High‐Fidelity Profiling of Multiple Nearby Mutations via Cooperative Recognition

Detection methods with single-nucleotide specificity are essential tools for nucleic acid analysis in diverse clinical and biological settings. However, both hybridization-based and enzyme-based methods are only effective for discriminating single-nucleotide mutations at certain positions, making it difficult to detect nucleic acid targets having multiple nearby mutations. Herein, we describe the design of cooperative recognition probes (CRPs) that integrate both hybridization and ligation-based recognition mechanisms and thus are highly effective for discriminating mutations throughout all positions. The cooperative nature of CRPs further enables AND-gate-based detection of multiple nearby mutations with high fidelity and specificity. Moreover, CRPs generate circular or linear ligation products that can be readily amplified by rolling circle amplification or polymerase chain reaction, making our strategy readily adaptable to diverse biological and clinical settings. Leveraging CRPs, we demonstrate the detection of nucleic acid targets that are difficult to be discriminated using conventional strategies, such as the highly specific discrimination of microRNA from its family members and isoforms, and the high-fidelity identification of drug-resistant single-nucleotide variants in the presence of nearby synchronous mutations in lung cancer samples.

Authors

Institutions

Publication Details

Journal
Angewandte Chemie International Edition
Published
2025-09-01
DOI
https://doi.org/10.1002/anie.202509901
Citations
1
Primary Topic
Advanced biosensing and bioanalysis techniques
Type
article
Field-Weighted Citation Impact
0.81

Funders

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

High‐Fidelity Profiling of Multiple Nearby Mutations via Cooperative Recognition

Ruonan He, Ruijie Deng, Hao Yang, Zhen Zeng et al.
1 citations
Angewandte Chemie International Edition
Advanced biosensing and bioanalysis techniques
0.81
article

High‐Fidelity Profiling of Multiple Nearby Mutations via Cooperative Recognition

Ruonan He, Ruijie Deng, Hao Yang, Zhen Zeng, Feng Li, Yong Zhang, Feng Lin, Xianglin Zhu, Xuhan Xia
article en
1 citations

Abstract

Detection methods with single-nucleotide specificity are essential tools for nucleic acid analysis in diverse clinical and biological settings. However, both hybridization-based and enzyme-based methods are only effective for discriminating single-nucleotide mutations at certain positions, making it difficult to detect nucleic acid targets having multiple nearby mutations. Herein, we describe the design of cooperative recognition probes (CRPs) that integrate both hybridization and ligation-based recognition mechanisms and thus are highly effective for discriminating mutations throughout all positions. The cooperative nature of CRPs further enables AND-gate-based detection of multiple nearby mutations with high fidelity and specificity. Moreover, CRPs generate circular or linear ligation products that can be readily amplified by rolling circle amplification or polymerase chain reaction, making our strategy readily adaptable to diverse biological and clinical settings. Leveraging CRPs, we demonstrate the detection of nucleic acid targets that are difficult to be discriminated using conventional strategies, such as the highly specific discrimination of microRNA from its family members and isoforms, and the high-fidelity identification of drug-resistant single-nucleotide variants in the presence of nearby synchronous mutations in lung cancer samples.

Angewandte Chemie International EditionVol. 64(42)
Sichuan University (CN), Chengdu University of Technology (CN)
National Natural Science Foundation of China, National Key Research and Development Program of China
Openalex Percentile: Top 24%
Advanced biosensing and bioanalysis techniques
0.81
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.