Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS¹) and in silico Peptide Mass Data

Abstract

1.ABSTRACTOver the past decade, modern methods of mass spectrometry (MS) have emerged that allow reliable, fast and cost-effective identification of pathogenic microorganisms. While MALDI-TOF MS has already revolutionized the way microorganisms are identified, recent years have witnessed also substantial progress in the development of liquid chromatography (LC)-MS based proteomics for microbiological applications. For example, LC-tandem mass spectrometry (LC-MS2) has been proposed for microbial characterization by means of multiple discriminative peptides that enable identification at the species, or sometimes at the strain level. However, such investigations can be very time-consuming, especially if the experimental LC-MS2 data are tested against sequence databases covering a broad panel of different microbiological taxa.In this proof of concept study, we present an alternative bottom-up proteomics method for microbial identification. The proposed approach involves efficient extraction of proteins from cultivated microbial cells, digestion by trypsin and LC-MS measurements. MS1 data are then extracted and systematically tested against an in silico library of peptide mass data compiled in house. The library has been computed from the UniProt Knowledgebase Swiss-Prot and TrEMBL databases and comprises more than 12,000 strain-specific in silico profiles, each containing tens of thousands of peptide mass entries. Identification analysis involves computation of score values derived from spectral distances between experimental and in silico peptide mass data and compilation of score ranking lists. The taxonomic positions of the microbial samples are then determined by using the best-matching database entries. The suggested method is computationally efficient – less than two minutes per sample - and has been successfully tested by a set of 19 different microbial pathogens. The approach is rapid, accurate and automatable and holds great potential for future microbiological applications.

Identification of Microbial Strains via 2D Cross-Correlation of LC-MS Data

“…In a 2020 article, Lasch et al proposed a protocol for an LC-MS-based fingerprinting procedure, and the associated software package MicrobeMS, 12 that combines data published in peptide databases (e.g., UniProtKB, SwissProt) to construct an in silico library of microbial peptide fingerprints. 13 MicrobeMS eliminates the retention time dimension from the test data during the preprocessing steps, creating instead a mass spectrum that is analogous to the results attained directly via MALDI-based procedures.…”