Issue 37, 2024

Cu-MOFs@AuPtNPs nanozyme-based immunosorbent assay for colorimetric detection of alpha-fetoprotein

Abstract

Accurate detection of tumor biomarkers in blood is crucial for diagnosing and treating tumor disease. In this study, a metal enzyme-linked immunosorbent assay (MeLISA) was fabricated for the ultrasensitive and naked-eye detection of tumor biomarker alpha-fetoprotein (AFP) in clinical serum samples. Herein, novel copper metal–organic frameworks and gold platinum nanoparticle composites (Cu-MOFs@AuPtNPs) were synthesized for the first time by an in situ method, which showed an enormous specific surface area and excellent peroxidase (POx) mimicking properties. Cu-MOFs@AuPtNPs linked with antibodies targeting AFP served as a signal nanoprobe to amplify the detection signal. Additionally, the specificity of MeLISA was significantly enhanced through a conventional antigen–antibody reaction and efficient blocking of non-specific sites with BSA. Under optimal conditions, the sandwich-type MeLISA exhibited a wide range from 0.001 to 1000 ng mL−1 (R2 = 0.997) and a low detection limit of 0.86 pg mL−1 (S/N = 3) with acceptable stability, selectivity, and reproducibility. It is noteworthy that the suggested MeLISA performed exceptionally well in detecting clinical serum samples, which were visible to the naked eye and did not require complex platforms. To sum up, the innovative MeLISA based on Cu-MOFs@AuPtNPs provides an alternative method for early cancer diagnosis, particularly in economically backward areas where simple diagnostic apparatus is extremely desirable.

Graphical abstract: Cu-MOFs@AuPtNPs nanozyme-based immunosorbent assay for colorimetric detection of alpha-fetoprotein

Supplementary files

Article information

Article type
Paper
Submitted
28 Jul 2024
Accepted
15 Aug 2024
First published
20 Aug 2024

Anal. Methods, 2024,16, 6443-6450

Cu-MOFs@AuPtNPs nanozyme-based immunosorbent assay for colorimetric detection of alpha-fetoprotein

S. Tang, J. Cai, K. Zhou, Z. Mei, D. Huang, L. Liu, L. Yang, D. Yin and L. Hu, Anal. Methods, 2024, 16, 6443 DOI: 10.1039/D4AY01410C

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