Molecularly Imprinted Nanozyme Intelligent Array Sensing Platform Realizes the Enrichment and Quantitative Detection of Small Extracellular Vesicles

化学 分子印迹 纳米技术 普鲁士蓝 堆积 分析物 传感器阵列 分子识别 分子印迹聚合物 辣根过氧化物酶 印记(心理学) 生物传感器 生物物理学 磁性纳米粒子 细胞外小泡 双层 疾病监测 二氧化硅 荧光 小泡
作者
Xiao Wang,Qian-Qian Li,Jing-Yuan Ma,Xun Zhang,Xue Wu,Qi Han,Ziying Jian,Luhai Wang,Bin Yan,Haoan Wu,Ming Ma,Zheng Ge,Yu Zhang
出处
期刊:Analytical Chemistry [American Chemical Society]
卷期号:98 (14): 10444-10456
标识
DOI:10.1021/acs.analchem.5c06664
摘要

As tumor-associated biomarkers, proteins on small extracellular vesicles (sEVs) play a critical role in revealing tumor subtypes and disease progression. Nevertheless, the rapid isolation of sEVs and the quantitative detection of their surface proteins remain challenging. In this study, we developed a nanozyme (PB-MIP-PEG) array sensing platform based on molecular imprinting technology (MIT), which can efficiently enrich sEVs and simultaneously detect three proteins: CD20, CD19, and PD-L1. Fe3O4@TiO2 magnetic beads interact with phosphate groups in the sEVs phospholipid bilayer and, under an external magnetic field, achieve effective separation from complex samples. Prussian blue (PB) nanozymes with peroxidase (POD)-like activity, coated with a silicon dioxide (SiO2) imprinting layer, showed enhanced enrichment toward 3,3′,5,5′-tetramethylbenzidine (TMB), which contributed to improved catalytic activity and detection sensitivity. Furthermore, density functional theory (DFT) and molecular dynamics (MD) simulations further revealed that hydrogen bonding and π–π stacking are the dominant interactions during the imprinting process. By integration of colorimetric imaging with a position-encoding system and employing a convolutional neural network-long short-term memory (CNN-LSTM) model for automated feature extraction, the concentrations of CD20, CD19, and PD-L1 were rapidly distinguished. This array sensing platform, which integrates molecularly imprinted nanozymes with neural network models, provides a new approach for the intelligent analysis of sEVs surface proteins and demonstrates a potential application value in lymphoma-related monitoring.
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