Universal, untargeted detection of bacteria in tissues using metabolomics workflows
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...
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| Format: | Article |
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Nature Portfolio
2025-01-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-55457-7 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Wei Chen |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-5f4c4baa1bcc4a1fb184f4e80a9fe7be |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-5f4c4baa1bcc4a1fb184f4e80a9fe7be2025-01-05T12:37:12ZengNature PortfolioNature Communications2041-17232025-01-0116111210.1038/s41467-024-55457-7Universal, untargeted detection of bacteria in tissues using metabolomics workflowsWei Chen0Min Qiu1Petra Paizs2Miriam Sadowski3Toma Ramonaite4Lieby Zborovsky5Raquel Mejias-Luque6Klaus-Peter Janßen7James Kinross8Robert D. Goldin9Monica Rebec10Manuel Liebeke11Zoltan Takats12James S. McKenzie13Nicole Strittmatter14Department of Bioscience, School of Natural Sciences, Technical University of MunichDepartment of Bioscience, School of Natural Sciences, Technical University of MunichDepartment of Metabolism, Digestion and Reproduction, Imperial College LondonDepartment of Symbiosis, Max Planck Institute for Marine MicrobiologyDepartment of Metabolism, Digestion and Reproduction, Imperial College LondonDepartment of Bioscience, School of Natural Sciences, Technical University of MunichInstitute for Medical Microbiology, Immunology and Hygiene, School of Medicine and Health, Technical University of MunichDepartment of Surgery, School of Medicine and Health, Technical University of MunichDepartment of Surgery and Cancer, Imperial College LondonDepartment of Metabolism, Digestion and Reproduction, Imperial College LondonNorth West London Pathology, Imperial College Healthcare NHS TrustDepartment of Symbiosis, Max Planck Institute for Marine MicrobiologyDepartment of Metabolism, Digestion and Reproduction, Imperial College LondonDepartment of Metabolism, Digestion and Reproduction, Imperial College LondonDepartment of Bioscience, School of Natural Sciences, Technical University of MunichAbstract 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.https://doi.org/10.1038/s41467-024-55457-7 |
| spellingShingle | 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 Universal, untargeted detection of bacteria in tissues using metabolomics workflows Nature Communications |
| title | Universal, untargeted detection of bacteria in tissues using metabolomics workflows |
| title_full | Universal, untargeted detection of bacteria in tissues using metabolomics workflows |
| title_fullStr | Universal, untargeted detection of bacteria in tissues using metabolomics workflows |
| title_full_unstemmed | Universal, untargeted detection of bacteria in tissues using metabolomics workflows |
| title_short | Universal, untargeted detection of bacteria in tissues using metabolomics workflows |
| title_sort | universal untargeted detection of bacteria in tissues using metabolomics workflows |
| url | https://doi.org/10.1038/s41467-024-55457-7 |
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