Raman Spectroscopy-Compatible Inactivation Method for Pathogenic Endospores

被引:46
作者
Stoeckel, S. [1 ]
Schumacher, W. [1 ]
Meisel, S. [1 ]
Elschner, M. [2 ]
Roesch, P. [1 ]
Popp, J. [1 ,3 ]
机构
[1] Univ Jena, Inst Phys Chem, D-07743 Jena, Germany
[2] Inst Bacterial Infect & Zoonoses, Fed Res Inst Anim Hlth, Friedrich Loeffler Inst, D-07743 Jena, Germany
[3] Inst Photon Technol, D-07745 Jena, Germany
关键词
BACILLUS-SUBTILIS; BACTERIAL ENDOSPORES; SINGLE BACTERIA; SPORES; IDENTIFICATION; ACID; CLASSIFICATION; CELLS; SPECTRA; RESISTANCE;
D O I
10.1128/AEM.02481-09
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
Micro-Raman spectroscopy is a fast and sensitive tool for the detection, classification, and identification of biological organisms. The vibrational spectrum inherently serves as a fingerprint of the biochemical composition of each bacterium and thus makes identification at the species level, or even the subspecies level, possible. Therefore, microorganisms in areas susceptible to bacterial contamination, e. g., clinical environments or food-processing technology, can be sensed. Within the scope of point-of-care-testing also, detection of intentionally released biosafety level 3 (BSL-3) agents, such as Bacillus anthracis endospores, or their products is attainable. However, no Raman spectroscopy-compatible inactivation method for the notoriously resistant Bacillus endospores has been elaborated so far. In this work we present an inactivation protocol for endospores that permits, on the one hand, sufficient microbial inactivation and, on the other hand, the recording of Raman spectroscopic signatures of single endospores, making species-specific identification by means of highly sophisticated chemometrical methods possible. Several physical and chemical inactivation methods were assessed, and eventually treatment with 20% formaldehyde proved to be superior to the other methods in terms of sporicidal capacity and information conservation in the Raman spectra. The latter fact has been verified by successfully using self-learning machines (such as support vector machines or artificial neural networks) to identify inactivated B. anthracis-related endospores with adequate accuracies within the range of the limited model database employed.
引用
收藏
页码:2895 / 2907
页数:13
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