High-throughput analysis of the activities of xCas9, SpCas9-NG and SpCas9 at matched and mismatched target sequences in human cells
Open accessAbstract
The applications of clustered regularly interspaced short palindromic repeats (CRISPR)-based genome editing can be limited by a lack of compatible protospacer adjacent motifs (PAMs), insufficient on-target activity and off-target effects. Here, we report an extensive comparison of the PAM-sequence compatibilities and the on-target and off-target activities of Cas9 from Streptococcus pyogenes (SpCas9) and the SpCas9 variants xCas9 and SpCas9-NG (which are known to have broader PAM compatibility than SpCas9) at 26,478 lentivirally integrated target sequences and 78 endogenous target sites in human cells. We found that xCas9 has the lowest tolerance for mismatched target sequences and that SpCas9-NG has the broadest PAM compatibility. We also show, on the basis of newly identified non-NGG PAM sequences, that SpCas9-NG and SpCas9 can edit six previously unedited endogenous sites associated with genetic diseases. Moreover, we provide deep-learning models that predict the activities of xCas9 and SpCas9-NG at the target sequences. The resulting deeper understanding of the activities of xCas9, SpCas9-NG and SpCas9 in human cells should facilitate their use.
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The OFF-target library contained thousands of known off-target sites: 431 off target sites of 13 gRNAs detected by GUIDE-seq or Digenome-seq, 805 gRNA-target pairs of 3 gRNAs evaluated by similarly oligonucleotide synthesis method.
“The “OFF-target” library contained thousands of known off-target sites: 431 off target sites of 13 gRNAs detected by GUIDE-seq or Digenome-seq, 805 gRNA-target pairs of 3 gRNAs evaluated by similarly oligonucleotide synthesis method.”
From Machine learning-based prediction models to guide the selection of Cas9 variants for efficient gene editing (2024) · cites this paper as [30]
Although some Cas9 variants have comparable editing activity and specificity with SpCas9 at a few target sites, high-throughput analysis is still lacking.
“Although some Cas9 variants have comparable editing activity and specificity with SpCas9 at a few target sites, high-throughput analysis is still lacking.”
From Machine learning-based prediction models to guide the selection of Cas9 variants for efficient gene editing (2024) · cites this paper as [30]
Across all four Cas9 variants and WT Cas9, wobble transition was more tolerated, reflected by higher relative indel frequency compared to other mismatch types.
“Across all four Cas9 variants and WT Cas9, the wobble transition was more tolerated, which was reflected by higher relative indel frequency compared to other mismatch types (Figure 3C).”
From Machine learning-based prediction models to guide the selection of Cas9 variants for efficient gene editing (2024) · cites this paper as [30]
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“Cas9 Variant Activity Prediction Developing deep learning models to predict the activity and specificity of different Cas9 variants Kim et al. (2020b)”
MethodRapid DNA unwinding accelerates genome editing by engineered CRISPR-Cas9
Eggers, A. R., Chen, K., Soczek, K. M., Tuck, O. T., Doherty, E. E., Xu, B., Trinidad, M. I., Thornton, B. W., Yoon, P. H., & Doudna, J. A. (2024) · Cell · doi:10.1016/j.cell.2024.04.031 · Cited by 72 · see citing works
“37 This may be because we lack a proper understanding of the essential relationship be-tween Cas9 structure and its genome-editing activities.”
Deep learning models to predict the editing efficiencies and outcomes of diverse base editors
Kim, N., Choi, S.-C., Kim, S., Song, M., Seo, J. H., Min, S., Park, J., Cho, S., & Kim, S. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01792-x · Cited by 75 · see citing works
“…Cas12a nuclease activities showed good performance for predicting the relative efficiencies (activity ranks) of CRISPR nucleases across cell types 26,28, 40 , DeepCas9variants should be useful for selecting the most efficient Cas9 variant and sgRNA pair in both HEK293T cells and other cell types.”
Revolutionizing CRISPR technology with artificial intelligence
Kim, M., Go, M.-J., Kang, S.-H., Jeong, S.-H., & Lim, K. (2025) · Experimental & Molecular Medicine · doi:10.1038/s12276-025-01462-9 · Cited by 80 · see citing works
“After that, Kim et al. 56 conducted high-throughput screening, targeting more than 26,000 lentiviral integration sites and 78 endogenous sites in human cells to evaluate and compare the protospacer adjacent motif (PAM) compatibility of SpCas9, xCas9 57 and SpCas9-NG 58 .”
InfluentialHigh-throughput functional evaluation of human cancer-associated mutations using base editors
Kim, Y., Lee, S., Cho, S., Park, J., Chae, D., Park, T., Minna, J. D., & Kim, S. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01276-4 · Cited by 66 · see citing works
In-depth assessment of the PAM compatibility and editing activities of Cas9 variants
Zhang, W., Yin, J., Zhangding, Z., Xin, C., Liu, M., Wang, Y., Chen, A., & Hu, J. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab507 · Cited by 63 · see citing works
“multiplexing system with tens of thousands of parallel target sites in the same library in order to cover as many as different types of SpCas9 variant-targeting sites in the genome (18,26,37), here we used a complementary strategy to assess the PAM compatibility, editing efficiency and targeting specificity of these S…”
BackgroundMethodBenchmarking of SpCas9 variants enables deeper base editor screens of BRCA1 and BCL2
Sangree, A. K., Griffith, A. L., Szegletes, Z. M., Roy, P., DeWeirdt, P. C., Hegde, M., McGee, A. V., Hanna, R. E., & Doench, J. G. (2022) · Nature Communications · doi:10.1038/s41467-022-28884-7 · Cited by 63 · see citing works
GO: a functional reporter system to identify and enrich base editing activity
Katti, A., Foronda, M., Zimmerman, J., Diaz, B. J., Zafra, M. P., Goswami, S., & Dow, L. E. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa124 · Cited by 60 · see citing works
“These data are consistent with recent reports quantifying DNA cleavage and BE of Cas9, xCas9 and Cas9-NG variants across different genomic loci (22,23).”
ResultKim, H. S., Kweon, J., & Kim, Y. (2024) · Experimental & Molecular Medicine · doi:10.1038/s12276-024-01212-3 · Cited by 64 · see citing works
Development and Applications of CRISPR/Cas9-Based Genome Editing in Lactobacillus
Mu, Y., Zhang, C., Li, T., Jin, F., Sung, Y.-J., Oh, H., Lee, H., & Jin, L. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms232112852 · Cited by 58 · see citing works
Genome dependent Cas9/gRNA search time underlies sequence dependent gRNA activity
Moreb, E. A., & Lynch, M. (2021) · Nature Communications · doi:10.1038/s41467-021-25339-3 · Cited by 51 · see citing works
“…Cas9-NG xCas9 Cas9 Doench2014 Chari2015 Xu2015 Doench2016 Kim2019-SA Kim2020b Kim2020a A375 MOLM13/ NB4/TF1 HL60 KBM7 A375 HEK293T (lentivirus) HEK293T (lentivirus) HEK293T (lentivirus) HEK293T (lentivirus) Cas9 Cas9 Hart2015…”
InfluentialCurrent Strategies for Increasing Knock-In Efficiency in CRISPR/Cas9-Based Approaches
Leal, A., Herreno-Pachón, A., Benincore-Flórez, E., Karunathilaka, A., & Tomatsu, S. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25052456 · Cited by 58 · see citing works
Systematic decomposition of sequence determinants governing CRISPR/Cas9 specificity
Fu, R., He, W., Dou, J., Villarreal, O. D., Bedford, E., Wang, H. H., Hou, C., Zhang, L., Wang, Y., Ma, D., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-28028-x · Cited by 56 · see citing works
Single-Base Resolution: Increasing the Specificity of the CRISPR-Cas System in Gene Editing
Rabinowitz, R., & Offen, D. (2020) · Molecular Therapy · doi:10.1016/j.ymthe.2020.11.009 · Cited by 54 · see citing works
Scalable characterization of the PAM requirements of CRISPR–Cas enzymes using HT-PAMDA
Walton, R. T., Hsu, J. Y., Joung, J. K., & Kleinstiver, B. P. (2021) · Nature Protocols · doi:10.1038/s41596-020-00465-2 · Cited by 45 · see citing works
Compendium of Plant-Specific CRISPR Vectors and Their Technical Advantages
Alok, A., Chauhan, H., Upadhyay, S. K., Pandey, A. K., Kumar, J., & Singh, K. (2021) · Life · doi:10.3390/life11101021 · Cited by 50 · see citing works
Benchmarking deep learning methods for predicting CRISPR/Cas9 sgRNA on- and off-target activities
Zhang, G., Luo, Y., Dai, X., & Dai, Z. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad333 · Cited by 47 · see citing works
Engineering of efficiency-enhanced Cas9 and base editors with improved gene therapy efficacies
Yin, S., Zhang, M., Liu, Y., Sun, X., Guan, Y., Chen, X., Yang, L., Huo, Y., Yang, J., Zhang, X., et al. (2022) · Molecular Therapy · doi:10.1016/j.ymthe.2022.11.014 · Cited by 44 · see citing works
“26,27 In this study, through the fusion of a non-sequence-speci fi c HMG-D dsDBD to Cas9 protein, a series of eeCas9 tools with significantly enhanced activity has been engineered.”
BackgroundMethodLarge-scale multiplexed mosaic CRISPR perturbation in the whole organism
Liu, B., Jing, Z., Zhang, X., Chen, Y., Mao, S., Kaundal, R. K., Zou, Y., Ge, W., Zang, Y., Wang, X., et al. (2022) · Cell · doi:10.1016/j.cell.2022.06.039 · Cited by 42 · see citing works
Recording of elapsed time and temporal information about biological events using Cas9
Park, J., Lim, J. M., Jung, I., Heo, S., Park, J., Chang, Y. J., Kim, H. K., Jung, D., Yu, J. H., Min, S., et al. (2021) · Cell · doi:10.1016/j.cell.2021.01.014 · Cited by 39 · see citing works
A short overview of CRISPR-Cas technology and its application in viral disease control
Ghorbani, A., Hadifar, S., Salari, R., Izadpanah, K., Burmistrz, M., Afsharifar, A., Eskandari, M. H., Niazi, A., Denes, C. E., & Neely, G. G. (2021) · Transgenic Research · doi:10.1007/s11248-021-00247-w · Cited by 38 · see citing works
“The first SpCas9 mutants reported to target altered PAM sites with improved activity were SpCas9-EQR, SpCas9-VQR and, SpCas9-VRER (Kleinstiver et al. 2015).”
BackgroundEngineering Cas9 for human genome editing
Slaymaker, I. M., & Gaudelli, N. M. (2021) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2021.03.004 · Cited by 34 · see citing works
Deep sampling of gRNA in the human genome and deep-learning-informed prediction of gRNA activities
Zhang, H., Yan, J., Lu, Z., Zhou, Y., Zhang, Q., Cui, T., Li, Y., Chen, H., & Ma, L. (2023) · Cell Discovery · doi:10.1038/s41421-023-00549-9 · Cited by 33 · see citing works
“…in the hidden layers and batch size were optimized by using Hyperopt based on the following con fi gurations: the number of fi lters was chosen from [32, 64, 128, 256, 512], kernel size was selected from [2, 3, 4], the number of neurons in the hidden layers was chosen from [64, 128, 256], and batch…”
Systematic functional interrogation of human pseudogenes using CRISPRi
Sun, M., Wang, Y., Zheng, C., Wei, Y., Hou, J., Zhang, P., He, W., Lv, X., Ding, Y., Liang, H., et al. (2021) · Genome biology · doi:10.1186/s13059-021-02464-2 · Cited by 30 · see citing works
“Because the off-target effect is much weaker when there are > 1 nucleotides (nt) of mismatches [32] or there is any RNA/DNA bulge [33] in the potential off-target sites, we focused on the predicted off-target sites with 1-nt mismatch from a given sgRNA sequence.”
BackgroundSuperior Fidelity and Distinct Editing Outcomes of SaCas9 Compared with SpCas9 in Genome Editing
Yang, Z.-X., Fu, Y.-W., Zhao, J.-J., Zhang, F., Li, S.-A., Zhao, M., Wen, W., Zhang, L., Cheng, T., Zhang, J., et al. (2022) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2022.12.003 · Cited by 32 · see citing works
“As a general principle, 20 nt is the optimal length for high activity, consistent with another report that extension with one nucleotide decreases its activity [34].”
BackgroundMekler, V. M., Kuznedelov, K. D., & Severinov, K. (2020) · Journal of Biological Chemistry · doi:10.1074/jbc.ra119.012239 · Cited by 28 · see citing works
“In addition to its broader PAM compatibility, xCas9 has considerably reduced off-target activity compared with WT SpCas9 (17, 38, 41).”
BackgroundLi, J., Wu, P., Cao, Z., Huang, G., Lu, Z., Yan, J., Zhang, H., Zhou, Y., Liu, R., Chen, H., et al. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.113765 · Cited by 26 · see citing works
Interpreting CRISPR-Cas12a enzyme kinetics through free energy change of nucleic acids
Zhang, J., Guan, X., Moon, J., Zhang, S., Jia, Z., Yang, R., Hou, C., Guo, C., Pei, M., & Liu, C. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae1124 · Cited by 24 · see citing works
CRISPR-Cas13a as a next-generation tool for rapid and precise plant RNA virus diagnostics
Karimi, M., Ghorbani, A., Niazi, A., Rostami, M., & Tahmasebi, A. (2025) · Plant Methods · doi:10.1186/s13007-025-01401-9 · Cited by 25 · see citing works
Engineered dual selection for directed evolution of SpCas9 PAM specificity
Goldberg, G. W., Spencer, J. M., Giganti, D. O., Camellato, B. R., Agmon, N., Ichikawa, D. M., Boeke, J. D., & Noyes, M. B. (2021) · Nature Communications · doi:10.1038/s41467-020-20650-x · Cited by 25 · see citing works
Singh, S., Mishra, V., Riyazuddin, R., Chugh, V., & Upadhyay, S. K. (2024) · Plant Breeding · doi:10.1111/pbr.13234 · Cited by 25 · see citing works
Engineering of Cas12a nuclease variants with enhanced genome-editing specificity
Chen, P., Zhou, J., Liu, H., Zhou, E.-C., He, B., Wu, Y., Wang, H., Sun, Z., Paek, C., Lei, J., et al. (2024) · PLoS Biology · doi:10.1371/journal.pbio.3002514 · Cited by 23 · see citing works
CRISPR-Cas “Non-Target” Sites Inhibit On-Target Cutting Rates
Moreb, E. A., Hutmacher, M., & Lynch, M. (2020) · The CRISPR Journal · doi:10.1089/crispr.2020.0065 · Cited by 22 · see citing works
Increasing the Targeting Scope of CRISPR Base Editing System Beyond NGG
Yu, S.-Y., Birkenshaw, A., Thomson, T., Carlaw, T., Zhang, L., & Ross, C. J. (2022) · The CRISPR Journal · doi:10.1089/crispr.2021.0109 · Cited by 23 · see citing works
“63 This could potentially be attributed to slower cleavage kinetics due to the amino acid modi-fications in xCas9.”
Naeem, M., Alkhodairy, H. F., Ashraf, I., & Khalil, A. B. (2022) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-022-07266-7 · Cited by 26 · see citing works
Guide-specific loss of efficiency and off-target reduction with Cas9 variants
Zhang, L., He, W., Fu, R., Wang, S., Chen, Y., & Xu, H. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad702 · Cited by 25 · see citing works
“Over the past years, machine learning models have been developed and trained to predict on-target efficiency and off-target effects of WT Sp-Cas9 and a few Cas9 variants based on a large volume of datasets ( 17 , 22 , 35 , 49 ).”
BackgroundCRISPR screening in hematology research: from bulk to single-cell level
Meyers, S., Demeyer, S., & Cools, J. (2023) · Journal of Hematology & Oncology · doi:10.1186/s13045-023-01495-5 · Cited by 23 · see citing works
Asano, Y., Yamashita, K., Hasegawa, A., Ogasawara, T., Iriki, H., & Muramoto, T. (2021) · Scientific Reports · doi:10.1038/s41598-021-89546-0 · Cited by 21 · see citing works
Seo, S.-Y., Min, S., Lee, S., Seo, J. H., Park, J., Kim, H. K., Song, M., Baek, D., Cho, S., & Kim, S. (2023) · Nature Methods · doi:10.1038/s41592-023-01875-2 · Cited by 21 · see citing works
Gong, G., Zhang, Y., Wang, Z., Liu, L., Shi, S., Siewers, V., Yuan, Q., Nielsen, J., Zhang, X., & Liu, Z. (2021) · ACS Synthetic Biology · doi:10.1021/acssynbio.0c00560 · Cited by 20 · see citing works
Using the dCas9-KRAB system to repress gene expression in hiPSC-derived NGN2 neurons
Li, A., Cartwright, S., Yu, A., Ho, S., Schrode, N., Deans, P. J. M., Matos, M. R., García, M. F., Townsley, K. G., Zhang, B., et al. (2021) · STAR Protocols · doi:10.1016/j.xpro.2021.100580 · Cited by 18 · see citing works
“A Guanine nucleotide must be included on one of the 5’ ends of the sgRNA, if it is not already included, to ensure that there is complete and efficient transcription (Kim et al., 2020).”
BackgroundXia, K., Wang, F., Tan, Z., Zhang, S., Lai, X., Ou, W., Yang, C., Chen, H., Peng, H. Y., Luo, P., et al. (2023) · Advanced Science · doi:10.1002/advs.202300993 · Cited by 19 · see citing works
Ansari, A. H., Kumar, M., Sarkar, S., Maiti, S., & Chakraborty, D. (2023) · eLife · doi:10.7554/elife.77976 · Cited by 17 · see citing works
Engineered PsCas9 enables therapeutic genome editing in mouse liver with lipid nanoparticles
Degtev, D., Bravo, J. P. K., Emmanouilidi, A., Zdravković, A., Choong, O. K., Touza, J. L., Selfjord, N., Weisheit, I., Francescatto, M., Akçakaya, P., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-53418-8 · Cited by 17 · see citing works
Highly parallel profiling of the activities and specificities of Cas12a variants in human cells
Chen, P., Wu, Y., Wang, H., Liu, H., Zhou, J., Chen, J., Lei, J., Sun, Z., Paek, C., & Yin, L. (2025) · Nature Communications · doi:10.1038/s41467-025-57150-9 · Cited by 16 · see citing works
“The viral titer was estimated by counting the number of surviving cells in the virus-treated population when almost all of the untransduced cells had died 68,69 .”
Dimauro, G., Barletta, V. S., Catacchio, C. R., Colizzi, L., Maglietta, R., & Ventura, M. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.10.013 · Cited by 15 · see citing works
Xia, Y., Sun, L., Liang, Z., Guo, Y., Li, J., Tang, D., Huo, Y., & Guo, S. (2023) · Chemical Engineering Journal · doi:10.1016/j.cej.2023.143865 · Cited by 15 · see citing works
CRISPR/Cas in Grapevine Genome Editing: The Best Is Yet to Come
Ren, C., Mohamed, M. S. M., Aini, N., Kuang, Y., & Liang, Z. (2024) · Horticulturae · doi:10.3390/horticulturae10090965 · Cited by 17 · see citing works
“7 exhibits higher activities on NGT and NGA PAMs than that of SpCas9 [32,33].”
Neggers, J. E., Jacquemyn, M., Dierckx, T., Kleinstiver, B. P., Thibaut, H. J., & Daelemans, D. (2020) · Molecular Therapy · doi:10.1016/j.ymthe.2020.09.025 · Cited by 13 · see citing works
Chen, Y., Fang, L., Ying, X., Cheng, M., Wang, L., Sun, P., Zhang, Z., Shi, L., Cao, Y., & Song, H. (2022) · Advanced Biology · doi:10.1002/adbi.202101296 · Cited by 15 · see citing works
Development and expansion of the CRISPR/Cas9 toolboxes for powerful genome engineering in yeast
Guang, C., Lin, Z., & Shi, S. (2022) · Enzyme and Microbial Technology · doi:10.1016/j.enzmictec.2022.110056 · Cited by 13 · see citing works
An efficient miRNA knockout approach using CRISPR-Cas9 in Xenopus
Godden, A. M., Antonaci, M., Ward, N. J., van der Lee, M., Abu‐Daya, A., Guille, M., & Wheeler, G. N. (2021) · Developmental Biology · doi:10.1016/j.ydbio.2021.12.015 · Cited by 13 · see citing works
Deng, L., Zhou, Y., Cai, Z., Zhu, J., Li, Z., & Bao, Z. (2024) · Science Advances · doi:10.1126/sciadv.adj9382 · Cited by 12 · see citing works
“Surprisingly, SpiCas9 was also able to edit at the noncanonical GGT PAM, which echoes a previous study ( 27 ).”
CGD: Comprehensive guide designer for CRISPR-Cas systems
Menon, A. V., Sohn, J.-I., & Nam, J. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.03.020 · Cited by 12 · see citing works
“We thus applied our machine learning framework to non-canonical PAMs (NGH) using a dataset published by Kim et al. [45], resulting in performances comparable with that of the previous deep learning model (Supplementary Fig.”
BackgroundNucleases in gene-editing technologies: past and prologue
Li, D., Li, L.-Q., & Liu, J. (2023) · National Science Open · doi:10.1360/nso/20220067 · Cited by 12 · see citing works
“Remarkably, xCas9 displayed the lowest tolerance for mismatched target sequences compared to SpCas9 and SpCas9-NG [160].”
BackgroundPotts, M. A., McDonald, J. A., Sutherland, K. D., & Herold, M. J. (2020) · European Journal of Immunology · doi:10.1002/eji.202048712 · Cited by 11 · see citing works
Chakraborty, S. S., Dutta, J. R., Ganesan, R., & Mináry, P. (2024) · Methods in molecular biology · doi:10.1007/978-1-0716-4079-1_17 · Cited by 11 · see citing works
DeepMEns: an ensemble model for predicting sgRNA on-target activity based on multiple features
Ding, S., Zheng, J., & Jia, C. (2024) · Briefings in Functional Genomics · doi:10.1093/bfgp/elae043 · Cited by 11 · see citing works
CRISPR-Cas Systems: A Functional Perspective and Innovations
Navarro, C., Diaz, M. P., Durán, P., Castro, A., Díaz, A., Cano, C., Carbonell, A., Solano-Jimenez, D.-S., Rivera-Porras, D., Contreras-Velásquez, J., et al. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26083645 · Cited by 14 · see citing works
“…is Cas9 mutant versions such as xCas9-NG (a hybrid resulting from the fusion of xCas9 and Cas-NG, both Cas9 variations with PAM flexibilisation) [197], which, although it has reported markedly less activity than traditional Cas9, also greatly expands the system’s field of action of CRISPR [198].”
Zhu, D., Wang, J., Yang, D., Xi, J., & Li, J. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms222413301 · Cited by 11 · see citing works
Deconstructing cancer with precision genome editing
Johnson, G. A., Gould, S. I., & Rivera, F. J. S. (2024) · Biochemical Society Transactions · doi:10.1042/bst20230984 · Cited by 9 · see citing works
Evaluation and prediction of guide RNA activities in genome-editing tools
Kim, H. K., & Kim, S. (2025) · Nature Reviews Bioengineering · doi:10.1038/s44222-025-00352-z · Cited by 11 · see citing works
Li, Y., & Zhou, L. (2021) · BioEssays · doi:10.1002/bies.202100086 · Cited by 10 · see citing works
“The SpCas9 mutants such as xCas9,[34] eSpCas9,[35] SpCas9-HF1,[36] SpCas9 D1135E,[31] hyper-accurate Cas9 (HypaCas9),[37] and highfidelity Cas9-R691A[38] globally reduced off-target activity by refining the protein-DNA-RNA interactions in human cells.”
BackgroundMoreb, E. A., & Lynch, M. (2022) · The CRISPR Journal · doi:10.1089/crispr.2021.0084 · Cited by 10 · see citing works
Sun, L., Zhang, T., Lan, X., Zhang, N., Wang, R., Ma, S., Zhao, P., & Xia, Q. (2024) · Insects · doi:10.3390/insects15040241 · Cited by 9 · see citing works
Analysis of Nucleoporin Function Using Inducible Degron Techniques
Aksenova, V., Arnaoutov, A., & Dasso, M. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-2337-4_9 · Cited by 9 · see citing works
Advances in CRISPR/Cas technologies and their application in plants
Chao, D., Xi, Y., Satheesh, V., & Lei, M. (2023) · Tropical Plants · doi:10.48130/tp-2023-0002 · Cited by 9 · see citing works
CRISPR Challenges and Opportunities for Space Travel
Gouw, A. M. (2020) · Space and society · doi:10.1007/978-3-030-42036-9_2 · Cited by 9 · see citing works
Prime editing enables precise genome editing in mouse liver and retinaPreprint
Jang, H., Shin, J. H., Jo, D. H., Seo, J. H., Yu, G., Gopalappa, R., Cho, S., Kim, J. H., & Kim, S. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.01.08.425835 · Cited by 8 · see citing works
Zhou, X., Gao, J., Luo, L., Huang, C., Wu, J., & Wang, X. (2024) · Communications Biology · doi:10.1038/s42003-024-07078-5 · Cited by 10 · see citing works
“Forlibraryconstruction,synthesizedsgRNA-targetpairs werePCRampli fi ed and assembled into a lentiviral plasmid by Gibson assembly 45,46 .”
CRISPR-Based Gene Editing Techniques in Pediatric Neurologic Disorders
Chrzanowski, S. M., & Batra, R. (2022) · Pediatric Neurology · doi:10.1016/j.pediatrneurol.2024.01.021 · Cited by 7 · see citing works
Marsiglia, J. D. C., Vaalavirta, K., Knight, E., Nakamura, M., Cong, L., & Hughes, N. W. (2024) · Cell Reports Methods · doi:10.1016/j.crmeth.2024.100882 · Cited by 8 · see citing works
“11 17 In addition to these approaches, there is interest in developing strategies that use an orthogonal modulator of CRISPR-Cas to tune its editing activity.”
Experimental strategies to achieve efficient targeted knock-in via tandem paired nicking
Rahman, M. L., Hyodo, T., Karnan, S., Ota, A., Hasan, M. N., Mihara, Y., Wahiduzzaman, M., Tsuzuki, S., Hosokawa, Y., & Konishi, H. (2021) · Scientific Reports · doi:10.1038/s41598-021-01978-w · Cited by 7 · see citing works
Lee, D., Lee, J. H., & Bang, D. (2021) · ACS Synthetic Biology · doi:10.1021/acssynbio.1c00056 · Cited by 7 · see citing works
Kim, B.-H., Woo, T., Kang, S., Park, S., & Park, B.-J. (2022) · Genes · doi:10.3390/genes13020165 · Cited by 7 · see citing works
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“44 Hence, we evaluated whether gN19 sgRNAs could also enhance the activity of SpRY.”
Computational analysis of cas proteins unlocks new potential in HIV-1 targeted gene therapy
Dampier, W., Berman, R. E., Nonnemacher, M. R., & Wigdahl, B. L. (2024) · Frontiers in Genome Editing · doi:10.3389/fgeed.2023.1248982 · Cited by 6 · see citing works
CRISPR/Cas9 Editing: Sparking Discussion on Safety in Light of the Need for New Therapeutics
Carlaw, T., Zhang, L., & Ross, C. J. (2020) · Human Gene Therapy · doi:10.1089/hum.2020.111 · Cited by 7 · see citing works
Application of novel CRISPR tools in brain therapy
Shang, J., Song, F., Zhang, Z., Chen, D., & Yang, S. (2024) · Life Sciences · doi:10.1016/j.lfs.2024.122855 · Cited by 5 · see citing works
Chen, P., Li, X., Zhou, Q., Chen, J., Lu, L., Wang, P., Zhang, G., Sun, D., Huang, X., Liu, J., et al. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf522 · Cited by 4 · see citing works
“Although a PAM-flexible uPEn version might be adapted to precisely place the cleavages, this countermeasure would be hampered by the often inferior activities of designer Cas9 modules [ 18 , 19 ].”
Improving the efficiency of high-fidelity Cas9 by enhancing PAM-distal interactions
Zheng, R., Lu, Z., Wei, R., Shin, Y.-C., Du, J., Zhang, Q., Li, J., Wang, X., Wei, Y., Liu, B., et al. (2026) · Nature Structural & Molecular Biology · doi:10.1038/s41594-026-01753-3 · Cited by 4 · see citing works
Fueling chromosomal gene diversification and artificial evolution with CRISPR
Zhu, R.-Y., Ren, C., & Bao, Z. (2025) · Genome biology · doi:10.1186/s13059-025-03756-7 · Cited by 4 · see citing works
“However, these Cas mutants are associated with improved off-target effects [127, 128].”
An Efficient CRISPR-Cas9 Method to Knock Out MiRNA Expression in Xenopus Tropicalis
Godden, A. M., Antonaci, M., & Wheeler, G. N. (2023) · Methods in molecular biology · doi:10.1007/978-1-0716-2982-6_15 · Cited by 4 · see citing works
Chen, Y., Ke, Y., Jiang, Z., & Yang, G. (2025) · Biomaterials Science · doi:10.1039/d5bm00194c · Cited by 5 · see citing works
- Authors
- Kim, H. K., Lee, S., Kim, Y., Park, J., Min, S., Choi, J. W., Huang, T. P., Yoon, S., Liu, D. R., & Kim, H. H.
- Affiliations
- Yonsei University (KR)
- Seoul National University (KR)
- Broad Institute (US)
- ORCID
- Venue
- Nature Biomedical Engineering
- Published
- 2020-01-14
- Volume
- 4
- Issue
- 1
- Pages
- 111-124
- ISSN
- 2157-846X (ISSN-L)
- OpenAlex
- W2999201570
- Cited by
- 145
- References
- 66
- Citations by year
- 2026: 19 · 2025: 18 · 2024: 23 · 2023: 22 · 2022: 20 · 2021: 26 · 2020: 17
- FWCI
- 6.89
- Type
- journal article
- Language
- en
- Open access
- Licence
- Locations
- Nature Biomedical Engineering (publishedVersion)
- PubMed (publishedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion)
- YUHSpace (Yonsei University Medical Library) (submittedVersion, cc-by-nc-nd)
- Funders
- Foundation for the National Institutes of Health (RM1 HG009490, R01 EB022376, R35 GM118062)
- Howard Hughes Medical Institute (David Liu)
- National Research Foundation of Korea (2017R1A2B3004198, 2017M3A9B4062403, 2018R1A5A2025079)
- Ministry of Health and Welfare (HI17C0676, HI16C1012)
- 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
- Health Sciences › Medicine › Oncology › CAR-T cell therapy research
- Keywords
- SpCas9, protospacer adjacent motif, on-target activity, off-target effects, CRISPR gene editing
- MeSH terms
- Gene Editing, CRISPR-Associated Protein 9, Deep Learning, Genetic Vectors, Humans, Streptococcus pyogenes, Lentivirus, HEK293 Cells, CRISPR-Cas Systems
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