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通过特别设计的 SERS 活性基底和光谱分析检测活 SARS-CoV-2 病毒及其变体。

Detection of live SARS-CoV-2 virus and its variants by specially designed SERS-active substrates and spectroscopic analyses.

机构信息

Engineered Materials for Biomedical Applications Laboratory, Department of Materials Science and Engineering, National Cheng Kung University, Tainan, 701, Taiwan.

Department of Pathology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, 704, Taiwan; Department of Medical Laboratory Science and Biotechnology, College of Medicine, National Cheng Kung University, Tainan, 701, Taiwan.

出版信息

Anal Chim Acta. 2023 May 22;1256:341151. doi: 10.1016/j.aca.2023.341151. Epub 2023 Mar 30.

Abstract

A method using label-free surface enhanced Raman spectroscopy (SERS) based on substrate design is provided for an early detection and differentiation of spike glycoprotein mutation sites in live SARS-CoV-2 variants. Two SERS-active substrates, Au nanocavities (Au NCs) and Au NPs on porous ZrO (Au NPs/pZrO), were used to identify specific peaks of A.3, Alpha, and Delta variants at different concentrations and demonstrated the ability to provide their SERS spectra with detection limits of 0.1-1.0% (or 10 copies/mL). Variant identification can be achieved by cross-examining reference spectra and analyzing the substrate-analyte relationship between the suitability of the analyte upon the hotspot(s) formed at high concentrations and the effective detection distance at low concentrations. Mutation sites on the S1 chain of the spike glycoprotein for each variant may be related and distinguishable. This method does not require sample preprocessing and therefore allows for fast screening, which is of high value for more comprehensive and specific studies to distinguish upcoming variants.

摘要

提供了一种基于基底设计的无标记表面增强拉曼光谱(SERS)方法,用于早期检测和区分活 SARS-CoV-2 变体中的刺突糖蛋白突变位点。使用两种 SERS 活性基底,金纳米腔(Au NCs)和多孔 ZrO 上的金纳米颗粒(Au NPs/pZrO),以不同浓度鉴定 A.3、Alpha 和 Delta 变体的特定峰,并证明能够提供其 SERS 光谱,检测限为 0.1-1.0%(或 10 拷贝/mL)。通过交叉检查参考光谱并分析基底-分析物之间的关系,可以实现变体识别,即在高浓度下形成热点时分析物的适用性与在低浓度下的有效检测距离之间的关系。每个变体的刺突糖蛋白 S1 链上的突变位点可能相关且可区分。该方法不需要样品预处理,因此允许快速筛选,对于更全面和特定的研究来区分即将出现的变体具有很高的价值。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae14/10060322/f445bb9a2f39/ga1_lrg.jpg

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