Uncovering PFAS and Other 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,
transformation, excretion, and coexistence with other endogenous molecules,
however, greatly complicate the interpretation of features detected
in nontargeted studies. While mass spectrometry (MS)-based platforms
are commonly used in metabolomic measurements, deconvoluting endogenous
metabolites from xenobiotics is also 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 a xenobiotic structural annotation
workflow using ion mobility spectrometry coupled with MS (IMS–MS),
mass defect filtering, and machine learning to uncover potential xenobiotic
classes and species in large metabolomic feature lists. Xenobiotic
classes examined included those of known high toxicities, including
per- and polyfluoroalkyl substances (PFAS), polycyclic aromatic hydrocarbons
(PAHs), polychlorinated biphenyls (PCBs), polybrominated diphenyl
ethers (PBDEs), and pesticides. Specifically, when the workflow was
applied to identify PFAS in the NIST SRM 1957 and 909c human serum
samples, it greatly reduced the hundreds of detected liquid chromatography
(LC)–IMS–MS features by utilizing both mass defect filtering
and m/z versus IMS collision cross
sections relationships. These potential PFAS features were then compared
to the EPA CompTox entries, and while some matched within specific m/z tolerances, there were still many unknowns
illustrating the importance of nontargeted studies for detecting new
molecules with known chemical characteristics. Additionally, this
workflow can also be utilized to evaluate other xenobiotics and enable
more confident annotations from nontargeted studies.

非靶向代谢组学分析中外源性物质（xenobiotics）的鉴定，是解析人体暴露情况的关键环节。然而，外源性物质的代谢、转化、排泄以及与其他内源性分子的共存，极大地复杂化了非靶向研究中检测到的特征信号的解析工作。尽管基于质谱（mass spectrometry, MS）的检测平台已广泛应用于代谢组学检测，但从外源性物质中区分内源性代谢物仍常面临诸多挑战：一方面缺乏外源性物质母本及其代谢物的标准品，另一方面每个小分子的m/z特征均存在大量同分异构体。本研究评估了一套结合离子迁移谱-质谱（ion mobility spectrometry coupled with MS, IMS–MS）、质量亏损过滤与机器学习的外源性物质结构注释流程，用于在大规模代谢组学特征列表中挖掘潜在的外源性物质类别与单体。本研究考察的外源性物质类别均为已知高毒性物质，包括全氟和多氟烷基物质（per- and polyfluoroalkyl substances, PFAS）、多环芳烃（polycyclic aromatic hydrocarbons, PAHs）、多氯联苯（polychlorinated biphenyls, PCBs）、多溴二苯醚（polybrominated diphenyl ethers, PBDEs）以及各类农药。具体而言，将该流程应用于美国国家标准与技术研究院（National Institute of Standards and Technology, NIST）标准参考物质（Standard Reference Material, SRM）1957与909c人血清样本中的全氟和多氟烷基物质（PFAS）鉴定时，通过结合质量亏损过滤以及m/z与离子迁移谱碰撞截面的关联关系，大幅缩减了数百个液相色谱-离子迁移谱-质谱（liquid chromatography, LC）–IMS–MS检测到的特征信号数量。随后将这些潜在的PFAS特征信号与美国环境保护署（Environmental Protection Agency, EPA）的CompTox数据库条目进行比对，尽管部分信号在特定m/z误差范围内可匹配到已知物质，但仍存在大量未知信号，这凸显了非靶向研究在检测具有已知化学特性的新型分子方面的重要价值。此外，该流程还可用于评估其他外源性物质，助力非靶向研究实现可信度更高的注释结果。