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Identification of Microbial Strains via 2D Cross-Correlation of LC-MS Data
Journal article   Open access   Peer reviewed

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

Tucker James Collins, Cathy Muste and Kevin G Owens
Journal of the American Society for Mass Spectrometry, v 35(6), pp 1352-1362
14 May 2024
DOI: https://doi.org/10.1021/jasms.4c00101
PMID: 38742647
Featured in Collection :   Research Supported by Drexel Libraries' OA Programs
url
https://doi.org/10.1021/jasms.4c00101View
Published, Version of Record (VoR)Open Access via Drexel Libraries Read and Publish Program 2024CC BY V4.0,  Open

Abstract

correlation analysis LC-MS Orbitrap strain identification micro-organism identification Proteomics
Mass spectrometry is commonly used in the identification of species present in microbial samples, but the high similarity in the peptide composition between strains of a single species has made analysis at the subspecies level challenging. Prior research in this area has employed methods such as Principal Component Analysis (PCA), the k-Nearest Neighbors' (kNN) algorithm, and Pearson correlation. Previously, 1D cross-correlation of mass spectra has been shown to be useful in the classification of small molecule compounds as well as in the identification of peptide sequences via the SEQUEST algorithm and its variants. While direct application of cross-correlation to mass spectral data has been shown to aid in the identification of many other types of compounds, this type of analysis has not been demonstrated in the literature for the purpose of LC-MS based identification of microbial strains. A method of identifying microbial strains is presented here that applies the principle of 2D cross-correlation to LC-MS data. For a set of = 30 yeast isolate samples representing 5 yeast strains (K-97, S-33, T-58, US-05, WB-06), high-resolution LC-MS-Orbitrap data were collected. Reference spectra were then generated for each strain from the combined data of each sample of that strain. Sample strains were then predicted by computing the 2D cross-correlation of each sample against the reference spectra, followed by application of correction factors measuring the asymmetry of the 2D correlation functions.

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Web of Science research areas
Biochemical Research Methods
Chemistry, Analytical
Chemistry, Physical
Spectroscopy
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