Issue 33, 2025

Enhancing the sensitivity of a monoclonal antibody-based heterologous coating strategy for the detection of 6-benzyladenine through the LSPR effect of AuNRs

Abstract

6-Benzyladenine (6-BA), a toxic, synthetic plant growth regulator, is widely used in agriculture despite its harmful properties. However, the detection of 6-BA remains a challenge due to the insufficient sensitivity of current monoclonal antibodies. In this study, the synthetic pathways of 6-BA hapten were optimized based on structural analogues by introducing a carboxyl spacer arm into the benzene ring and combining with a heterologous coating strategy. Through molecular simulation and experiments, a monoclonal antibody with extremely high sensitivity (IC50 = 0.13 ng mL−1) and specificity was developed. The characteristic structure of the distal purine of the binding protein was easily recognized by the immune system, thereby producing a high-sensitivity antibody by linking the carboxy spacer arm to the benzene ring. Based on this antibody, a visual colorimetric sensor based on TMB2+-mediated etching of gold nanorods (AuNRs) was further constructed. A linear relationship was established between Δλ of the LSPR peak and the concentration of 6-BA in the range of 0.04 to 0.63 ng mL−1, with a low detection limit of 0.04 ng mL−1. In addition, the accuracy and precision of the immunosensor were further confirmed by recovery tests (81.0–92.0%) and HPLC validation, which showed the potential application of this immunosensor in the rapid detection of 6-BA.

Graphical abstract: Enhancing the sensitivity of a monoclonal antibody-based heterologous coating strategy for the detection of 6-benzyladenine through the LSPR effect of AuNRs

Supplementary files

Article information

Article type
Paper
Submitted
05 Jun 2025
Accepted
29 Jul 2025
First published
31 Jul 2025

Anal. Methods, 2025,17, 6694-6706

Enhancing the sensitivity of a monoclonal antibody-based heterologous coating strategy for the detection of 6-benzyladenine through the LSPR effect of AuNRs

J. Han, L. Hu, Z. Chen, J. Wen, R. Chen, L. Luo, Q. Aori and Z. Xu, Anal. Methods, 2025, 17, 6694 DOI: 10.1039/D5AY00932D

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