Discrimination of Common E. coli Strains in Urine by Liquid Chromatography-Ion Mobility-Tandem Mass Spectrometry and Machine Learning.

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 Escherichia coli (E. coli) 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 E. coli 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.