Assessing How Chemical Exposures Affect Human Health

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Measuring chemical exposure is extremely challenging due to the range and number of anthropogenic molecules encountered in our daily lives, as well as their complex transformations throughout the body. To broadly characterize how chemical exposures influence human health, a combination of genomic, transcriptomic, proteomic, endogenous metabolomic, and xenobiotic measurements must be performed. However, while genomic, transcriptomic, and proteomic analyses have rapidly progressed over the last two decades, advancements in instrumentation and computations for nontargeted xenobiotic and endogenous small molecule measurements are still greatly needed.

Upon completion of The Human Genome Project, it was determined that greater than 90% of human diseases are not solely due to a person’s genetics but a combination of genetic factors and environmental influences (1–4). Linking environmental exposures and biological effects is however extremely challenging. Humans are exposed daily to a broad range of xenobiotic chemicals in drinking water, foods, household items, and everyday consumer products, such as plastic bottles, containers, clothes, and cosmetics. While many xenobiotics do not cause harm when contacted or ingested, some disrupt normal bodily processes and change how hormones, organs, and the immune system function, either instantly or later in life. Identifying xenobiotics with adverse effects and evaluating how their chemical exposure influences human health is therefore of utmost importance, but it requires advanced instrumental and computational analysis capabilities.

While genetic factors have been readily assessed to date using rapid genome sequencing technologies, measuring environmental factors is much more challenging and requires the implementation of direct and indirect molecular measurements (5,6). In direct measurements, specific xenobiotic chemicals of interest are analyzed in environmental and biological samples such as water, plants, biofluids, and human tissues. These measurements are extremely complex, as thousands of xenobiotics exist and can also be transformed in the environment and body. Furthermore, many xenobiotics and their transformation products are often excreted before biological responses ever occur, causing difficulties in linking specific exposures to phenotypic outcomes. Indirect analyses are therefore utilized to evaluate molecular changes occurring due to chemical exposure. In indirect measurements, one or more complementary omic techniques such as transcriptomics, proteomics, metabolomics, or lipidomics are often performed on each sample. However, these multi-omic measurements must be completed in a relatively short period of time to quickly assess exposure effects and potentially provide treatments. Novel developments in both analytical and computational methods are therefore imperative.

Traditionally, both direct and indirect measurements have focused on a subset of target compounds largely selected from toxicology studies that have highlighted hazardous characteristics or known functional disruptions. In targeted approaches, the extraction, analysis, and detection of the known chemicals of interest is optimized to enable the highest sensitivity for the limited set of molecules. However, targeted direct analyses create a huge knowledge gap in understanding exposure, since thousands of new chemicals are introduced each year but not included in targeted studies until deemed to be of concern. Furthermore, the additional transformation products that occur due to degradation and metabolism of the xenobiotics are often not monitored by targeted assays, even though some can be more toxic than the parent molecules. Nontargeted analyses have therefore become a focus of recent exposure assessments (7). However, these evaluations are challenged by the great number and structural diversity of xenobiotics and the endogenous small molecules they affect. The chemical makeup of these molecules is unlike the biopolymers in genomics, transcriptomics, and proteomics studies, which have a limited number of building blocks, that is, nucleotides and amino acids), with some synthetic xenobiotics pushing the boundaries of chemistry itself. For example, xenobiotics such as per- and polyfluoroalkyl substances (PFAS) have numerous fluorine atoms within each species, and some pesticides have various types of halogens within the same molecule. Broad coverage of nontargeted measurements therefore requires different extraction methods, chromatography techniques, ionization sources on mass spectrometers (electrospray ionization vs. atmospheric pressure chemical ionization), and analysis polarities (6,8–12). This multiplies the number of instrumental and data analyses performed on each sample and brings forth infinite possibilities for molecular studies. Budgets are therefore used to prioritize and limit the number of nontargeted analyses, even making them somewhat targeted and biased to certain molecules. Thus, to extend the number of nontargeted studies possible, the development of higher throughput approaches capable of assessing many samples following distinct extractions, ionization sources, polarities, and separations is essential.


The identification of unknown features is another important challenge for nontargeted analyses (13,14). While thousands of features are often detected in small molecule nontargeted analyses of complex matrices, normally only a few hundred can be identified confidently (15). Furthermore, the fragmentation of small molecules is not as intuitive or informative as peptides, which always break at known locations based on the fragmentation method used. Molecular standards are therefore often utilized for the identification of unknown features in these studies. Unfortunately, this is also a limitation, as only a fraction of the small molecules that exist have a commercially available standard. Two alternate approaches often used to identify unknowns are nuclear magnetic resonance (NMR), which allows accurate structural identification (16), and the application of in silico tools that provide plausible chemical class or structural matches to the experimentally measured characteristics (17,18). While NMR has proven very useful for unknown structure identification, it also has challenges, such as the need for high concentrations of the molecule of interest and its low throughput, ultimately limiting the number of novel molecules identified per year (19). In silico tools are much faster, but small testing sets and errors between the theoretical numbers and experimental analyses (for example, chromatography retention time differences) often only allow plausible candidate matches until validated with a standard (17). Therefore, in my keynote presentation at HPLC 2023, I will address the challenges of xenobiotic and endogenous small molecule measurements and demonstrate how multidimensional analyses combining liquid chromatography, ion mobility spectrometry, collision induced dissociation, and mass spectrometry (LC–IMS–CID–MS) and computational developments enhance direct xenobiotic analyses and indirect multi-omic evaluations. I will also showcase applications where these developments are leading to a better understanding of molecular responses occurring due to chemical exposures.


  1. Vermeulen, R.; Schymanski, E. L.; Barabási, A. -L.; Miller, G. W. The Exposome and Health: Where Chemistry Meets Biology. Science 2020, 367 (6476), 392–396. DOI: 10.1126/science.aay3164
  2. Wild, C. P. Complementing the Genome with an “Exposome”: The Outstanding Challenge of Environmental Exposure Measurement in Molecular Epidemiology. Cancer Epidemiol. Biomarkers Prev. 2005, 14 (8), 1847–50. DOI: 10.1158/1055-9965.EPI-05-0456
  3. Wild, C. P. The Exposome: From Concept to Utility. Int. J. Mol. Epidemiol. Genet. 2012, 41 (1), 24–32. DOI: 10.1093/ije/dyr236
  4. Miller, G. W; Jones, D. P. The Nature of Nurture: Refining the Definition of the Exposome. Toxicol. Sci. 2014, 137 (1), 1–2. DOI: 10.1093/toxsci/kft251
  5. Thomas, D. Gene--Environment-Wide Association Studies: Emerging Approaches. Nat. Rev. Genet. 2010, 11 (4), 259–72. DOI: 10.1038/nrg2764
  6. Walker, D. I.; Valvi, D.; Rothman, N.; et al. The Metabolome: A Key Measure for Exposome Research in Epidemiology. Curr. Epidemiol. Rep. 2019, 6, 93–103. DOI: 10.1007/s40471-019-00187-4
  7. National Research Council. Exposure Science in the 21st Century: A Vision and a Strategy; National Academies Press, 2012.
  8. Athersuch, T. Metabolome Analyses in Exposome Studies: Profiling Methods for a Vast Chemical Space. Arch. Biochem. Biophys. 2016, 589, 177–86. DOI: 10.1016/
  9. Ruddigkeit, L.; Awale, M.; Reymond, J. L. Expanding the Fragrance Chemical Space for Virtual Screening. J. Cheminf. 2014, 6, 27. DOI: 10.1186/1758-2946-6-27
  10. O’Hagan, S.; Kell, D. B. Understanding the Foundations of the Structural Similarities Between Marketed Drugs and Endogenous Human Metabolites. Front. Pharmacol. 2015, 6, 105. DOI: 10.3389/fphar.2015.00105
  11. Wishart, D. S. Advances in Metabolite Identification. Bioanal. 2011, 3 (15), 1769–82. DOI: 10.4155/bio.11.155
  12. Peironcely, J. E.; Reijmers, T.; Coulier, L.; Bender, A.; Hankemeier, T. Understanding and Classifying Metabolite Space and Metabolite-Likeness. PLoS One 2011, 6 (12), e28966. DOI: 10.1371/journal.pone.0028966
  13. Schymanski, E. L.; Jeon, J.; Gulde, R.; et al. Identifying Small Molecules via High Resolution Mass Spectrometry: Communicating Confidence. Environ. Sci. Technol. 2014, 48 (4), 2097–8. DOI: 10.1021/es5002105
  14. Schrimpe-Rutledge, A. C.; Codreanu, S. G.; Sherrod, S. D.; McLean, J. A. Untargeted Metabolomics Strategies–Challenges and Emerging Directions. J. Am. Soc. Mass Spectrom. 2016, 27 (12), 1897–1905. DOI: 10.1007/s13361-016-1469-y
  15. Monge, M. E.; Dodds, J. N.; Baker, E. S.; Edison, A. S.; Fernández, F. M. Challenges in Identifying the Dark Molecules of Life. Annu. Rev. Anal. Chem. 2019, 12 (1), 177–199. DOI: 10.1146/annurev-anchem-061318-114959
  16. Garcia-Perez, I.; Posma, J. M.; Serrano-Contreras, J. I.; et al. Identifying Unknown Metabolites Using NMR-Based Metabolic Profiling Techniques. Nat. Protoc. 2020, 15 (8), 2538–2567. DOI: 10.1038/s41596-020-0343-3
  17. Plante, P. L.; Francovic-Fontaine, É.; May, J. C.; et al. Predicting Ion Mobility Collision Cross-Sections Using a Deep Neural Network: DeepCCS. Anal. Chem. 2019, 91 (8), 5191–5199. DOI: 10.1021/acs.analchem.8b05821
  18. Foster, M.; Rainey, M.; Watson, C.; et al. Uncovering PFAS and Other Xenobiotics in the Dark Metabolome Using Ion Mobility Spectrometry, Mass Defect Analysis, and Machine Learning. Environ. Sci. Technol. 2022, 56 (12), 9133–9143. DOI: 10.1021/acs.est.2c00201
  19. Socha, O.; Osifová, Z.; Dračínský, M. An Interactive Website with Exercises in Solving Structures from NMR Spectra. J. Chem. Educ. 2023, 100 (2), 962–968. DOI:10.1021/acs.jchemed.2c01067

Erin S. Baker is an associate professor at the University of North Carolina in Chapel Hill, North Carolina, USA. To date, she has published over 150 peer-reviewed papers utilizing different analytical chemistry techniques to study both environmental and biological systems. Over the last four years, Erin also helped grow the Females in Mass Spectrometry group, where she served as the Events Committee Chair from 2019–2022 and hosted 35 online and two in-person events. She is currently serving as the Vice President of Education for the International Lipidomics Society, a mentor for Females in Mass Spectrometry, and as an associate editor for the Journal of the American Society for Mass Spectrometry. She has received seven US patents, two R&D 100 Awards, and was a recipient of the 2016 ACS Rising Star Award for Top Midcareer Women Chemists, 2022 ASMS Biemann Medal, and 2022 IMSF Curt Brunnée Award. Currently, her research group utilizes advanced separations and novel software capabilities to examine how chemical exposure affects human health.

Erin S. Baker will present her keynote lecture on Wednesday 21 June at 10:30 in Hall Y.