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Updated: Jun 2, 2026

Highly Efficient Gene Disruption of Murine and Human Hematopoietic Progenitor Cells by CRISPR/Cas9
Published on: April 10, 2018
DNA-PKcs inhibitor AZD7648 reveals sgRNA cross-contaminants and enhanced sensitivity of genome engineering off-target
Nathan White1, Yi-Ting Hu1, John Alexander Chalk1,2
1Infection, Immunity, and Inflammation Teaching and Research Department, Great Ormond Street Institute of Child Health, University College London, London, WC1N 1DZ, United Kingdom.
Abstract:
Therapeutic gene editing with designer nucleases can be compromised by undesired repair outcomes. DNA repair inhibitors are used to bias DSB repair toward HDR, but their impact on larger structural rearrangements, including large deletions and translocations, remains unclear. We quantify the mutational burden associated with end-joining inhibitor compounds. With a highly precise Cas9 nuclease, repair inhibition yields modest increases in aberrations, whereas promiscuous single guide RNAs (sgRNAs) amplify aberrant outcomes by orders of magnitude. Donor templates mitigate mutational burden at on-target sites, and in rare cases donor sequences bridge translocations between on- and off-target loci. Because DNA-PKcs inhibition does not itself induce instability over short intervals but increases the likelihood of capturing chromosomal aberrations postediting, we leveraged this to enhance assay performance. Compared to CAST-Seq, high-resolution CAST-Seq achieved a median ~12-fold increase in detected aberrations and, in this higher-sensitivity context, revealed unintended, target-specific sgRNA contaminants in GMP-like batches, underscoring direct genotoxicity risk and the need for stricter guide purity controls. A modified, translocation-quantitative rhAmpSeq reports all translocation combinations between two loci, enabling robust off-target validation beyond indel-only readouts. Finally, we evaluate AZD7648, finding limited aberration increases with precise nucleases and reconciling reports of extensive large deletions by quantifying assay- and design-dependent biases.

