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Orthogonal dual molecularly imprinted polymer-based plasmonic immunosandwich assay: A double characteristic recognition strategy for specific detection of glycoproteins

分子印迹聚合物 分子印迹 对偶(语法数字) 糖蛋白 化学 纳米技术 材料科学 色谱法 生物化学 选择性 文学类 艺术 催化作用
作者
Lingli Zhou,Yijia Wang,Rongrong Xing,Jin Chen,Jia Liu,Wei Li,Zhen Liu
出处
期刊:Biosensors and Bioelectronics [Elsevier]
卷期号:145: 111729-111729 被引量:64
标识
DOI:10.1016/j.bios.2019.111729
摘要

Sensitive and specific detection methods are critical to the detection of glycoproteins. Immunoassay has been a powerful tool for this purpose, in which antibodies or their mimics particularly molecularly imprinted polymers (MIPs) are used for specific recognition. Epitope and glycan are two structure features of a glycoprotein. However, immunoassays based on simultaneous recognition towards the two characteristics have been scarcely explored so far. Herein we present a new strategy called orthogonal dual molecularly imprinted polymer-based plasmonic immunosandwich assay (odMIP-PISA). It relies on double recognition towards a target glycoprotein by two different types of MIPs, using epitope-imprinted gold nanoparticles (AuNPs)-coated slide as capturing substrate to recognize the peptide epitope and glycans-imprinted Raman-active silver nanoparticles as labeling nanotags to recognize the glycans. Carcinoembryonic antigen (CEA), a routinely used marker for colon cancer, was used as a test glycoprotein. The orthogonal double recognition apparently improved the specificity, reducing the maximum cross-reactivity from 14.4% for epitope recognition and 15.2% for glycan recognition to 8.2% for double recognition. Meanwhile, the plasmonic nanostructure-based Raman detection provided ultrahigh sensitivity, yielding a limit of detection of 5.56 × 10-14 M (S/N = 10). Through measuring the CEA level in human serum, this method permitted differentiation of colon cancer patient from healthy individual. Compared with the traditional immunoassay, odMIP-PISA exhibited multiple advantages, including simplified procedure (6 steps), speed (30 min), reduced cost, and so on. Therefore, this new approach holds great promise in many applications particularly clinical diagnosis.
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