Exploiting Differential Signal Filtering (DSF) and Image Structure Filtering (ISF) Methods for Untargeted Mass Spectrometry Imaging of Bacterial Metabolites

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Abstract
Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) is a label-free technique, producing images where pixels contain mass spectra. The technique allows the visualization of the spatial distribution of (bio)molecules from metabolites to proteins, on surfaces such as tissues sections or bacteria culture media. One particularly exciting example of MALDI-MSI use rests on its potential to localize ionized compounds produced during microbial interactions and chemical communication, offering a molecular snapshot of metabolomes at a given time. The huge size and the complexity of generated MSI data make the processing of the data challenging, which requires the use of computational methods. Despite recent advances, currently available commercial software relies mainly on statistical tools to identify patterns, similarities, and differences within data sets. However, grouping m/z values unique to a given data set according to microbiological contexts, such as coculture experiments, still requires tedious manual analysis. Here we propose a nontargeted method exploiting the differential signals between negative controls and tested experimental conditions, i.e., differential signal filtering (DSF), and a scoring of the ion images using image structure filtering (ISF) coupled with a fold change score between the controls and the conditions of interest. These methods were first applied to coculture experiments involving Escherichia coli and Streptomyces coelicolor, revealing specific MS signals during bacterial interaction. Two case studies were also investigated: (i) cellobiose-mediated induction for the pathogenicity of Streptomyces scabiei, the causative agent of common scab on root and tuber crops, and (ii) iron-repressed production of siderophores of S. scabiei. This report proposes guidelines for MALDI-MSI data treatment applied in the case of microbiology contexts, with enhanced ion peak annotation in specific culture conditions. The strengths and weaknesses of the methods are discussed.
Funding Information
  • Universit? de Li?ge
  • European Regional Development Fund (2.2.1/996)
  • ARC MIND (19/23-32)

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