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Molecular & Cellular Proteomics 7:448-456, 2008.
© 2008 by The American Society for Biochemistry and Molecular Biology, Inc.

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Research

Highly Efficient Classification and Identification of Human Pathogenic Bacteria by MALDI-TOF MS^*,S

Sen-Yung Hsieh^,,¶,||, Chiao-Li Tseng, Yun-Shien Lee^**, An-Jing Kuo, Chien-Feng Sun, Yen-Hsiu Lin and Jen-Kun Chen

From the Clinical Proteomics Center, Liver Research Unit, and Department of Clinical Pathology, Chang Gung Memorial Hospital, Taoyuan 333, Taiwan, ^¶ Department of Microbiology and Immunology, School of Medicine, Chang Gung University, Taoyuan 333, Taiwan, and ^** Department of Biotechnology, Ming Chuan University, Taoyuan 333, Taiwan

Accurate and rapid identification of pathogenic microorganisms is of critical importance in disease treatment and public health. Conventional work flows are time-consuming, and procedures are multifaceted. MS can be an alternative but is limited by low efficiency for amino acid sequencing as well as low reproducibility for spectrum fingerprinting. We systematically analyzed the feasibility of applying MS for rapid and accurate bacterial identification. Directly applying bacterial colonies without further protein extraction to MALDI-TOF MS analysis revealed rich peak contents and high reproducibility. The MS spectra derived from 57 isolates comprising six human pathogenic bacterial species were analyzed using both unsupervised hierarchical clustering and supervised model construction via the Genetic Algorithm. Hierarchical clustering analysis categorized the spectra into six groups precisely corresponding to the six bacterial species. Precise classification was also maintained in an independently prepared set of bacteria even when the numbers of m/z values were reduced to six. In parallel, classification models were constructed via Genetic Algorithm analysis. A model containing 18 m/z values accurately classified independently prepared bacteria and identified those species originally not used for model construction. Moreover bacteria fewer than 10⁴ cells and different species in bacterial mixtures were identified using the classification model approach. In conclusion, the application of MALDI-TOF MS in combination with a suitable model construction provides a highly accurate method for bacterial classification and identification. The approach can identify bacteria with low abundance even in mixed flora, suggesting that a rapid and accurate bacterial identification using MS techniques even before culture can be attained in the near future.

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