Issue 40, 2023

Colorimetric immunoassay of carcinoembryonic antigen based on the glucose oxidase/MnO2 nanosheet cascade reaction with self-supplying oxygen

Abstract

The detection of carcinoembryonic antigen (CEA) has profound implications in cancer diagnostics and therapeutic monitoring. In this work, we developed a colorimetric immunoassay for the detection of CEA. This assay involves the utilization of zinc(II)-based coordination polymers (ZnCPs) as a host for integrating glucose oxidase (GOx) and anti-carcinoembryonic antigen antibody (anti-CEA), which results in the formation of a detection antibody (anti-CEA/GOx@ZnCPs). The adaptable inclusion properties of ZnCPs enable the preservation of the original catalytic behavior of GOx and antigen capture ability of anti-CEA. Consequently, the anti-CEA/GOx@ZnCPs can act as a detection antibody to facilitate the development of an immunoassay. The combination of anti-CEA/GOx@ZnCPs in the immunoassay triggers a cascade reaction involving GOx and MnO2 nanosheets, leading to the generation of an amplified colorimetric signal through self-supplying oxygen. This colorimetric immunoassay exhibits a linear response ranging from 2 to 180 ng mL−1 CEA and has a detection limit of 50 pg mL−1. The practicality of this colorimetric immunoassay in biological matrices was demonstrated by the successful determination of CEA in serum samples with good recovery and precision. We believe that this study will pave the way to rationally design multifunctional CP-based composites for a wide range of applications in bioanalysis.

Graphical abstract: Colorimetric immunoassay of carcinoembryonic antigen based on the glucose oxidase/MnO2 nanosheet cascade reaction with self-supplying oxygen

Supplementary files

Article information

Article type
Paper
Submitted
14 Aug 2023
Accepted
19 Sep 2023
First published
19 Sep 2023

Anal. Methods, 2023,15, 5351-5359

Colorimetric immunoassay of carcinoembryonic antigen based on the glucose oxidase/MnO2 nanosheet cascade reaction with self-supplying oxygen

Y. Yao, J. Wang, Z. Wang, S. Li and H. Tan, Anal. Methods, 2023, 15, 5351 DOI: 10.1039/D3AY01425H

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