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

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 <i>bla</i><sub>-<i>TEM</i></sub> and <i>bla</i><sub>-<i>CTX-M-1</i></sub>. 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 <i>bla</i><sub>-<i>TEM</i></sub> and 0.29 zM for <i>bla</i><sub>-<i>CTX-M-1</i></sub>. Furthermore, the developed detection method enabled accurate quantitative analysis when compared with conventional polymerase chain reaction (PCR) assays.

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Publication Details

Journal
The Analyst
Published
2025-01-01
DOI
https://doi.org/10.1039/d5an00536a
Primary Topic
Biosensors and Analytical Detection
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Q.P. Wang, Xinli Shi, Xiao Gong, Xinyue Song et al.
The Analyst
Biosensors and Analytical Detection
article

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

Q.P. Wang, Xinli Shi, Xiao Gong, Xinyue Song, Yuqi Zhang, Chen Ge, Yan Zhang, Shusheng Zhang, Yujun Sun, Mengmeng Li
article en

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 <i>bla</i><sub>-<i>TEM</i></sub> and <i>bla</i><sub>-<i>CTX-M-1</i></sub>. 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 <i>bla</i><sub>-<i>TEM</i></sub> and 0.29 zM for <i>bla</i><sub>-<i>CTX-M-1</i></sub>. Furthermore, the developed detection method enabled accurate quantitative analysis when compared with conventional polymerase chain reaction (PCR) assays.

The AnalystVol. 150(16)
Shandong University (CN), Linyi University (CN), Linyi People's Hospital (CN), Livestock Breeding and Veterinary Department (MM), Nanjing University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 84%
Biosensors and Analytical Detection
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