Abstract
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(4) 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.
Highlights
Accurate and rapid identification of pathogenic microorganisms is of critical importance in disease treatment and public health
We aimed to demonstrate that directly subjecting intact bacterial colonies for protein profiling using MALDI-TOF MS can be a simple and reliable approach [21,22,23,24,25,26] and that bacteria of independently prepared groups can be accurately classified and identified by two analytic approaches: the unsupervised and supervised (Genetic Algorithm) [27] approaches
Directly subjecting the bacterial colonies without further extraction to MALDI-TOF MS analysis resulted in rich peak contents of the spectra and the highest reproducibility
Summary
Collection and Isolation of Pure Bacterial Colonies—All bacterial isolates used in this study were collected and characterized by the Clinical Pathology Laboratory of Chang Gung Memorial Hospital, Taoyuan, Taiwan. 57 well characterized bacterial isolates (training set) composed of the six most common species of human pathogenic bacteria, namely Staphylococcus aureus (10 isolates), Streptococcus serogroup B (eight isolates), Escherichia coli (eight isolates), Klebsiella pneumoniae (10 isolates), Salmonella enterica serogroup B (11 isolates), and Pseudomonas aeruginosa (10 isolates), were used for both unsupervised hierarchical clustering analysis (HCA) and supervised analysis (see below) for construction of the classification models. The second set contained 37 well characterized bacterial isolates composed of the same bacterial species as the first set and was used for external validation of the constructed classification models (independent set 2). RC is essential to internally evaluate the fitness of the classification models, whereas CVA is crucial to measure robustness of the resulting classification models For external validation, another set containing 37 characterized isolates was analyzed in a blind study (independent set 2) to examine the robustness of the models. One microliter of each aliquot sample was subsequently spotted onto MALDI target plates for analysis
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