massively targeted evaluation of CRISPR off-targets in cells by surrogate sequencing

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.

开发灵敏且高通量的细胞内CRISPR RNA引导核酸酶（CRISPR RNA-guided nucleases, RGNs）脱靶位点（off-targets, OTs）评估方法，对于推进基于RGN的基因治疗至关重要。本文报道了一种可在细胞内同时评估数千个潜在RGN脱靶位点的方法，命名为SURRO-seq。该方法通过慢病毒载体库捕获细胞内潜在替代脱靶位点（surrogate off-target sites）中RGN诱导的插入缺失突变（indels），并结合靶向深度测序实现检测。我们首先利用SURRO-seq在HEK293T细胞中评估了来自11种RGN的170个已研究过的脱靶位点。SURRO-seq可灵敏捕获T7E1检测到的近100%脱靶位点、GUIDEseq鉴定的大部分（70%）脱靶位点，以及CIRCLE-seq发现的约一半（51%）脱靶位点。随后，我们将SURRO-seq应用于111种治疗性RGN的7160个潜在脱靶位点评估，鉴定出754个具有显著可检测插入缺失突变的潜在脱靶位点，并在5种人类细胞的23个内源性基因组位点中进一步验证了这些位点。对具有显著高插入缺失突变频率的潜在脱靶位点（pOTs）分析表明，热力学稳定的摆动碱基对（rG•dT）显著增强RGN的脱靶效应。利用最新RGN预测工具对SURRO-seq鉴定的脱靶位点进行基准分析，进一步证实基于热力学能量的预测工具可提供最准确的RGN脱靶预测。因此，SURRO-seq为评估RGN脱靶位点和推进基于RGN的基因治疗应用提供了一种可扩展、高通量、灵敏且互补的方法。