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<p>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 &gt;90% of samples, suggesting the general applicability of this workflow to detect bacterial presence with standard MS-based analytical methods.</p>

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Universal, untargeted detection of bacteria in tissues using metabolomics workflows

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年1月2日

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  • 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,
  • Nicole Strittmatter

摘要

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.