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.

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.

The chemical exposome is the totality of environmental chemical exposures that an individual experience throughout the life course, but few studies have measured it any person at more than one time-point. Here we apply a gas chromatography high resolution-mass spectrometry (GC-HRMS) workflow to the first longitudinal study of the nonpolar chemical exposome in human blood. In a multiomic wellness cohort, 46 healthy adults presented for plasma sampling at 6 time points over 2 years, thereby allowing for the temporal dynamics and interindividual variability of the nonpolar chemical exposome to be evaluated and compared to other omic-level profiles. The extraction and combined target/nontarget analysis of 276 samples was robust throughout 800 injections, and 52 substances were detected and quantified among a diverse set of target analytes (n=103) and confirmed molecular discoveries (n=9). In nontarget analysis, 148 substances were confidently annotated among 532 consensus molecular features, and molecular networking aided the grouping of many unknown features to chemical classes of toxicological significance. Considering all identified (Level 1) and structurally annotated (Level 2) molecules in our analysis, multivariate analyses showed that 159 environmental substances had lower temporal stability compared to 45 endogenous molecules. Moreover, intraclass correlation coefficients (ICCs) demonstrated significantly lower longitudinal stability of the chemical exposome (mean ICC = 0.24) than the proteome, metabolome, lipidome or microbiome, emphasizing the importance of repeated sampling in future exposome studies. Hierarchical clustering revealed novel coexposures to diverse mixtures, raising questions about the exposure sources and cumulative toxicological risk to individuals and whole populations.

Comprehensive chemical exposomics requires multiple analytical workflows to detect the diverse range of environmental substances present in human blood. This places great pressure on the limited volumes of biobanked samples in cohort studies, and consequently very few exposome studies have datasets that combine liquid chromatography (LC) and gas chromatography (GC) high-resolution mass spectrometry (HRMS) analysis of the same samples. By bridging two literature methods, here for the first time we demonstrate and validate a workflow using one small aliquot of plasma (100–200 μL) to generate two separate extracts, enabling target and nontarget analysis by both LC- and GC-HRMS. A nonpolar extract is obtained for GC-HRMS through hexane-acetonitrile extraction of plasma (HA-P), and the residual acetonitrile-plasma is subsequently treated by solid-phase extraction for phospholipid removal prior to LC-HRMS analysis. Both extracts were suitable for sensitive large-volume injections, and most target analyte recoveries and matrix effects in LC-HRMS were improved relative to the direct literature method. Application to 32 individual plasma samples (100 μL) enabled quantification of 46 LC- and 52 GC-target analytes and many molecular discoveries. Additional 106 substances were annotated and later confirmed (Level 1), and 187 Level 2 annotations were made, including for a diverse range of PCBs, PFAS, pesticides, industrial chemicals, cosmetics, hormones and dietary substances spanning 10 orders of magnitude in hydrophobicity. Hierarchical clustering of a combined list of 348 substances revealed a wide range of rare or common exposures, some of which correlated with endogenous metabolites, highlighting the power of comprehensive chemical exposomics to reveal new insights of relevance to precision health.