Classification and identification of pigmented cocci bacteria relevant to the soil environment via Raman spectroscopy
Classification and identification of pigmented cocci bacteria relevant to the soil environment via Raman spectroscopy
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DOI：
10.1007/s11356-015-4593-5
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发表时间：
2015-12-01
影响因子：
5.8
通讯作者：
Popp, Juergen
中科院分区：
文献类型：
--
作者：
Kumar, Vinay;Kampe, Bernd;Popp, Juergen
土壤生境由大量不能用常规方法培养的细菌组成，因此对它们的分类和鉴定造成了明显的困难。这一困难需要先进的技术，其中由已经可培养的细菌组成的精心编制的生物分子数据库可以作为参考，试图将不可培养的细菌与它们最接近的系统发生类群联系起来。拉曼光谱已经成功地应用于细菌、真菌和植物等许多系统的分类学研究，这些系统依赖于它们整体生物分子组成的变化所产生的光谱差异。然而，由于类胡萝卜素等光敏微生物色素的拉曼信号显示出巨大的信号强度差异，阻碍了分类学研究，这些光谱差异可能被掩盖。在这项研究中，我们应用激光诱导光漂白来清除有色球菌中的类胡萝卜素特征。利用这种方法，我们利用径向核支持向量机的化学计量学工具，基于拉曼光谱，研究了土壤生境中丰富的12种有色细菌，它们属于3个属，主要是微球菌、葡萄球菌和Kocuria。我们的结果证明了拉曼光谱作为一种在单细胞水平上鉴定着色球菌土壤细菌的微创分类工具的潜力。
A soil habitat consists of a significant number of bacteria that cannot be cultivated by conventional means, thereby posing obvious difficulties in their classification and identification. This difficulty necessitates the need for advanced techniques wherein a well-compiled biomolecular database consisting of the already cultivable bacteria can be used as a reference in an attempt to link the noncultivable bacteria to their closest phylogenetic groups. Raman spectroscopy has been successfully applied to taxonomic studies of many systems like bacteria, fungi, and plants relying on spectral differences contributed by the variation in their overall biomolecular composition. However, these spectral differences can be obscured due to Raman signatures from photosensitive microbial pigments like carotenoids that show enormous variation in signal intensity hindering taxonomic investigations. In this study, we have applied laser-induced photobleaching to expel the carotenoid signatures from pigmented cocci bacteria. Using this method, we have investigated 12 species of pigmented bacteria abundant in soil habitats belonging to three genera mainly Micrococcus, Deinococcus, and Kocuria based on their Raman spectra with the assistance of a chemometric tool known as the radial kernel support vector machine (SVM). Our results demonstrate the potential of Raman spectroscopy as a minimally invasive taxonomic tool to identify pigmented cocci soil bacteria at a single-cell level.