Chen Wei, Qiu Min, Paizs Petra, Sadowski Miriam, Ramonaite Toma, Zborovsky Lieby, Mejias-Luque Raquel, Janßen Klaus-Peter, Kinross James, Goldin Robert D, Rebec Monica, Liebeke Manuel, Takats Zoltan, McKenzie James S, Strittmatter Nicole
Department of Bioscience, School of Natural Sciences, Technical University of Munich, Garching, Germany.
Department of Metabolism, Digestion and Reproduction, Imperial College London, London, United Kingdom.
Nat Commun. 2025 Jan 2;16(1):165. doi: 10.1038/s41467-024-55457-7.
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.
在临床样本中直接快速且可靠地鉴定细菌是临床微生物诊断中的关键因素。目前的方法需要耗时的细菌分离和富集程序，从而延误分层治疗。在此，我们描述了一种基于生物标志物的策略，该策略利用细菌小分子代谢物和脂质，通过质谱（MS）直接检测复杂样本中的细菌。我们从一个包含233种细菌的光谱代谢文库中挖掘在不同系统发育水平上具有特异性的标志物。使用单变量统计分析方法，我们确定了359个所谓的分类群特异性标志物（TSM）。我们将这些TSM应用于使用健康和癌性胃肠道组织以及粪便样本进行细菌的原位检测。为了证明该MS方法的通用性，使用空间代谢组学和传统的基于整体样本的代谢组学方法对样本进行分析。在这项工作中，在超过90%的样本中发现了TSM，这表明该工作流程使用基于MS的标准分析方法检测细菌存在具有普遍适用性。
