Issue 17, 2024

A CRISPR/Cas12a-powered gold/nickel foam surface-enhanced Raman spectroscopy biosensor for nucleic acid specific detection in foods

Abstract

Food is a necessary source of energy, but it also serves as a pathway for transmitting infectious pathogens, making food safety a matter of great concern. Rapid, accurate, and specific detection methods for foodborne viruses are crucial. Surface-Enhanced Raman Scattering (SERS), due to its superior sensitivity and characteristic fingerprint spectra, holds enormous potential. However, due to the limitations of SERS, it requires specific conditions to achieve specificity. In order to enhance the specificity and accuracy of nucleic acid detection based on SERS, we have developed a CRISPR-Cas12a-mediated SERS technique to identify target DNA, harnessing the targeting recognition capability of CRISPR-Cas12a and ultra-sensitive SERS tags and successfully addressing SERS’ lack of specific detection capability. This system includes a gold/nickel foam substrate (Au-NFs) and a reporter (ssDNA–ROX). The phenomenon of colloidal gold/silver nano-aggregation due to magnesium ions, which is commonly encountered in CRISPR-SERS, was simultaneously solved using AuNFs. The qualitative and quantitative analysis of target DNA in drinking water was performed by monitoring the intensity change of ROX Raman reporter molecules. The results showed that the sensor detected DNA within 30 min and the limit of detection (LOD) was 8.23 fM. This is expected to become one of the alternative methods for nucleic acid detection for its rapid detection and high specificity.

Graphical abstract: A CRISPR/Cas12a-powered gold/nickel foam surface-enhanced Raman spectroscopy biosensor for nucleic acid specific detection in foods

Supplementary files

Article information

Article type
Paper
Submitted
31 May 2024
Accepted
14 Jul 2024
First published
17 Jul 2024

Analyst, 2024,149, 4343-4350

A CRISPR/Cas12a-powered gold/nickel foam surface-enhanced Raman spectroscopy biosensor for nucleic acid specific detection in foods

Y. Liu, S. Gou, L. Qiu, Z. Xu, H. Yang, S. Yang and Y. Zhao, Analyst, 2024, 149, 4343 DOI: 10.1039/D4AN00778F

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