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Comprehensive profiling of activity and specificity of CRISPR/Cas9 under cellular environment by deep learning

ABSTRACT: This study aims to predict the activity and specificity of CRISPR/Cas9 by deep learning at genome-scale among different cell lines. Here, we have focused on embracing and modifying a system for evaluating SpCas9 activity of on-target and off-target using >1,000,000 guide RNAs (gRNAs) covering ~20,000 protein-coding genes and ~10,000 non-coding genes in synthetic constructs with a high-throughput manner. With the help of deep learning algorithms in the field of artificial intelligence, three prediction models with the best generalization performance now are constructed: Aidit_Cas9-ON, Aidit_Cas9-OFF, and Aidit_Cas9-DSB. Moreover, through systematically investigating the influence of diverse cellular environment on gRNA activity and specificity, we noticed that distinct features are favored from H1 cell line compared with the other 2 cell lines for on-target activity and the overall distribution of repair outcomes is markedly different across 3 cell lines, especially in Jurkat. Finally, we identify a key effect protein DNTT strongly influences editing outcomes induced by CRISPR/Cas9. We confirm that this study will greatly facilitate CRISPR-based genome editing.

ORGANISM(S): Homo sapiens

PROVIDER: GSE181774 | GEO | 2023/05/24

REPOSITORIES: GEO

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Project description:Clustered regularly interspaced short palindromic repeat (CRISPR) RNA-guided nucleases have gathered considerable excitement as a tool for genome engineering. However, questions remain about the specificity of their target site recognition. Most previous studies have examined predicted off-target binding sites that differ from the perfect target site by one to four mismatches, which represent only a subset of genomic regions. Here, we used ChIP-seq to examine genome-wide CRISPR binding specificity at gRNA-specific and gRNA-independent sites. For two guide RNAs targeting the murine Snurf gene promoter, we observed very high binding specificity at the intended target site while off-target binding was observed at 2- to 6-fold lower intensities. We also identified significant gRNA-independent off-target binding. Interestingly, we found that these regions are highly enriched in the PAM site, a sequence required for target site recognition by CRISPR. To determine the relationship between Cas9 binding and endonuclease activity, we used targeted sequence capture as a high-throughput approach to survey a large number of the potential off-target sites identified by ChIP-seq or computational prediction. A high frequency of indels was observed at both target sites and one off-target site, while no cleavage activity could be detected at other ChIP-bound regions. Our results demonstrate that even a simple configuration of a Cas9:gRNA nuclease can support very specific DNA cleavage activity and that most interactions between the CRISPR nuclease complex and genomic PAM sites do not lead to DNA cleavage. ChIP-seq using dCas9 to determine genome-wide binding of CRISPR/Cas9 noED: Cas9 doublemutant protein without an effector domain KRAB: Cas9 doublemutant protein fused to the KRAB repressor domain S1 gRNA: guide RNA targeting GCTCCCTACGCATGCGTCCC(AGG) in the mouse genome S2 gRNA: guide RNA targeting AATGGCTCAGGTTTGTCGCG(CGG) in the mouse genome VEGFA TS3 gRNA: guide RNA targeting GGTGAGTGAGTGTGTGCGTG(TGG) in the human genome

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Comprehensive profiling of activity and specificity of CRISPR/Cas9 under cellular environment by deep learning

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