Universal, untargeted detection of bacteria in tissues using metabolomics workflows
Wei Chen, Min Qiu, Petra Paizs, Miriam Sadowski, Toma Ramonaite, Lieby Zborovsky, Raquel Mejías‐Luque, Klaus‐Peter Janssen, James Kinross, Robert Goldin, Monica Rebec, Manuel Liebeke, Zoltán Takáts, James S. McKenzie, Nicole Strittmatter
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.