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

Abstract

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.

Keywords: chemical space; chromatography; environmental exposures; exposome; high-resolution mass spectrometry; metabolomics; non-targeted analysis; toxicants.

Figure 1

Schematic illustration of human chemical exposome consisting of both external and internal components, which embraces a vast chemical space by number, dynamic range, structural diversity, and physicochemical properties. The external component encompasses environmental chemicals humans are being exposed to and accumulate in the body, can have indoor, ambient, and occupational sources, and likely varies in individuals with distinct diet, drug, and lifestyle choices and psychosocial influences. The internal component is a dynamic reservoir of (i) parent exposure agents taken in, (ii) their biotransformation products, and (iii) endogenous biomolecules indicative of a toxicological and/or etiologic effect. Abbreviations: PPCP, pharmaceuticals and personal care products; ADME, absorption, distribution, metabolism, and excretion; e-cigs: E-cigarettes.

Figure 2

Critical steps for expanding the analytical coverage by HRMS-based exposomics. Modular components at the front end, from (a) laboratory measurement to (b) data analytics, are essential to generating quality feature tables for (c) advanced statistics successes at the later stages of analysis. To generate a feature table, the data analytics entails (d) feature detection and (e) compound annotation for best results, both of which have been critically reviewed in this article. The figure was generated using BioRender under a paid subscription. Abbreviations: GC, gas chromatography; LC, liquid chromatography; RP, reverse phase; HILIC, hydrophilic interaction chromatography; EI, electron ionization; CI, chemical ionization; API, atmospheric pressure ionization; ESI, electrospray ionization; APCI, atmospheric pressure chemical ionization; APPI, atmospheric pressure photoionization; IMS, ion mobility spectrometry; SIM, selective ion monitoring; DDA, data-dependent acquisition; DIA, data-independent acquisition; minFrac, minimum fraction (proportion of minimum samples where a peak has to be present in a group); QC, quality control; RT, retention time; CCS, collision cross section; ExWAS, exposome-wide association studies; PCA, principal component analysis; FA, factor analysis; NMF, non-negative matrix factorization; BKMR, Bayesian Kernel Machine Regression; WQS, Weighted Quantile Sum.

Figure 3

Conceptual navigation of analytical scenarios and approaches for expanding the chemical space coverage by HRMS-based exposomics. (a) The “known-unknown” quadrant chart as built from the Rumsfeld Matrix as a framework to consider both the influences of hypothesis/knowledge-driven activities (i.e., expecting a feature to be detected and/or identified in a sample) (y-axis) and HRMS workflow capabilities (from feature detection to structural annotation) (x-axis) on the analytical coverage outcome. Within each quadrant, the definition of “known/unknown” can be relative, subjective, and context-specific; the first-place “known/unknown” term (in bold) describes the hypothesis/knowledge-driven activities (from unknown to known on the y-axis), while the second-place “known/unknown” term (not in bold) denotes the analytical outcome (from unknown to known on the x-axis). (b) Pie chart illustration of the analytical coverage by HRMS-based approaches including targeted analysis (screening/quantitation), suspect screening, and NTA. The double arrow does not indicate a quantitation continuum (i.e., absolute/semiquantitation is binary for individual compounds) across analysis modes. Rather, it illustrates the tendency or commonality for targeted and non-targeted approaches (or alternative analysis modes) to achieve different quantitative goals with affordable accuracy and sensitivity for target analytes/features.

Figure 4

Patterns in the mass spectral measurements can be leveraged for de novo structural elucidation of exposome molecules, using FluoroMatch and PFAS for a showcase.^, (a) The Kendrick plot reveals distinct mass defect patterns indicative of the CF₂ repeating units in the PFAS homologous series. (b) Similar patterns of RT and m/z space further confirm the homologous series orthogonally; homologues with differing repeat units will follow a different trend and can be readily removed. (c) The EIC plot view of the PFAS homologous series. (d) The full-scan MS spectral view of the PFAS homologous series.

Figure 5

Select challenges in expanding the analytical coverage of human chemical exposome using HRMS-based approaches.