MALDI-TOF MS has been shown capable of rapidly and accurately characterizing bacteria. Highly reproducible spectra are required to ensure reliable characterization. Prior work has shown that spectra acquired manually can have higher reproducibility than those acquired automatically. For this reason, the objective of this study was to optimize automated data acquisition to yield spectra with reproducibility comparable to those acquired manually. Fractional factorial design was used to design experiments for robust optimization of settings, in which values of five parameters (peak selection mass range, signal to noise ratio (S:N), base peak intensity, minimum resolution and number of shots summed) commonly used to facilitate automated data acquisition were varied. Pseudomonas aeruginosa was used as a model bacterium in the designed experiments, and spectra were acquired using an intact cell sample preparation method. Optimum automated data acquisition settings (i.e., those settings yielding the highest reproducibility of replicate mass spectra) were obtained based on statistical analysis of spectra of P. aeruginosa. Finally, spectrum quality and reproducibility obtained from non-optimized and optimized automated data acquisition settings were compared for P. aeruginosa, as well as for two other bacteria, Klebsiella pneumoniae and Serratia marcescens. Results indicated that reproducibility increased from 90% to 97% (p-value [~ over =] 0.002) for P. aeruginosa when more shots were summed and, interestingly, decreased from 95% to 92% (p-value [~ over =] 0.013) with increased threshold minimum resolution. With regard to spectrum quality, highly reproducible spectra were more likely to have high spectrum quality as measured by several quality metrics, except for base peak resolution. Interaction plots suggest that, in cases of low threshold minimum resolution, high reproducibility can be achieved with fewer shots. Optimization yielded more reproducible spectra than non-optimized settings for all three bacteria.
MALDI-TOF MS profiling has been shown to be a rapid and reliable method to characterize pure cultures of bacteria. Currently, there is keen interest in using this technique to identify bacteria in mixtures. Promising results have been reported with two- or three-isolate model systems using biomarker-based approaches. In this work, we applied MALDI-TOF MS-based methods to a more complex model mixture containing six bacteria. We employed: 1) a biomarker-based approach that has previously been shown to be useful in identification of individual bacteria in pure cultures and simple mixtures and 2) a similarity coefficient-based approach that is routinely and nearly exclusively applied to identification of individual bacteria in pure cultures. Both strategies were developed and evaluated using blind-coded mixtures. With regard to the biomarker-based approach, results showed that most peaks in mixture spectra could be assigned to those found in spectra of each component bacterium; however, peaks shared by two isolates as well as peaks that could not be assigned to any individual component isolate were observed. For two-isolate blind-coded samples, bacteria were correctly identified using both similarity coefficient- and biomarker-based strategies, while for blind-coded samples containing more than two isolates, bacteria were more effectively identified using a biomarker-based strategy.