Discrimination of bacteria using surface-enhanced Raman spectroscopy
Discrimination of bacteria using surface-enhanced Raman spectroscopy
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DOI：
10.1021/ac034689c
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发表时间：
2004-01-01
影响因子：
7.4
通讯作者：
Goodacre, R
中科院分区：
文献类型：
--
作者：
Jarvis, RM;Goodacre, R
拉曼光谱最近被证明是一种潜在的强大的全生物体指纹技术，并吸引了微生物系统学的兴趣，用于快速鉴定细菌和真菌。然而，虽然拉曼效应非常弱，只有十分之一的入射光子被拉曼散射（因此收集时间大约为几分钟），但如果分子附着在或显微镜下接近适当粗糙的表面，这种效应可以大大增强（大约10（3）-10（6）倍），这种技术称为表面增强拉曼散射（Sers）。在这项研究中，表面增强Sers，采用聚集的银胶体基板，用于分析临床细菌分离与尿路感染的集合。虽然每个光谱需要10秒收集，以获得可重复的数据，50个光谱收集，使每个细菌的光谱采集时间类似于8分钟。多元统计技术的判别函数分析（DFA）和层次聚类分析（HCA），以分组这些生物体的基础上，他们的光谱指纹。由此产生的排序图和树状图显示正确的分组，这些生物体，包括歧视菌株水平的样品组大肠杆菌，这是验证了测试光谱投影到DFA和HCA空间。我们相信这是第一份使用Sers显示细菌歧视的报告。
Raman spectroscopy has recently been shown to be a potentially powerful whole-organism fingerprinting technique and is attracting interest within microbial systematics for the rapid identification of bacteria and fungi. However, while the Raman effect is so weak that only similar to1 in 10(8) incident photons are Raman scattered (so that collection times are in the order of minutes), it can be greatly enhanced (by some 10(3)-10(6)-fold) if the molecules are attached to, or microscopically close to, a suitably roughened surface, a technique known as surface-enhanced Raman scattering (SERS). In this study, SERS, employing an aggregated silver colloid substrate, was used to analyze a collection of clinical bacterial isolates associated with urinary tract infections. While each spectrum took 10 s to collect, to acquire reproducible data, 50 spectra were collected making the spectral acquisition times per bacterium similar to8 min. The multivariate statistical techniques of discriminant function analysis (DFA) and hierarchical cluster analysis (HCA) were applied in order to group these organisms based on their spectral fingerprints. The resultant ordination plots and dendrograms showed correct groupings for these organisms, including discrimination to strain level for a sample group of Escherichia coli, which was validated by projection of test spectra into DFA and HCA space. We believe this to be the first report showing bacterial discrimination using SERS.