Department of Chemistry and Biochemistry, Auburn University, 179 Chemistry Building, Auburn, Alabama 36849, United States.
Department of Mathematics and Statistics, Auburn University, 221 Roosevelt Concourse, Auburn, Alabama 36849, United States.
J Am Soc Mass Spectrom. 2024 Nov 6;35(11):2706-2713. doi: 10.1021/jasms.4c00189. Epub 2024 Aug 5.
Accurate identification of bacterial strains in clinical samples is essential to provide an appropriate antibiotherapy to the patient and reduce the prescription of broad-spectrum antimicrobials, leading to antibiotic resistance. In this study, we utilized the combination of a multidimensional analytical technique, liquid chromatography-ion mobility-tandem mass spectrometry (LC-IM-MS/MS), and machine learning to accurately identify and distinguish 11 () strains in artificially contaminated urine samples. Machine learning was utilized on the LC-IM-MS/MS data of the inoculated urine samples to reveal lipid, metabolite, and peptide isomeric biomarkers for the identification of the bacteria strains. Tandem MS and LC separation proved effective in discriminating diagnostic isomers in the negative ion mode, while IM separation was more effective in resolving conformational biomarkers in the positive ion mode. Using hierarchical clustering, the strains are clustered accurately according to their group highlighting the uniqueness of the discriminating biomarkers to the class of each strain. These results show the great potential of using LC-IM-MS/MS and machine learning for targeted omics applications to diagnose infectious diseases in various environmental and clinical samples accurately.
准确识别临床样本中的细菌菌株对于为患者提供适当的抗生素治疗至关重要，可减少广谱抗生素的处方使用，从而降低抗生素耐药性。在这项研究中，我们结合使用多维分析技术、液相色谱-离子淌度-串联质谱（LC-IM-MS/MS）和机器学习，准确识别和区分人工污染尿液样本中的 11 种（）菌株。我们对接种尿液样本的 LC-IM-MS/MS 数据进行机器学习分析，以揭示用于鉴定细菌菌株的脂质、代谢物和肽同型生物标志物。串联质谱和 LC 分离在负离子模式下有效地区分了诊断异构体，而 IM 分离在正离子模式下更有效地解析了构象生物标志物。使用层次聚类，根据其组准确地对菌株进行聚类，突出了区分标志物对每个菌株类别的独特性。这些结果表明，LC-IM-MS/MS 和机器学习在靶向组学应用方面具有巨大潜力，可用于准确诊断各种环境和临床样本中的传染病。
