Maximizing the Performance of MALDI-TOF MS-based Microbial Characterization: Challenges, Strategies, and Successes Beyond Species-level and Pure Culture Applications

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Todd Sandrin (Presenter) Arizona State University

Presenter Bio: As a faculty member in the School of Mathematical and Natural Sciences, I teach a variety of courses ranging from introductory biology to more advanced courses in microbiology and cell biology. Research in my lab employs unique, often multidisciplinary approaches to mysteries in the natural sciences deemed intractable by strategies relying on approaches from a single discipline. Current research projects use tools in proteomics and functional genomics to: 1) leverage beneficial aspects of microorganisms (e.g., biodegradation/bioremediation of harmful chemical pollutants) and 2) develop technologies to mitigate negative effects of microorganisms (e.g., disease, microbial contamination of water). Most recently, my research group has been particularly active and has placed considerable focus on the development and optimization of rapid, mass spectrometry-enabled approaches to characterize microorganisms at the strain level.

Relevant Financial Disclosures (within past 24 months) Not yet reported.

Matrix-Assisted Laser Desorption Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF MS) has emerged over the past decade as a rapid and accurate alternative to traditional methods of microbial characterization in the clinical laboratory. Countless successes have been reported with species-level identification of diverse microorganisms of clinical and environmental importance, but challenges have been described in the literature when applying the method at taxonomic levels beyond the species level (e.g., at the strain level). Furthermore, application of MALDI-TOF MS has largely been restricted to pure cultures of bacteria. Our group has developed and optimized strategies to enhance performance of MALDI-TOF MS-based approaches at the strain level and with polymicrobial mixtures. Collectively, our results suggest that maximizing performance beyond the species level and with polymicrobial mixtures requires rigorous optimization and standardization of culture conditions, sample preparation methods, data acquisition modes, and data analysis approaches.