CHANGE-seq reveals genetic and epigenetic effects on CRISPR–Cas9 genome-wide activity

• Published in: Nature biotechnology Vol. 38; no. 11; pp. 1317 - 1327
• Main Authors: Lazzarotto, Cicera R., Malinin, Nikolay L., Li, Yichao, Zhang, Ruochi, Yang, Yang, Lee, GaHyun, Cowley, Eleanor, He, Yanghua, Lan, Xin, Jividen, Kasey, Katta, Varun, Kolmakova, Natalia G., Petersen, Christopher T., Qi, Qian, Strelcov, Evgheni, Maragh, Samantha, Krenciute, Giedre, Ma, Jian, Cheng, Yong, Tsai, Shengdar Q.
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.11.2020; Nature Publishing Group
• Subjects: 631/1647; 631/208; 631/61; 631/80; Agriculture; Analysis; Base Sequence; Bioinformatics; Biomedical and Life Sciences; Biomedical Engineering/Biotechnology; Biomedicine; Biotechnology; Cell Line; Chromatin; Chromatin - genetics; CRISPR; CRISPR-Associated Protein 9 - metabolism; CRISPR-Cas Systems - genetics; DNA sequencing; Enhancers; Epigenesis, Genetic; Epigenetic inheritance; Epigenetics; Gene Editing; Genetic Variation; Genome, Human; Genome-wide association studies; Genomes; High-Throughput Nucleotide Sequencing; Humans; Influence; Learning algorithms; Life Sciences; Lymphocytes; Lymphocytes T; Machine Learning; Measurement methods; Nuclease; Nucleotide sequencing; Nucleotides; Ribonucleic acid; RNA; Target recognition; Technology application; Transcription; United States
• ISSN: 1087-0156; 1546-1696; 1546-1696
• DOI: 10.1038/s41587-020-0555-7

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. The genome-wide activity of Cas9 is measured in unprecedented detail.