Recombinase polymerase amplification via proximity hybridization-triggered amplification conversion for detecting diverse nucleic acid targets

Abstract

In this study, we present a novel nucleic acid testing paradigm termed "amplification conversion," which decouples target recognition from amplification to create a detection platform adaptable to diverse target types. Unlike conventional target-dependent amplification or CRISPR-RPA strategies that rely on complex enzyme systems, our approach innovatively integrates proximity hybridization assay (PHA) and recombinase polymerase amplification (RPA). Crucially, the recombinase from the RPA system empowers PHA to overcome its inherent limitation, enabling specific recognition of double-stranded DNA. Concurrently, the structure-switching mechanism of PHA converts amplification from a target-specific to a universal reporter sequence, thereby decoupling specific identification from nonspecific, template-driven amplification. This enables a one pot, closed tube isothermal reaction based on shared amplification module and unified recognition mechanism. We demonstrated its suitability for the detection of diverse nucleic acid targets by successfully detecting the wild-type MTHFR gene, the MTHFR C677T mutation, and the SMN1 exon 7 deletion in clinical samples and achieving a detection limit of 0.1 ng/μL (nearly 15 copies) for genomic DNA within 120 min. The method exhibited 100% concordance with clinical RT-PCR results, confirming its robust accuracy. By offering a simple, one-pot, low-cost (<$5 per test), and multiplex target compatible solution, this amplification conversion-based RPA strategy showcases significant potential for application in molecular diagnostics.

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Article information

Article type
Paper
Submitted
27 Feb 2026
Accepted
07 May 2026
First published
07 May 2026

Anal. Methods, 2026, Accepted Manuscript

Recombinase polymerase amplification via proximity hybridization-triggered amplification conversion for detecting diverse nucleic acid targets

L. Zhang, Y. Ma, W. Wang, J. Zhang, Y. Zhou, Y. Huang, X. Yao, L. Xu and Y. Ge, Anal. Methods, 2026, Accepted Manuscript , DOI: 10.1039/D6AY00342G

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