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.

Imaging mass spectrometry (IMS) has recently established itself in the field of “spatial metabolomics.” Merging the sensitivity and fast screening of high-throughput mass spectrometry with spatial and temporal chemical information, IMS visualizes the production, location, and distribution of metabolites in intact biological models. Since metabolite profiling and morphological features are combined in single images, IMS offers an unmatched chemical detail on complex biological and microbiological systems. Thus, IMS-type “spatial metabolomics” emerges as a powerful and complementary approach to genomics, transcriptomics, and classical metabolomics studies. In this review, we summarize the current state-of-the-art IMS methods with a strong focus on desorption electrospray ionization (DESI)-IMS. DESI-IMS utilizes the original principle of electrospray ionization, but in this case solvent droplets are rastered and desorbed directly on the sample surface. The rapid and minimally destructive DESI-IMS chemical screening is achieved at ambient conditions and enables the accurate view of molecules in tissues at the µm-scale resolution. DESI-IMS analysis does not require complex sample preparation and allows repeated measurements on samples from different biological sources, including microorganisms, plants, and animals. Thanks to its easy workflow and versatility, DESI-IMS has successfully been applied to many different research fields, such as clinical analysis, cancer research, environmental sciences, microbiology, chemical ecology, and drug discovery. Herein we discuss the present applications of DESI-IMS in natural product research.

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.

Introduction: The oxylipin methyl jasmonate (MeJA) is a plant hormone active in response signalling and defence against herbivores. Although MeJA is applied experimentally to mimic herbivory and induce plant defences, its downstream effects on the plant metabolome are largely uncharacterized, especially in the context of primary growth and tissue-specificity of the response. Objectives: We investigated the effects of MeJA-simulated and real caterpillar herbivory on the foliar metabolome of the wild plant Brassica nigra and monitored the herbivore-induced responses in relation to leaf ontogeny. Methods: As single or multiple herbivory treatments, MeJA- and mock-sprayed plants were consecutively exposed to caterpillars or left untreated. Gas chromatography (GC) and liquid chromatography (LC) time-of-flight mass-spectrometry (TOF-MS) were combined to analyse foliar compounds, including central primary and specialized defensive plant metabolites. Results: Plant responses were stronger in young leaves, which simultaneously induced higher chlorophyll levels. Both MeJA and caterpillar herbivory induced similar, but not identical, accumulation of tricarboxylic acids (TCAs), glucosinolates (GSLs) and phenylpropanoids (PPs), but only caterpillar feeding led to depletion of amino acids. MeJA followed by caterpillars caused higher induction of defence compounds, including a three-fold increase in the major defence compound allyl-GSL (sinigrin). When feeding on MeJA-treated plants, caterpillars gained less weight indicative of the reduced host-plant quality and enhanced resistance. Conclusions: The metabolomics approach showed that plant responses induced by herbivory extend beyond the regulation of defence metabolism and are tightly modulated throughout leaf development. This leads to a new understanding of the plant metabolic potential that can be exploited for future plant protection strategies.

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.