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Genome-scale CRISPR-Cas9 knockout and transcriptional activation screening

Nature Protocols volume 12, pages 828863 (2017) | Download Citation

This article has been updated

Abstract

Forward genetic screens are powerful tools for the unbiased discovery and functional characterization of specific genetic elements associated with a phenotype of interest. Recently, the RNA-guided endonuclease Cas9 from the microbial CRISPR (clustered regularly interspaced short palindromic repeats) immune system has been adapted for genome-scale screening by combining Cas9 with pooled guide RNA libraries. Here we describe a protocol for genome-scale knockout and transcriptional activation screening using the CRISPR-Cas9 system. Custom- or ready-made guide RNA libraries are constructed and packaged into lentiviral vectors for delivery into cells for screening. As each screen is unique, we provide guidelines for determining screening parameters and maintaining sufficient coverage. To validate candidate genes identified by the screen, we further describe strategies for confirming the screening phenotype, as well as genetic perturbation, through analysis of indel rate and transcriptional activation. Beginning with library design, a genome-scale screen can be completed in 9–15 weeks, followed by 4–5 weeks of validation.

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Change history

  • 13 April 2017

    In the version of this article initially published, the wrong version of Supplementary Data 4 was provided, and links to the GitHub page hosting the same files (which will provide ongoing access to updated future versions) were omitted. This error and omission have been corrected for the PDF and HTML versions of this article.

  • 22 October 2018

    In the published version of this paper, Step 64 of the Procedure reads, "Refer to Steps 37-39 for NGS analysis of the sgRNA distribution." This step should refer the reader to Steps 35-39. This text has not been corrected in the original paper.

References

  1. 1.

    & The art and design of genetic screens: RNA interference. Nat. Rev. Genet. 9, 554–566 (2008).

  2. 2.

    & High-throughput RNAi screening in cultured cells: a user's guide. Nat. Rev. Genet. 7, 373–384 (2006).

  3. 3.

    , & High-throughput functional genomics using CRISPR-Cas9. Nat. Rev. Genet. 16, 299–311 (2015).

  4. 4.

    et al. Duplexes of 21-nucleotide RNAs mediate RNA interference in cultured mammalian cells. Nature 411, 494–498 (2001).

  5. 5.

    et al. Potent and specific genetic interference by double-stranded RNA in Caenorhabditis elegans. Nature 391, 806–811 (1998).

  6. 6.

    & Gene silencing in mammals by small interfering RNAs. Nat. Rev. Genet. 3, 737–747 (2002).

  7. 7.

    & Mechanisms of gene silencing by double-stranded RNA. Nature 431, 343–349 (2004).

  8. 8.

    et al. A large-scale RNAi screen in human cells identifies new components of the p53 pathway. Nature 428, 431–437 (2004).

  9. 9.

    et al. Genome-wide RNAi analysis of growth and viability in Drosophila cells. Science 303, 832–835 (2004).

  10. 10.

    & Building mammalian signalling pathways with RNAi screens. Nat. Rev. Mol. Cell Biol. 7, 177–187 (2006).

  11. 11.

    et al. A resource for large-scale RNA-interference-based screens in mammals. Nature 428, 427–431 (2004).

  12. 12.

    , , , & Genome-scale loss-of-function screening with a lentiviral RNAi library. Nat. Methods 3, 715–719 (2006).

  13. 13.

    et al. Second-generation shRNA libraries covering the mouse and human genomes. Nat. Genet. 37, 1281–1288 (2005).

  14. 14.

    et al. 3′ UTR seed matches, but not overall identity, are associated with RNAi off-targets. Nat. Methods 3, 199–204 (2006).

  15. 15.

    et al. Expression profiling reveals off-target gene regulation by RNAi. Nat. Biotechnol. 21, 635–637 (2003).

  16. 16.

    & Recognizing and avoiding siRNA off-target effects for target identification and therapeutic application. Nat. Rev. Drug Discov. 9, 57–67 (2010).

  17. 17.

    , & Biology and applications of CRISPR systems: harnessing nature's toolbox for genome engineering. Cell 164, 29–44 (2016).

  18. 18.

    CRISPR-Cas immunity in prokaryotes. Nature 526, 55–61 (2015).

  19. 19.

    , & Development and applications of CRISPR-Cas9 for genome engineering. Cell 157, 1262–1278 (2014).

  20. 20.

    et al. The CRISPR/Cas bacterial immune system cleaves bacteriophage and plasmid DNA. Nature 468, 67–71 (2010).

  21. 21.

    et al. CRISPR RNA maturation by trans-encoded small RNA and host factor RNase III. Nature 471, 602–607 (2011).

  22. 22.

    et al. A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science 337, 816–821 (2012).

  23. 23.

    , , & Cas9-crRNA ribonucleoprotein complex mediates specific DNA cleavage for adaptive immunity in bacteria. Proc. Natl. Acad. Sci. USA 109, E2579–E2586 (2012).

  24. 24.

    et al. Multiplex genome engineering using CRISPR/Cas systems. Science 339, 819–823 (2013).

  25. 25.

    et al. RNA-guided human genome engineering via Cas9. Science 339, 823–826 (2013).

  26. 26.

    , & Introduction of double-strand breaks into the genome of mouse cells by expression of a rare-cutting endonuclease. Mol. Cell Biol. 14, 8096–8106 (1994).

  27. 27.

    et al. Highly efficient Cas9-mediated transcriptional programming. Nat. Methods 12, 326–328 (2015).

  28. 28.

    et al. Genome-scale CRISPR-mediated control of gene repression and activation. Cell 159, 647–661 (2014).

  29. 29.

    et al. CRISPR-mediated modular RNA-guided regulation of transcription in eukaryotes. Cell 154, 442–451 (2013).

  30. 30.

    et al. Genome-scale transcriptional activation by an engineered CRISPR-Cas9 complex. Nature 517, 583–588 (2015).

  31. 31.

    et al. CRISPR RNA-guided activation of endogenous human genes. Nat. Methods 10, 977–979 (2013).

  32. 32.

    et al. CAS9 transcriptional activators for target specificity screening and paired nickases for cooperative genome engineering. Nat. Biotechnol. 31, 833–838 (2013).

  33. 33.

    et al. RNA-guided gene activation by CRISPR-Cas9-based transcription factors. Nat. Methods 10, 973–976 (2013).

  34. 34.

    et al. Repurposing CRISPR as an RNA-guided platform for sequence-specific control of gene expression. Cell 152, 1173–1183 (2013).

  35. 35.

    et al. Optical control of mammalian endogenous transcription and epigenetic states. Nature 500, 472–476 (2013).

  36. 36.

    et al. Comparison of Cas9 activators in multiple species. Nat. Methods 13, 563–567 (2016).

  37. 37.

    et al. Genome-scale CRISPR-Cas9 knockout screening in human cells. Science 343, 84–87 (2014).

  38. 38.

    , , & Genetic screens in human cells using the CRISPR-Cas9 system. Science 343, 80–84 (2014).

  39. 39.

    , , , & Genome-wide recessive genetic screening in mammalian cells with a lentiviral CRISPR-guide RNA library. Nat. Biotechnol. 32, 267–273 (2014).

  40. 40.

    et al. High-throughput screening of a CRISPR/Cas9 library for functional genomics in human cells. Nature 509, 487–491 (2014).

  41. 41.

    et al. Rational design of highly active sgRNAs for CRISPR-Cas9-mediated gene inactivation. Nat. Biotechnol. 32, 1262–1267 (2014).

  42. 42.

    et al. Genome-wide CRISPR screen in a mouse model of tumor growth and metastasis. Cell 160, 1246–1260 (2015).

  43. 43.

    et al. Discovery of cancer drug targets by CRISPR-Cas9 screening of protein domains. Nat. Biotechnol. 33, 661–667 (2015).

  44. 44.

    et al. A genome-wide CRISPR screen in primary immune cells to dissect regulatory networks. Cell 162, 675–686 (2015).

  45. 45.

    et al. Identification and characterization of essential genes in the human genome. Science 350, 1096–1101 (2015).

  46. 46.

    et al. Multiplexed barcoded CRISPR-Cas9 screening enabled by CombiGEM. Proc. Natl. Acad. Sci. USA 113, 2544–2549 (2016).

  47. 47.

    et al. Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nat. Biotechnol. 34, 184–191 (2016).

  48. 48.

    et al. Hypoxia as a therapy for mitochondrial disease. Science 352, 54–61 (2016).

  49. 49.

    et al. Genetic dissection of Flaviviridae host factors through genome-scale CRISPR screens. Nature 535, 159–163 (2016).

  50. 50.

    et al. A CRISPR screen defines a signal peptide processing pathway required by flaviviruses. Nature 535, 164–168 (2016).

  51. 51.

    et al. CRISPR/Cas9 screens reveal requirements for host cell sulfation and fucosylation in bacterial type III secretion system-mediated cytotoxicity. Cell Host Microbe 20, 226–237 (2016).

  52. 52.

    et al. Discovery of a proteinaceous cellular receptor for anorovirus. Science 353, 933–936 (2016).

  53. 53.

    et al. BCL11A enhancer dissection by Cas9-mediated in situ saturating mutagenesis. Nature 527, 192–197 (2015).

  54. 54.

    et al. Functional genetic screens for enhancer elements in the human genome using CRISPR-Cas9. Nat. Biotechnol. 34, 192–198 (2016).

  55. 55.

    et al. A new class of temporarily phenotypic enhancers identified by CRISPR/Cas9-mediated genetic screening. Genome Res. 26, 397–405 (2016).

  56. 56.

    et al. High-resolution interrogation of functional elements in the noncoding genome. Science 353, 1545–1549 (2016).

  57. 57.

    et al. Systematic mapping of functional enhancer-promoter connections with CRISPR interference. Science 354, 769–773 (2016).

  58. 58.

    et al. High-frequency off-target mutagenesis induced by CRISPR-Cas nucleases in human cells. Nat. Biotechnol. 31, 822–826 (2013).

  59. 59.

    et al. DNA targeting specificity of RNA-guided Cas9 nucleases. Nat. Biotechnol. 31, 827–832 (2013).

  60. 60.

    et al. High-throughput profiling of off-target DNA cleavage reveals RNA-programmed Cas9 nuclease specificity. Nat. Biotechnol. 31, 839–843 (2013).

  61. 61.

    et al. CRISPR knockout screening outperforms shRNA and CRISPRi in identifying essential genes. Nat. Biotechnol. 34, 631–633 (2016).

  62. 62.

    et al. CRISPR screens provide a comprehensive assessment of cancer vulnerabilities but generate false-positive hits for highly amplified genomic regions. Cancer Discov. 6, 900–913 (2016).

  63. 63.

    et al. Genomic copy number dictates a gene-independent cell response to CRISPR/Cas9 targeting. Cancer Discov. 6, 914–929 (2016).

  64. 64.

    , , & Systematic comparison of CRISPR/Cas9 and RNAi screens for essential genes. Nat. Biotechnol. 34, 634–636 (2016).

  65. 65.

    et al. High-content genome-wide RNAi screens identify regulators of parkin upstream of mitophagy. Nature 504, 291–295 (2013).

  66. 66.

    et al. A lentiviral RNAi library for human and mouse genes applied to an arrayed viral high-content screen. Cell 124, 1283–1298 (2006).

  67. 67.

    et al. High-throughput RNAi screening by time-lapse imaging of live human cells. Nat. Methods 3, 385–390 (2006).

  68. 68.

    , & Improved vectors and genome-wide libraries for CRISPR screening. Nat. Methods 11, 783–784 (2014).

  69. 69.

    et al. A probability-based approach for the analysis of large-scale RNAi screens. Nat. Methods 4, 847–849 (2007).

  70. 70.

    et al. Highly parallel identification of essential genes in cancer cells. Proc. Natl. Acad. Sci. USA 105, 20380–20385 (2008).

  71. 71.

    et al. MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens. Genome Biol. 15, 554 (2014).

  72. 72.

    et al. Genome engineering using the CRISPR-Cas9 system. Nat. Protoc. 8, 2281–2308 (2013).

  73. 73.

    et al. Double nicking by RNA-guided CRISPR Cas9 for enhanced genome editing specificity. Cell 154, 1380–1389 (2013).

  74. 74.

    et al. Cpf1 is a single RNA-guided endonuclease of a class 2 CRISPR-Cas system. Cell 163, 759–771 (2015).

  75. 75.

    et al. Orthogonal gene knockout and activation with a catalytically active Cas9 nuclease. Nat. Biotechnol. 33, 1159–1161 (2015).

  76. 76.

    et al. Cas9 gRNA engineering for genome editing, activation and repression. Nat. Methods 12, 1051–1054 (2015).

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Acknowledgements

We thank O. Shalem, D.A. Scott and P.D. Hsu for helpful discussions and insights; R. Belliveau for overall research support; R. Macrae for critical reading of the manuscript; and the entire Zhang laboratory for support and advice. O.O.A. was supported by a Paul and Daisy Soros Fellowship, a Friends of the McGovern Institute Fellowship, and the Poitras Center for Affective Disorders. J.S.G. was supported by a DOE Computational Science Graduate Fellowship. F.Z. was supported by the NIH through the National Institute of Mental Health (NIMH; grants 5DP1-MH100706 and 1R01-MH110049), the National Science Foundation (NSF), the Howard Hughes Medical Institute (HHMI), the New York Stem Cell Foundation, the Simons Foundation, the Paul G. Allen Family Foundation, and the Vallee Foundation, and James and Patricia Poitras, Robert Metcalfe, and David Cheng. F.Z. is a New York Stem Cell Foundation-Robertson Investigator. Reagents are available through Addgene; support forums and computational tools are available via the Zhang laboratory website (http://www.genome-engineering.org).

Author information

Author notes

    • Julia Joung
    •  & Silvana Konermann

    These authors contributed equally to this work.

    • Silvana Konermann
    • , Randall J Platt
    •  & Neville E Sanjana

    Present addresses: Salk Institute for Biological Studies, La Jolla, California, USA (S.K.); Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland (R.J.P.); New York Genome Center, New York, New York, USA (N.E.S.); Department of Biology, New York University, New York, New York, USA (N.E.S.).

Affiliations

  1. Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.

    • Julia Joung
    •  & Feng Zhang
  2. Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.

    • Julia Joung
    • , Silvana Konermann
    • , Jonathan S Gootenberg
    • , Omar O Abudayyeh
    • , Randall J Platt
    • , Mark D Brigham
    • , Neville E Sanjana
    •  & Feng Zhang
  3. McGovern Institute for Brain Research at MIT, Cambridge, Massachusetts, USA.

    • Julia Joung
    • , Silvana Konermann
    • , Jonathan S Gootenberg
    • , Omar O Abudayyeh
    • , Randall J Platt
    • , Mark D Brigham
    • , Neville E Sanjana
    •  & Feng Zhang
  4. Department of Brain and Cognitive Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.

    • Julia Joung
    • , Silvana Konermann
    • , Jonathan S Gootenberg
    • , Omar O Abudayyeh
    • , Randall J Platt
    • , Mark D Brigham
    • , Neville E Sanjana
    •  & Feng Zhang
  5. Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, USA.

    • Jonathan S Gootenberg
  6. Department of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.

    • Omar O Abudayyeh

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Contributions

J.J., S.K., J.S.G., O.O.A., R.J.P., M.D.B., N.E.S. and F.Z. designed and performed the experiments. J.J., S.K. and F.Z. wrote the manuscript with help from all authors.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Feng Zhang.

Supplementary information

Zip files

  1. 1.

    Supplementary Data 1-4

    Design _library.py, Design_targeted_library.py, Count_spacers.py, and Calculate_indel.py. See https://github.com/fengzhanglab/Screening_Protocols_manuscript for latest version. (Note: In the version of this article initially published, the wrong version of Supplementary Data 4 was provided, and the GitHub link was omitted. This error and omission are now corrected as of 13 April 2017.)

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DOI

https://doi.org/10.1038/nprot.2017.016

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