Unknown

Dataset Information

0

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


ABSTRACT: 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.

SUBMITTER: Lazzarotto CR 

PROVIDER: S-EPMC7652380 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

altmetric image

Publications


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 thera  ...[more]

Similar Datasets

2020-06-16 | GSE149363 | GEO
2020-09-22 | GSE149361 | GEO
2020-06-15 | GSE149295 | GEO
2020-09-22 | GSE149362 | GEO
| S-EPMC6512799 | biostudies-literature
| S-EPMC4772022 | biostudies-literature
| S-EPMC9331158 | biostudies-literature
| S-EPMC7818936 | biostudies-literature
| S-EPMC5924695 | biostudies-literature
| S-EPMC5012946 | biostudies-literature