Using Multidimensional Separations to Distinguish Isomeric Amino Acid-Bile Acid Conjugates and Assess Their Presence and Perturbations in Model Systems.

Bile acids play key roles in nutrient uptake, inflammation, signaling, and microbiome composition. While previous bile acid analyses have primarily focused on profiling 5 canonical primary and secondary bile acids and their glycine and taurine amino acid-bile acid (AA-BA) conjugates, recent studies suggest that many other microbial conjugated bile acids (or MCBAs) exist. MCBAs are produced by the gut microbiota and serve as biomarkers, providing information about early disease onset and gut health. Here we analyzed 8 core bile acids synthetically conjugated with 22 proteinogenic and nonproteogenic amino acids totaling 176 MCBAs. Since many of the conjugates were isomeric and only 42 different m/z values resulted from the 176 MCBAs, a platform coupling liquid chromatography, ion mobility spectrometry, and mass spectrometry (LC-IMS-MS) was used for their separation. Their molecular characteristics were then used to create an in-house extended bile acid library for a combined total of 182 unique compounds. Additionally, ∼250 rare bile acid extracts were also assessed to provide additional resources for bile acid profiling and identification. This library was then applied to healthy mice dosed with antibiotics and humans having fecal microbiota transplantation (FMT) to assess the MCBA presence and changes in the gut before and after each perturbation.

Combining Isotopologue Workflows and Simultaneous Multidimensional Separations to Detect, Identify, and Validate Metabolites in Untargeted Analyses.

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-13C 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 12C 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.

High-Resolution Demultiplexing (HRdm) Ion Mobility Spectrometry-Mass Spectrometry for Aspartic and Isoaspartic Acid Determination and Screening.

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.

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.

Utilizing ion mobility spectrometry-mass spectrometry for the characterization and detection of persistent organic pollutants and their metabolites.

Persistent organic pollutants (POPs) are xenobiotic chemicals of global concern due to their long-range transport capabilities, persistence, ability to bioaccumulate, and potential to have negative effects on human health and the environment. Identifying POPs in both the environment and human body is therefore essential for assessing potential health risks, but their diverse range of chemical classes challenge analytical techniques. Currently, platforms coupling chromatography approaches with mass spectrometry (MS) are the most common analytical methods employed to evaluate both parent POPs and their respective metabolites and/or degradants in samples ranging from d rinking water to biofluids. Unfortunately, different types of analyses are commonly needed to assess both the parent and metabolite/degradant POPs from the various chemical classes. The multiple time-consuming analyses necessary thus present a number of technical and logistical challenges when rapid evaluations are needed and sample volumes are limited. To address these challenges, we characterized 64 compounds including parent per- and polyfluoroalkyl substances (PFAS), pesticides, polychlorinated biphenyls (PCBs), industrial chemicals, and pharmaceuticals and personal care products (PPCPs), in addition to their metabolites and/or degradants, using ion mobility spectrometry coupled with MS (IMS-MS) as a potential rapid screening technique. Different ionization sources including electrospray ionization (ESI) and atmospheric pressure photoionization (APPI) were employed to determine optimal ionization for each chemical. Collectively, this study advances the field of exposure assessment by structurally characterizing the 64 important environmental pollutants, assessing their best ionization sources, and evaluating their rapid screening potential with IMS-MS.

Characterization of Compositional Variability in Petroleum Substances.

In the process of registration of substances of Unknown or Variable Composition, Complex Reaction Products or Biological Materials (UVCBs), information sufficient to enable substance identification must be provided. Substance identification for UVCBs formed through petroleum refining is particularly challenging due to their chemical complexity, as well as variability in refining process conditions and composition of the feedstocks. This study aimed to characterize compositional variability of petroleum UVCBs both within and across product categories. We utilized ion mobility spectrometry (IMS)-MS as a technique to evaluate detailed chemical composition of independent production cycle-derived samples of 6 petroleum products from 3 manufacturing categories (heavy aromatic, hydrotreated light paraffinic, and hydrotreated heavy paraffinic). Atmospheric pressure photoionization and drift tube IMS-MS were used to identify structurally related compounds and quantified between- and within-product variability. In addition, we determined both individual molecules and hydrocarbon blocks that were most variable in samples from different production cycles. We found that detailed chemical compositional data on petroleum UVCBs obtained from IMS-MS can provide the information necessary for hazard and risk characterization in terms of quantifying the variability of the products in a manufacturing category, as well as in subsequent production cycles of the same product.

Improving the Speed and Selectivity of Newborn Screening Using Ion Mobility Spectrometry-Mass Spectrometry.

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.

Rapid Characterization of Per- and Polyfluoroalkyl Substances (PFAS) by Ion Mobility Spectrometry-Mass Spectrometry (IMS-MS).

Per- and polyfluoroalkyl substances (PFAS) are an ensemble of persistent organic pollutants of global interest because of their associations with adverse health outcomes. Currently, environmental PFAS pollution is prolific as a result of the widespread manufacturing of these compounds and their chemical persistence. In this work, we demonstrate the advantages of adding ion mobility spectrometry (IMS) separation to existing LC-MS workflows for PFAS analysis. Using a commercially available drift tube IMS-MS, we characterized PFAS species and isomeric content in both analytical standards and environmental water samples. Molecular trendlines based on intrinsic mass and structural relationships were also explored for individual PFAS subclasses (e.g. PFSA, PFCA, etc.). Results from rapid IMS-MS analyses provided a link between mass and collision cross sections (CCS) for specific PFAS families and are linked to compositional differences in molecular structure. In addition, CCS values provide additional confidence of annotating prioritized features in untargeted screening studies for potential environmental pollutants. Results from this study show that the IMS separation provides novel information to support traditional LC-MS PFAS analyses and will greatly benefit the evaluation of unknown pollutants in future environmental studies.

Rapid Characterization of Emerging Per- and Polyfluoroalkyl Substances in Aqueous Film-Forming Foams Using Ion Mobility Spectrometry-Mass Spectrometry.

Aqueous film-forming foams (AFFF) are mixtures formulated with numerous hydrocarbon- and fluoro-containing surfactants. AFFF use leads to environmental releases of unknown per- and polyfluoroalkyl substances (PFAS). AFFF composition is seldom disclosed, and their use elicits concerns from both regulatory agencies and the public because PFAS are persistent in the environment and potentially associated with adverse health effects. In this study, we demonstrate the use of coupled liquid chromatography, ion mobility spectrometry, and mass spectrometry (LC-IMS-MS) to rapidly characterize both known and unknown PFAS in AFFF. Ten AFFF formulations from seven brands were analyzed using LC-IMS-MS in both negative and positive ion modes. Untargeted analysis of the formulations was followed by feature identification of PFAS-like features utilizing database matching, mass defect and homologous series evaluation, and MS/MS fragmentation experiments. Across the tested AFFF formulations, we identified 33 homologous series; only ten of these homologous series have been previously reported. Among tested AFFF, the FireStopper (n = 85) contained the greatest number of PFAS-like features and Phos-Check contained zero. This work demonstrates that LC-IMS-MS-enabled untargeted analysis of complex formulations, followed by feature identification using data-processing algorithms, can be used for rapid exposure characterization of known and putative PFAS during fire suppression-related contamination events.

Ion Mobility Spectrometry and the Omics: Distinguishing Isomers, Molecular Classes and Contaminant Ions in Complex Samples.

Ion mobility spectrometry (IMS) is a widely used analytical technique providing rapid gas phase separations. IMS alone is useful, but its coupling with mass spectrometry (IMS-MS) and various front-end separation techniques has greatly increased the molecular information achievable from different omic analyses. IMS-MS analyses are specifically gaining attention for improving metabolomic, lipidomic, glycomic, proteomic and exposomic analyses by increasing measurement sensitivity (e.g. S/N ratio), reducing the detection limit, and amplifying peak capacity. Numerous studies including national security-related analyses, disease screenings and environmental evaluations are illustrating that IMS-MS is able to extract information not possible with MS alone. Furthermore, IMS-MS has shown great utility in salvaging molecular information for low abundance molecules of interest when high concentration contaminant ions are present in the sample by reducing detector suppression. This review highlights how IMS-MS is currently being used in omic analyses to distinguish structurally similar molecules, isomers, molecular classes and contaminant ions.

Untargeted Molecular Discovery in Primary Metabolism: Collision Cross Section as a Molecular Descriptor in Ion Mobility-Mass Spectrometry.

In this work, we established a collision cross section (CCS) library of primary metabolites based on analytical standards in the Mass Spectrometry Metabolite Library of Standards (MSMLS) using a commercially available ion mobility-mass spectrometer (IM-MS). From the 554 unique compounds in the MSMLS plate library, we obtained a total of 1246 CCS measurements over a wide range of biochemical classes and adduct types. Resulting data analysis demonstrated that the curated CCS library provides broad molecular coverage of metabolic pathways and highlights intrinsic mass-mobility relationships for specific metabolite superclasses. The separation and characterization of isomeric metabolites were assessed, and all molecular species contained within the plate library, including isomers, were critically evaluated to determine the analytical separation efficiency in both the mass ( m/ z) and mobility (CCS/ΔCCS) dimension required for untargeted metabolomic analyses. To further demonstrate the analytical utility of CCS as an additional molecular descriptor, a well-characterized biological sample of human plasma serum (NIST SRM 1950) was examined by LC-IM-MS and used to provide a detailed isomeric analysis of carbohydrate constituents by ion mobility.

Correlating Resolving Power, Resolution, and Collision Cross Section: Unifying Cross-Platform Assessment of Separation Efficiency in Ion Mobility Spectrometry.

Here we examine the relationship among resolving power (Rp), resolution (Rpp), and collision cross section (CCS) for compounds analyzed in previous ion mobility (IM) experiments representing a wide variety of instrument platforms and IM techniques. Our previous work indicated these three variables effectively describe and predict separation efficiency for drift tube ion mobility spectrometry experiments. In this work, we seek to determine if our previous findings are a general reflection of IM behavior that can be applied to various instrument platforms and mobility techniques. Results suggest IM distributions are well characterized by a Gaussian model and separation efficiency can be predicted on the basis of the empirical difference in the gas-phase CCS and a CCS-based resolving power definition (CCS/ΔCCS). Notably traveling wave (TWIMS) was found to operate at resolutions substantially higher than a single-peak resolving power suggested. When a CCS-based Rp definition was utilized, TWIMS was found to operate at a resolving power between 40 and 50, confirming the previous observations by Giles and co-workers. After the separation axis (and corresponding resolving power) is converted to cross section space, it is possible to effectively predict separation behavior for all mobility techniques evaluated (i.e., uniform field, trapped ion mobility, traveling wave, cyclic, and overtone instruments) using the equations described in this work. Finally, we are able to establish for the first time that the current state-of-the-art ion mobility separations benchmark at a CCS-based resolving power of >300 that is sufficient to differentiate analyte ions with CCS differences as small as 0.5%.

Investigation of the Complete Suite of the Leucine and Isoleucine Isomers: Toward Prediction of Ion Mobility Separation Capabilities.

In this study we investigated 11 isomers with the molecular formula C6H13NO2 (m/z 131) to ascertain the potential of utilizing drift tube ion mobility mass spectrometry to aid in the separation of isomeric mixtures. This study of small molecules provides a detailed examination of the application of uniform field ion mobility for a narrow scope of isomers with variations in both bond coordination and stereochemistry. For small molecules, it was observed that in general constitutional isomers are more readily separated by uniform field mobility in comparison to stereoisomers such as enantiomers or diastereomers. Diastereomers exhibited differences in their collision cross section (CCS), but were unresolvable in a mixture, whereas the enantiomers studied did not exhibit statistically different CCS values. A mathematical relationship relating the CCS to resolving power was developed in order to predict the required ion mobility resolving power needed to separate the various isomer classes. For the majority of isomers evaluated in this study, a uniform field-based resolving power of 100 was predicted to be sufficient to resolve over half (∼60%) of all hypothetical isomer pairs, including leucine and isoleucine, whereas their stereoisomers (d- and l-forms) are predicted to be significantly more challenging, if not impossible, to separate by conventional drift tube techniques.

Broadscale resolving power performance of a high precision uniform field ion mobility-mass spectrometer.

An extensive study of two current ion mobility resolving power theories ("conditional" and "semi-empirical") was undertaken using a recently developed drift tube ion mobility-mass spectrometer. The current study investigates the quantitative agreement between experiment and theory at reduced pressure (4 Torr) for a wide range of initial ion gate widths (100 to 500 µs), and ion mobility values (K0 from 0.50 to 3.0 cm(2) V(-1) s(-1)) representing measurements obtained in helium, nitrogen, and carbon dioxide drift gas. Results suggest that the conditional resolving power theory deviates from experimental results for low mobility ions (e.g., high mass analytes) and for initial ion gate widths beyond 200 µs. A semi-empirical resolving power theory provided close-correlation of predicted resolving powers to experimental results across the full range of mobilities and gate widths investigated. Interpreting the results from the semi-empirical theory, the performance of the current instrumentation was found to be highly linear for a wide range of analytes, with optimal resolving powers being accessible for a narrow range of drift fields between 14 and 17 V cm(-1). While developed using singly-charged ion mobility data, preliminary results suggest that the semi-empirical theory has broader applicability to higher-charge state systems.

Investigating mouse hepatic lipidome dysregulation following exposure to emerging per- and polyfluoroalkyl substances (PFAS).

Per- and polyfluoroalkyl substances (PFAS) are environmental pollutants that have been associated with adverse health effects including liver damage, decreased vaccine responses, cancer, developmental toxicity, thyroid dysfunction, and elevated cholesterol. The specific molecular mechanisms impacted by PFAS exposure to cause these health effects remain poorly understood, however there is some evidence of lipid dysregulation. Thus, lipidomic studies that go beyond clinical triglyceride and cholesterol tests are greatly needed to investigate these perturbations. Here, we have utilized a platform coupling liquid chromatography, ion mobility spectrometry, and mass spectrometry (LC-IMS-MS) separations to simultaneously evaluate PFAS bioaccumulation and lipid metabolism disruptions. For the study, liver samples collected from C57BL/6 mice exposed to either of the emerging PFAS hexafluoropropylene oxide dimer acid (HFPO-DA or "GenX") or Nafion byproduct 2 (NBP2) were assessed. Sex-specific differences in PFAS accumulation and liver size were observed for both PFAS, in addition to disturbed hepatic liver lipidomic profiles. Interestingly, GenX resulted in less hepatic bioaccumulation than NBP2 yet gave a higher number of significantly altered lipids when compared to the control group, implying that the accumulation of substances in the liver may not be a reliable measure of the substance's capacity to disrupt the liver's natural metabolic processes. Specifically, phosphatidylglycerols, phosphatidylinositols, and various specific fatty acyls were greatly impacted, indicating alteration of inflammation, oxidative stress, and cellular signaling processes due to emerging PFAS exposure. Overall, these results provide valuable insight into the liver bioaccumulation and molecular mechanisms of GenX- and NBP2-induced hepatotoxicity.

AutonoMS: Automated Ion Mobility Metabolomic Fingerprinting.

Automation is dramatically changing the nature of laboratory life science. Robotic lab hardware that can perform manual operations with greater speed, endurance, and reproducibility opens an avenue for faster scientific discovery with less time spent on laborious repetitive tasks. A major bottleneck remains in integrating cutting-edge laboratory equipment into automated workflows, notably specialized analytical equipment, which is designed for human usage. Here we present AutonoMS, a platform for automatically running, processing, and analyzing high-throughput mass spectrometry experiments. AutonoMS is currently written around an ion mobility mass spectrometry (IM-MS) platform and can be adapted to additional analytical instruments and data processing flows. AutonoMS enables automated software agent-controlled end-to-end measurement and analysis runs from experimental specification files that can be produced by human users or upstream software processes. We demonstrate the use and abilities of AutonoMS in a high-throughput flow-injection ion mobility configuration with 5 s sample analysis time, processing robotically prepared chemical standards and cultured yeast samples in targeted and untargeted metabolomics applications. The platform exhibited consistency, reliability, and ease of use while eliminating the need for human intervention in the process of sample injection, data processing, and analysis. The platform paves the way toward a more fully automated mass spectrometry analysis and ultimately closed-loop laboratory workflows involving automated experimentation and analysis coupled to AI-driven experimentation utilizing cutting-edge analytical instrumentation. AutonoMS documentation is available at https://autonoms.readthedocs.io.

Uncovering per- and polyfluoroalkyl substances (PFAS) with nontargeted ion mobility spectrometry-mass spectrometry analyses.

Because of environmental and health concerns, legacy per- and polyfluoroalkyl substances (PFAS) have been voluntarily phased out, and thousands of emerging PFAS introduced as replacements. Traditional analytical methods target a limited number of mainly legacy PFAS; therefore, many species are not routinely assessed in the environment. Nontargeted approaches using high-resolution mass spectrometry methods have therefore been used to detect and characterize unknown PFAS. However, their ability to elucidate chemical structures relies on generation of informative fragments, and many low concentration species are not fragmented in typical data-dependent acquisition approaches. Here, a data-independent method leveraging ion mobility spectrometry (IMS) and size-dependent fragmentation was developed and applied to characterize aquatic passive samplers deployed near a North Carolina fluorochemical manufacturer. From the study, 11 PFAS structures for various per- and polyfluorinated ether sulfonic acids and multiheaded perfluorinated ether acids were elucidated in addition to 36 known PFAS. Eight of these species were previously unreported in environmental media, and three suspected species were validated.

Assessing Per- and Polyfluoroalkyl Substances (PFAS) in Fish Fillet Using Non-Targeted Analyses.

Per- and polyfluoroalkyl substances (PFAS) are a class of thousands of man-made chemicals that are persistent and highly stable in the environment. The diverse structures of PFAS give them different chemical properties that influence their solubility in different environmental matrices and biological tissues. PFAS in drinking water have been extensively studied, but information on their presence in fish and other exposure routes is limited. To address this, a non-targeted analysis using liquid chromatography, ion mobility spectrometry, and mass spectrometry (LC-IMS-MS) was performed to evaluate PFAS in fish fillets from in central North Carolina and compare with PFAS data from previously published water. A total of 22 different PFAS were detected in the fillets, including only 4 of the PFAS reported in water. Both more PFAS types and higher concentrations were observed in fish caught near a known PFAS point-source compared to those from a reservoir used for drinking water and recreation. Median fillet PFOS levels were 54 ppb in fish closest to the point source and 14-20 ppb in fish from the reservoir. Thus, future PFAS monitoring should include both targeted and non-targeted analyses of both water and fish to increase understanding of human exposure risks and ecosystem impacts. SYNOPSIS: Fish fillet samples were collected from five sites in North Carolina. PFAS were detected in all samples and differences in analytes and abundances were observed at the different sites. GRAPHICAL ABSTRACT: For use in table of contents only.

Application of Ion Mobility Spectrometry-Mass Spectrometry for Compositional Characterization and Fingerprinting of a Library of Diverse Crude Oil Samples.

Exposure characterization of crude oils, especially in time-sensitive circumstances such as spills and disasters, is a well-known analytical chemistry challenge. Gas chromatography-mass spectrometry is commonly used for "fingerprinting" and origin tracing in oil spills; however, this method is both time-consuming and lacks the resolving power to separate co-eluting compounds. Recent advances in methodologies to analyze petroleum substances using high-resolution analytical techniques have demonstrated both improved resolving power and higher throughput. One such method, ion mobility spectrometry-mass spectrometry (IMS-MS), is especially promising because it is both rapid and high-throughput, with the ability to discern among highly homologous hydrocarbon molecules. Previous applications of IMS-MS to crude oil analyses included a limited number of samples and did not provide detailed characterization of chemical constituents. We analyzed a diverse library of 195 crude oil samples using IMS-MS and applied a computational workflow to assign molecular formulas to individual features. The oils were from 12 groups based on geographical and geological origins: non-US (1 group), US onshore (3), and US Gulf of Mexico offshore (8). We hypothesized that information acquired through IMS-MS data would provide a more confident grouping and yield additional fingerprint information. Chemical composition data from IMS-MS was used for unsupervised hierarchical clustering, as well as machine learning-based supervised analysis to predict geographic and source rock categories for each sample; the latter also yielded several novel prospective biomarkers for fingerprinting of crude oils. We found that IMS-MS data have complementary advantages for fingerprinting and characterization of diverse crude oils and that proposed polycyclic aromatic hydrocarbon biomarkers can be used for rapid exposure characterization. Environ Toxicol Chem 2023;42:2336-2349. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.