Issue 3, 2021

A redox modulated fluorescence nanoplatform for the detection of alkaline phosphatase activity with fluorescent polydopamine nanoparticles

Abstract

Herein, we simply synthesized intrinsic fluorescent polydopamine nanoparticles (PDA NPs) in sodium hydroxide solution (NaOH, pH 11), and constructed a new fluorescence nanoplatform for the detection of alkaline phosphatase (ALP) using PDA NPs as an effective signal reporter. The nanoplatform was constructed by the combination of enzymatic hydrolysis of ALP to the substrate L-ascorbic acid-2-phosphate (AA2P) and the chemical redox reaction between L-ascorbic acid (AA) and mercury ion (Hg2+). The fluorescence of PDA NPs could be effectively quenched by Hg2+ through the coordination effect between Hg2+ and the functional groups on the surface of PDA NPs. However, the quenching effect was greatly inhibited by the addition of AA into the solution. Based on this point, the activity of ALP could be monitored by hydrolysis of the substrate AA2P to AA and the fluorescence output of PDA NPs. The nanoplatform exhibited high sensitivity and desirable selectivity for ALP detection. With a wide linear range of 0 to 18 U L−1, a detection limit of 0.4 U L−1 was achieved using the developed nanosensor. The proposed method could not only be used to screen the inhibitor of ALP but also be used to detect ALP activity in human serum samples successfully. Moreover, the strategy can easily be expanded to determining other kinds of enzymes participating in AA-generation reactions.

Graphical abstract: A redox modulated fluorescence nanoplatform for the detection of alkaline phosphatase activity with fluorescent polydopamine nanoparticles

Supplementary files

Article information

Article type
Paper
Submitted
17 Nov 2020
Accepted
09 Dec 2020
First published
09 Dec 2020

Anal. Methods, 2021,13, 322-326

A redox modulated fluorescence nanoplatform for the detection of alkaline phosphatase activity with fluorescent polydopamine nanoparticles

J. Tian, Y. Fang, R. Yu, Z. Zhang, Y. Zhuo, J. He, S. Wu, Q. Xiao and X. Kong, Anal. Methods, 2021, 13, 322 DOI: 10.1039/D0AY02129F

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