stanford, CA, USA

    Geoff Stanley & Lei S. Qi

  20. Institute for Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada

    Jason Moffat

  21. Department of Chemical and Systems Biology, Stanford University, Stanford, CA, USA

    Lei S. Qi

  22. ChEM-H, Stanford University, Stanford, CA, USA

    Lei S. Qi

  23. Brotman Baty Institute for Precision Medicine, Seattle, WA, USA

    Jay Shendure

  24. Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA

    Jay Shendure

  25. Whitehead Institute for Biomedical Research, Cambridge, MA, USA

    Jonathan S. Weissman

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  1. Christoph Bock
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  2. Paul Datlinger
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  3. Florence Chardon
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  20. Xiaowei Zhuang
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Contributions

Introduction (C.B.); Experimentation (C.B., P.D., F.C., M.A.C., M.B.D., K.A.L., T.L., T.M.N., G.S., S.C., M.G., J.M., L.S.Q., J.S., J.S.W. and X.Z.); Results (C.B., B.S., G.S., W.L. and L.S.Q.); Applications (C.B., F.C., M.A.C., M.B.D., K.A.L., L.M., T.M.N., S.C., M.G., J.M., R.S.S., J.S. and J.S.W.); Reproducibility and data deposition (C.B., B.S., G.S., W.L. and L.S.Q.); Limitations and optimizations (C.B.); Outlook (C.B.); Figures (C.B., P.D., L.M., B.S., W.L. and R.S.S.); Overview of the Primer (C.B.); Editing (all authors).

Corresponding author

Correspondence to Christoph Bock.

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Competing interests

C.B. is a co-founder and scientific advisor of Aelian Biotechnology and Neurolentech. S.C. is a co-founder of EvolveImmune Therapeutics and Cellinfinity Bio. M.G. has performed consultancy for Sanofi, receives research funding from AstraZeneca and GlaxoSmithKline, and is a co-founder of Mosaic Therapeutics. J.M. is a shareholder of Northern Biologics and Pionyr Immunotherapeutics, and a scientific advisor and shareholder of Century Therapeutics and Aelian Biotechnology. L.S.Q. is a co-founder and scientific advisor of Epicrispr Biotechnologies and Refuge Biotechnologies. J.S. is a scientific advisor of Maze Therapeutics, Camp4 Therapeutics, Cajal Biosciences, Adaptive Biotechnologies and Guardant Health, and a co-founder of Scale Bio and Phase Genomics. J.S.W. consults for and holds equity in KSQ Therapeutics, Maze Therapeutics and Tenaya Therapeutics, is a venture partner at 5AM Ventures and is a member of the Amgen Scientific Advisory Board. X.Z. is a co-founder and consultant of Vizgen. The other authors declare no competing interests.

Additional information

Peer review information

Nature Reviews Methods Primers thanks Sabrina D’Agosto, Francesco Iorio and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Related links

Addgene: https://www.addgene.org/

DepMap: https://depmap.org/

EBI ArrayExpress: https://www.ebi.ac.uk/arrayexpress/

EBI European Genome-phenome Archive (EGA): https://ega-archive.org/

International Nucleotide Sequence Database Collaboration: https://www.insdc.org/

NCBI database of Genotypes and Phenotypes (dbGAP): https://www.ncbi.nlm.nih.gov/gap/

NCBI Gene Expression Omnibus (GEO): https://www.ncbi.nlm.nih.gov/geo/

Zenodo: https://zenodo.org/

Glossary

Forward genetics

Screening approach in which genes involved in the phenotype of interest are identified by screening genetically perturbed cells.

Pooled CRISPR screen

A technique in which genetically encoded perturbations are introduced in bulk and read out with sequencing or imaging technology.

Arrayed CRISPR screens

A technique in which perturbations are introduced in individual reaction compartments and remain physically separated.

High-content CRISPR screens

Screens combining complex models, perturbations and stimuli with data-rich read-outs.

Biological replicates

Separately conducted repetitions of the same CRISPR screen using cells from different individuals or different passages of a cell line

Coverage

The average number of cells per guide RNA (gRNA) in a CRISPR screen.

Multiplicity of infection

(MOI). The average number of virions (and, by extension, guide RNAs (gRNAs)) delivered per cell during infection.

Genetic interactions

The combined effect of the simultaneous perturbation of several genes, which may deviate from the sum of the individual effects.

Positive selection screens

Also known as enrichment screens. Cells with the phenotype of interest are selected (enriched) in the screens; other cells are depleted.

Negative selection screens

Also known as dropout screens. Cells with the phenotype of interest are depleted in the screen; other cells are maintained.

Effective dose

The intensity of a perturbation (such as a drug or virus) that causes an effect (such as cell death) in a specified percentage of cells.

Screening hits

Target genes identified as being associated with the phenotype of interest in a CRISPR screen.

scCRISPR-seq

An umbrella term for a group of methods that combine pooled CRISPR sequencing with a single-cell sequencing read-out.

Manifold learning

A set of machine learning methods that seek to uncover hidden structures in the data through dimensionality reduction.

Variants of uncertain significance

Genetic variants for which there is insufficient genetic evidence to support or exclude a causal phenotypic effect.

Saturation genome editing

Introduction of many genome edits into a gene or regulatory element, with the goal of comprehensively assessing their phenotypic impact.

Synthetic lethality

Special case of a genetic interaction in which knockouts of two genes are individually tolerated, but their combination is lethal to cells.

False positives

Putative CRISPR screening hits that do not validate, which can be caused by technical biases.

False negatives

Potential CRISPR screening hits that would have validated but were missed, which can be caused by suboptimal screening conditions.

Population bottlenecks

Reductions in the genetic diversity among a pool of cells owing to external events.

Genetic drift

Genetic changes over time in a pool of cells, caused by random and uncontrolled events.

Unique molecular identifiers

Short barcodes that uniquely identify individual DNA or RNA molecules.

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Bock, C., Datlinger, P., Chardon, F. et al. High-content CRISPR screening. Nat Rev Methods Primers 2, 8 (2022). https://doi.org/10.1038/s43586-021-00093-4

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