Predictable and precise template-free CRISPR editing of pathogenic variants
Open accessAbstract
Following Cas9 cleavage, DNA repair without a donor template is generally considered stochastic, heterogeneous and impractical beyond gene disruption. Here, we show that template-free Cas9 editing is predictable and capable of precise repair to a predicted genotype, enabling correction of disease-associated mutations in humans. We constructed a library of 2,000 Cas9 guide RNAs paired with DNA target sites and trained inDelphi, a machine learning model that predicts genotypes and frequencies of 1- to 60-base-pair deletions and 1-base-pair insertions with high accuracy (r = 0.87) in five human and mouse cell lines. inDelphi predicts that 5-11% of Cas9 guide RNAs targeting the human genome are 'precise-50', yielding a single genotype comprising greater than or equal to 50% of all major editing products. We experimentally confirmed precise-50 insertions and deletions in 195 human disease-relevant alleles, including correction in primary patient-derived fibroblasts of pathogenic alleles to wild-type genotype for Hermansky-Pudlak syndrome and Menkes disease. This study establishes an approach for precise, template-free genome editing.
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Repair events in 3272-26A>G patient organoids were largely similar to those in pMG3272-26A>G model, with the 18-nucleotide deletion as the most frequent repair.
“In agreement with previous reports49,50, and despite the heterogeneity of the observed editing, the repair events in patient’s organoids were largely similar to those observed in pMG3272-26A>G model, with the 18 nucleotide deletion as the most frequent repair (compare Fig. 3e with Fig. 1e).”
From Allele specific repair of splicing mutations in cystic fibrosis through AsCas12a genome editing (2019) · cites this paper as [49]
The Results section (‘Template-free Cas9 editing is predictable’; page not provided) reports consistent indel frequencies across cell types at 86 target sites, supporting the general point that repair patterns can be similar across contexts. It does not report results for 3272-26A>G patient organoids or the pMG3272-26A>G model, or identify an 18-nucleotide deletion as their most frequent repair. (LLM, conf=0.83)
Experimental — accuracy not yet measuredThree guide RNAs were designed per target using Benchling and Indelphi, and guides with predicted high on-target (>40), off-target (>80) and frameshift (>80%) scores were selected, preferentially targeting an early exon of the gene.
“Three guide RNAs were designed per target using Benchling and Indelphi (Shen et al., 2018). Guides with predicted high on-target (>40), off-target (>80) and frameshift (>80%) scores were selected, preferentially targeting an early exon of the gene.”
From Efficient genetic editing of human intestinal organoids using ribonucleoprotein-based CRISPR (2023) · cites this paper as Shen et al., 2018
The Introduction and Results describe using inDelphi to design and select guide RNAs based on predicted editing outcomes. Our copy does not say that the guides were designed using Benchling or that three were designed per target. The score thresholds and early-exon preference appear in a separate, uncited sentence and are not part of this verdict. (LLM, conf=0.87)
Experimental — accuracy not yet measured
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Cites
Multiplex Genome Engineering Using CRISPR/Cas Systems
Cong, L., Ran, F. A., Cox, D., Lin, S., Barretto, R., Habib, N., Hsu, P. D., Wu, X., Jiang, W., Marraffini, L. A., et al. (2013) · Science · doi:10.1126/science.1231143 · Cited by 15,339 · see citing works
Programmable editing of a target base in genomic DNA without double-stranded DNA cleavage
Komor, A. C., Kim, Y. B., Packer, M. S., Zuris, J. A., & Liu, D. R. (2016) · Nature · doi:10.1038/nature17946 · Cited by 5,437 · see citing works
Self-citationOptimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9
Doench, J. G., Fusi, N., Sullender, M., Hegde, M., Vaimberg, E. W., Donovan, K. F., Smith, I., Tothova, Z., Wilen, C., Orchard, R., et al. (2016) · Nature Biotechnology · doi:10.1038/nbt.3437 · Cited by 4,637 · see citing works
Programmable base editing of A•T to G•C in genomic DNA without DNA cleavage
Gaudelli, N. M., Komor, A. C., Rees, H. A., Packer, M. S., Badran, A. H., Bryson, D. I., & Liu, D. R. (2017) · Nature · doi:10.1038/nature24644 · Cited by 4,184 · see citing works
Self-citationIn vivo genome editing using Staphylococcus aureus Cas9
Ran, F. A., Cong, L., Yan, W. X., Scott, D. A., Gootenberg, J. S., Kriz, A. J., Zetsche, B., Shalem, O., Wu, X., Makarova, K. S., et al. (2015) · Nature · doi:10.1038/nature14299 · Cited by 2,687 · see citing works
RNA-programmed genome editing in human cells
Jinek, M., East, A., Cheng, A., Lin, S., Ma, E., & Doudna, J. (2013) · eLife · doi:10.7554/elife.00471 · Cited by 2,098 · see citing works
Koike-Yusa, H., Li, Y., Tan, E.-P., Velasco-Herrera, M. D. C., & Yusa, K. (2014) · Nature Biotechnology · doi:10.1038/nbt.2800 · Cited by 1,064 · see citing works
Efficient introduction of specific homozygous and heterozygous mutations using CRISPR/Cas9
Paquet, D., Kwart, D., Chen, A., Sproul, A., Jacob, S., Teo, S., Olsen, K. M., Gregg, A., Noggle, S., & Tessier-Lavigne, M. (2016) · Nature · doi:10.1038/nature17664 · Cited by 951 · see citing works
Flagged first, then by citations
Research works printed but not linked: 21
- [2]Mali P RNA-Guided Human Genome Engineering via Cas9. Science 339, 823– 826 (2013). 23287722 PMC3712628
- [5]Adli M The CRISPR tool kit for genome editing and beyond. Nat. Commun 9, 1911 (2018). 29765029 PMC5953931
- [9]Landrum MJ ClinVar: Public archive of interpretations of clinically relevant variants. Nucleic Acids Res. 44, D862– D868 (2016). 26582918 PMC4702865
- [10]Stenson PD Human Gene Mutation Database: towards a comprehensive central mutation database. J. Med. Genet 45, 124 (2008). 18245393
- [11]Suzuki K In vivo genome editing via CRISPR/Cas9 mediated homology-independent targeted integration. Nature 540, 144– 149 (2016). 27851729 PMC5331785
- [12]Nakade S Microhomology-mediated end-joining-dependent integration of donor DNA in cells and animals using TALENs and CRISPR/Cas9. Nat. Commun 5, 5560– 5560 (2014). 25410609 PMC4263139
- [14]van Overbeek M DNA Repair Profiling Reveals Nonrandom Outcomes at Cas9-Mediated Breaks. Mol. Cell 63, 633– 646 (2016). 27499295
- [15]Urasaki A, Morvan G & Kawakami K Functional dissection of the Tol2 transposable element identified the minimal cis-sequence and a highly repetitive sequence in the subterminal region essential for transposition. Genetics 174, 639– 649 (2006). 16959904 PMC1602067
- [16]Ceccaldi R, Rondinelli B & D’Andrea AD Repair Pathway Choices and Consequences at the Double-Strand Break. Spec. Issue Qual. Control 26, 52– 64 (2016). PMC4862604 26437586
- [17]Deriano L & Roth DB Modernizing the Nonhomologous End-Joining Repertoire: Alternative and Classical NHEJ Share the Stage. Annu. Rev. Genet 47, 433– 455 (2013). 24050180
- [18]Bae S, Kweon J, Kim HS & Kim J-S Microhomology-based choice of Cas9 nuclease target sites. Nat Methods 11, 705– 706 (2014). 24972169
- [19]Cornu TI, Mussolino C & Cathomen T Refining strategies to translate genome editing to the clinic. Nat. Med 23, 415 (2017). 28388605
- [20]Davis AJ & Chen DJ DNA double strand break repair via non-homologous end-joining. Transl. Cancer Res 2, 130– 143 (2013). 24000320 PMC3758668
- [21]Arbab M, Srinivasan S, Hashimoto T, Geijsen N & Sherwood RI Cloning-free CRISPR. Stem Cell Rep 5, 908– 917 (2015). PMC4649464 26527385
- [22]Bourbon M, Alves AC & Sijbrands EJ Low-density lipoprotein receptor mutational analysis in diagnosis of familial hypercholesterolemia. Curr. Opin. Lipidol 28, 120– 129 (2017). 28169869
- [24]Oh J Positional cloning of a gene for Hermansky-Pudlak syndrome, a disorder of cytoplasmic organelles. Nat. Genet 14, 300– 306 (1996). 8896559
- [25]Biehs R DNA Double-Strand Break Resection Occurs during Non-homologous End Joining in G1 but Is Distinct from Resection during Homologous Recombination. Mol. Cell 671– 684 (2017). doi: 10.1016/j.molcel.2016.12.016 28132842 PMC5316416 · DOI
- [26]Shin HY CRISPR/Cas9 targeting events cause complex deletions and insertions at 17 sites in the mouse genome. Nat. Commun 8, 1– 10 (2017). 28561021 PMC5460021
- [27]Kosicki M, Tomberg K & Bradley A Repair of double-strand breaks induced by CRISPR–Cas9 leads to large deletions and complex rearrangements. Nat. Biotechnol (2018). PMC6390938 30010673
- [28]Kleinstiver BP Broadening the targeting range of Staphylococcus aureus CRISPR-Cas9 by modifying PAM recognition. Nat. Biotechnol 33, 1293– 1298 (2015). 26524662 PMC4689141
- [29]Sherwood RI Discovery of directional and nondirectional pioneer transcription factors by modeling DNase profile magnitude and shape. Nat. Biotechnol 32, 171– 178 (2014). 24441470 PMC3951735
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Xiang, X., Corsi, G. I., Anthon, C., Qu, K., Pan, X., Liang, X., Han, P., Dong, Z., Liu, L., Zhong, J., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-23576-0 · Cited by 209 · see citing works
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Michlits, G., Jude, J., Hinterndorfer, M., de Almeida, M., Vainorius, G., Hubmann, M., Neumann, T., Schleiffer, A., Burkard, T. R., Fellner, M., et al. (2020) · Nature Methods · doi:10.1038/s41592-020-0850-8 · Cited by 154 · see citing works
Precision genome editing using cytosine and adenine base editors in mammalian cells
Huang, T. P., Newby, G. A., & Liu, D. R. (2021) · Nature Protocols · doi:10.1038/s41596-020-00450-9 · Cited by 175 · see citing works
Self-citationKim, H. K., Lee, S., Kim, Y., Park, J., Min, S., Choi, J. W., Huang, T. P., Yoon, S., Liu, D. R., & Kim, H. H. (2020) · Nature Biomedical Engineering · doi:10.1038/s41551-019-0505-1 · Cited by 145 · see citing works
Cas9-induced large deletions and small indels are controlled in a convergent fashion
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Tools for efficient genome editing; ZFN, TALEN, and CRISPR
Shamshirgaran, Y., Liu, J., Sumer, H., Verma, P. J., & Taheri-Ghahfarokhi, A. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-2301-5_2 · Cited by 114 · see citing works
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Jo, T., Nho, K., Bice, P., Saykin, A. J., & Initiative, F. T. A. D. N. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac022 · Cited by 80 · see citing works
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He, W., Zhang, L., Villarreal, O. D., Fu, R., Bedford, E., Dou, J., Patel, A. Y., Bedford, M. T., Shi, X., Chen, T., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-12489-8 · Cited by 69 · see citing works
Current Bioinformatics Tools to Optimize CRISPR/Cas9 Experiments to Reduce Off-Target Effects
Naeem, M., & Alkhnbashi, O. S. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24076261 · Cited by 70 · see citing works
Profiling the Landscape of Drug Resistance Mutations in Neosubstrates to Molecular Glue Degraders
Gosavi, P. M., Ngan, K. C., Yeo, M. J. R., Su, C., Li, J., Lue, N. Z., Hoenig, S. M., & Liau, B. B. (2022) · ACS Central Science · doi:10.1021/acscentsci.1c01603 · Cited by 56 · see citing works
Massively targeted evaluation of therapeutic CRISPR off-targets in cells
Pan, X., Qu, K., Yuan, H., Xiang, X., Anthon, C., Pashkova, L., Liang, X., Han, P., Corsi, G. I., Xu, F., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-31543-6 · Cited by 42 · see citing works
Design of efficacious somatic cell genome editing strategies for recessive and polygenic diseases
Carlson-Stevermer, J., Das, A., Abdeen, A. A., Fiflis, D., Grindel, B. I., Saxena, S., Akcan, T., Alam, T., Kletzien, H., Kohlenberg, L., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-20065-8 · Cited by 32 · see citing works
Efficient genetic editing of human intestinal organoids using ribonucleoprotein-based CRISPR
Skoufou-Papoutsaki, N., Adler, S., D'Santos, P., Mannion, L., Mehmed, S., Kemp, R., Smith, A., Perrone, F., Nayak, K., Russell, A., et al. (2023) · Disease Models & Mechanisms · doi:10.1242/dmm.050279 · Cited by 22 · see citing works
“Three guide RNAs were designed per target using Benchling and Indelphi (Shen et al., 2018).”
Arber, C., Villegas-Llerena, C., Toombs, J., Pocock, J. M., Ryan, N. S., Fox, N. C., Zetterberg, H., Hardy, J., & Wray, S. (2019) · Brain Communications · doi:10.1093/braincomms/fcz024 · Cited by 19 · see citing works
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- Authors
- Shen, M. W., Arbab, M., Hsu, J. Y., Worstell, D., Culbertson, S. J., Krabbe, O., Cassa, C. A., Liu, D. R., Gifford, D. K., & Sherwood, R. I.
- Affiliations
- MIT Computer Science and Artificial Intelligence Laboratory (US)
- Broad Institute (US)
- Massachusetts General Hospital (US)
- Brigham and Women's Hospital (US)
- Corresponding author
- Liu, David R.
- Gifford, David K.
- Sherwood, Richard I.
- ORCID
- Venue
- Nature
- Published
- 2018-11
- Volume
- 563
- Issue
- 7733
- Pages
- 646-651
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC6517069
- OpenAlex
- W2899807648
- Cited by
- 569
- References
- 8
- Citations by year
- 2026: 61 · 2025: 72 · 2024: 54 · 2023: 50 · 2022: 94 · 2021: 70 · 2020: 106 · 2019: 57 · 2018: 4
- FWCI
- 20.69
- Type
- journal article
- Language
- en
- Update notices
- correction (2019-02-14)
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- Funders
- National Science Foundation
- Howard Hughes Medical Institute
- Broad Institute
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek
- National Institutes of Health
- Defense Advanced Research Projects Agency (HR0011-17-2-0049)
- Harvard Stem Cell Institute
- Brigham Research Institute
- National Human Genome Research Institute (R01HG008754, R01HG008363, R01HG010372, RM1HG009490)
- National Institute of General Medical Sciences (T32GM087237, R35GM118062)
- National Institute of Diabetes and Digestive and Kidney Diseases (K01DK101684)
- National Institute of Biomedical Imaging and Bioengineering (R01EB022376)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › CRISPR and Genetic Engineering
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA regulation and disease
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › DNA Repair Mechanisms
- Keywords
- Cas9, non-homologous end joining, DNA repair, indels, pathogenic variants, disease-associated mutations, Hermansky-Pudlak syndrome, Menkes disease, patient-derived fibroblasts, guide RNA library
- MeSH terms
- Machine Learning, Gene Editing, CRISPR-Associated Protein 9, Alleles, Base Sequence, DNA Repair, Fibroblasts, Humans, Menkes Kinky Hair Syndrome, Substrate Specificity, Templates, Genetic, Reproducibility of Results, K562 Cells, Hermanski-Pudlak Syndrome, HCT116 Cells, HEK293 Cells, CRISPR-Cas Systems
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