UNCOVERseq enables sensitive and controlled gene editing off-target nomination across CRISPR-Cas modalities and systems
TL;DR
An in-cell method that sensitively detects rare off-target edits, benchmarks performance across editors, and improves risk assessment in therapeutic cells is presented, establishing UNCOVERseq as a robust framework for informed off-target risk assessment in translational gene-editing systems.
Abstract
The rapid expansion of CRISPR-Cas gene editing enables new therapeutic strategies but complicates assessment of unintended editing risks due to emerging modalities and unclear analytical standards. We present UNCOVERseq (Unbiased Nomination of CRISPR Off-target Variants using Enhanced RhPCR), an improved in cellulo off-target nomination workflow that sensitively identifies rare off-target events using defined inputs and analytical process controls. Using an inter-method off-target confirmation benchmarking dataset, UNCOVERseq demonstrates high analytical sensitivity (97.6%) and precision (78%), outperforming published nomination methods. We apply UNCOVERseq across 192 guide RNAs and identify six guides spanning a broad specificity range, enabling relative risk assessment across S. pyogenes Cas9, high-fidelity variants, and base editors in hematopoietic stem and progenitor cells. We further show that double-strand break nomination sites retain strong rank-order concordance with single-strand break–mediated base editing. Together, these results establish UNCOVERseq as a robust framework for informed off-target risk assessment in translational gene-editing systems. CRISPR gene editing promises new therapies but raises concerns about unintended changes. Here, authors present UNCOVERseq, an in-cell method that sensitively detects rare off-target edits, benchmarks performance across editors, and improves risk assessment in therapeutic cells.