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Integrated design, execution, and analysis of arrayed and pooled CRISPR genome-editing experiments

Nature Protocols volume 13, pages 946–986 (2018)Cite this article

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Abstract

CRISPR (clustered regularly interspaced short palindromic repeats) genome-editing experiments offer enormous potential for the evaluation of genomic loci using arrayed single guide RNAs (sgRNAs) or pooled sgRNA libraries. Numerous computational tools are available to help design sgRNAs with optimal on-target efficiency and minimal off-target potential. In addition, computational tools have been developed to analyze deep-sequencing data resulting from genome-editing experiments. However, these tools are typically developed in isolation and oftentimes are not readily translatable into laboratory-based experiments. Here, we present a protocol that describes in detail both the computational and benchtop implementation of an arrayed and/or pooled CRISPR genome-editing experiment. This protocol provides instructions for sgRNA design with CRISPOR (computational tool for the design, evaluation, and cloning of sgRNA sequences), experimental implementation, and analysis of the resulting high-throughput sequencing data with CRISPResso (computational tool for analysis of genome-editing outcomes from deep-sequencing data). This protocol allows for design and execution of arrayed and pooled CRISPR experiments in 4–5 weeks by non-experts, as well as computational data analysis that can be performed in 1–2 d by both computational and noncomputational biologists alike using web-based and/or command-line versions.

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Figure 1: Schematic of an arrayed genome-editing experiment.
Figure 2: Schematic of a pooled genome-editing experiment.
Figure 3: Design, experimental execution, and data analysis workflows for arrayed and pooled genome-editing experiments.
Figure 4: Locus-specific deep-sequencing analysis of coding and noncoding targeting by CRISPResso.

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References

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

    CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Barrangou, R. et al. CRISPR provides acquired resistance against viruses in prokaryotes. Science 315, 1709–1712 (2007).

    CAS  PubMed  Google Scholar 

  4. Zetsche, B. et al. Cpf1 is a single RNA-guided endonuclease of a class 2 CRISPR-Cas system. Cell 163, 759–971 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Canver, M.C. et al. Characterization of genomic deletion efficiency mediated by clustered regularly interspaced palindromic repeats (CRISPR)/Cas9 nuclease system in mammalian cells. J. Biol. Chem. 289, 21312–21324 (2014).

    PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Haeussler, M. et al. Evaluation of off-target and on-target scoring algorithms and integration into the guide RNA selection tool CRISPOR. Genome Biol. 17, 148 (2016).

    PubMed  PubMed Central  Google Scholar 

  10. Pinello, L. et al. Analyzing CRISPR genome-editing experiments with CRISPResso. Nat. Biotechnol. 34, 695–697 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Canver, M.C. et al. Variant-aware saturating mutagenesis using multiple Cas9 nucleases identifies regulatory elements at trait-associated loci. Nat. Genet. 49, 625–634 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Canver, M.C., Bauer, D.E. & Orkin, S.H. Functional interrogation of non-coding DNA through CRISPR genome editing. Methods 121–122, 118–129 (2017).

    PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  15. Koike-Yusa, H., Li, Y., Tan, E.-P., Velasco-Herrera, M.D.C. & Yusa, K. Genome-wide recessive genetic screening in mammalian cells with a lentiviral CRISPR-guide RNA library. Nat. Biotechnol. 32, 267–273 (2014).

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Sanjana, N.E., Shalem, O. & Zhang, F. Improved vectors and genome-wide libraries for CRISPR screening. Nat. Methods 11, 783–784 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Joung, J. et al. Genome-scale CRISPR-Cas9 knockout and transcriptional activation screening. Nat. Protoc. 12, 828–863 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26, 589–595 (2010).

    PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Park, J., Kim, J. & Bae, S. Cas-Database: web-based genome-wide guide RNA library design for gene knockout screens using CRISPR-Cas9. Bioinformatics 32, 2017–2023 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Bae, S., Park, J. & Kim, J.S. Cas-OFFinder: a fast and versatile algorithm that searches for potential off-target sites of Cas9 RNA-guided endonucleases. Bioinformatics 30, 1473–1475 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Xiao, A. et al. CasOT: a genome-wide Cas9/gRNA off-target searching tool. Bioinformatics 30, 1180–1182 (2014).

    CAS  PubMed  Google Scholar 

  28. Stemmer, M., Thumberger, T., Del Sol Keyer, M., Wittbrodt, J. & Mateo, J.L. CCTop: an intuitive, flexible and reliable CRISPR/Cas9 target prediction tool. PLoS One 10, 1–11 (2015).

    Google Scholar 

  29. Cradick, T.J., Qiu, P., Lee, C.M., Fine, E.J. & Bao, G. COSMID: a web-based tool for identifying and validating CRISPR/Cas off-target sites. Mol. Ther. Nucleic Acids 3, e214 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Montague, T.G., Cruz, J.M., Gagnon, J.A., Church, G.M. & Valen, E. CHOPCHOP: a CRISPR/Cas9 and TALEN web tool for genome editing. Nucleic Acids Res. 42, 401–407 (2014).

    Google Scholar 

  31. Labun, K., Montague, T.G., Gagnon, J.A., Thyme, S.B. & Valen, E. CHOPCHOP v2: a web tool for the next generation of CRISPR genome engineering. Nucleic Acids Res. 44, W272–W276 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Naito, Y., Hino, K., Bono, H. & Ui-Tei, K. CRISPRdirect: software for designing CRISPR/Cas guide RNA with reduced off-target sites. Bioinformatics 31, 1120–1123 (2015).

    CAS  PubMed  Google Scholar 

  33. Ma, J. et al. CRISPR-DO for genome-wide CRISPR design and optimization. Bioinformatics 32, 3336–3338 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Liu, H. et al. CRISPR-ERA: a comprehensive design tool for CRISPR-mediated gene editing, repression and activation. Bioinformatics 31, 3676–3678 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Lei, Y. et al. CRISPR-P: a web tool for synthetic single-guide RNA design of CRISPR-system in plants. Mol. Plant 7, 1494–1496 (2014).

    CAS  PubMed  Google Scholar 

  36. Singh, R., Kuscu, C., Quinlan, A., Qi, Y. & Adli, M. Cas9-chromatin binding information enables more accurate CRISPR off-target prediction. Nucleic Acids Res. 43, e118 (2015).

    PubMed  PubMed Central  Google Scholar 

  37. Heigwer, F., Kerr, G. & Boutros, M. E-CRISP: fast CRISPR target site identification. Nat. Methods 11, 122–123 (2014).

    CAS  PubMed  Google Scholar 

  38. Gratz, S.J. et al. Highly specific and efficient CRISPR/Cas9-catalyzed homology-directed repair in Drosophila. Genetics 196, 961–971 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Meier, J.A., Zhang, F. & Sanjana, N. GUIDES: sgRNA design for loss-of-function screens. Nat. Methods 14, 831–832 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Perez, A.R. et al. GuideScan software for improved single and paired CRISPR guide RNA design. Nat. Biotechnol. 35, 347–349 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. O'Brien, A. & Bailey, T.L. GT-Scan: identifying unique genomic targets. Bioinformatics 30, 2673–2675 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Wong, N., Liu, W. & Wang, X. WU-CRISPR: characteristics of functional guide RNAs for the CRISPR/Cas9 system. Genome Biol. 16, 218 (2015).

    PubMed  PubMed Central  Google Scholar 

  43. Zhu, L.J., Holmes, B.R., Aronin, N. & Brodsky, M.H. CRISPRseek: a bioconductor package to identify target-specific guide RNAs for CRISPR-Cas9 genome-editing systems. PLoS One 9, e108424 (2014).

    PubMed  PubMed Central  Google Scholar 

  44. Xie, S., Shen, B., Zhang, C., Huang, X. & Zhang, Y. SgRNAcas9: a software package for designing CRISPR sgRNA and evaluating potential off-target cleavage sites. PLoS One 9, e100448 (2014).

    PubMed  PubMed Central  Google Scholar 

  45. Prykhozhij, S.V., Rajan, V., Gaston, D. & Berman, J.N. CRISPR multitargeter: a web tool to find common and unique CRISPR single guide RNA targets in a set of similar sequences. PLoS One 10, e0119372 (2015).

    PubMed  PubMed Central  Google Scholar 

  46. Tycko, J., Myer, V.E. & Hsu, P.D. Methods for optimizing CRISPR-Cas9 genome editing specificity. Mol. Cell 63, 355–370 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Fusi, N., Smith, I., Doench, J. & Listgarten, J. In silico predictive modeling of CRISPR/Cas9 guide efficiency. Preprint at bioRxiv, doi.org/10.1101/021568 (2015).

  48. Chari, R., Mali, P., Moosburner, M. & Church, G.M. Unraveling CRISPR-Cas9 genome engineering parameters via a library-on-library approach. Nat. Methods 12, 823–826 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Xu, H. et al. Sequence determinants of improved CRISPR sgRNA design. Genome Res. 25, 1147–1157 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Wang, T., Wei, J.J., Sabatini, D.M. & Lander, E.S. Genetic screens in human cells using the CRISPR-Cas9 system. Science 343, 80–84 (2014).

    CAS  PubMed  Google Scholar 

  52. Moreno-Mateos, M.A. et al. CRISPRscan: designing highly efficient sgRNAs for CRISPR-Cas9 targeting in vivo. Nat. Methods 12, 982–988 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Housden, B.E. et al. Identification of potential drug targets for tuberous sclerosis complex by synthetic screens combining CRISPR-based knockouts with RNAi. Sci. Signal. 8, rs9 (2015).

    PubMed  PubMed Central  Google Scholar 

  54. Ren, X. et al. Enhanced specificity and efficiency of the CRISPR/Cas9 system with optimized sgRNA parameters in Drosophila. Cell Rep. 9, 1151–1162 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Farboud, B. & Meyer, B.J. Dramatic enhancement of genome editing by CRISPR/cas9 through improved guide RNA design. Genetics 199, 959–971 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Bae, S., Kweon, J., Kim, H.S. & Kim, J.-S. Microhomology-based choice of Cas9 nuclease target sites. Nat. Methods 11, 705–706 (2014).

    CAS  PubMed  Google Scholar 

  57. Güell, M., Yang, L. & Church, G.M. Genome editing assessment using CRISPR genome analyzer (CRISPR-GA). Bioinformatics 30, 2968–2970 (2014).

    PubMed  PubMed Central  Google Scholar 

  58. Park, J., Lim, K., Kim, J.-S. & Bae, S. Cas-Analyzer: an online tool for assessing genome editing results using NGS data. Bioinformatics 33, 286–288 (2017).

    CAS  PubMed  Google Scholar 

  59. Xue, L.J. & Tsai, C.J. AGEseq: analysis of genome editing by sequencing. Mol. Plant 8, 1428–1430 (2015).

    CAS  PubMed  Google Scholar 

  60. Lindsay, H. et al. CrispRVariants charts the mutation spectrum of genome engineering experiments. Nat. Biotechnol. 34, 701–702 (2016).

    CAS  PubMed  Google Scholar 

  61. Boel, A. et al. BATCH-GE: batch analysis of next-generation sequencing data for genome editing assessment. Sci. Rep. 6, 30330 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  63. Nelson, C.E. & Gersbach, C.A. Engineering delivery vehicles for genome editing. Annu. Rev. Chem. Biomol. Eng. 7, 637–662 (2016).

    CAS  PubMed  Google Scholar 

  64. Yin, H., Kauffman, K.J. & Anderson, D.G. Delivery technologies for genome editing. Nat. Rev. Drug Discov. 16, 387–399 (2017).

    CAS  PubMed  Google Scholar 

  65. Montalbano, A., Canver, M.C. & Sanjana, N.E. High-throughput approaches to pinpoint function within the noncoding genome. Mol. Cell 68, 44–59 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Tsai, S.Q. et al. GUIDE-seq enables genome-wide profiling of off-target cleavage by CRISPR-Cas nucleases. Nat. Biotechnol. 33, 187–197 (2015).

    CAS  PubMed  Google Scholar 

  67. Kim, D. et al. Digenome-seq: genome-wide profiling of CRISPR-Cas9 off-target effects in human cells. Nat. Methods 12, 237–243 (2015).

    CAS  PubMed  Google Scholar 

  68. Frock, R.L. et al. Genome-wide detection of DNA double-stranded breaks induced by engineered nucleases. Nat. Biotechnol. 33, 179–186 (2015).

    CAS  PubMed  Google Scholar 

  69. Yan, W.X. et al. BLISS is a versatile and quantitative method for genome-wide profiling of DNA double-strand breaks. Nat. Commun. 8, 15058 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. Tsai, S.Q. et al. CIRCLE-seq: a highly sensitive in vitro screen for genome-wide CRISPR–Cas9 nuclease off-targets. Nat. Methods 14, 607–614 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  71. Park, J. et al. Digenome-seq web tool for profiling CRISPR specificity. Nat. Methods 14, 548–549 (2017).

    CAS  PubMed  Google Scholar 

  72. Cameron, P. et al. Mapping the genomic landscape of CRISPR–Cas9 cleavage. Nat. Methods 14, 600–606 (2017).

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  75. Cheng, A.W. et al. Multiplexed activation of endogenous genes by CRISPR-on, an RNA-guided transcriptional activator system. Cell Res. 23, 1163–1171 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  77. Horlbeck, M.A. et al. Compact and highly active next-generation libraries for CRISPR-mediated gene repression and activation. Elife 5, e19760 (2016).

    PubMed  PubMed Central  Google Scholar 

  78. Horlbeck, M.A. et al. Nucleosomes impede Cas9 access to DNA in vivo and in vitro. Elife 5, e12677 (2016).

    PubMed  PubMed Central  Google Scholar 

  79. Liu, S.J. et al. CRISPRi-based genome-scale identification of functional long noncoding RNA loci in human cells. Science 355, aah7111 (2017).

    PubMed  Google Scholar 

  80. Joung, J. et al. Genome-scale activation screen identifies a lncRNA locus regulating a gene neighbourhood. Nature 548, 343–346 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  81. Kleinstiver, B.P. et al. High-fidelity CRISPR–Cas9 nucleases with no detectable genome-wide off-target effects. Nature 529, 490–495 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  82. Slaymaker, I.M. et al. Rationally engineered Cas9 nucleases with improved specificity. Science 351, 84–88 (2016).

    CAS  PubMed  Google Scholar 

  83. Chen, J.S. et al. Enhanced proofreading governs CRISPR-Cas9 targeting accuracy. Nature 550, 407–410 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  84. JoVE Science Education Database. Basic Methods in Cellular and Molecular Biology. Gel Purification. JoVE, Cambridge, MA. https://www.jove.com/science-education/5063/gel-purification (2018).

  85. Froger, A. & Hall, J.E. Transformation of plasmid DNA into E. coli using the heat shock method. J. Vis. Exp. e253 (6) http://dx.doi.org/10.3791/253 (2007)

  86. JoVE Science Education Database. Basic Methods in Cellular and Molecular Biology. Bacterial Transformation: The Heat Shock Method. JoVE, Cambridge, MA. https://www.jove.com/science-education/5059/bacterial-transformation-the-heat-shock-method (2018).

  87. Kutner, R.H., Zhang, X.-Y. & Reiser, J. Production, concentration and titration of pseudotyped HIV-1-based lentiviral vectors. Nat. Protoc. 4, 495–505 (2009).

    CAS  PubMed  Google Scholar 

  88. Coufal, N.G. et al. L1 retrotransposition in human neural progenitor cells. Nature 460, 1127–1131 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  89. Ewing, B. & Green, P. Base-calling of automated sequencer traces using phred. II. Error probabilities. Genome Res. 8, 186–194 (1998).

    CAS  PubMed  Google Scholar 

  90. Bolger, A.M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  91. Ellis, E.L. & Delbrück, M. The growth of bacteriophage. J. Gen. Physiol. 22, 365–384 (1939).

    CAS  PubMed  PubMed Central  Google Scholar 

  92. Stent, G. Molecular Biology of Bacterial Viruses (Freeman, 1963).

  93. Choi, C., Kuatsjah, E., Wu, E. & Yuan, S. The effect of cell size on the burst size of T4 bacteriophage infections of Escherichia coli B23. J. Exp. Microbiol. Immunol. 14, 85–91 (2010).

    Google Scholar 

  94. Brendel, C. & Williams, D.A. Unexpected help: mTOR meets lentiviral vectors. Blood 124, 832–833 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  95. O'Doherty, U., Swiggard, W.J. & Malim, M.H. Human immunodeficiency virus type 1 spinoculation enhances infection through virus binding. J. Virol. 74, 10074–10080 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  96. Sims, D. et al. High-throughput RNA interference screening using pooled shRNA libraries and next generation sequencing. Genome Biol. 12, R104 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

M.C.C. was supported by a National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) Award (F30DK103359). M.H. was funded by National Institutes of Health (NIH)/National Human Genome Research Institute (NHGRI) grant 5U41HG002371-15 and NIH/National Cancer Institute (NCI) grant 5U54HG007990-02 and by a grant from the California Institute of Regenerative Medicine, CIRM GC1R-06673C. D.E.B. was supported by the National Heart, Lung, and Blood Institute (NHLBI) (DP2OD022716, P01HL032262), the Burroughs Wellcome Fund, and a Doris Duke Charitable Foundation Innovations in Clinical Research Award. S.H.O. was supported by an award from the NHLBI (P01HL032262) and an award from the NIDDK (P30DK049216, Center of Excellence in Molecular Hematology). N.E.S. was supported by the NIH through the NHGRI (R00-HG008171). L.P. was supported by an NHGRI Career Development Award (R00HG008399) and the Defense Advanced Research Projects Agency (HR0011-17-2-0042).

Author information

Author notes

  1. Matthew C Canver and Maximilian Haeussler: These authors contributed equally to this work.

Authors and Affiliations

  1. Molecular Pathology Unit and Cancer Center, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA

    Matthew C Canver & Luca Pinello

  2. Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, California, USA

    Maximilian Haeussler

  3. Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA

    Daniel E Bauer & Stuart H Orkin

  4. Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Stem Cell Institute, Harvard University, Boston, Massachusetts, USA

    Daniel E Bauer & Stuart H Orkin

  5. Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA

    Daniel E Bauer & Stuart H Orkin

  6. Howard Hughes Medical Institute, Boston, Massachusetts, USA

    Stuart H Orkin

  7. New York Genome Center and Department of Biology, New York University, New York, New York, USA

    Neville E Sanjana

  8. Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA

    Ophir Shalem

  9. Department of Genetics, University of Pennsylvania, Philadelphia, Pennsylvania, USA

    Ophir Shalem

  10. Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA

    Guo-Cheng Yuan

  11. The Broad Institute, Cambridge, Massachusetts, USA

    Feng Zhang & Luca Pinello

  12. McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Feng Zhang

  13. Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Feng Zhang

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

    Feng Zhang

  15. INSERM U1154, CNRS UMR 7196, Muséum National d'Histoire Naturelle, Paris, France

    Jean-Paul Concordet

Authors
  1. Matthew C Canver
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  2. Maximilian Haeussler
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  3. Daniel E Bauer
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  5. Neville E Sanjana
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  6. Ophir Shalem
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  7. Guo-Cheng Yuan
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  8. Feng Zhang
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  9. Jean-Paul Concordet
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  10. Luca Pinello
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Contributions

M.C.C., M.H., and L.P. conceived this project. M.H. and J.-P.C. created CRISPOR. L.P., M.C.C., D.E.B., and G.-C.Y. created CRISPResso. M.C.C. and D.E.B. performed the experiments. M.C.C., D.E.B., S.H.O., N.E.S., O.S., G.-C.Y., F.Z., and L.P. analyzed the experimental data. M.C.C., M.H., and L.P. wrote the manuscript with input from all authors.

Corresponding author

Correspondence to Luca Pinello.

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Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Screenshot of sharing disk volumes and allocating memory with Docker containers.

a, Screenshot using Docker to ensure that the drive(s) you want to be available to the container is/are checked (under Settings…/ Shared Drives). b, Screenshot using Docker to allocate enough memory to the container (under Settings…/ Advanced).

Supplementary Figure 2 Pooled sgRNA library preparation and analysis.

a, Representative results for lsPCR1. b, Representative results for lsPCR2. c, Gradient PCR for lentiGuide-Puro-specific primers or locus-specific primers for laPCR1. d, Representative results for laPCR2.

Supplementary Figure 3 Example amplicon and regions description files.

a, Example of a properly formatted amplicon description file. This file is a tab delimited text file with up to 5 columns (first 2 columns required). No column heading is required. b, Example of a properly formatted regions description file. This file is a tab delimited text file with up to 7 columns (4 required) and contains the coordinates of the regions to analyze and some additional information. No column heading is required.

Supplementary Figure 4 Visualization of the distribution of identified alleles generated from targeting BCL11A exon 2.

Nucleotides are indicated by unique colors (A = green; C = red; G = yellow; T = purple). Substitutions are shown in bold font. Red rectangles highlight inserted sequences. Horizontal dashed lines indicate deleted sequences. The vertical dashed line indicates the predicted double-strand break position.

Supplementary Figure 5 Direct comparison of BCL11A exon 2 sequence between a BCL11A exon 2 targeted sgRNA sample (“edited”) and a non-edited control sample (“non-edited”).

a, Distribution of editing outcomes (unmodified, NHEJ, HDR, and mixed alleles) for treated (edited) and control (non- edited) samples. b, Comparison of the percent different editing outcomes (unmodified, NHEJ, HDR, and mixed alleles) between the treated (edited) and control (non-edited) samples. c, Combined (substitutions/deletions/insertions) mutation position distribution for treated (edited) and control (non- edited) samples. The vertical dashed line indicates the position of predicted Cas9 cleavage. The position of the sgRNA is shown in gray. d, Comparison of the percent different combined mutations (substitutions/deletions/insertions) between the treated (edited) and control (non-edited) samples. The vertical dashed line indicates the position of predicted Cas9 cleavage. The position of the sgRNA is shown in gray.

Supplementary information

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Supplementary Figures 1–5 and Supplementary Tables 1–8. (PDF 1462 kb)

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Canver, M., Haeussler, M., Bauer, D. et al. Integrated design, execution, and analysis of arrayed and pooled CRISPR genome-editing experiments. Nat Protoc 13, 946–986 (2018). https://doi.org/10.1038/nprot.2018.005

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