Current methods can illuminate the genome-wide activity of CRISPR–Cas9 nucleases, but are not easily scalable to the throughput needed to fully understand the principles that govern Cas9 specificity. Here we describe ‘circularization for high-throughput analysis of nuclease genome-wide effects by sequencing’ (CHANGE-seq), a scalable, automatable tagmentation-based method for measuring the genome-wide activity of Cas9 in vitro. We applied CHANGE-seq to 110 single guide RNA targets across 13 therapeutically relevant loci in human primary T cells and identified 201,934 off-target sites, enabling the training of a machine learning model to predict off-target activity. Comparing matched genome-wide off-target, chromatin modification and accessibility, and transcriptional data, we found that cellular off-target activity was two to four times more likely to occur near active promoters, enhancers and transcribed regions. Finally, CHANGE-seq analysis of six targets across eight individual genomes revealed that human single-nucleotide variation had significant effects on activity at ~15.2% of off-target sites analyzed. CHANGE-seq is a simplified, sensitive and scalable approach to understanding the specificity of genome editors.

目前的方法可以阐明 CRISPR–Cas9 核酸酶的全基因组活性，但不容易扩展到完全理解控制 Cas9 特异性原理所需的通量。在这里，我们描述了“通过测序对核酸酶全基因组效应进行高通量分析的环化”（CHANGE-seq），这是一种可扩展的、基于自动化标签的方法，用于在体外测量 Cas9 的全基因组活性。我们将 CHANGE-seq 应用于人类原代 T 细胞中 13 个治疗相关位点的 110 个单向导 RNA 靶点，并确定了 201,934 个脱靶位点，从而能够训练机器学习模型来预测脱靶活动。比较匹配的全基因组脱靶、染色质修饰和可及性以及转录数据，我们发现，在活性启动子、增强子和转录区域附近发生细胞脱靶活动的可能性要高出两到四倍。最后，对八个个体基因组中六个靶点的 CHANGE-seq 分析表明，人类单核苷酸变异对所分析的约 15.2% 的脱靶位点的活性有显着影响。CHANGE-seq 是一种用于了解基因组编辑器特异性的简化、敏感和可扩展的方法。