Aug 2026· Nature Communications· Vol 17· 3 citations· 62 references
Medicine
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.
Accurate detection of off-target activity in primary human cells is crucial for ensuring the safety of gene therapies, yet existing methods often lack sufficient sensitivity. To address this limitation, we develop Tracking-seq2, an advanced technology that integrates exogenous 5′ → 3′ exonuclease treatment and non-homologous end joining (NHEJ) pathway inhibitors with the original Tracking-seq. Tracking-seq2 exhibits enhanced sensitivity in profiling off-target sites of diverse genome editors—including Cas9, Cas12a, cytosine base editors (CBEs), adenine base editors (ABEs), and prime editors (PEs). Critically, Tracking-seq2 is directly applicable to clinically relevant primary human cell types, such as T cells and CD34
+
hematopoietic stem and progenitor cells (HSPCs). Furthermore, our findings reveal that genomic variations drive distinct off-target heterogeneity across different individuals, highlighting the necessity for personalized safety assessment in clinical genome editing applications. Tracking-seq2 provides a robust platform for sensitive off-target detection in primary cells, with sensitivity comparable to or exceeding current state-of-the-art methods.
This review focuses on analytical and translational frameworks for CRISPR fidelity assessment, with emphasis on the strengths and limitations of current bioanalytical platforms.
Arpita Mukherjee· Journal of Rare Diseases· 1 citation
Accurate identification of CRISPR-Cas9 off-target sites is essential for the safety assessment of genome-editing-based therapies. While numerous in silico prediction tools have been developed, their comparative performance and practical utility in preclinical workflows remain incompletely defined. We performed a systematic benchmarking of 14 in silico CRISPR-Cas9 off-target prediction tools, including both standard approaches and machine learning-based models. The analysis was based on a curated dataset derived from the CRISPRoffT database, comprising 3,827 deep-sequenced genomic sites across 26 guide RNA/Cas9 combinations in human cells. Sites with indel frequencies ≥0.1% were operationally defined as true off-targets. We evaluated tool performance using score distributions, correlation with indel frequencies, precision-recall characteristics, recall among top-ranked candidate sites, and the effect of combining tools. All tools assigned higher scores to true off-target sites compared with nontarget sites, although substantial overlap between classes was observed. Correlation between prediction scores and indel frequencies was weak to moderate, indicating limited ability to predict editing magnitude. Precision-recall performance was moderate across all tools, reflecting inherent trade-offs between sensitivity and specificity. Recall increased with the number of predicted sites considered, reaching approximately 77% among the top 500 and up to 83% among the top 1,250 sites, but leaving a substantial fraction of true off-targets undetected. Combining tools yielded only modest improvements. Current in silico tools enable prioritization of CRISPR-Cas9 off-target candidates but remain limited in their ability to comprehensively identify and quantitatively predict off-target activity. Our findings highlight the importance of considering both ranking performance and candidate site coverage and support the use of combined computational and experimental strategies for robust off-target assessment in preclinical gene editing workflows.
M. M. Kaufmann, Maren Hackenberg, William Jobson Pargeter et al.· Human Gene Therapy· 0 citations
It is shown that off-target editing and translocations vary widely between individual cells and organs, motivating the development of more sensitive and organ-specific safety assays for CRISPR therapies.
Alexandra Madsen, Niklas Selfjord, M. Martinez-Lage et al.· Nature Communications· 0 citations
The current developments in engineering of Cas effector protein, optimizing guide RNA designs, developing advanced reporter probes, and utilizing chemical additives are summarized and their significant contribution to increasing the fidelity of editing and diagnostic sensitivity is highlighted.
Sheng Li, Han Li, Bojie Chen et al.· Interdisciplinary Medicine· 0 citations
ABSTRACT Amplification‐free Cas12a diagnostics with split crRNA enable rapid and programmable target recognition, yet insufficient understanding of DNA activator architecture prevents predictable control over trans‐cleavage activity and sensitivity. Here we systematically map over 200 split DNA activator configurations by introducing nicks at every position across both strands. The mapping reveals that target strand nicks suppress activity with position‐dependent severity, while non‐target strand nicks enhance activity. Guided by these rules, we engineer an optimized split activator pair that achieves attomolar microRNA detection (LOD: 112 aM), ∼480‐fold higher sensitivity than intact activators. The enhanced sensitivity supports multiplexed live‐cell profiling of five miRNAs for machine learning‐based cancer cell stratification, and is further generalized to non‐nucleic acid targets, including APE1 enzyme (0.0073 U/L) and HClO (2.37 pM) through position‐informed cleavable sites. This work provides a generalizable methodology for engineering CRISPR‐Cas12a performance across diagnostic and biosensing applications.
Xiaoyan Tang, Zhe Li, Yuning Lu et al.· Advancement of science· 0 citations