For comprehensive chemical exposomics in blood, analytical workflows are evolving through advances in sample preparation and instrumental methods. We hypothesized that gas chromatography-high-resolution mass spectrometry (GC-HRMS) workflows could be enhanced by minimizing lipid coextractives, thereby enabling larger injection volumes and lower matrix interference for improved target sensitivity and nontarget molecular discovery. A simple protocol was developed for small plasma volumes (100-200 μL) by using isohexane (H) to extract supernatants of acetonitrile-plasma (A-P). The HA-P method was quantitative for a wide range of hydrophobic multiclass target analytes (i.e., log Kow > 3.0), and the extracts were free of major lipids, thereby enabling robust large-volume injections (LVIs; 25 μL) in long sequences (60-70 h, 70-80 injections) to a GC-Orbitrap HRMS. Without lipid removal, LVI was counterproductive because method sensitivity suffered from the abundant matrix signal, resulting in low ion injection times to the Orbitrap. The median method quantification limit was 0.09 ng/mL (range 0.005-4.83 ng/mL), and good accuracy was shown for a certified reference serum. Applying the method to plasma from a Swedish cohort (n = 32; 100 μL), 51 of 103 target analytes were detected. Simultaneous nontarget analysis resulted in 112 structural annotations (12.8% annotation rate), and Level 1 identification was achieved for 7 of 8 substances in follow-up confirmations. The HA-P method is potentially scalable for application in cohort studies and is also compatible with many liquid-chromatography-based exposomics workflows.

Offshore oil platforms discharge enormous volumes of produced water that contain mixtures of petrochemicals and production chemicals. It is crucial to avoid the discharge of particularly those chemicals that are persistent in the marine environment. This study aims to (1) develop a biodegradation testing approach for discharged chemicals by native marine microorganism, (2) determine how dilution affects biodegradation, and (3) determine biodegradation kinetics for many discharged chemicals at low and noninhibitory concentrations. Produced water from an offshore oil platform was diluted in the ratio of 1:20, 1:60, and 1:200 in seawater from the same location and incubated for 60 days at 10 °C. Automated solid-phase microextraction GC-MS was used as a sensitive analytical technique, and chemical-specific primary degradation was determined based on peak area ratios between biotic test systems and abiotic controls. Biodegradation was inhibited at lower dilutions, consistent with ecotoxicity tests. Biodegradation kinetics were determined at the highest dilution for 139 chemicals (43 tentatively identified), and 6 chemicals were found persistent (half-life >60 days). Nontargeted analysis by liquid chromatography-high-resolution MS was demonstrated as a proof-of-principle for a comprehensive assessment. Biodegradation testing of chemicals in discharges provides the possibility to assess hundreds of chemicals at once and find the persistent ones.

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.

Chemical exposomes can now be comprehensively measured in human blood, but knowledge of their variability and longitudinal stability is required for robust application in cohort studies. Here, we applied high-resolution chemical exposomics to plasma of 46 adults, each sampled 6 times over 2 years in a multiomic cohort, resulting in 276 individual exposomes. In addition to quantitative analysis of 83 priority target analytes, we discovered and semiquantified substances that have rarely or never been reported in humans, including personal care products, pesticide transformation products, and polymer additives. Hierarchical cluster analysis for 519 confidently annotated substances revealed unique and distinctive coexposures, including clustered pesticides, poly(ethylene glycols), chlorinated phenols, or natural substances from tea and coffee; interactive heatmaps were publicly deposited to support open exploration of the complex (meta)data. Intraclass correlation coefficients (ICC) for all annotated substances demonstrated the relatively low stability of the exposome compared to that of proteome, microbiome, and endogenous small molecules. Implications are that the chemical exposome must be measured more frequently than other omics in longitudinal studies and four longitudinal exposure types are defined that can be considered in study design. In this small cohort, mixed-effect models nevertheless revealed significant associations between testosterone and perfluoroalkyl substances, demonstrating great potential for longitudinal exposomics in precision health research.

Chemical exposomics in human plasma wasenhanced by an optimizedphospholipid removal step that increased targeted method sensitivitywhile also revealing >13,000 new molecular features by LC-HRMSnon-targetedacquisition. The challenge of chemical exposomics in human plasmais the 1000-foldconcentration gap between endogenous substances and environmentalpollutants. Phospholipids are the major endogenous small moleculesin plasma, thus we validated a chemical exposomics protocol with anoptimized phospholipid-removal step prior to targeted and non-targetedliquid chromatography high-resolution mass spectrometry. Increasedinjection volume with negligible matrix effect permitted sensitivemulticlass targeted analysis of 77 priority analytes; median MLOQ= 0.05 ng/mL for 200 & mu;L plasma.In non-targeted acquisition, mean total signal intensities of non-phospholipidswere enhanced 6-fold in positive (max 28-fold) and 4-fold in negativemode (max 58-fold) compared to a control method without phospholipidremoval. Moreover, 109 and 28% more non-phospholipid molecular featureswere detected by exposomics in positive and negative mode, respectively,allowing new substances to be annotated that were non-detectable withoutphospholipid removal. In individual adult plasma (100 & mu;L, n = 34), 28 analytes were detected and quantified among10 chemical classes, and quantitation of per- and polyfluoroalkylsubstances (PFAS) was externally validated by independent targetedanalysis. Retrospective discovery and semi-quantification of PFAS-precursorswas demonstrated, and widespread fenuron exposure is reported in plasmafor the first time. The new exposomics method is complementary tometabolomics protocols, relies on open science resources, and canbe scaled to support large studies of the exposome.

The airborne chemical exposome is a dynamic complex mixture of gases and particles, and despite clear links to chronic disease and premature death, its molecular composition and variability remains largely uncharacterized. To overcome this, we aimed to pair nontarget analysis by high-resolution mass spectrometry (HRMS) with an inexpensive and stable passive sampling media for airborne gases and particles. To this end, we synthesized silicone (polydimethylsiloxane; PDMS) foam disks resulting in a low cost (0.02$/disk) and ultraclean material suitable for analysis by gas or liquid chromatography (GC/LC)HRMS. When tested for indoor passive sampling over 1-3 months, alongside a PDMS sheet, PDMS foam accumulated many nonpolar gas phase environmental contaminants (e.g., polychlorinated biphenyls), and a surprisingly complex mixture of larger polar substances (e.g., oxygen, nitrogen and sulfur-containing) that were absent from the PDMS sheet, suggesting sampling of the particulate phase. The airborne molecular discovery potential was further demonstrated using an open-science LC-HRMS workflow integrating molecular networks and in silico structural predictions tailored on PubChemLite for Exposomics, which revealed series of known and unknown substances, including aromatic nitrophenols and sulfonyls. Future studies may benefit from implementing PDMS foam as wearable or stationary passive samplers to support advances in understanding exposure and contaminant sources in the indoor, outdoor, and personal airborne exposomes.

Omics-based technologies have enabled comprehensive characterization of our exposure to environmental chemicals (chemical exposome) as well as assessment of the corresponding biological responses at the molecular level (eg, metabolome, lipidome, proteome, and genome). By systematically measuring personal exposures and linking these stimuli to biological perturbations, researchers can determine specific chemical exposures of concern, identify mechanisms and biomarkers of toxicity, and design interventions to reduce exposures. However, further advancement of metabolomics and exposomics approaches is limited by a lack of standardization and approaches for assigning confidence to chemical annotations. While a wealth of chemical data is generated by gas chromatography high-resolution mass spectrometry (GC-HRMS), incorporating GC-HRMS data into an annotation framework and communicating confidence in these assignments is challenging. It is essential to be able to compare chemical data for exposomics studies across platforms to build upon prior knowledge and advance the technology. Here, we discuss the major pieces of evidence provided by common GC-HRMS workflows, including retention time and retention index, electron ionization, positive chemical ionization, electron capture negative ionization, and atmospheric pressure chemical ionization spectral matching, molecular ion, accurate mass, isotopic patterns, database occurrence, and occurrence in blanks. We then provide a qualitative framework for incorporating these various lines of evidence for communicating confidence in GC-HRMS data by adapting the Schymanski scoring schema developed for reporting confidence levels by liquid chromatography HRMS (LC-HRMS). Validation of our framework is presented using standards spiked in plasma, and confident annotations in outdoor and indoor air samples, showing a false-positive rate of 12% for suspect screening for chemical identifications assigned as Level 2 (when structurally similar isomers are not considered false positives). This framework is easily adaptable to various workflows and provides a concise means to communicate confidence in annotations. Further validation, refinements, and adoption of this framework will ideally lead to harmonization across the field, helping to improve the quality and interpretability of compound annotations obtained in GC-HRMS.

A combination of high-resolution mass spectrometry and computational molecular characterization techniques can structurally annotate up to 17% of organic compounds in fine particulate matter in highly polluted air sampled in the Maldives. Fine particulate-matter is an important component of air pollution that impacts health and climate, and which delivers anthropogenic contaminants to remote global regions. The complex composition of organic molecules in atmospheric particulates is poorly constrained, but has important implications for understanding pollutant sources, climate-aerosol interactions, and health risks of air pollution exposure. Here, comprehensive nontarget high-resolution mass spectrometry was combined with in silico structural prediction to achieve greater molecular-level insight for fine particulate samples (n = 40) collected at a remote receptor site in the Maldives during January to April 2018. Spectral database matching identified 0.5% of 60,030 molecular features observed, while a conservative computational workflow enabled structural annotation of 17% of organic structures among the remaining molecular dark matter. Compared to clean air from the southern Indian Ocean, molecular structures from highly-polluted regions were dominated by organic nitrogen compounds, many with computed physicochemical properties of high toxicological and climate relevance. We conclude that combining nontarget analysis with computational mass spectrometry can advance molecular-level understanding of the sources and impacts of polluted air.

The concept of the exposome was introduced over 15 years ago to reflect the important role that the environment exerts on health and disease. While originally viewed as a call-to-arms to develop more comprehensive exposure assessment methods applicable at the individual level and throughout the life course, the scope of the exposome has now expanded to include the associated biological response. In order to explore these concepts, a workshop was hosted by the Gunma University Initiative for Advanced Research (GIAR, Japan) to discuss the scope of exposomics from an international and multidisciplinary perspective. This Global Perspective is a summary of the discussions with emphasis on (1) top-down, bottom-up, and functional approaches to exposomics, (2) the need for integration and standardization of LC- and GC-based high-resolution mass spectrometry methods for untargeted exposome analyses, (3) the design of an exposomics study, (4) the requirement for open science workflows including mass spectral libraries and public databases, (5) the necessity for large investments in mass spectrometry infrastructure in order to sequence the exposome, and (6) the role of the exposome in precision medicine and nutrition to create personalized environmental exposure profiles. Recommendations are made on key issues to encourage continued advancement and cooperation in exposomics.