Uncovering Xenobiotics in the Dark Metabolome using Ion Mobility Spectrometry, Mass Defect Analysis and Machine Learning

The identification of xenobiotics in nontargeted metabolomic analyses is a vital step in understanding human exposure. Xenobiotic metabolism, excretion, and co-existence with other endogenous molecules however greatly complicate nontargeted studies. While mass spectrometry (MS)-based platforms are commonly used in metabolomic measurements, deconvoluting endogenous metabolites and xenobiotics is often challenged by the lack of xenobiotic parent and metabolite standards as well as the numerous isomers possible for each small molecule m/z feature. Here, we evaluate the use of ion mobility spectrometry coupled with MS (IMS-MS) and mass defect filtering in a xenobiotic structural annotation workflow to reduce large metabolomic feature lists and uncover potential xenobiotic classes and species detected in the metabolomic studies. To evaluate the workflow, xenobiotics having known high toxicities including per- and polyfluoroalkyl substances (PFAS), polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs) and polybrominated diphenyl ethers (PBDEs) were examined. Initially, to address the lack of available IMS collision cross section (CCS) values for per- and polyfluoroalkyl substances (PFAS), 88 PFAS standards were evaluated with IMS-MS to both develop a targeted PFAS CCS library and for use in machine learning predictions. The CCS values for biomolecules and xenobiotics were then plotted versus m/z, clearly distinguishing the biomolecules and halogenated xenobiotics. The xenobiotic structural annotation workflow was then used to annotate potential PFAS features in NIST human serum. The workflow reduced the 2,423 detected LC-IMS-MS features to 80 possible PFAS with 17 confidently identified through targeted analyses and 48 additional features correlating with possible CompTox entries.