Issue 19, 2019

An antifouling interface integrated with HRP-based amplification to achieve a highly sensitive electrochemical aptasensor for lysozyme detection

Abstract

We report here a highly sensitive sandwich type electrochemical aptasensor for lysozyme (lys) detection by the integration of an antifouling interface with HRP-based signal amplification. The biosensing interface with antifouling ability is designed, consisting of a lys-binding aptamer (LBA), dithiothreitol (DTT) and mercaptohexanol (MCH). When lys is captured by the immobilized LBA due to the specific recognition of the aptamer, gold nanoparticles (AuNPs) functionalized with HRP and LBA (HRP-AuNP-LBA) are further conjugated to the surface-bound lys, forming a sandwich assay format. HRP catalyzes the chemical oxidation of hydroquinone (HQ) by hydrogen peroxide (H2O2) to produce benzoquinone (BQ) which results in a large electrochemical reduction signal of BQ. Therefore, this reduction signal measured by differential pulse voltammetry (DPV) is used to detect lys. The catalytic behavior of HRP toward the reaction between HQ and H2O2, together with the high loading of HRP on AuNPs, remarkably amplifies the signal. A linear relationship between the DPV response and the logarithm of lys concentration from 0.01 pg mL−1 to 105 pg mL−1 with a detection limit of 0.003 pg mL−1 (S/N = 3) is obtained. The proposed biosensing platform combines antifouling ability and signal amplification, resulting in high sensitivity, providing an effective way for ultrasensitive assay of protein biomarkers in complex media.

Graphical abstract: An antifouling interface integrated with HRP-based amplification to achieve a highly sensitive electrochemical aptasensor for lysozyme detection

Article information

Article type
Paper
Submitted
28 Jul 2019
Accepted
12 Aug 2019
First published
13 Aug 2019

Analyst, 2019,144, 5794-5801

An antifouling interface integrated with HRP-based amplification to achieve a highly sensitive electrochemical aptasensor for lysozyme detection

Z. Liu and H. Wang, Analyst, 2019, 144, 5794 DOI: 10.1039/C9AN01430F

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