Department of Chemistry, University of Georgia, Athens, GA 30602, USA.
Analyst. 2010 Dec;135(12):3103-9. doi: 10.1039/c0an00453g. Epub 2010 Sep 13.
A spectroscopic assay based on surface-enhanced Raman spectroscopy (SERS) has been developed for rapid genotyping of the measles virus (MeV). Silver nanorods fabricated using an oblique angle vapor deposition method acted as the SERS-active substrate. The SERS spectra of four separate MeV genotypes, i.e. A, H1, D4 and D9, and two separate negative media control samples were analyzed using multivariate statistical methods. Principal components analysis (PCA) and hierarchical cluster analysis (HCA) successfully separated three of the four MeV genotypes studied. The MeV genotypes used in this study had >96% sequence similarity as monitored using the MeV hemagglutinin (H) gene, and the clustering seen in PCA and HCA mirrored this sequence diversity. For example, the MeV genotypes with the highest sequence diversity (3%, A and H1) were the most widely separated in the PCA scores plot and HCA dendogram. Conversely, the MeV genotypes with the lowest sequence diversity (0.5%, D4 and D9) could not be statistically differentiated. However, a supervised chemometric method, partial least squares-discriminant analysis (PLS-DA) was able to separate each of the four MeV strains, the two negative controls, and the background, with >90% sensitivity and >96% selectivity based solely on their inherent SERS spectra. These results demonstrate that SERS, in combination with multivariate statistical methods, is a highly sensitive and rapid viral identification and classification method that can be applied to MeV genotyping.
基于表面增强拉曼光谱(SERS)的光谱分析已被开发用于快速对麻疹病毒(MeV)进行基因分型。使用斜角气相沉积法制造的银纳米棒作为 SERS 活性基底。使用多元统计方法分析了四个单独的 MeV 基因型(A、H1、D4 和 D9)以及两个单独的阴性介质对照样品的 SERS 光谱。主成分分析(PCA)和层次聚类分析(HCA)成功分离了研究的四个 MeV 基因型中的三个。本研究中使用的 MeV 基因型在使用 MeV 血凝素(H)基因监测时具有>96%的序列相似性,PCA 和 HCA 中的聚类反映了这种序列多样性。例如,具有最高序列多样性(3%,A 和 H1)的 MeV 基因型在 PCA 得分图和 HCA 树状图中分离得最开。相反,具有最低序列多样性(0.5%,D4 和 D9)的 MeV 基因型无法进行统计学区分。然而,基于其固有 SERS 光谱,监督化学计量学方法,偏最小二乘判别分析(PLS-DA)能够分离出四个 MeV 菌株中的每一个、两个阴性对照和背景,具有>90%的灵敏度和>96%的选择性。这些结果表明,SERS 与多元统计方法相结合,是一种高度敏感和快速的病毒识别和分类方法,可用于 MeV 基因分型。