An Algorithm for Automated Bacterial Identification Using Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry

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• 作者：Kristin H. Jarman ; Sharon T. Cebula ; Adam J. Saenz ; Catherine E. Petersen ; Nancy B. Valentine ; Mark T. Kingsley ; and Karen L. Wahl
• 刊名：Analytical Chemistry
• 出版年：2000
• 出版时间：March 15, 2000
• 年：2000
• 卷：72
• 期：6
• 页码：1217 - 1223
• 全文大小：80K
• 年卷期：v.72,no.6(March 15, 2000)
• ISSN：1520-6882

文摘

An algorithm for bacterial identification using matrix-assisted laser desorption/ionization (MALDI) mass spectrometry is being developed. This mass spectral fingerprint comparison algorithm is fully automated andstatistically based, providing objective analysis of samplesto be identified. Based on extraction of reference fingerprint ions from test spectra, this approach should lenditself well to real-world applications where samples arelikely to be impure. This algorithm is illustrated using ablind study. In the study, MALDI-MS fingerprints forBacillus atrophaeus ATCC 49337, Bacillus cereusATCC 14579^T, Escherichia coli ATCC 33694, Pantoeaagglomerans ATCC 33243, and Pseudomonas putidaF1 are collected and form a reference library. The identification of test samples containing one or more referencebacteria, potentially mixed with one species not in thelibrary (Shewanella alga BrY), is performed by comparison to the reference library with a calculated degreeof association. Out of 60 samples, no false positives arepresent, and the correct identification rate is 75%. Missedidentifications are largely due to a weak B. cereus signalin the bacterial mixtures. Potential modifications to thealgorithm are presented and result in a higher than 90%correct identification rate for the blind study data, suggesting that this approach has the potential for reliableand accurate automated data analysis of MALDI-MS.