Issue 16, 2019

Hybridization chain reaction-enhanced enzyme biomineralization for ultrasensitive colorimetric biosensing of a protein biomarker

Abstract

By employment of an aptamer-initiated hybridization chain reaction (HCR) to enhance the enzyme biomineralization of cupric subcarbonate, this work develops a novel colorimetric biosensing method for protein analysis. The HCR product was used to specifically attach a large amount of urease-functionalized gold nanoparticles (Au NPs) for the preparation of a gold nanoprobe. After the sandwich biorecognition reactions, this nanoprobe could be quantitatively captured onto the antibody-functionalized magnetic bead (MB) platform. Then, numerous copper ions would be enriched onto the MB surface through the urease-induced biomineralization of cupric subcarbonate. Based on the complete release of Cu2+ ions for the sensitive copper chromogenic reaction, convenient colorimetric signal transduction was thus achieved for the quantitative analysis of the target analyte of the carcinoembryonic antigen. The HCR product provides a large number of biotin sites for the attachment of Au NP nanotags. The biomineralization reaction of high-content urease loaded onto Au NPs leads to highly efficient Cu2+ enrichment for signal amplification. So this method features excellent performance including a very wide linear range and a low detection limit down to 0.071 pg mL−1. In addition, the satisfactory results of real sample experiments reveal that this method possesses huge potential for practical applications.

Graphical abstract: Hybridization chain reaction-enhanced enzyme biomineralization for ultrasensitive colorimetric biosensing of a protein biomarker

Supplementary files

Article information

Article type
Paper
Submitted
16 May 2019
Accepted
11 Jul 2019
First published
12 Jul 2019

Analyst, 2019,144, 5003-5009

Hybridization chain reaction-enhanced enzyme biomineralization for ultrasensitive colorimetric biosensing of a protein biomarker

B. Li, L. Qin, J. Zhou, X. Cai, G. Lai and A. Yu, Analyst, 2019, 144, 5003 DOI: 10.1039/C9AN00898E

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