Ultrasensitive inner filter effect fluorescence sensing platform for alkaline phosphatase based on arginine surface-engineered gold nanoclusters

纳米团簇 碱性磷酸酶 荧光 精氨酸 化学 纳米技术 滤波器(信号处理) 表面改性 材料科学 生物化学 计算机科学 光学 物理 物理化学 氨基酸 计算机视觉
作者
Jincheng Xiong,Shuai Zhang,Linqian Qin,Wenchong Shan,Boyan Sun,Jianzhong Shen,Haiyang Jiang
出处
期刊:Sensors and Actuators B-chemical [Elsevier]
卷期号:378: 133177-133177 被引量:13
标识
DOI:10.1016/j.snb.2022.133177
摘要

Developing a simple yet powerful method for enzyme activity monitoring is of great significance in the early diagnosis of diseases. Herein, we designed a precise spectral overlap-triggered inner filter effect (IFE) fluorescence sensing platform for alkaline phosphatase (ALP) detection. ALP could catalyze hydrolysis of p-nitrophenylphosphate (PNPP) to generate p-nitrophenol (PNP) and PNP acts as a powerful acceptor in IFE reaction system to influence the excitation of fluorescent donors. Based on this principle, a standardized procedure was proposed for rational screening of high-performance donor/acceptor pairs to maximize IFE efficiency. The results proved that perfect spectral overlap and high quantum yield of fluorescent donors synergistically contribute to maximizing IFE efficiency. In this case, a high quantum yield of 51.7% of the arginine surface-engineered gold nanoclusters (Arg/ATT-AuNCs) produced perfect spectral overlap with PNP and achieved maximal IFE efficiency, which was eventually selected as fluorescent signal tag for ALP detection. The Arg/ATT-AuNCs-based approach achieved ultrasensitive detection of ALP with a detection limit of 0.0062 U/L, which exhibited 4.2–25.8-fold improvement compared to other donors. Furthermore, the accuracy and reliability of established method in real human serum samples were verified by spiking analysis and commercial kits, thus providing a promising platform for monitoring ALP. This work aims to provide a standardized procedure for rational screening high-performance donor/acceptor pairs and to develop an ultrasensitive IFE sensing platform for biomarkers detection, which will open up a promising horizon for other biomarkers detection in clinical diagnostics.
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