Amplification-free long-read sequencing reveals unforeseen CRISPR-Cas9 off-target activity

Autor: Susana Häggqvist, Ulf Gyllensten, Marie-Louise Bondeson, Anastasia Emmanouilidou, Sanna Gudmundsson, Adam Ameur, Ida Höijer, Marcel den Hoed, Josefin Johansson, Ignas Bunikis, Chen-Shan Chin, Lars Feuk, Maria Wilbe
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Zdroj: Genome Biology
Genome Biology, Vol 21, Iss 1, Pp 1-19 (2020)
Popis: BackgroundOne ongoing concern about CRISPR-Cas9 genome editing is that unspecific guide RNA (gRNA) binding may induce off-target mutations. However, accurate prediction of CRISPR-Cas9 off-target activity is challenging. Here, we present SMRT-OTS and Nano-OTS, two novel, amplification-free, long-read sequencing protocols for detection of gRNA-driven digestion of genomic DNA by Cas9 in vitro.ResultsThe methods are assessed using the human cell line HEK293, re-sequenced at 18x coverage using highly accurate HiFi SMRT reads. SMRT-OTS and Nano-OTS are first applied to three different gRNAs targeting HEK293 genomic DNA, resulting in a set of 55 high-confidence gRNA cleavage sites identified by both methods. Twenty-five of these sites are not reported by off-target prediction software, either because they contain four or more single nucleotide mismatches or insertion/deletion mismatches, as compared with the human reference. Additional experiments reveal that 85% of Cas9 cleavage sites are also found by other in vitro-based methods and that on- and off-target sites are detectable in gene bodies where short-reads fail to uniquely align. Even though SMRT-OTS and Nano-OTS identify several sites with previously validated off-target editing activity in cells, our own CRISPR-Cas9 editing experiments in human fibroblasts do not give rise to detectable off-target mutations at the in vitro-predicted sites. However, indel and structural variation events are enriched at the on-target sites.ConclusionsAmplification-free long-read sequencing reveals Cas9 cleavage sites in vitro that would have been difficult to predict using computational tools, including in dark genomic regions inaccessible by short-read sequencing.
Databáze: OpenAIRE