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

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 se...

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Detalles Bibliográficos
Autores principales: 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.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7652380/
https://www.ncbi.nlm.nih.gov/pubmed/32541958
http://dx.doi.org/10.1038/s41587-020-0555-7
Descripción
Sumario: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 sgRNA 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 6 targets across 8 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.