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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.

Isomeric peptide analyses are an analytical challenge of great importance to therapeutic monoclonal antibody and other biotherapeutic product development workflows. Aspartic acid (Asp, D) to isoaspartic acid (isoAsp, isoD) isomerization is a critical quality attribute (CQA) that requires careful control, monitoring, and quantitation during the drug discovery and production processes. While the formation of isoAsp has been implicated in a variety of disease states such as autoimmune diseases and several types of cancer, it is also understood that the formation of isoAsp results in a structural change impacting efficacy, potency, and immunogenic properties, all of which are undesirable. Currently, lengthy ultrahigh-performance liquid chromatography (UPLC) separations are coupled with MS for CQA analyses; however, these measurements often take over an hour and drastically limit analysis throughput. In this manuscript, drift tube ion mobility spectrometry–mass spectrometry (DTIMS–MS) and both a standard and high-resolution demultiplexing approach were utilized to study eight isomeric Asp and isoAsp peptide pairs. While the limited resolving power associated with the standard DTIMS analysis only separated three of the eight pairs, the application of HRdm distinguished seven of the eight and was only unable to separate DL and isoDL. The rapid high-throughput HRdm DTIMS–MS method was also interfaced with both flow injection and an automated solid phase extraction system to present the first application of HRdm for isoAsp and Asp assessment and demonstrate screening capabilities for isomeric peptides in complex samples, resulting in a workflow highly suitable for biopharmaceutical research needs.

Photooxidation can alter the environmental fate and effects of spilled oil. To better understand this process, oil slicks were generated on seawater mesocosms and exposed to sunlight for 8 days. The molecular composition of seawater under irradiated and non-irradiated oil slicks was characterized using ion mobility spectrometry-mass spectrometry and polyaromatic hydrocarbons analyses. Biomimetic extraction was performed to quantify neutral and ionized constituents. Results show that seawater underneath irradiated oil showed significantly higher amounts of hydrocarbons with oxygen- and sulfur-containing by-products peaking by day 4-6; however, concentrations of dissolved organic carbon were similar. Biomimetic extraction indicated toxic units in irradiated mesocosms increased, mainly due to ionized components, but remained1, suggesting limited potential for ecotoxicity. Because the experimental design mimicked important aspects of natural conditions (freshly collected seawater, natural sunlight, and relevant oil thickness and concentrations), this study improves our understanding of the effects of photooxidation during a marine oil spill.

While the combination of liquid chromatography and tandem mass spectrometry (LC-MS/MS) is commonly used for feature annotation in untargeted omics experiments, ensuring these prioritized features originate from endogenous metabolism remains challenging. Isotopologue workflows, such as isotopic ratio outlier analysis (IROA), mass isotopomer ratio analysis of U-(13)C labeled extracts (MIRACLE), and credentialing incorporate isotopic labels directly into metabolic precursors, guaranteeing that all features of interest are unequivocal byproducts of cellular metabolism. Furthermore, comprehensive separation and annotation of small molecules continue to challenge the metabolomics field, particularly for isomeric systems. In this paper, we evaluate the analytical utility of incorporating ion mobility spectrometry (IMS) as an additional separation mechanism into standard LC-MS/MS isotopologue workflows. Since isotopically labeled molecules codrift in the IMS dimension with their (12)C versions, LC-IMS-CID-MS provides four dimensions (LC, IMS, MS, and MS/MS) to directly investigate the metabolic activity of prioritized untargeted features. Here, we demonstrate this additional selectivity by showcasing how a preliminary data set of 30 endogeneous metabolites are putatively annotated from isotopically labeled Escherichia coli cultures when analyzed by LC-IMS-CID-MS. Metabolite annotations were based on several molecular descriptors, including accurate mass measurement, carbon number, annotated fragmentation spectra, and collision cross section (CCS), collectively illustrating the importance of incorporating IMS into isotopologue workflows. Overall, our results highlight the enhanced separation space and increased annotation confidence afforded by IMS for metabolic characterization and provide a unique perspective for future developments in isotopically labeled MS experiments.

Detection and diagnosis of congenital disorders is the principal aim of newborn screening (NBS) programs worldwide. Mass spectrometry (MS) has become the preferred primary testing method for high-throughput NBS sampling because of its speed and selectivity. However, the ever-increasing list of NBS biomarkers included in expanding panels creates unique analytical challenges for multiplexed MS assays due to isobaric/isomeric overlap and chimeric fragmentation spectra. Since isobaric and isomeric systems limit the diagnostic power of current methods and require costly follow-up exams due to many false-positive results, here, we explore the utility of ion mobility spectrometry (IMS) to enhance the accuracy of MS assays for primary (tier 1) screening. Our results suggest that ∼400 IMS resolving power would be required to confidently assess most NBS biomarkers of interest in dried blood spots (DBSs) that currently require follow-up testing. While this level of selectivity is unobtainable with most commercially available platforms, the separations detailed here for a commercially available drift tube IMS (Agilent 6560 with high-resolution demultiplexing, HRdm) illustrate the unique capabilities of IMS to separate many diagnostic NBS biomarkers from interferences. Furthermore, to address the need for increased speed of NBS analyses, we utilized an automated solid-phase extraction (SPE) system for ∼10 s sampling of simulated NBS samples prior to IMS-MS. This proof-of-concept work demonstrates the unique capabilities of SPE-IMS-MS for high-throughput sample introduction and enhanced separation capacity conducive for increasing speed and accuracy for NBS.

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.