Identifying and verifying low frequencies of genomic alterations resulting from off-target editing, gRNA synthesis errors, cross-contamination, or other unintended gRNA activity is critical to preventing unexpected genotoxic effects for the gene editing therapeutic community. However, assembling the necessary components and expertise for genotoxicity characterization studies is expensive and labor intensive. To better enable the gene editing community, we demonstrate a series of tools, workflows, and services that can be leveraged to perform genotoxicity characterization of CRISPR reagents. First, we demonstrate an empirical in vivo tag-based off-target nomination service rooted in our RNaseH2-dependent amplification technology as a method to generate an off-target list for a target gRNA. Then, we demonstrate how outputs of this service can be fed into rhAmpSeq to identify and classify indels and chromosomal re-arrangements at on/off-target loci with increased effectivity compared to existing methods. Finally, we demonstrate a method to sensitively identify gRNA cross-contamination down to sub-0.1% gRNA contamination levels by leveraging unique NHEJ DNA repair fingerprint information of select gRNAs. Using this method, we demonstrate that editing attributable to a characterized gRNA activity can be identified at frequencies far below detection limits of canonically utilized tools, and we further use this method to characterize the ability of IDT’s synthesis platform to prevent cross-contamination reproducibly. From this work, we demonstrate the importance of manufacturing processes in mitigating unintended genomic alterations and present new tooling/services to improve genotoxicity characterization in gene therapy workflows.

Learning Objectives:

• Discuss key challenges in CRISPR genotoxicity characterization and their impact on CRISPR-based gene editing therapies

• Explore advanced tools and workflows for detecting off-target effects and genomic alterations.

• Demonstrate methods to identify gRNA cross-contamination in CRISPR workflows.