Methods to Assess Off-Target CRISPR Genome Editing

CRISPR Therapeutics scientists compared three widely used methods for detecting off-target gene editing activity of the CRISPR-Cas9 machinery and found that they were all similarly effective, though each had its own set of strengths and weaknesses.

The report  “Evaluation of Homology-Independent CRISPR-Cas9 Off-Target Assessment Methods” was published today in The CRISPR Journal. The most important takeaway is that this research validates the use of CRISPR-Cas9 as a gene editing method for clinical drug development,” says senior author Andrew Kernytsky, PhD.

The Nobel Prize-winning CRISPR-Cas9 genome editing technology is rapidly approaching a bench-to-bedside translation. CRISPR Therapeutics reported promising preliminary findings in clinical trials treating sickle-cell disease and beta-thalassemia patients. Earlier this month. Editas Medicine and Intelia Therapeutics, two other genome editing biotechs, have already begun CRISPR-based clinical trials.

The development of methods for detecting and mitigating unintended off-target edits has contributed to this advancement. The specificity of CRISPR-Cas9 genome editing is based on guide RNAs, which recognize the desired DNA target while also directing the Cas9 nuclease to it. Off-target and potentially deleterious mutations may occur if the guide RNA recognizes a genomic site with a similar sequence to the target.

Off-target sites are usually identified using a combination of homology-dependent and homology-independent (bioinformatic) methods, followed by deep sequencing to validate whether CRISPR-Cas editing activity occurs at these locations.

Based on the degree of similarity between the target DNA sequence and the hybridizing portion of the guide RNA, as well as the existence of an adjacent PAM sequence, homology-dependent approaches indicate off-target sites computationally. Sites with a near match to the goal have a higher risk of being incorrectly edited, and therefore have a shorter "editing distance." Homology-independent methods use genome-wide assays to identify off-target sites, which are then confirmed using next-generation sequencing (NGS).

Kernytsky and colleagues from CRISPR Therapeutics conducted a systematic review of three homology-independent off-target assessment approaches, using 75,000 homology-nominated sites as a benchmark.

“One of the concerns that prompted us to conduct this research was that one or more of the approaches would fail to identify such off-target sites,” says the researcher.

The researchers used Cas9 and eight different guide RNAs to evaluate the efficiency of the cell-based assay GUIDE-seq, as well as two biochemical assays, CIRCLE-seq and SITE-seq, to bioinformatically nominated sites in HEK293T cells. To label the editing sites, cell-based assays use the cell's internal repair machinery. To detect edits, these labelled sites are then enriched and sequenced.

The authors point out that “the main distinction between the two assay types is that cell-based assays nominate sites in accessible regions of the genome in a cell type-specific manner, while biochemical assays theoretically nominate cleavable sites regardless of chromatin accessibility.”

At this size, the relative abilities of cell-based and biochemical assays to classify true sequence-confirmed off-targets have not been examined. Researchers will benefit from knowing how these assays compare in terms of efficiency while choosing methods and designing experiments.

“It's exciting that guides have been used without detectable off-target cutting, which is why this analysis concentrated on evaluating the three assays with eight guide RNAs that have previously been reported to have off-target events,” says the researcher. T.J. Cradick, PhD, co-senior author on the report, says that using clinical guide RNAs with no off-target events offered a more detailed contrast. (Cradick, who was previously with CRISPR Therapeutics, is now with Excision BioTherapeutics, a gene editing start-up).

“The three methods performed similarly in nominating sequence-confirmed off-target sites,” the authors write, “but there were significant variations in the total number of sites nominated.” “All three off-target nomination methods include a systematic evaluation of off-target behavior when combined with homology-dependent nomination methods and confirmation by sequencing.”

Kernytsky claims that “none of the three approaches reveals a systemic bias toward detecting one kind of off-target site over another.”

The cellular assay GUIDE-seq has the highest precision, while CIRCLE-seq has the lowest, according to the authors.

Further, the researchers found a connection between the nomination signal and cell editing rates. “Nomination assays would be more useful outside off-target site nomination if the read counts they provided quantitatively reflected cell editing. This would allow gRNAs to be prioritized based on the frequency of editing at off-target sites, according to the researchers.

The indel mutation frequency difference calculated by sequencing was strongly associated with GUIDE-seq read counts, with on-target sites having the highest GUIDE-seq read counts. There was no association between read counts and observed indel mutation frequency difference between treated and control samples for CIRCLE-seq, and there was none for SITE-seq.

GUIDE-seq is a strong option for nominating off-target sites for ex vivo CRISPR-Cas therapies because of its low false-positive rate and high association of its signal with observed editing.

The authors give some realistic recommendations for assessing CRISPR-Cas9 off-target effects based on this detailed review. These include using quantitative off-target and on-target site prediction methods to prioritize candidate guide RNAs; identifying possible off-target sites using both homology-dependent computational methods and homology-independent empirical methods to increase the sensitivity of detecting low-frequency editing activity; and sequencing at a depth of more than 5,000x to increase the sensitivity of detecting low-frequency editing activity.