AviTag-seq unifies nucleotide-resolution maps of CRISPR off-targets and AAV vector integrations

Comprehensive safety assessment of gene-editing therapies requires quantifying both off-target cleavage and vector integration. However, current double-strand break (DSB)-dependent assays are fundamentally limited when evaluating nickase-based editors and are hindered by tag polarity constraints. Here, we present AviTag-seq, a platform repurposing AAV Inverted Terminal Repeats (ITRs) as universal capture tags. By exploiting the ITRs' single-stranded hairpin structure, AviTag-seq overcomes polarity issues, enabling high-sensitivity detection with a single primer pair, particularly in iPSCs. Crucially, it captures off-target events from prime and base editors that evade conventional detection. In vivo, AviTag-seq outperformed DISCOVER-Seq+ in profiling Pcsk9 off-targets in mouse liver while simultaneously mapping AAV integration sites. This dual profiling revealed that, unlike in vitro, AAV vectors in vivo preferentially integrate into active gene promoters, highlighting a specific genotoxic risk for liver-directed therapies. AviTag-seq thus offers a unified, regulatory-grade solution for evaluating diverse genome-editing modalities.

Authors

Institutions

Publication Details

Journal
Communications Biology
Published
2026-05-22
DOI
https://doi.org/10.1038/s42003-026-10298-6
Primary Topic
CRISPR and Genetic Engineering
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

AviTag-seq unifies nucleotide-resolution maps of CRISPR off-targets and AAV vector integrations

Dong-Hao Deng, Shan-Chen PANG, Juan-Juan Zhao, Xiao-Bing Zhang et al.
Communications Biology
CRISPR and Genetic Engineering
article

AviTag-seq unifies nucleotide-resolution maps of CRISPR off-targets and AAV vector integrations

Dong-Hao Deng, Shan-Chen PANG, Juan-Juan Zhao, Xiao-Bing Zhang, Shu-Man Zhang, An Gong, Jian-Ping Zhang, Xiao-Yu Ma, Jia-Xin Li, Pei-Dong Bai, Shu-Dong Wang, Fang Dong
article en

Abstract

Comprehensive safety assessment of gene-editing therapies requires quantifying both off-target cleavage and vector integration. However, current double-strand break (DSB)-dependent assays are fundamentally limited when evaluating nickase-based editors and are hindered by tag polarity constraints. Here, we present AviTag-seq, a platform repurposing AAV Inverted Terminal Repeats (ITRs) as universal capture tags. By exploiting the ITRs' single-stranded hairpin structure, AviTag-seq overcomes polarity issues, enabling high-sensitivity detection with a single primer pair, particularly in iPSCs. Crucially, it captures off-target events from prime and base editors that evade conventional detection. In vivo, AviTag-seq outperformed DISCOVER-Seq+ in profiling Pcsk9 off-targets in mouse liver while simultaneously mapping AAV integration sites. This dual profiling revealed that, unlike in vitro, AAV vectors in vivo preferentially integrate into active gene promoters, highlighting a specific genotoxic risk for liver-directed therapies. AviTag-seq thus offers a unified, regulatory-grade solution for evaluating diverse genome-editing modalities.

Communications Biology
Tianjin University of Technology (CN), Chinese Academy of Medical Sciences & Peking Union Medical College (CN), Tianjin Anding Hospital (CN), Institute of Hematology & Blood Diseases Hospital (CN), China University of Petroleum, East China (CN), Tianjin haihe hospital (CN), Tianjin Medical University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 8%
CRISPR and Genetic Engineering
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.