CRISPR off-target quantification with next generation sequencing

Support your CRISPR gene editing with NGS

Next generation sequencing (NGS) is the gold standard for analyzing CRISPR edits and is used for 1) unbiased identification and nomination of off-target sites across the genome and 2) accurate evaluation of CRISPR editing events. A combination of assays rooted in this technology allows for an efficient, quantifiable, and comprehensive approach to measuring the levels of on- and off-target editing.

NGS can be used to evaluate the sequence of either the entire genome (whole genome sequencing, WGS) or a specific region of interest by targeted enrichment techniques such as hybridization capture and amplicon sequencing. CRISPR applications typically utilize amplicon sequencing, which makes more economical use of sequencing capacity compared to WGS. The IDT rhAmpSeq CRISPR Analysis System is based on amplicon sequencing. 

NGS is the only method that can fully characterize insertion/deletion (indel) profiles after a CRISPR genome editing experiment. Amplicon sequencing with NGS can identify and quantify the frequency of the indels that result from non-homologous end joining (NHEJ) following introduction of CRISPR-generated double-strand breaks (DSB). Furthermore, with amplicon sequencing, you also can quantify the correct homology-directed repair (HDR) events and determine what percent of your targeted alleles successfully underwent perfect HDR, thus defining a well-resolved picture of editing events at the target site. As such, NGS generates more data than all methods used to analyze Sanger traces, including such methods as TIDE (Tracking of Indels by Decomposition) and ICE (Inference of CRISPR Edits).

An additional advantage of amplicon sequencing is the ability to evaluate multiple genomic targets at a time—this can happen on two levels. First, individual samples are uniquely barcoded for deconvolution, which allows you to combine hundreds or even thousands of samples on a single flow cell. Second, you can use IDT rhAmpSeq technology with Illumina sequencing platforms to generate multiplexed amplicons on a per-sample basis to investigate multiple on- and off-target sites in a single reaction. The rhAmpSeq CRISPR Analysis System also includes an advanced but accessible cloud-based data analysis pipeline for quantification of on- and off-target edits.

Genome-wide NGS to determine off-target editing

Although it is possible to do targeted sequencing of the on-target edited site without knowing anything about off-target effects (OTEs), it is often critical to understand the OTEs in your experiment. Therefore, before you can properly assess off-target edits, you must first identify potential sites of off-target effects. This is often called “nominating hotspots” and can be done effectively using unbiased techniques such as WGS.

Many techniques have been and are being developed for empirical nomination of hotspots. These techniques include “breaks labeling and enrichment on streptavidin and sequencing” (BLESS) [1], DigenomeSeq [2], “circularization for in vitro reporting of cleavage effects by sequencing” (CIRCLE-seq) [3], “selective enrichment and identification of tagged genomic DNA ends by sequencing” (SITE-seq) [4], genome-wide unbiased identification of DSBs evaluated by sequencing (GUIDE-seq) [5,6], and “discovery of in situ Cas off-targets and verification by sequencing” (DISCOVER-Seq) [7]. The best method for your project depends on your experimental system and especially on which Cas enzyme you use for genome editing. 

When you use a Cas9 enzyme such as Alt-R™ Cas9 V3 or Alt-R HiFi Cas9 for your genome editing, we recommend that you nominate the hotspots with GUIDE-seq [5,6] which is uniquely suited for the discovery phase of off-target identification. This method will generate a semi-quantitative assessment of the accumulation of on- and off-target editing across the genome. With GUIDE-seq, DSB sites across the genome are reported. There are controls included in the assays to mitigate false-positive identification of non-CRISPR breaks. In addition, there are guide RNA alignments to the nominated loci to help identify CRISPR-specific breaks. While GUIDE-seq provides a quantitative measurement of total reads aligned to a CRISPR-targeted site, the output does not perfectly correlate to the frequency of editing at each nominated site. However, quantification of Cas9-mediated edits at these sites can be accomplished with targeted amplicon sequencing.

With Cas12a genome editing, some of the above techniques for nominating hotspots may work better than others in your experimental system. DISCOVER-Seq may be a good starting point for nominating hotspots in many Cas12a experiments [7]. However, you may need to do empirical evaluation in your own system to be confident about which technique you use for unbiased investigation of off-target Cas12a genome editing.

Like with Cas9 experiments, once you have nominated the hotspots in your Cas12a experiment, we recommend targeted sequencing with the rhAmpSeq CRISPR Analysis System to quantify genome editing results accurately.

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References

1. Crosetto N, Mitra A, Silva MJ, et al. Nucleotide-resolution DNA double-strand breaks mapping by next-generation sequencing. Nat Methods. 2013;10(4):361–365.
2. Kim D, Bae S, Park J, et al. Digenome-seq: genome-wide profiling of CRISPR-Cas9 off-target effects in human cells. Nat Methods. 2015;12(3):237–243.
3. Tsai SQ, Nguyen NT, Malagon-Lopez J, et al. CIRCLE-seq: a highly sensitive in vitro screen for genome-wide CRISPR-Cas9 nuclease off-targets. Nat Methods. 2017;14(6):607–614.
4. Cameron P, Fuller CK, Donohoue PD, et al. Mapping the genomic landscape of CRISPR-Cas9 cleavage. Nat Methods. 2017;14(6):600–606.
5. Tsai SQ, Zheng Z, Nguyen NT, et al. GUIDE-seq enables genome-wide profiling of off-target cleavage by CRISPR-Cas nucleases. Nat Biotechnol. 2015;33(2):187–197.
6. Chaudhari HG, Penterman J, Whitton HJ, et al. Evaluation of homology-independent CRISPR-Cas9 off-target assessment methods. CRISPR J. 2020;3(6):440–453.
7. Wienert B, Wyman SK, Richardson CD, et al. Unbiased detection of CRISPR off-targets in vivo using DISCOVER-Seq. Science. 2019;364(6437):286–289.