Raman sensor assisted by cascade signal amplification for simultaneous detection of two extracellular antibiotic resistance genes

Abstract

Accompanied by the widespread use of antibiotics, antibiotic resistance genes (ARGs) have rapidly proliferated in the environment, necessitating the urgent development of sensitive detection methods. However, most reported methods focus on the determination of only one subtype of ARGs, which leads to an inadequate evaluation of ARG pollution levels. Surface-enhanced Raman scattering (SERS) assays are highly feasible and can serve as universal detection methods through Raman molecules. Conventional SERS assays may face background interference from complex matrices and demonstrate unsatisfactory sensitivity for trace analysis. In this study, a multifunctional magnetic recognition probe was designed to effectively separate and enrich two subtypes of penicillin ARGs. Subsequently, a cascade nicking-polymerization signal amplification process was successfully conducted for the captured ARGs, resulting in a SERS detection probe featuring two distinct Raman molecules capable of sensitive detection for bla-TEM and bla-CTX-M-1. This achievement can be likened to “hitting two birds with one stone”. Due to the signal amplification strategy, the developed sensor obtained good sensitivities with limits of detection (LOD) of 0.33 zM for bla-TEM and 0.29 zM for bla-CTX-M-1. Furthermore, the developed detection method enabled accurate quantitative analysis when compared with conventional polymerase chain reaction (PCR) assays.

Graphical abstract: Raman sensor assisted by cascade signal amplification for simultaneous detection of two extracellular antibiotic resistance genes

Supplementary files

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Article information

Article type
Paper
Submitted
15 May 2025
Accepted
24 Jun 2025
First published
08 Jul 2025

Analyst, 2025, Advance Article

Raman sensor assisted by cascade signal amplification for simultaneous detection of two extracellular antibiotic resistance genes

X. Shi, Q. Wang, Y. Zhang, C. Ge, Y. Sun, Y. Zhang, M. Li, X. Gong, X. Song and S. Zhang, Analyst, 2025, Advance Article , DOI: 10.1039/D5AN00536A

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