Universal, untargeted detection of bacteria in tissues using metabolomics workflows

Wei Chen, Min Qiu, Petra Paizs, Miriam Sadowski, Toma Ramonaite, Lieby Zborovsky, Raquel Mejias-Luque, Klaus-Peter Janßen, James Kinross, Robert D. Goldin, Monica Rebec, Manuel Liebeke, Zoltan Takats, James S. McKenzie () and Nicole Strittmatter ()
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Wei Chen: Technical University of Munich
Min Qiu: Technical University of Munich
Petra Paizs: Imperial College London
Miriam Sadowski: Max Planck Institute for Marine Microbiology
Toma Ramonaite: Imperial College London
Lieby Zborovsky: Technical University of Munich
Raquel Mejias-Luque: Technical University of Munich
Klaus-Peter Janßen: Technical University of Munich
James Kinross: Imperial College London
Robert D. Goldin: Imperial College London
Monica Rebec: Imperial College Healthcare NHS Trust
Manuel Liebeke: Max Planck Institute for Marine Microbiology
Zoltan Takats: Imperial College London
James S. McKenzie: Imperial College London
Nicole Strittmatter: Technical University of Munich

Nature Communications, 2025, vol. 16, issue 1, 1-12

Abstract: Abstract Fast and reliable identification of bacteria directly in clinical samples is a critical factor in clinical microbiological diagnostics. Current approaches require time-consuming bacterial isolation and enrichment procedures, delaying stratified treatment. Here, we describe a biomarker-based strategy that utilises bacterial small molecular metabolites and lipids for direct detection of bacteria in complex samples using mass spectrometry (MS). A spectral metabolic library of 233 bacterial species is mined for markers showing specificity at different phylogenetic levels. Using a univariate statistical analysis method, we determine 359 so-called taxon-specific markers (TSMs). We apply these TSMs to the in situ detection of bacteria using healthy and cancerous gastrointestinal tissues as well as faecal samples. To demonstrate the MS method-agnostic nature, samples are analysed using spatial metabolomics and traditional bulk-based metabolomics approaches. In this work, TSMs are found in >90% of samples, suggesting the general applicability of this workflow to detect bacterial presence with standard MS-based analytical methods.

Date: 2025
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DOI: 10.1038/s41467-024-55457-7

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