Description: Sensitive and high-throughput methods for evaluating CRISPR RNA-guided nucleases (RGNs) off-targets (OTs) in cells are essential for advancing RGN-based gene therapies. Here we report a method for simultaneously evaluating thousands of potential RGN off-target sites in cells, called SURRO-seq, which relies on capturing RGN-induced indels in potential SURROgate off-target sites in cells by a pool of lentiviral vectors and targeted deep sequencing. We first evaluate 170 previously investigated OTs from 11 RGNs with SURRO-seq in HEK293T cells. SURRO-seq sensitively captures nearly 100% OTs found by T7E1, most (70%) GUIDEseq-identified OTs, and about half (51%) OTs found by CIRCLE-seq. We next apply SURRO-seq to evaluate 7160 potential OTs from 111 therapeutic RGNs and identify 754 potential OTs with significantly detectable indels, which are further validated in 23 endogenous genome loci in five human cells. Analyses of pOTs with significantly high indel frequencies reveal that thermodynamically stable wobble base pair (rG•dT) strongly increases RGN OT effect. Benchmark analysis of SURRO-seq identified OTs with latest RGN prediction tools further confirms that thermodynamic energy-based predictors provide the most accurate RGN OT prediction. SURRO-seq thus offers a scalable, high-throughput, sensitive and complementary for evaluating RGN OTs and advancing RGN-based gene therapy applications.

Data type: Raw sequence reads

Sample scope: Synthetic

Release date: 2022-01-26

Last updated: 2022-01-26

Data size: 4.74GB