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

Prioritization strategies for non-target screening in environmental samples by chromatography - High-resolution mass spectrometry: A tutorial

Non-target screening (NTS) using chromatography coupled to high-resolution mass spectrometry (HRMS), has become fundamental for detecting and prioritizing chemicals of emerging concern (CECs) in complex environmental matrices. The vast number of generated features (m/z, retention time, and intensity) necessitate effective prioritization strategies to identify environmentally and toxicologically relevant CECs. Since compound identification remains a major bottleneck in NTS, prioritization is critical to focus identification efforts where they matter most. This tutorial presents seven prioritization strategies: (1) Target and suspect screening for identifying known or suspected compounds using reference libraries. (2) Data quality filtering to apply quality control measures to reduce noise and the number of false positives. (3) Chemistry-driven prioritization using HRMS data properties to prioritize specific compound classes (e.g., halogenated substances, transformation products). (4) Process-driven - using spatial, temporal, or process-based comparisons (pre- and post-technical processes) to identify key features. (5) Effect-Directed Analysis (EDA) and Virtual Effect-Directed Analysis (vEDA) prioritization to link chemical features to biological effects. (6) Prediction-based prioritization such as quantitative structure-property relationships (QSPR) and machine learning to estimate risk or concentration levels, and (7) Pixel- or tile-based analysis where the chromatographic image (2D data) is used to pin-point regions of interest or for comparison of larger sample sets. By integrating these prioritization strategies, this tutorial provides a structured foundation to evaluate both identified and unidentified features, prioritize high-risk compounds, and advance environmental risk assessment and regulatory decision-making.

Advancements in artificial intelligence-based technologies for PFAS detection, monitoring, and management

Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants with strong carbon‑fluorine (CF) bonds that contribute to bioaccumulation and long-term environmental and health risks. Traditional PFAS detection and treatment methods are often time-consuming, costly, and limited in scope. Recently, artificial intelligence (AI)-based technologies, particularly machine learning (ML), have emerged as powerful tools for enhancing PFAS monitoring, source identification, and remediation. ML models such as random forest (RF), gradient boosting decision trees (GBDT), support vector machines (SVM), and artificial neural networks (ANN) have been successfully applied to classify PFAS contamination sources with over 96 % accuracy, predict PFAS concentrations in groundwater with an AUC of 0.90, and optimize removal processes such as nanofiltration and adsorption with R2 values exceeding 0.93. Despite these advancement, challenges remain in ensuring high-quality datasets, addressing data imbalance and improving model interpretability. Future research should focus on expanding public datasets, leveraging Automated ML (AutoML) for optimization, and integrating Al-driven sensors for real-time detection. AI-based approaches present a transformative opportunity to enhance efficiency, accuracy, and cost-effectiveness in PFAS management, aiding regulatory decision-making and environmental protection.

Xenometabolomics in Ecotoxicology: Concepts and Applications

Nontargeted high-resolution mass spectrometry (HRMS) allows for the characterization of a large fraction of the exposome, i.e., the entirety of chemicals an organism is exposed to, and helps detect important exogenous chemical compounds that could be key drivers of toxicological impact. Along with these chemical compounds occur endogenous metabolites that are essential for the health of the host organism. Chemical compounds derived from the biotransformation of xenobiotics present in the exposome are referred to as the xenometabolome, while endogenous metabolites derived from the host organism are referred to as the endometabolome. Recent advancements in HRMS technology allow for the detection of chemical features of biological and ecological importance in the context of chemical safety assessments with unprecedented sensitivity and resolution. In this perspective, we highlight the application of HRMS-based metabolomics of organisms in the context of ecotoxicology, the complexity of comprehensively characterizing the endometabolome, and distinguishing chemical compounds of the xenometabolome.

FluoroMatch IM: An Interactive Software for PFAS Analysis by Ion Mobility Spectrometry

Per- and polyfluoroalkyl substances (PFASs) are often present in complex mixtures at trace levels in environmental samples, posing difficulties for analytical chemists. Ion mobility offers highly replicable identifiers, enabling the use of community-based libraries for PFAS annotation in nontargeted analysis. Currently, limited software exists to leverage the capabilities of liquid chromatography ion mobility high-resolution mass spectrometry (LC-IM-HRMS) for nontargeted analysis. FluoroMatch IM is a free vendor-neutral open-source tool for rapid annotation of PFASs in LC-IM-HRMS datasets. Annotation algorithms include collision cross-section (CCS) matching, formula prediction, homologous series detection, mass defect filtering, and accurate mass matching with a database of 194 PFAS ions that can be continuously expanded by the community. Results from FluoroMatch IM were compared to a targeted approach with a laboratory-prepared mixture of 63 PFASs and real wastewater samples. A nontarget workflow incorporating FluoroMatch IM revealed additional likely PFASs (n = 16) while confirming most targeted annotations (11/12) in wastewater samples. Validation of the standard mix showed a low false negative rate of 5% and a 5% false positive rate for features included in the CCS library, with a 0% false positive rate for features assigned confident scores. This study demonstrates the promise of FluoroMatch IM for streamlining PFAS analysis workflows.

The Role of Artificial Intelligence and Machine Learning in Polymer Characterization: Emerging Trends and Perspectives

The application of artificial intelligence (AI) and machine learning (ML) is rapidly expanding and has begun to make a significant impact on polymer development and characterization. This perspective article explores the current state of AI in this field and highlights areas where its potential remains underutilized. While the optimization of polymer synthesis to achieve desired properties and the classification of polymer types are well-established, opportunities for AI integration in detailed characterization, analytical method development, and data processing remain largely untapped. Greater automation of the analytical laboratory, whether through dedicated algorithms or AI-driven solutions, will enable analytical chemists to focus more on addressing research questions and interpreting results, rather than on method development and routine measurements.

Bromine-Based Derivatization of Carboxyl-Containing Metabolites for Liquid Chromatography-Trapped Ion Mobility Spectrometry-Mass Spectrometry

The analysis of small carboxyl-containing metabolites (CCMs), such as tricarboxylic acid (TCA) cycle intermediates, provides highly useful information about the metabolic state of cells. However, their detection using liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS) methods can face sensitivity and specificity challenges given their low ionization efficiency and the presence of isomers. Ion mobility spectrometry (IMS), such as trapped ion mobility spectrometry (TIMS), provides additional specificity, but further signal loss can occur during the mobility separation process. We, therefore, developed a solution to boost CCM ionization and chromatographic separation as well as leverage specificity of IMS. Inspired by carbodiimide-mediated coupling of carboxylic acids with 4-bromo-N-methylbenzylamine (4-BNMA) for quantitative analysis, we newly report the benefits of this reagent for TIMS-based measurement. We observed a pronounced (orders of magnitude) increase in signal and enhanced isomer separations, particularly by LC. We found that utilization of a brominated reagent, such as 4-BNMA, offered unique benefits for untargeted CCM measurement. Derivatized CCMs displayed shifted mobility out of the metabolite and lipid region of the TIMS-MS space as well as characteristic isotope patterns, which were leveraged for data mining with Mass Spectrometry Query Language (MassQL) and indication of the number of carboxyl groups. The utility of our LC-ESI-TIMS-MS/MS method with 4-BMA derivatization was demonstrated via the characterization of alterations in CCM expression in bone marrow-derived macrophages upon activation with lipopolysaccharide. While metabolic reprogramming in activated macrophages has been characterized previously, especially with respect to TCA cycle intermediates, we report a novel finding that isomeric itaconic, mesaconic, and citraconic acid increase after 24 h, indicating possible roles in the inflammatory response.

Structural Characterization of Dimeric Perfluoroalkyl Carboxylic Acid Using Experimental and Theoretical Ion Mobility Spectrometry Analyses

Per- and polyfluoroalkyl substances (PFAS) are contaminants of increasing concern, with over seven million compounds currently inventoried in the PubChem PFAS Tree. Recently, ion mobility spectrometry has been combined with liquid chromatography and high-resolution mass spectrometry (LC-IMS-HRMS) to assess PFAS. Interestingly, using negative electrospray ionization, perfluoroalkyl carboxylic acids (PFCAs) form homodimers ([2M-H]-), a phenomenon observed with trapped, traveling wave, and drift-tube IMS. In addition to the limited research on their effect on analytical performance, there is little information on the conformations these dimers can adopt. This study aimed to propose most probable conformations for PFCA dimers. Based on qualitative analysis of how collision cross section (CCS) values change with the mass-to-charge ratio (m/z) of PFCA ions, the PFCA dimers were hypothesized to likely adopt a V-shaped structure. To support this assumption, in silico geometry optimizations were performed to generate a set of conformers for each possible dimer. A CCS value was then calculated for each conformer using the trajectory method with Lennard-Jones and ion-quadrupole potentials. Among these conformers, at least one of the ten lowest-energy conformers identified for each dimer exhibited theoretical CCS values within a ±2% error margin compared to the experimental data, qualifying them as plausible structures for the dimers. Our findings revealed that the fluorinated alkyl chains in the dimers are close to each other due to a combination of C-F···O=C and C-F···F-C stabilizing interactions. These findings, together with supplementary investigations involving environmentally relevant cations, may offer valuable insights into the interactions and environmental behavior of PFAS.

Multidimensional Separations for Characterization of Isomeric PFAS Using SLIM High-Resolution Ion Mobility and Tandem Mass Spectrometry

Per- and polyfluoroalkyl substances (PFAS) are synthetic organofluorine compounds that accumulate in the environment due to significant industrial use and resistance to degradation. PFAS are of global interest because of their environmental and health concerns. They exist in a variety of linear and nonlinear forms containing a variety of isomers, as well as differing functional headgroups for each class. That structural complexity requires advanced analytical techniques, beyond current high-resolution mass spectrometry (HRMS) methods, for their accurate identification and quantification in a wide range of samples. Herein, we demonstrate the power of Structures for Lossless Ion Manipulations (SLIM)-based high-resolution ion mobility (HRIM) for separation of complex PFAS branched isomers. SLIM is integrated into a multidimensional LC-SLIM IM-MS/MS workflow, developed for the extensive characterization of a wide range of PFAS compounds. As we surveyed sulfonate and carboxylic acid classes of PFAS, we observed unique arrival time vs m/z trend lines that were representative of each class; these trend lines are important for allowing identification of emerging species based on their placement in that two-dimensional space. Next, we used complementary tandem mass spectrometry (MS/MS) approaches with all ion fragmentation (AIF), as well as energy-resolved MS/MS, to further investigate the structure of mobility-separated species. This allowed both investigation of fragmentation mechanism and identification of unique fragment ions that could allow differentiation of isomers when ion mobility was insufficient. Overall, the combination of chromatography, high-resolution SLIM, and MS/MS provided a comprehensive workflow capable of identifying unknown emerging PFAS compounds in complex environmental samples.

Novel PFAS in Alligator Blood Discovered with Non-Targeted Ion Mobility-Mass Spectrometry

Per- and polyfluoroalkyl substances (PFAS) are a large and growing class of chemicals gaining global attention due to their persistence, mobility, and toxicity. Given the diverse chemical properties of PFAS and their varying distributions in water and tissue, monitoring of different matrices is critical to determine their presence and accumulation. Here, we used a platform combining liquid chromatography, ion mobility spectrometry, and high-resolution mass spectrometry (LC-IMS-HRMS) for non-targeted analysis (NTA) to detect and identify PFAS in alligator plasma from North Carolina (5 years, 2018-2022) and Florida (2021 only). Structures for 12 PFAS were elucidated, including 2 novel structures, and an additional 34 known PFAS were detected. Three of these compounds were previously unreported in environmental media. More PFAS were detected in NC alligators than FL and no novel PFAS were detected in FL gators. Quantitative analysis of 21 of the known PFAS revealed that plasma concentrations did not change over the 5 year study, indicating that exposure is ongoing.

Liquid and gas-chromatography-mass spectrometry methods for exposome analysis

Mass spectrometry-based methods have become fundamental to exposome research, providing the capability to explore a broad spectrum of chemical exposures. Liquid and gas chromatography coupled with low/high-resolution mass spectrometry (MS) are among the most frequently employed platforms due to their sensitivity and accuracy. However, these approaches present challenges, such as the inherent complexity of MS data and the expertise of biologists, chemists, clinicians, and data analysts to integrate and interpret MS data with other datasets effectively. The "omics" era advances rapidly, driven by developments of AI-based algorithms and an increase in accessible data; nevertheless, further efforts are necessary to ensure that exposomics outputs are comparable and reproducible, thus enhancing research findings. This review outlines the principles of MS-based methods for the exposome analytical pipeline, from sample collection to data analysis. We summarize and review both standard and cutting-edge strategies in exposome research, covering sample preparation, focusing on MS-based platforms, data acquisition strategies, and data annotation. The ultimate goal of this review is to highlight applications that enable the simultaneous analysis of endogenous metabolites and xenobiotics, which can help enhance our understanding of the impact of human exposure on health and disease and support personalized healthcare.

Reference Library for Suspect Non-targeted Screening of Environmental Toxicants Using Ion Mobility Spectrometry-Mass Spectrometry

As our health is affected by the xenobiotic chemicals we are exposed to, it is important to rapidly assess these molecules both in the environment and our bodies. Targeted analytical methods coupling either gas or liquid chromatography with mass spectrometry (GC-MS or LC-MS) are commonly utilized in current exposure assessments. While these methods are accepted as the gold standard for exposure analyses, they often require multiple sample preparation steps and more than 30 minutes per sample. This throughput limitation is a critical gap for exposure assessments and has resulted in an evolving interest in using ion mobility spectrometry and MS (IMS-MS) for non-targeted studies. IMS-MS is a unique technique due to its rapid analytical capabilities (millisecond scanning) and detection of a wide range of chemicals based on unique collision cross section (CCS) and mass-to-charge (m/z) values. To increase the availability of IMS-MS information for exposure studies, here we utilized drift tube IMS-MS to evaluate 4,685 xenobiotic chemical standards from the Environmental Protection Agency Toxicity Forecaster (ToxCast) program including pesticides, industrial chemicals, pharmaceuticals, consumer products, and per- and polyfluoroalkyl substances (PFAS). In the analyses, 3,993 [M+H]+, [M+Na]+, [M-H]- and [M+]+ ion types were observed with high confidence and reproducibility (≤1% error intra-laboratory and ≤2% inter-laboratory) from 2,140 unique chemicals. These values were then assembled into an openly available multidimensional database and uploaded to PubChem to enable rapid IMS-MS suspect screening for a wide range of environmental contaminants, faster response time in environmental exposure assessments, and assessments of xenobiotic-disease connections.

Challenges in PFAS Postdegradation Analysis: Insights from the PFAS-CTAB Model System

Per- and polyfluoroalkyl substances (PFAS) are synthetic chemicals widely used for their oil and water-repellent properties. Their environmental persistence and potential health risks have raised significant concerns. As PFAS degrades through remediation or natural processes, they form complex mixtures of the original chemicals, transformation byproducts, and degradation additives. Analyzing PFAS after degradation presents analytical challenges due to possible chemical and physical interactions, including ion pairing, micelle formation, and complexation. These factors can significantly impact the precision and accuracy of PFAS measurements, yet they are often overlooked in PFAS degradation studies. In this work, we demonstrate that with the addition of ppb-level cetyltrimethylammonium bromide (CTAB), a cationic surfactant used in PFAS plasma-based degradation, the PFAS calibration curve linearity, sensitivity, and reproducibility are severely compromised. Isotopically labeled internal standards cannot fully correct these issues. Furthermore, the standard EPA methods 537.1, 533, and 1633 could not accurately recover PFAS concentrations in the PFAS and CTAB mixtures, with severe matrix effects observed for longer-chain and nitrogen-containing PFAS. Among these methods, Method 1633 is currently the most suitable option for postdegradation analysis. Method 1633 showed the lowest CTAB interference because this method used another weak ion pair additive, formic acid or acetic acid (in commercial lab analysis), to acidify the sample before LC-MS/MS analysis and added an isotopically labeled internal standard. For future PFAS degradation studies, we recommend systematically evaluating the matrix effect on the PFAS quantification using a recovery matrix to validate the analytical methods before use.

PubChemLite Plus Collision Cross Section (CCS) Values for Enhanced Interpretation of Nontarget Environmental Data

Finding relevant chemicals in the vast (known) chemical space is a major challenge for environmental and exposomics studies leveraging nontarget high resolution mass spectrometry (NT-HRMS) methods. Chemical databases now contain hundreds of millions of chemicals, yet many are not relevant. This article details an extensive collaborative, open science effort to provide a dynamic collection of chemicals for environmental, metabolomics, and exposomics research, along with supporting information about their relevance to assist researchers in the interpretation of candidate hits. The PubChemLite for Exposomics collection is compiled from ten annotation categories within PubChem, enhanced with patent, literature and annotation counts, predicted partition coefficient (logP) values, as well as predicted collision cross section (CCS) values using CCSbase. Monthly versions are archived on Zenodo under a CC-BY license, supporting reproducible research, and a new interface has been developed, including historical trends of patent and literature data, for researchers to browse the collection. This article details how PubChemLite can support researchers in environmental and exposomics studies, describes efforts to increase the availability of experimental CCS values, and explores known limitations and potential for future developments. The data and code behind these efforts are openly available. PubChemLite can be browsed at https://pubchemlite.lcsb.uni.lu.

Updated Guidance for Communicating PFAS Identification Confidence with Ion Mobility Spectrometry

Over the last decade, global contamination from per- and polyfluoroalkyl substances (PFAS) has become apparent due to their detection in countless matrices worldwide, from consumer products to human blood to drinking water. As researchers implement non-targeted analyses (NTA) to more fully understand the PFAS present in the environment and human bodies, clear guidance is needed for consistent and objective reporting of the identified molecules. While confidence levels for small molecules analyzed and identified with high-resolution mass spectrometry (HRMS) have existed since 2014, unification and automation of these levels is needed due to inconsistencies in reporting and continuing innovations in analytical methods. Here, we (i) investigate current practices for confidence level reporting of PFAS identified with liquid chromatography (LC), gas chromatography (GC), and/or ion mobility spectrometry (IMS) coupled with high resolution mass spectrometry (HRMS) and (ii) propose a simple, unified confidence level guidance that incorporates both PFAS-specific attributes and IMS collision cross section (CCS) values.

Multidimensional library for the improved identification of per- and polyfluoroalkyl substances (PFAS)

As the occurrence of human diseases and conditions increase, questions continue to arise about their linkages to chemical exposure, especially for per-and polyfluoroalkyl substances (PFAS). Currently, many chemicals of concern have limited experimental information available for their use in analytical assessments. Here, we aim to increase this knowledge by providing the scientific community with multidimensional characteristics for 175 PFAS and their resulting 281 ion types. Using a platform coupling reversed-phase liquid chromatography (RPLC), electrospray ionization (ESI) or atmospheric pressure chemical ionization (APCI), drift tube ion mobility spectrometry (IMS), and mass spectrometry (MS), the retention times, collision cross section (CCS) values, and m/z ratios were determined for all analytes and assembled into an openly available multidimensional dataset. This information will provide the scientific community with essential characteristics to expand analytical assessments of PFAS and augment machine learning training sets for discovering new PFAS.

PFAS biodegradation by Labrys portucalensis F11: Evidence of chain shortening and identification of metabolites of PFOS, 6:2 FTS, and 5:3 FTCA

The biodegradation of three per- and polyfluoroalkyl substances (PFAS), namely perfluorooctane sulfonic acid (PFOS), 6:2-fluorotelomer sulfonic acid (6:2 FTS), and 5:3-fluorotelomer carboxylic acid (5:3 FTCA), were evaluated using Labrys portucalensis F11, an aerobic bacteria known to defluorinate fluorine-containing compounds. Cultures of L. portucalensis F11 were grown in minimal salts media and treated with 10,000 μg/L of individual PFAS as the sole carbon source in separate flasks. In PFOS-spiked media, several metabolites were detected, including perfluoroheptane sulfonic acid (PFHpS), perfluorohexane sulfonic acid (PFHxS), perfluorohexanoic acid (PFHxA), perfluoropentanoic acid (PFPeA), perfluorobutanoic acid (PFBA), and perfluoropropanoic acid (PFPrA). After 194-day incubation three de-fluorinated metabolites were identified: PFOS-F (m/z = 480.940, PFOS-2F (m/z = 462.980), and unsaturated PFOS-3F (m/z = 442.943). During the biodegradation of 5:3 FTCA, the following metabolites were observed: PFHxA, PFPeA, PFBA, PFPrA, and two fluorotelomer unsaturated carboxylic acids (5:3 FTUCA and 7:2 FTUCA). The biodegradation of 6:2 FTS was slower, with only 21 % decrease in concentration observed after 100 days, and subsequent formation of 4:2 FTS. On the contrary, 90 % of PFOS and 58 % of 5:3 FTCA were degraded after 100 days. These results indicate that L. portucalensis F11 can be potentially used for PFAS biodegradation in contaminated environments.

PubChem 2025 update

PubChem (https://pubchem.ncbi.nlm.nih.gov) is a large and highly-integrated public chemical database resource at NIH. In the past two years, significant updates were made to PubChem. With additions from over 130 new sources, PubChem contains >1000 data sources, 119 million compounds, 322 million substances and 295 million bioactivities. New interfaces, such as the consolidated literature panel and the patent knowledge panel, were developed. The consolidated literature panel combines all references about a compound into a single list, allowing users to easily find, sort, and export all relevant articles for a chemical in one place. The patent knowledge panels for a given query chemical or gene display chemicals, genes, and diseases co-mentioned with the query in patent documents, helping users to explore relationships between co-occurring entities within patent documents. PubChemRDF was expanded to include the co-occurrence data underlying the literature knowledge panel, enabling users to exploit semantic web technologies to explore entity relationships based on the co-occurrences in the scientific literature. The usability and accessibility of information on chemicals with non-discrete structures (e.g. biologics, minerals, polymers, UVCBs and glycans) were greatly improved with dedicated web pages that provide a comprehensive view of all available information in PubChem for these chemicals.

Target, suspect, and non-target screening of per- and poly-fluoroalkyl substances in wastewater treatment plant effluents in South Korea using ion mobility spectrometry-mass spectrometry

This study used target, suspect, and non-target screening, to assess the presence of per- and polyfluoroalkyl substances (PFASs) in domestic (municipal) and industrial wastewater treatment plants (WWTPs) in South Korea. Target analysis quantified 20 PFASs in the WWTP effluents. Total concentration of PFASs ranged from 69.1 to 79.6 ng/L and the concentrations of perfluorobutanoic acid (PFBA) (mean: 15.6 ng/L, median: 14.9 ng/L) and perfluorooctanoic acid (PFOA) (mean: 14.7 ng/L, median: 12.7 ng/L) were higher than those of other PFASs. Compared to 2010, there was an overall increase in perfluoroalkyl carboxylic acids (PFCAs), particularly perfluoroheptanoic acid, (PFHpA), which showed a nearly 10-fold increase, with current concentrations reaching 9.5 ng/L. Suspect and non-target screening with ion mobility spectrometry (IMS)-mass spectrometry was used to identify additional PFASs based on their exact mass, collision cross-section (CCS), and tandem mass spectrometry fragmentation patterns. Twenty compounds were identified as PFAS compounds through suspect screening at a confidence level (CL) of 3 or higher, with five compounds identified at CL 2. Additionally, fragment-based, suspect and non-target screening identified emerging PFASs, including FBSA, a n:2 fluorotelomer-based non-polymer, and bistriflimide, all with CL 2. Semi-quantification of identified PFASs revealed that the concentrations of PFASs identified by suspect and non-target screening were higher than those of the target PFASs, especially in industrial wastewater effluents.

Investigating the Mechanism of Neurotoxic Effects of PFAS in Differentiated Neuronal Cells through Transcriptomics and Lipidomics Analysis

Per- and polyfluorinated alkyl substances (PFAS) are pervasive environmental contaminants that bioaccumulate in tissues and pose risks to human health. Increasing evidence links PFAS to neurodegenerative and behavioral disorders, yet the underlying mechanisms of their effects on neuronal function remain largely unexplored. In this study, we utilized SH-SY5Y neuroblastoma cells, differentiated into neuronal-like cells, to investigate the impact of six PFAS compounds─perfluorooctanoic acid (PFOA), perfluorooctanesulfonic acid (PFOS), perfluorodecanoic acid (PFDA), perfluorodecanesulfonic acid (PFDS), 8:2 fluorotelomer sulfonate (8:2 FTS), and 8:2 fluorotelomer alcohol (8:2 FTOH)─on neuronal health. Following a 30 μM exposure for 24 h, PFAS accumulation ranged from 40-6500 ng/mg of protein. Transcriptomic analysis revealed 721 differentially expressed genes (DEGs) across treatments (padj < 0.05), with 11 DEGs shared among all PFAS exposures, indicating potential biomarkers for neuronal PFAS toxicity. PFOA-treated cells showed downregulation of genes involved in synaptic growth and neural function, while PFOS, PFDS, 8:2 FTS, and 8:2 FTOH exposures resulted in the upregulation of genes related to hypoxia response and amino acid metabolism. Lipidomic profiling further demonstrated significant increases in fatty acid levels with PFDA, PFDS, and 8:2 FTS and depletion of triacylglycerols with 8:2 FTOH treatments. These findings suggest that the neurotoxic effects of PFAS are structurally dependent, offering insights into the molecular processes that may drive PFAS-induced neuronal dysfunction.

Mass Spectrometry Imaging for Spatial Toxicology Research

The spatial information of xenobiotics distribution, metabolism, and toxicity mechanisms in situ has drawn increasing attention in both pharmaceutical and environmental toxicology research to aid drug development and environmental risk assessments. Mass spectrometry imaging (MSI) provides a label-free, multiplexed, and high-throughput tool to characterize xenobiotics, their metabolites, and endogenous molecules in situ with spatial resolution, providing knowledge on spatially resolved absorption, distribution, metabolism, excretion, and toxicity on the molecular level. In this perspective, we briefly summarize applications of MSI in toxicology on xenobiotic distribution and metabolism, quantification, toxicity mechanisms, and biomarker discovery. We identified several challenges regarding how we can fully harness the power of MSI in both fundamental toxicology research and regulatory practices. First, how can we increase the coverage, sensitivity, and specificity in detecting xenobiotics and their metabolites in complex biological matrices? Second, how can we link the spatial molecular information of xenobiotics to toxicity consequences to understand toxicity mechanisms, predict exposure outcomes, and aid biomarker discovery? Finally, how can we standardize the MSI experiment and data analysis workflow to provide robust conclusions for regulation and drug development? With these questions in mind, we provide our perspectives on the future directions of MSI as a promising tool in spatial toxicology research.

Evaluating Solid Phase Adsorption Toxin Tracking (SPATT) for passive monitoring of per- and polyfluoroalkyl substances (PFAS) with Ion Mobility Spectrometry-Mass Spectrometry (IMS-MS)

Detection and monitoring of per- and polyfluoroalkyl substances (PFAS) in aquatic environments has become an increasingly higher priority of regulatory agencies as public concern for human intake of these chemicals continues to grow. While many methods utilize active sampling strategies ("grab samples") for precise PFAS quantitation, here we evaluate the efficacy of low-cost passive sampling devices (Solid Phase Adsorption Toxin Tracking, or SPATTs) for spatial and temporal PFAS assessment of aquatic systems. For this study, passive samplers were initially deployed in North Carolina along the Cape Fear River during the summer and fall of 2016 and 2017. These were originally intended for the detection of microcystins and monitoring potentially harmful algal blooms, though this period also coincided with occurrences of PFAS discharge from a local fluorochemical manufacturer into the river. Additional samplers were then deployed in 2022 to evaluate changes in PFAS fingerprint and abundances. Assessment of PFAS showed legacy compounds were observed across almost all sampling sites over all 3 years (PFHxS, PFOS, PFHxA, etc.), while emerging replacement PFAS (e.g., Nafion byproducts) were predominantly localized downstream from the manufacturer. Furthermore, samplers deployed downstream from the manufacturer in 2022 noted sharp decreases in observed signal for replacement PFAS in comparison to samplers deployed in 2016 and 2017, indicating mitigation and remediation efforts in the area were able to reduce localized fluorochemical contamination.

Streamlining Phenotype Classification and Highlighting Feature Candidates: A Screening Method for Non-Targeted Ion Mobility Spectrometry-Mass Spectrometry (IMS-MS) Data

Nontargeted analysis (NTA) is increasingly utilized for its ability to identify key molecular features beyond known targets in complex samples. NTA is particularly advantageous in exploratory studies aimed at identifying phenotype-associated features or molecules able to classify various sample types. However, implementing NTA involves extensive data analyses and labor-intensive annotations. To address these limitations, we developed a rapid data screening capability compatible with NTA data collected on a liquid chromatography, ion mobility spectrometry, and mass spectrometry (LC-IMS-MS) platform that allows for sample classification while highlighting potential features of interest. Specifically, this method aggregates the thousands of IMS-MS spectra collected across the LC space for each sample and collapses the LC dimension, resulting in a single summed IMS-MS spectrum for screening. The summed IMS-MS spectra are then analyzed with a bootstrapped Lasso technique to identify key regions or coordinates for phenotype classification via support vector machines. Molecular annotations are then performed by examining the features present in the selected coordinates, highlighting potential molecular candidates. To demonstrate this summed IMS-MS screening approach, we applied it to clinical plasma lipidomic NTA data and exposomic NTA data from water sites with varying contaminant levels. Distinguishing coordinates were observed in both studies, enabling the evaluation of phenotypic molecular annotations and resulting in screening models capable of classifying samples with up to a 25% increase in accuracy compared to models using annotated data.

Mobile Phase Contaminants Affect Neutral Lipid Analysis in LC-MS-Based Lipidomics Studies

Lipidomics is a well-established field, enabled by modern liquid chromatography mass spectrometry (LC-MS) technology, rapidly generating large amounts of data. Lipid extracts derived from biological samples are complex, and most spectral features in LC-MS lipidomics data sets remain unidentified. In-depth analyses of commercial triacylglycerol, diacylglycerol, and cholesterol ester standards revealed the expected ammoniated and sodiated ions as well as five additional unidentified higher mass peaks with relatively high intensities. The identities and origin of these unknown peaks were investigated by modifying the chromatographic mobile-phase components and LC-MS source parameters. Tandem MS (MS/MS) of each unknown adduct peak yielded no lipid structural information, producing only an intense ion of the adducted species. The unknown adducts were identified as low-mass contaminants originating from methanol and isopropanol in the mobile phase. Each contaminant was determined to be an alkylated amine species using their monoisotopic masses to calculate molecular formulas. Analysis of bovine liver extract identified 33 neutral lipids with an additional 73 alkyl amine adducts. Analysis of LC-MS-grade methanol and isopropanol from different vendors revealed substantial alkylated amine contamination in one out of three different brands that were tested. Substituting solvents for ones with lower levels of alkyl amine contamination increased lipid annotations by 36.5% or 27.4%, depending on the vendor, and resulted in >2.5-fold increases in peak area for neutral lipid species without affecting polar lipid analysis. These findings demonstrate the importance of solvent selection and disclosure for lipidomics protocols and highlight some of the major challenges when comparing data between experiments.

Assessing Per- and Polyfluoroalkyl Substances 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. Fish consumption has been identified as a key route of PFAS exposure for humans. However, routine fish monitoring targets only a handful of PFAS, and non-targeted analyses have largely only evaluated fish from heavily PFAS-impacted waters. Here, we evaluated PFAS in fish fillets from recreational and drinking water sources in central North Carolina to assess whether PFAS are present in these fillets that would not be detected by conventional targeted methods. We used liquid chromatography, ion mobility spectrometry, and mass spectrometry (LC-IMS-MS) to collect full scan feature data, performed suspect screening using an in-house library of 100 PFAS for high confidence feature identification, searched for additional PFAS features using non-targeted data analyses, and quantified perfluorooctanesulfonic acid (PFOS) in the fillet samples. A total of 36 PFAS were detected in the fish fillets, including 19 that would not be detected using common targeted methods, with a minimum of 6 and a maximum of 22 in individual fish. Median fillet PFOS levels were concerningly high at 11.6 to 42.3 ppb, and no significant correlation between PFOS levels and number of PFAS per fish was observed. Future PFAS monitoring in this region should target more of these 36 PFAS, and other regions not considered heavily PFAS contaminated should consider incorporating non-targeted analyses into ongoing fish monitoring studies.

Why are nH-perfluoroalkanoate ions more mobile than expected? Implications for identifying an emerging environmental pollutant

nH-Perfluoroalkyl carboxylic acids (nH-PFCAs) are emerging pollutants. Their identification by ion mobility is frustrated by the nH-PFCAs having unexpectedly small collision cross sections (CCS). Theory and experiment agree that this is because nH-PFCA ions undergo internal hydrogen bridging, and this insight will help guide the creation of more accurate methods for pollutant identification.

High-Resolution Mass Spectrometry for Human Exposomics: Expanding Chemical Space Coverage

In the modern "omics" era, measurement of the human exposome is a critical missing link between genetic drivers and disease outcomes. High-resolution mass spectrometry (HRMS), routinely used in proteomics and metabolomics, has emerged as a leading technology to broadly profile chemical exposure agents and related biomolecules for accurate mass measurement, high sensitivity, rapid data acquisition, and increased resolution of chemical space. Non-targeted approaches are increasingly accessible, supporting a shift from conventional hypothesis-driven, quantitation-centric targeted analyses toward data-driven, hypothesis-generating chemical exposome-wide profiling. However, HRMS-based exposomics encounters unique challenges. New analytical and computational infrastructures are needed to expand the analysis coverage through streamlined, scalable, and harmonized workflows and data pipelines that permit longitudinal chemical exposome tracking, retrospective validation, and multi-omics integration for meaningful health-oriented inferences. In this article, we survey the literature on state-of-the-art HRMS-based technologies, review current analytical workflows and informatic pipelines, and provide an up-to-date reference on exposomic approaches for chemists, toxicologists, epidemiologists, care providers, and stakeholders in health sciences and medicine. We propose efforts to benchmark fit-for-purpose platforms for expanding coverage of chemical space, including gas/liquid chromatography-HRMS (GC-HRMS and LC-HRMS), and discuss opportunities, challenges, and strategies to advance the burgeoning field of the exposome.

Emerging contaminants: A One Health perspective

Environmental pollution is escalating due to rapid global development that often prioritizes human needs over planetary health. Despite global efforts to mitigate legacy pollutants, the continuous introduction of new substances remains a major threat to both people and the planet. In response, global initiatives are focusing on risk assessment and regulation of emerging contaminants, as demonstrated by the ongoing efforts to establish the UN's Intergovernmental Science-Policy Panel on Chemicals, Waste, and Pollution Prevention. This review identifies the sources and impacts of emerging contaminants on planetary health, emphasizing the importance of adopting a One Health approach. Strategies for monitoring and addressing these pollutants are discussed, underscoring the need for robust and socially equitable environmental policies at both regional and international levels. Urgent actions are needed to transition toward sustainable pollution management practices to safeguard our planet for future generations.

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. Fish consumption has been identified as a key route of PFAS exposure for humans. However, routine fish monitoring targets only a handful of PFAS, and non-targeted analyses have largely only evaluated fish from heavily PFAS-impacted waters. Here, we evaluated PFAS in fish fillets from recreational and drinking water sources in central North Carolina to assess whether PFAS are present in these fillets that would not be detected by conventional targeted methods. We used liquid chromatography, ion mobility spectrometry, and mass spectrometry (LC-IMS-MS) to collect full scan feature data, performed suspect screening using an in-house library of 100 PFAS for high confidence feature identification, searched for additional PFAS features using non-targeted data analyses, and quantified perfluorooctane sulfonic acid (PFOS) in the fillet samples. A total of 36 PFAS were detected in the fish fillets, including 19 that would not be detected using common targeted methods, with a minimum of 6 and a maximum of 22 in individual fish. Median fillet PFOS levels were concerningly high at 11.6 to 42.3 ppb, and no significant correlation between PFOS levels and number of PFAS per fish was observed. Future PFAS monitoring in this region should target more of these 36 PFAS, and other regions not considered heavily PFAS contaminated should consider incorporating non-targeted analyses into ongoing fish monitoring studies.

Systematic approaches to machine learning models for predicting pesticide toxicity

Pesticides play an important role in modern agriculture by protecting crops from pests and diseases. However, the negative consequences of pesticides, such as environmental contamination and adverse effects on human and ecological health, underscore the importance of accurate toxicity predictions. To address this issue, artificial intelligence models have emerged as valuable methods for predicting the toxicity of organic compounds. In this review article, we explore the application of machine learning (ML) for pesticide toxicity prediction. This review provides a detailed summary of recent developments, prediction models, and datasets used for pesticide toxicity prediction. In this analysis, we compared the results of several algorithms that predict the harmfulness of various classes of pesticides. Furthermore, this review article identified emerging trends and areas for future direction, showcasing the transformative potential of machine learning in promoting safer pesticide usage and sustainable agriculture.

An artificial intelligence platform for automated PFAS subgroup classification: A discovery tool for PFAS screening

Since structural analyses and toxicity assessments have not been able to keep up with the discovery of unknown per- and polyfluoroalkyl substances (PFAS), there is an urgent need for effective categorization and grouping of PFAS. In this study, we presented PFAS-Atlas, an artificial intelligence-based platform containing a rule-based automatic classification system and a machine learning-based grouping model. Compared with previously developed classification software, the platform's classification system follows the latest Organization for Economic Co-operation and Development (OECD) definition of PFAS and reduces the number of uncategorized PFAS. In addition, the platform incorporates deep unsupervised learning models to visualize the chemical space of PFAS by clustering similar structures and linking related classes. Through real-world use cases, we demonstrate that PFAS-Atlas can rapidly screen for relationships between chemical structure and persistence, bioaccumulation, or toxicity data for PFAS. The platform can also guide the planning of the PFAS testing strategy by showing which PFAS classes urgently require further attention. Ultimately, the release of PFAS-Atlas will benefit both the PFAS research and regulation communities.

Toward Part-per-Million Precision in the Determination of an Ion's Collision Cross Section Using Multipass Cyclic Ion Mobility

In cyclic ion mobility (cIMS), ions are permitted to travel multiple passes around the drift cell, increasing the distance traveled and the relative separation between ions. This study tests the hypothesis that multiple passes around the cell can also result in improved precision when measuring an ion's mobility and the collision cross section (TWCCS) derived therefrom. Experiments were performed with a diverse set of compounds, including 16 polycyclic aromatic hydrocarbons using gas chromatographic atmospheric pressure chemical ionization and a set of drug molecules by direct infusion electrospray ionization. The average periodic drift time, viz., the average time required for the ion to travel around the cIMS cell once, shifts dramatically, approaching part-per-million (ppm) precision as the number of passes increases to ∼100. Extrapolation of the precision of the CCS values with respect to the number of passes led to the prediction that the precision will reach 1000 ppm after 50 passes, 100 ppm after 100 passes, and <10 ppm after 150 passes. Experiments wherein the number of passes exceeded 100 produced TWCCS values having within-run precisions ranging between 15 and 117 ppm. The improved precision with an increasing number of passes may be a consequence of mitigating space-charge effects by allowing the ions to occupy a larger region of the cIMS cell. A method is proposed to enable practical measurements of TWCCS with ppm precision and is demonstrated to characterize an unknown drug mixture.

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.

Fluorinated Propionic Acids Unmasked: Puzzling Fragmentation Phenomena of the Deprotonated Species

Environmental contamination by per- and polyfluorinated substances (PFAS) is an emerging concern for the public. In this study, short-chain PFAS such as deprotonated per- and polyfluorinated propionic acids are investigated using a combination of infrared multiple-photon dissociation (IRMPD) spectroscopy, collision-induced dissociation (CID), and density functional theory calculations. IRMPD and CID proceed via multiple competing pathways: (1) production of fluoroformate (FCO2-) and the associated ethylene derivative, (2) production of HF and the associated carbanion, or (3) loss of CO2 and the associated carbanion. Fluorinated propionic acids with at least one fluorine atom bound to the terminal carbon yield FCO2-, whereas loss of HF is observed in polyfluorinated species with at least one fluorine atom bound to the α-carbon. To explore the reaction pathways of the various fluorinated propionic acids, the nudged elastic band method is employed. The relative energy of the four-membered ring transition state leading to FCO2- dictates which product channel is observed in dissociation.

Formation of halogenated forms of bisphenol A (BPA) in water: Resolving isomers with ion mobility - mass spectrometry and the role of halogenation position in cellular toxicity

Halogenated BPA (XBPA) forms resulting from water chlorination can lead to increased toxicity and different biological effects. While previous studies have reported the occurrence of different XBPAs, analytical limitation have hindered the analysis and differentiation of the many potential isomeric forms. Using online solid-phase extraction - liquid chromatography - ion-mobility - high-resolution mass spectrometry (OSPE-LC-IM-HRMS), we demonstrated a rapid analysis method for the analysis of XBPA forms after water chlorination, with a total analysis time of less than 10 min including extraction and concentration and low detection limits (∼5-80 ng/L range). A multi in-vitro bioassay testing approach for the identified products revealed that cytotoxicity and bioenergetics impacts were largely associated with the presence of halogen atoms at positions 2 or 2' and the overall number of halogens incorporated into the BPA molecule. Different XBPA also showed distinct impacts on oxidative stress, peroxisome proliferator-activated receptor gamma - PPARγ, and inflammatory response. While increased DNA damage was observed for chlorinated water samples (4.14 ± 1.21-fold change), the additive effect of the selected 20 XBPA studied could not explain the increased DNA damage observed, indicating that additional species or synergistic effects might be at play.

PFAS ghosts: how to identify, evaluate, and exorcise new and existing analytical interference

With increasing public awareness of PFAS, and their presence in biological and environmental media across the globe, comes a matching increase in the number of PFAS monitoring studies. As more matrices and sample cohorts are examined, there are more opportunities for matrix interferents to appear as PFAS where there are none (i.e., "seeing ghosts"), impacting subsequent reports. Addressing these ghosts is vital for the research community, as proper analytical measurements are necessary for decision-makers to understand the presence, levels, and potential risks associated with PFAS and protect human and environmental health. To date, PFAS interference has been identified in several matrices (e.g., food, shellfish, blood, tissue); however, additional unidentified interferents are likely to be observed as PFAS research continues to expand. Therefore, the aim of this commentary is several fold: (1) to create and support a publicly available dataset of all currently known PFAS analytical interferents, (2) to allow for the expansion of that dataset as more sources of interference are identified, and (3) to advise the wider scientific community on how to both identify and eliminate current or new analytical interference in PFAS analyses.

Detection, Quantification, and Isomer Differentiation of Per- and Polyfluoroalkyl Substances (PFAS) Using MALDI-TOF with Trapped Ion Mobility

Per- and polyfluoroalkyl substances (PFAS) are a class of organic compounds that have attracted global attention for their persistence in the environment, exposure to biological organisms, and their adverse health effects. There is an urgent need to develop analytical methodologies for the characterization of PFAS in various sample matrices. Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) represents a chromatography-free MS method that performs laser-based ionization and in situ analysis on samples. In this study, we present PFAS analysis by MALDI-time-of-flight (TOF) MS with trapped ion mobility spectrometry (TIMS), which provides an additional dimension of gas phase separation based on the size-to-charge ratios. MALDI matrix composition and key instrument parameters were optimized to produce different ranges of calibration curves. Parts per billion (ppb) range of calibration curves were achieved for a list of legacy and alternative perfluorosulfonic acids (PFSAs) and perfluorocarboxylic acids (PFCAs), while ion mobility spectrum filtering enabled parts per trillion (ppt) range of calibration curves for PFSAs. We also successfully demonstrated the separation of three perfluorooctanesulfonic acid (PFOS) structural isomers in the gas phase using TIMS. Our results demonstrated the new development of utilizing MALDI-TOF-MS coupled with TIMS for fast, quantitative, and sensitive analysis of PFAS, paving ways to future high-throughput and in situ analysis of PFAS such as MS imaging applications.

OMICs Signatures Linking Persistent Organic Pollutants to Cardiovascular Disease in the Swedish Mammography Cohort

Cardiovascular disease (CVD) development may be linked to persistent organic pollutants (POPs), including organochlorine compounds (OCs) and perfluoroalkyl and polyfluoroalkyl substances (PFAS). To explore underlying mechanisms, we investigated metabolites, proteins, and genes linking POPs with CVD risk. We used data from a nested case-control study on myocardial infarction (MI) and stroke from the Swedish Mammography Cohort - Clinical (n = 657 subjects). OCs, PFAS, and multiomics (9511 liquid chromatography-mass spectrometry (LC-MS) metabolite features; 248 proteins; 8110 gene variants) were measured in baseline plasma. POP-related omics features were selected using random forest followed by Spearman correlation adjusted for confounders. From these, CVD-related omics features were selected using conditional logistic regression. Finally, 29 (for OCs) and 12 (for PFAS) unique features associated with POPs and CVD. One omics subpattern, driven by lipids and inflammatory proteins, associated with MI (OR = 2.03; 95% CI = 1.47; 2.79), OCs, age, and BMI, and correlated negatively with PFAS. Another subpattern, driven by carnitines, associated with stroke (OR = 1.55; 95% CI = 1.16; 2.09), OCs, and age, but not with PFAS. This may imply that OCs and PFAS associate with different omics patterns with opposite effects on CVD risk, but more research is needed to disentangle potential modifications by other factors.

The analysis of the migration of per and poly fluoroalkyl substances (PFAS) from food contact materials using ultrahigh performance liquid chromatography coupled to ion-mobility quadrupole time-of-flight mass spectrometry (UPLC- IMS-QTOF)

Per-poly fluoroalkyl substances (PFASs) are a group of synthetic fluorine compounds used in food packaging materials to repel water and fats. This study assessed the chemical migration of PFAS from different food contact materials, including cardboard, recycled cardboard, biopolymer, paper and Teflon trays, from various markets. Migration assays were conducted using Tenax® as a food simulant, which was optimized by subjecting it to three consecutive extractions with 3 mL of ethanol within an hour. The resulting extractions were combined and concentrated to 0.5 mL using a nitrogen stream. The analysis was performed using ultrahigh performance liquid chromatography (UPLC) coupled with ion-mobility (IMS) quadrupole-time-of-flight (QTOF) mass spectrometry, which provided a powerful and novel tool for identifying a library of targets containing collision cross section values (CCS) and increasing confidence in subsequent identifications. Eleven PFAS compounds belonging to the family of perfluorocarboxylic acid, perfluorosulfonic acid and perfluorooctanesulfonamidoacetic acid substances (PFCAs, PFSAs and FOSAAs) were found in packaging samples obtained from China, with migrant concentrations ranging 3.2 and 22.3 μg/kg. In contrast, no detectable levels of PFAS were observed in packaging samples obtained in Spain. All trays tested were deemed to be suitable for use as food contact materials due to the fact that their migrant values were lower than 0.025 mg/kg for PFOA and its salts, and lower than a maximum concentration of 1 mg/kg for PFOA-related compounds.

Application of Ion Mobility Spectrometry and the Derived Collision Cross Section in the Analysis of Environmental Organic Micropollutants

Ion mobility spectrometry (IMS) is a rapid gas-phase separation technique, which can distinguish ions on the basis of their size, shape, and charge. The IMS-derived collision cross section (CCS) can serve as additional identification evidence for the screening of environmental organic micropollutants (OMPs). In this work, we summarize the published experimental CCS values of environmental OMPs, introduce the current CCS prediction tools, summarize the use of IMS and CCS in the analysis of environmental OMPs, and finally discussed the benefits of IMS and CCS in environmental analysis. An up-to-date CCS compendium for environmental contaminants was produced by combining CCS databases and data sets of particular types of environmental OMPs, including pesticides, drugs, mycotoxins, steroids, plastic additives, per- and polyfluoroalkyl substances (PFAS), polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), and polybrominated diphenyl ethers (PBDEs), as well as their well-known transformation products. A total of 9407 experimental CCS values from 4170 OMPs were retrieved from 23 publications, which contain both drift tube CCS in nitrogen (DTCCSN2) and traveling wave CCS in nitrogen (TWCCSN2). A selection of publicly accessible and in-house CCS prediction tools were also investigated; the chemical space covered by the training set and the quality of CCS measurements seem to be vital factors affecting the CCS prediction accuracy. Then, the applications of IMS and the derived CCS in the screening of various OMPs were summarized, and the benefits of IMS and CCS, including increased peak capacity, the elimination of interfering ions, the separation of isomers, and the reduction of false positives and false negatives, were discussed in detail. With the improvement of the resolving power of IMS and enhancements of experimental CCS databases, the practicability of IMS in the analysis of environmental OMPs will continue to improve.

Sliding Windows in Ion Mobility (SWIM): A New Approach to Increase the Resolving Power in Trapped Ion Mobility-Mass Spectrometry Hyphenated with Chromatography

Over the past decade, the separation efficiency achieved by linear IMS instruments has increased substantially, with state-of-the-art IM technologies, such as the trapped ion mobility (TIMS), the cyclic traveling wave ion mobility (cTWIMS), and the structure for lossless ion manipulation (SLIM) platforms commonly demonstrating resolving powers in excess of 200. However, for complex sample analysis that require front end separation, the achievement of such high resolving power in TIMS is significantly hampered, since the ion mobility range must be broad enough to analyze all the classes of compounds of interest, whereas the IM analysis time must be short enough to cope with the time scale of the preseparation technique employed. In this paper, we introduce the concept of sliding windows in ion mobility (SWIM) for chromatography hyphenated TIMS applications that bypasses the need to use a wide and fixed IM range by using instead narrow and mobile ion mobility windows that adapt to the analytes' ion mobility during chromatographic separation. GC-TIMS-MS analysis of a mixture of 174 standards from several halogenated persistent organic pollutant (POP) classes, including chlorinated and brominated dioxins, biphenyls, and PBDEs, demonstrated that the average IM resolving power could be increased up to 40% when the SWIM mode was used, thereby greatly increasing the method selectivity for the analysis of complex samples.

Investigation of the Binding Fraction of PFAS in Human Plasma and Underlying Mechanisms Based on Machine Learning and Molecular Dynamics Simulation

More than 7000 per- and polyfluorinated alkyl substances (PFAS) have been documented in the U.S. Environmental Protection Agency's CompTox Chemicals database. These PFAS can be used in a broad range of industrial and consumer applications but may pose potential environmental issues and health risks. However, little is known about emerging PFAS bioaccumulation to assess their chemical safety. This study focuses specifically on the large and high-quality data set of fluorochemicals from the related environmental and pharmaceutical chemicals databases, and machine learning (ML) models were developed for the classification prediction of the unbound fraction of compounds in plasma. A comprehensive evaluation of the ML models shows that the best blending model yields an accuracy of 0.901 for the test set. The predictions suggest that most PFAS (∼92%) have a high binding fraction in plasma. Introduction of alkaline amino groups is likely to reduce the binding affinities of PFAS with plasma proteins. Molecular dynamics simulations indicate a clear distinction between the high and low binding fractions of PFAS. These computational workflows can be used to predict the bioaccumulation of emerging PFAS and are also helpful for the molecular design of PFAS to prevent the release of high-bioaccumulation compounds into the environment.

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.

Collision cross section measurement and prediction methods in omics

Omics studies such as metabolomics, lipidomics, and proteomics have become important for understanding the mechanisms in living organisms. However, the compounds detected are structurally different and contain isomers, with each structure or isomer leading to a different result in terms of the role they play in the cell or tissue in the organism. Therefore, it is important to detect, characterize, and elucidate the structures of these compounds. Liquid chromatography and mass spectrometry have been utilized for decades in the structure elucidation of key compounds. While prediction models of parameters (such as retention time and fragmentation pattern) have also been developed for these separation techniques, they have some limitations. Moreover, ion mobility has become one of the most promising techniques to give a fingerprint to these compounds by determining their collision cross section (CCS) values, which reflect their shape and size. Obtaining accurate CCS enables its use as a filter for potential analyte structures. These CCS values can be measured experimentally using calibrant-independent and calibrant-dependent approaches. Identification of compounds based on experimental CCS values in untargeted analysis typically requires CCS references from standards, which are currently limited and, if available, would require a large amount of time for experimental measurements. Therefore, researchers use theoretical tools to predict CCS values for untargeted and targeted analysis. In this review, an overview of the different methods for the experimental and theoretical estimation of CCS values is given where theoretical prediction tools include computational and machine modeling type approaches. Moreover, the limitations of the current experimental and theoretical approaches and their potential mitigation methods were discussed.

Right in two: capabilities of ion mobility spectrometry for untargeted metabolomics

This mini review focuses on the opportunities provided by current and emerging separation techniques for mass spectrometry metabolomics. The purpose of separation technologies in metabolomics is primarily to reduce complexity of the heterogeneous systems studied, and to provide concentration enrichment by increasing sensitivity towards the quantification of low abundance metabolites. For this reason, a wide variety of separation systems, from column chemistries to solvent compositions and multidimensional separations, have been applied in the field. Multidimensional separations are a common method in both proteomics applications and gas chromatography mass spectrometry, allowing orthogonal separations to further reduce analytical complexity and expand peak capacity. These applications contribute to exponential increases in run times concomitant with first dimension fractionation followed by second dimension separations. Multidimensional liquid chromatography to increase peak capacity in metabolomics, when compared to the potential of running additional samples or replicates and increasing statistical confidence, mean that uptake of these methods has been minimal. In contrast, in the last 15 years there have been significant advances in the resolution and sensitivity of ion mobility spectrometry, to the point where high-resolution separation of analytes based on their collision cross section approaches chromatographic separation, with minimal loss in sensitivity. Additionally, ion mobility separations can be performed on a chromatographic timescale with little reduction in instrument duty cycle. In this review, we compare ion mobility separation to liquid chromatographic separation, highlight the history of the use of ion mobility separations in metabolomics, outline the current state-of-the-art in the field, and discuss the future outlook of the technology. "Where there is one, you're bound to divide it. Right in two", James Maynard Keenan.

Cracked and shucked: GC-APCI-IMS-HRMS facilitates identification of unknown halogenated organic chemicals in French marine bivalves

High resolution mass spectrometry (HRMS)-based non-target analysis coupled with ion mobility spectrometry (IMS) is gaining momentum due to its ability to provide complementary information which can be useful in the identification of unknown organic chemicals in support of efforts in unraveling the complexity of the chemical exposome. The chemical exposome in the marine environment, though not as well studied as its freshwater counterparts, is not foreign to chemical diversity specially when it comes to potentially bioaccumulative and bioactive polyhalogenated organic contaminants and natural products. In this work we present in detail how we utilized IMS-HRMS coupled with gas chromatographic separation and atmospheric pressure chemical ionization (APCI) to annotate polyhalogenated organic chemicals in French bivalves collected from 25 sites along the French coasts. We describe how we used open cheminformatic tools to exploit isotopologue patterns, isotope ratios, Kendrick mass defect (Cl scale), and collisional cross section (CCS), in order to annotate 157 halogenated features (level 1: 54, level 2: 47, level 3: 50, and level 4: 6). Grouping the features into 11 compound classes was facilitated by a KMD vs CCS plot which showed co-clustering of potentially structurally-related compounds. The features were semi-quantified to gain insight into the distribution of these halogenated features along the French coast, ultimately allowing us to differentiate between sites that are more anthropologically impacted versus sites that are potentially biodiverse.

Analytical approaches for screening of per- and poly fluoroalkyl substances in food items: A review of recent advances and improvements

Per and polyfluoroalkyl substances (PFAS) are a group of man-made chemicals characterized by their ubiquitous nature in all environmental compartments which makes them of increasing concern due to their persistence, bioaccumulation, and toxicity (PBT). Several instrumental methodologies and separation techniques have been identified in the literature for the detection and quantification of PFAS in environmental samples. In this review, we have identified and compared common separation techniques adopted for the extraction of PFAS in food items, and analytical methodologies for identification and quantification of PFAS in food items of plant and animal origin, highlighting recent advances in tandem techniques for the high selectivity and separation of PFAS related compounds as well as knowledge gaps and research needs on current analytical methodologies.

Assessing How Chemical Exposures Affect Human Health

Measuring chemical exposure is extremely challenging due to the range and number of anthropogenic molecules encountered in our daily lives, as well as their complex transformations throughout the body. To broadly characterize how chemical exposures influence human health, a combination of genomic, transcriptomic, proteomic, endogenous metabolomic, and xenobiotic measurements must be performed. However, while genomic, transcriptomic, and proteomic analyses have rapidly progressed over the last two decades, advancements in instrumentation and computations for nontargeted xenobiotic and endogenous small molecule measurements are still greatly needed.

Collision cross-section as a universal molecular descriptor in the analysis of PFAS and use of ion mobility spectrum filtering for improved analytical sensitivities

The massive usage of per- and polyfluoroalkyl substances (PFAS), as well as their high chemical stability, have led to their ubiquitous presence in environmental matrices and direct human exposure through contaminated food, particularly fish. In the analysis of this large group of substances, the use of ion mobility coupled to mass spectrometry is of particular relevance because it uses an additional descriptor, the collision cross-section (CCS), which results in increased selectivity. In the present work, the ^TWCCS_N2 of 24 priority PFAS were experimentally obtained, and the reproducibility of these measurements was evaluated over seven weeks. The average values were employed to critically assess previously reported data and theoretical calculations. This gain in selectivity made it possible to increase the sensitivity of the detection on complex matrices (biota, food and human serum) by using the drift time associated to each analyte as a filter, thus reducing the interferences and background noise and allowing their detection at trace levels.

CCS Predictor 2.0: An Open-Source Jupyter Notebook Tool for Filtering Out False Positives in Metabolomics

Metabolite annotation continues to be the widely accepted bottleneck in nontargeted metabolomics workflows. Annotation of metabolites typically relies on a combination of high-resolution mass spectrometry (MS) with parent and tandem measurements, isotope cluster evaluations, and Kendrick mass defect (KMD) analysis. Chromatographic retention time matching with standards is often used at the later stages of the process, which can also be followed by metabolite isolation and structure confirmation utilizing nuclear magnetic resonance (NMR) spectroscopy. The measurement of gas-phase collision cross-section (CCS) values by ion mobility (IM) spectrometry also adds an important dimension to this workflow by generating an additional molecular parameter that can be used for filtering unlikely structures. The millisecond timescale of IM spectrometry allows the rapid measurement of CCS values and allows easy pairing with existing MS workflows. Here, we report on a highly accurate machine learning algorithm (CCSP 2.0) in an open-source Jupyter Notebook format to predict CCS values based on linear support vector regression models. This tool allows customization of the training set to the needs of the user, enabling the production of models for new adducts or previously unexplored molecular classes. CCSP produces predictions with accuracy equal to or greater than existing machine learning approaches such as CCSbase, DeepCCS, and AllCCS, while being better aligned with FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. Another unique aspect of CCSP 2.0 is its inclusion of a large library of 1613 molecular descriptors via the Mordred Python package, further encoding the fine aspects of isomeric molecular structures. CCS prediction accuracy was tested using CCS values in the McLean CCS Compendium with median relative errors of 1.25, 1.73, and 1.87% for the 170 [M - H]^-, 155 [M + H]⁺, and 138 [M + Na]⁺ adducts tested. For superclass-matched data sets, CCS predictions via CCSP allowed filtering of 36.1% of incorrect structures while retaining a total of 100% of the correct annotations using a Δ_CCS threshold of 2.8% and a mass error of 10 ppm.