Multiplexed Nucleic Acid Detection Using Sequence-Specific Cleavage Mediated by Repair Endonucleases

Rapid, portable detection of multiple nucleic acid targets is essential for infectious disease surveillance and precision oncology. CRISPR-based diagnostics have set a high bar for sensitivity and single-base specificity, yet their reliance on collateral nuclease activity complicates multiplexing, integration with amplification, and point-of-care deployment. Here, we present TIMBER (templated incision mediated by endonucleases of repair), a non-CRISPR platform that achieves comparable performance without nonspecific nuclease activity. TIMBER uses a repair endonuclease to cleave probes containing abasic sites only when they are hybridized to a matched target. The system exhibits strong specificity, enabling single-nucleotide polymorphism discrimination. TIMBER provides an analytical limit of detection of 12 pM without preamplification or one copy per μL (1.6 aM) with preamplification through RT-PCR or RT-LAMP, with results observable by visual fluorescence or on lateral flow. We deploy TIMBER to detect SARS-CoV-2 in clinical saliva samples and further demonstrate multiplex detection of four targets, enabling identification of EGFR mutations in lung cancer samples. This approach offers a flexible, rapid, and low-instrument-ready solution for diverse nucleic acid diagnostics, from viral detection to cancer genotyping.

Authors

Institutions

Publication Details

Journal
ACS Sensors
Published
2026-10-19
DOI
https://doi.org/10.1021/acssensors.6c02339
Primary Topic
Advanced biosensing and bioanalysis techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Multiplexed Nucleic Acid Detection Using Sequence-Specific Cleavage Mediated by Repair Endonucleases

Vel Murugan, Padric M. Garden, Alexander A. Green, Yudan Li
ACS Sensors
Advanced biosensing and bioanalysis techniques
article

Multiplexed Nucleic Acid Detection Using Sequence-Specific Cleavage Mediated by Repair Endonucleases

Vel Murugan, Padric M. Garden, Alexander A. Green, Yudan Li
article en

Abstract

Rapid, portable detection of multiple nucleic acid targets is essential for infectious disease surveillance and precision oncology. CRISPR-based diagnostics have set a high bar for sensitivity and single-base specificity, yet their reliance on collateral nuclease activity complicates multiplexing, integration with amplification, and point-of-care deployment. Here, we present TIMBER (templated incision mediated by endonucleases of repair), a non-CRISPR platform that achieves comparable performance without nonspecific nuclease activity. TIMBER uses a repair endonuclease to cleave probes containing abasic sites only when they are hybridized to a matched target. The system exhibits strong specificity, enabling single-nucleotide polymorphism discrimination. TIMBER provides an analytical limit of detection of 12 pM without preamplification or one copy per μL (1.6 aM) with preamplification through RT-PCR or RT-LAMP, with results observable by visual fluorescence or on lateral flow. We deploy TIMBER to detect SARS-CoV-2 in clinical saliva samples and further demonstrate multiplex detection of four targets, enabling identification of EGFR mutations in lung cancer samples. This approach offers a flexible, rapid, and low-instrument-ready solution for diverse nucleic acid diagnostics, from viral detection to cancer genotyping.

ACS Sensors
Boston University (US), Arizona State University (US)
Openalex Percentile: Top 23%
Advanced biosensing and bioanalysis techniques
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.