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.

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.

Nontarget mass spectrometry has great potential to reveal patterns of water contamination globally through community science, but few studies are conducted in low-income countries, nor with open-source workflows, and few datasets are FAIR (Findable, Accessible, Interoperable, Reusable). Water was collected from urban and rural rivers around Dhaka, Bangladesh, and analyzed by liquid chromatography high-resolution mass spectrometry in four ionization modes (electrospray ionization +/-, atmospheric pressure chemical ionization +/-) with data -independent MS2 acquisition. The acquisition strategy was complementary: 19,427 and 7365 features were unique to ESI and APCI, respectively. The complexity of water pollution was revealed by >26,000 unique molecular features resolved by MS-DIAL, among which >20,000 correlated with urban sources in Dhaka. A major wastewater treatment plant was not a dominant pollution source, consistent with major contributions from uncontrolled urban drainage, a result that encourages development of further wastewater infrastructures. Matching of deconvoluted MS2 spectra to public libraries resulted in 62 confident annotations (i.e., Level 1-2a) and allowed semiquantification of 42 analytes including pharmaceuticals, pesticides, and personal care products. In silico structure prediction for the top 100 unknown molecular features associated with an urban source allowed 15 additional chemicals of anthropogenic origin to be annotated (i.e., Level 3). The authentic MS2 spectra were uploaded to MassBank Europe, mass spectral data were openly shared on the MassIVE repository, a tool (i.e., MASST) that could be used for community science environmental surveillance was demonstrated, and current limitations were discussed.

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.

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.