Highly Efficient Classification and Identification of Human Pathogenic Bacteria by MALDI-TOF MS

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 10 cells and different species in bacterial mixtures were identified using the classification model approach. In conclusion, the application of MALDITOF 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. Molecular & Cellular Proteomics 7: 448–456, 2008. Currently the most popular methods for bacterial identification are based on microbiologic procedures, antibody recognition, and PCR amplification. Traditionally microbiologic methods are culture-based assays that examine the presence of bacterial species. These methods provide high sensitivity and specificity, but their efficiency is limited by the complexity of the procedures, including culture, selection, isolation, and morphologic and biochemical characterization, which usually take 48 h or longer. Serologic methods are presumptive and confined to the availability of antibodies and to bacteria that are included ahead in the assays. Molecular biology techniques, particularly PCR, have been regarded as non-culturebased methods with high efficiency and specificity (1). However, they are completely dependent on the known genetic sequences of the target bacteria. MS with its capability of de novo protein/peptide sequencing (such as electrospray ionization or MALDI-TOF MS for tandem MS/MS) or its high efficiency for proteome profiling (particularly MALDI-TOF MS) has been suggested as an alternative for microbial identification (2–7). In the past decade, extraction of bacterial proteins for sequencing using tandem MS/MS (8–11) or for proteome profiling followed by matching MS spectrum results to databases (fingerprinting) have been used for bacterial identification (6, 8, 12–15). Despite much improvement (10, 11, 16–20), neither de novo amino acid sequencing nor protein fingerprinting has been applied to clinical or epidemiologic uses because they are relatively time-consuming and technique-demanding or have low reproducibility (16, 19). Further evaluation of the feasibility of applying MALDI-TOF MS for rapid and accurate microorganism identification is warranted. In this study, we systematically evaluated procedures for the rapid profiling and data analysis for bacterial classification and identification. We aimed to demonstrate that directly subjecting intact bacterial colonies for protein profiling using MALDI-TOF MS can be a simple and reliable approach (21– 26) and that bacteria of independently prepared groups can be accurately classified and identified by two analytic approaches: the unsupervised (hierarchical clustering analysis) and supervised (Genetic Algorithm) (27) approaches. We further intended to demonstrate the capability to identify bacteria in the minimal number of cells and in mixed flora. 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 Received, July 25, 2007, and in revised form, October 1, 2007 Published, MCP Papers in Press, November 27, 2007, DOI 10.1074/mcp.M700339-MCP200 Research