Hu Cong, Xie Wen, Liu Jin, Zhang Yajing, Sun Ying, Cai Zongwei, Lin Zian
Ministry of Education Key Laboratory of Analytical Science for Food Safety and Biology, Fujian Provincial Key Laboratory of Analysis and Detection Technology for Food Safety, College of Chemistry, Fuzhou University, Fuzhou, Fujian 350108, China.
State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, 224 Waterloo Road, Kowloon Tong, Hong Kong 999077, Special Administrative Region, P. R. China.
ACS Appl Mater Interfaces. 2024 Dec 25;16(51):71048-71059. doi: 10.1021/acsami.4c18284. Epub 2024 Dec 12.
Given the crucial role of thiols in maintaining normal physiological functions, it is essential to establish a high-throughput and sensitive analytical method to identify and quantify various thiols accurately. Inspired by the iron porphyrin active center of natural horseradish peroxidase (HRP), we designed and synthesized two iron porphyrin covalent organic frameworks (Fe-COF-H and Fe-COF-OH) with notable peroxidase-like (POD) activity, capable of catalyzing 3,3',5,5'-tetramethylbenzidine (TMB) into oxidized TMB with three distinct absorption peaks. Based on these, a six-channel nanozyme colorimetric sensor array was constructed, which could map the specific fingerprints of various thiols. Subsequently, machine learning techniques, including supervised learning with linear discriminant analysis (LDA), decision trees (DT) and artificial neural networks (ANN), unsupervised learning with hierarchical cluster analysis (HCA), and ensemble learning with random forests (RF), were used for precise identification of thiols in complex systems, with a detection limit as low as 50 nM. Significantly, the sensor array demonstrated strong potential for practical applications, including analyzing homocysteine (Hcy) in human serum, mercaptoacetic acid (TGA) in depilatory creams, and glutathione (GSH) in cell lysates, thereby showing promise for use in disease diagnosis.
鉴于硫醇在维持正常生理功能中起着关键作用，建立一种高通量且灵敏的分析方法以准确识别和定量各种硫醇至关重要。受天然辣根过氧化物酶（HRP）的铁卟啉活性中心启发，我们设计并合成了两种具有显著类过氧化物酶（POD）活性的铁卟啉共价有机框架（Fe-COF-H和Fe-COF-OH），它们能够将3,3',5,5'-四甲基联苯胺（TMB）催化氧化为具有三个不同吸收峰的氧化型TMB。基于此，构建了一种六通道纳米酶比色传感器阵列，该阵列能够绘制各种硫醇的特定指纹图谱。随后，运用机器学习技术，包括采用线性判别分析（LDA）、决策树（DT）和人工神经网络（ANN）的监督学习，采用层次聚类分析（HCA）的无监督学习，以及采用随机森林（RF）的集成学习，对复杂体系中的硫醇进行精确识别，检测限低至50 nM。值得注意的是，该传感器阵列在实际应用中展现出强大潜力，包括分析人血清中的同型半胱氨酸（Hcy）、脱毛膏中的巯基乙酸（TGA）以及细胞裂解物中的谷胱甘肽（GSH），从而在疾病诊断方面显示出应用前景。
