Issue 2, 2024

An intelligent readable and capture-antibody-independent lateral flow immunoassay based on Cu2−xSe nanocrystals for point-of-care detection of Escherichia coli O157:H7

Abstract

Escherichia coli (E. coli) O157:H7 is a common foodborne pathogen which can cause serious harm. It is particularly important to establish a simple and portable method to achieve on-site pathogen detection. In this study, a capture-antibody-independent lateral flow immunoassay (LFIA) was constructed based on Cu2−xSe nanocrystals (Cu2−xSe NCs) for rapid detection of E. coli O157:H7. Cu2−xSe NCs can not only be regarded as the “nano-antibody” for the recognition of E. coli O157:H7 through electrostatic adsorption, but also as nanozymes that show good peroxidase-like catalytic activity. The formed compound of E. coli O157:H7 and Cu2−xSe NCs would be captured by a detection antibody on the T line due to the specific recognition of the antibody and E. coli O157:H7. Then, Cu2−xSe NCs could catalyze the oxidation of TMB by H2O2 to generate oxTMB, thereby generating blue bands. Meanwhile, we developed a mobile app for rapid data analysis. Under the optimal reaction conditions, E. coli O157:H7 could be detected within 70 min. The detection limit of this method was 2.65 × 105 CFU mL−1 with good specificity and stability. Additionally, it could achieve on-site rapid detection of E. coli O157:H7 in environmental water samples, providing a promising biosensor for portable pathogen detection.

Graphical abstract: An intelligent readable and capture-antibody-independent lateral flow immunoassay based on Cu2−xSe nanocrystals for point-of-care detection of Escherichia coli O157:H7

Supplementary files

Article information

Article type
Paper
Submitted
05 Oct 2023
Accepted
20 Nov 2023
First published
22 Nov 2023

Analyst, 2024,149, 357-365

An intelligent readable and capture-antibody-independent lateral flow immunoassay based on Cu2−xSe nanocrystals for point-of-care detection of Escherichia coli O157:H7

Y. Yao, L. Hou, F. Wei, T. Lin and S. Zhao, Analyst, 2024, 149, 357 DOI: 10.1039/D3AN01694C

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