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Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9
Open accessAbstract
CRISPR-Cas9–based genetic screens are a powerful new tool in biology. By simply altering the sequence of the single-guide RNA (sgRNA), one can reprogram Cas9 to target different sites in the genome with relative ease, but the on-target activity and off-target effects of individual sgRNAs can vary widely. Here, we use recently devised sgRNA design rules to create human and mouse genome-wide libraries, perform positive and negative selection screens and observe that the use of these rules produced improved results. Additionally, we profile the off-target activity of thousands of sgRNAs and develop a metric to predict off-target sites. We incorporate these findings from large-scale, empirical data to improve our computational design rules and create optimized sgRNA libraries that maximize on-target activity and minimize off-target effects to enable more effective and efficient genetic screens and genome engineering.
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“Additionally, it can identify genes related to drug resistance in cancer cells and facilitate research on overcoming this resistance [8].”
From A Review of Research on CRISPR/Cas9 Gene Editing Technology in Disease Treatment (2024) · cites this paper as [8]
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“The CFD algorithm is derived from a CRISPR/Cas9 genetic screen experiment that assessed the off-target effects of thousands of sgRNAs (Doench et al., 2016).”
From Off-target effects in CRISPR/Cas9 gene editing (2023) · cites this paper as Doench et al., 2016
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“Primarily, the sequence of sgRNAs is a crucial factor affecting on-target and off target efficiency.”
From Off-target effects in CRISPR/Cas9 gene editing (2023) · cites this paper as Doench et al., 2016
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“Different sgRNAs targeting the same gene locus can have distinct outcomes”
From Off-target effects in CRISPR/Cas9 gene editing (2023) · cites this paper as Doench et al., 2016
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“We used the STARS algorithm (Doench et al., 2016) to derive 38 hits, 18 of which were shared across at least two cell lines”
From Multimodal Analysis of Composition and Spatial Architecture in Human Squamous Cell Carcinoma (2020) · cites this paper as Doench et al., 2016
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“In addition, although our sgRNA pool was designed in silico to minimize the probability of SpCas9 off-target activity52, the GUIDE-seq assay for sgRNA+119 revealed 11 off-target sites throughout the genome (Supplementary Figs. 11h and 4d).”
From Allele specific repair of splicing mutations in cystic fibrosis through AsCas12a genome editing (2019) · cites this paper as [52]
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“Although TREM2 has been shown to bind to Aβ and facilitate its uptake80,81, the surface expression changes of other receptors involved in Aβ recognition and uptake might have counteracted the effects of increased TREM2 signaling upon INPP5D haploinsufficiency83,84.”
From INPP5D/SHIP1 regulates endo-lysosomal function and cargo-selective phagocytosis in human microglia (2025) · cites this paper as [83]
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“However, Aβ can bind to lipoproteins such as clusterin (ApoJ) and ApoE in the brain82–84, which facilitates its clearance by microglia via TREM281.”
From INPP5D/SHIP1 regulates endo-lysosomal function and cargo-selective phagocytosis in human microglia (2025) · cites this paper as [83]
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The NLRP1 and CARD8 inflammasomes detect reductive stress
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Self-citationThe transformative potential of AI-driven CRISPR-Cas9 genome editing to enhance CAR T-cell therapy
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Multimode drug inducible CRISPR/Cas9 devices for transcriptional activation and genome editing
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A Functional Landscape of Resistance to MEK1/2 and CDK4/6 Inhibition in NRAS-Mutant Melanoma
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A Novel Hybrid CNN-SVR for CRISPR/Cas9 Guide RNA Activity Prediction
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Functional interrogation of HOXA9 regulome in MLLr leukemia via reporter-based CRISPR/Cas9 screen
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FIREWORKS: a bottom-up approach to integrative coessentiality network analysis
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Metadata
- Authors
- Doench, J. G., Fusi, N., Sullender, M., Hegde, M., Vaimberg, E. W., Donovan, K. F., Smith, I., Tothova, Z., Wilen, C., Orchard, R., Virgin, H. W., Listgarten, J., & Root, D. E.
- Affiliations
- Broad Institute (US)
- Washington University in St. Louis (US)
- Microsoft Research New England (United States) (US)
- Corresponding author
- Doench, John G
- Listgarten, Jennifer
- Root, David E
- ORCID
- Venue
- Nature Biotechnology
- Published
- 2016-01-18
- Volume
- 34
- Issue
- 2
- Pages
- 184-191
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC4744125
- OpenAlex
- W2252568502
- Semantic Scholar
- b9e5fa707e804d6008e5011b058244437c656a93
- Cited by
- 4,637
- References
- 54
- Citations by year
- 2026: 494 · 2025: 658 · 2024: 569 · 2023: 572 · 2022: 538 · 2021: 499 · 2020: 463 · 2019: 337 · 2018: 258 · 2017: 170 · 2016: 75
- FWCI
- 144.68
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Biotechnology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Cancer Institute (K12CA087723)
- National Institute of Allergy and Infectious Diseases (U19AI109725, T32AI007163)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › CRISPR and Genetic Engineering
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Interference and Gene Delivery
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA regulation and disease
- Keywords
- sgRNA design, CRISPR/Cas9, off-target effects, on-target activity, genetic screen, off-target prediction, genome editing, human genome, mouse genome
- MeSH terms
- RNA, Guide, CRISPR-Cas Systems, Animals, Drug Resistance, Genetic Engineering, Humans, Gene Library, Genome, Genomics, Cell Line, Tumor, Mice, CRISPR-Cas Systems
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