Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Development of LC-HRMS untargeted analysis methods for nasal epithelial lining fluid exposomics

Abstract

Background

The nasal mucosa, as a primary site of entry for inhaled substances, contains both inhaled xenobiotic and endogenous biomarkers. Nasal mucosa can be non-invasively sampled (nasal epithelial lining fluid “NELF”) and analyzed for biological mediators. However, methods for untargeted analysis of compounds inhaled and/or retained in the nasal mucosa are needed.

Objectives

This study aimed to develop a high resolution LC-MS untargeted method to analyze collected NELF. Profiling of compounds in NELF samples will also provide baseline data for future comparative studies to reference.

Methods

Extracted NELF analytes were injected to LC-ESI-MS. After spectrum processing, an in-house library provided annotations with high confidence, while more tentative annotation proposals were obtained via ChemSpider database matching.

Results

The established method successfully detected unique molecular signatures within NELF. Baseline profiling of 27 samples detected 2002 unknown molecules, with 77 and 463 proposed structures by our in-house library and Chemspider matching. High confidence annotations revealed common metabolites and tentative annotations implied various environmental exposure biomarkers are also present in NELF.

Significance

The experimental pipeline for analyzing NELF samples serves as simple and robust method applicable for future studies to characterize identities/effects of inhaled substances and metabolites retained in the nasal mucosa.

Impact statement

The nasal mucosa contains exogenous and endogenous compounds. The development of an untargeted analysis is necessary to characterize the nasal exposome by deciphering the identity and influence of inhaled compounds on nasal mucosal biology. This study established a high resolution LC-MS based untargeted analysis of non-invasively collected nasal epithelial lining fluid. Baseline profiling of the nasal mucosa (n = 27) suggests the presence of environmental pollutants, along with detection of endogenous metabolites. Our results show high potential for the analytical pipeline to facilitate future respiratory health studies involving inhaled pollutants or pharmaceutical compounds and their effects on respiratory biology.

Your institute does not have access to this article

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Fig. 1: Schematic illustration of the analytical pipeline of profiling components in nasal epithelial lining fluid retained on strips.
Fig. 2: Feature detection in nasal epithelial lining fluid samples.
Fig. 3: Distribution of the 2002 abundant and representative compounds (clustered features) in nasal epithelial lining fluid samples (n = 27) at different prevalences (point colors from blue to coral represent low to high detected ratio) and signal strengths (point size).
Fig. 4: Compounds in nasal epithelial lining fluid annotated by both in-house library and ChemSpider chemical structure database.
Fig. 5: Compounds in nasal epithelial lining fluid annotated only by in-house library.

Data availability

Data are available from the corresponding author upon reasonable request.

References

  1. Colbeck I, Lazaridis M. Human exposure to pollutants via dermal absorption and inhalation. Dordrecht; London: Springer, © 2010.: Dordrecht London, 2010.

  2. Trivedi V, Apala DR, Iyer VN. Occupational asthma: diagnostic challenges and management dilemmas. Current Opinion Pulmonary Medicine. 2017;23:177–83.

    Article  Google Scholar 

  3. Schraufnagel DE, Balmes JR, Cowl CT, De Matteis S, Jung SH, Mortimer K, et al. Air pollution and noncommunicable diseases a review by the forum of international respiratory societies’ environmental committee, part 1: the damaging effects of air pollution. Chest. 2019;155:409–16.

    Article  Google Scholar 

  4. Avila-Tang E, Al-Delaimy WK, Ashley DL, Benowitz N, Bernert JT, Kim S, et al. Assessing secondhand smoke using biological markers. Tobacco Control. 2013;22:164–71.

    Article  Google Scholar 

  5. Gotts J E, Jordt S, McConnell R, Tarran R. What are the respiratory effects of e-cigarettes? BMJ 2019;366:l5275. https://doi.org/10.1136/bmj.l5275.

  6. Adams K, Greenbaum DS, Shaikh R, van Erp AM, Russell AG. Particulate matter components, sources, and health: Systematic approaches to testing effects. J Air Waste Manag Assoc. 2015;65:544–58.

    CAS  Article  Google Scholar 

  7. Hahn J, Monakhova YB, Hengen J, Kohl-Himmelseher M, Schüssler J, Hahn H, et al. Electronic cigarettes: overview of chemical composition and exposure estimation. Tob Induc Dis. 2014;12:23–23.

    Article  Google Scholar 

  8. Li Z, Trinidad D, Pittman EN, Riley EA, Sjodin A, Dills RL, et al. Urinary polycyclic aromatic hydrocarbon metabolites as biomarkers to woodsmoke exposure - results from a controlled exposure study. J Exposure Sci Environ Epidemiol. 2016;26:241–8.

    CAS  Article  Google Scholar 

  9. Benowitz NL, St Helen G, Nardone N, Cox LS, Jacob P. Urine metabolites for estimating daily intake of nicotine from cigarette smoking. Nicotine Tob Res. 2020;22:288–92.

    CAS  Article  Google Scholar 

  10. Smolders R, Schramm K-W, Nickmilder M, Schoeters G. Applicability of non-invasively collected matrices for human biomonitoring. Environ Health. 2009;8:8.

    Article  Google Scholar 

  11. Rebuli ME, Speen AM, Clapp PW, Jaspers I. Novel applications for a noninvasive sampling method of the nasal mucosa. Am J Physiol Lung Cell Mol Physiol. 2017;312:L288–96.

    Article  Google Scholar 

  12. Shilts MH, Rosas-Salazar C, Lynch CE, Tovchigrechko A, Boone HH, Russell PB, et al. Evaluation of the upper airway microbiome and immune response with nasal epithelial lining fluid absorption and nasal washes. Sci Rep. 2020;10:20618.

    CAS  Article  Google Scholar 

  13. Rebuli ME, Glista-Baker E, Hoffman JR, Duffney PF, Robinette C, Speen AM, et al. Electronic-cigarette use alters nasal mucosal immune response to live-attenuated influenza virus. a clinical trial. Am J Respiratory Cell Mol Biol. 2020;64:126–37.

    Article  Google Scholar 

  14. Elise H, Andrew H, Bryan Z, Meghan ER, Carole R, Matthew W et al. E-cigarette use, cigarette use, and sex modify the nasal microbiome and nasal host-microbiota interactions. Research Square 2021; Preprint (Ver. 2).

  15. Want EJ. LC-MS untargeted analysis. Methods Mol Biol. 2018;1738:99–116.

    CAS  Article  Google Scholar 

  16. Gika HG, Theodoridis GA, Plumb RS, Wilson ID. Current practice of liquid chromatography-mass spectrometry in metabolomics and metabonomics. J Pharm Biomed Anal. 2014;87:12–25.

    CAS  Article  Google Scholar 

  17. Wawrzyniak R, Kosnowska A, Macioszek S, Bartoszewski R, Jan Markuszewski M. New plasma preparation approach to enrich metabolome coverage in untargeted metabolomics: plasma protein bound hydrophobic metabolite release with proteinase K. Sci Rep. 2018;8:9541.

    Article  Google Scholar 

  18. Petrick L, Edmands W, Schiffman C, Grigoryan H, Perttula K, Yano Y, et al. An untargeted metabolomics method for archived newborn dried blood spots in epidemiologic studies. Metabolomics. 2017;13:27.

    Article  Google Scholar 

  19. Guo H, Chou W-C, Lai Y, Liang K, Tam JW, Brickey WJ, et al. Multi-omics analyses of radiation survivors identify radioprotective microbes and metabolites. Science. 2020;370:eaay9097.

    CAS  Article  Google Scholar 

  20. Lai Y, Liu C-W, Chi L, Ru H, Lu K. High-resolution metabolomics of 50 neurotransmitters and tryptophan metabolites in feces, serum, and brain tissues using UHPLC-ESI-Q exactive mass spectrometry. ACS Omega. 2021;6:8094–103.

    CAS  Article  Google Scholar 

  21. Hemmer S, Manier SK, Fischmann S, Westphal F, Wagmann L, Meyer MR. Comparison of three untargeted data processing workflows for evaluating LC-HRMS metabolomics data. Metabolites. 2020;10:378.

    CAS  Article  Google Scholar 

  22. Züllig T, Zandl-Lang M, Trötzmüller M, Hartler J, Plecko B, Köfeler HCA. Metabolomics workflow for analyzing complex biological samples using a combined method of untargeted and target-list based approaches. Metabolites. 2020;10:342.

    Article  Google Scholar 

  23. Mizuno H, Ueda K, Kobayashi Y, Tsuyama N, Todoroki K, Min JZ et al. The great importance of normalization of LC–MS data for highly-accurate non-targeted metabolomics. Biomed Chromatogr. 2017;31:e3864. https://doi.org/10.1002/bmc.3864.

  24. Pang Z, Chong J, Zhou G, de Lima Morais DA, Chang L, Barrette M, et al. MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights. Nucleic Acids Res. 2021;49:W388–W396.

    CAS  Article  Google Scholar 

  25. Barupal DK, Haldiya PK, Wohlgemuth G, Kind T, Kothari SL, Pinkerton KE, et al. MetaMapp: mapping and visualizing metabolomic data by integrating information from biochemical pathways and chemical and mass spectral similarity. BMC Bioinforma. 2012;13:99.

    Article  Google Scholar 

  26. Tautenhahn R, Patti GJ, Rinehart D, Siuzdak G. XCMS Online: a web-based platform to process untargeted metabolomic data. Anal Chem. 2012;84:5035–9.

    CAS  Article  Google Scholar 

  27. Tsugawa H, Cajka T, Kind T, Ma Y, Higgins B, Ikeda K, et al. MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis. Nat Methods. 2015;12:523–6.

    CAS  Article  Google Scholar 

  28. Thwaites RS, Jarvis HC, Singh N, Jha A, Pritchard A, Fan H et al. Absorption of nasal and bronchial fluids: precision sampling of the human respiratory mucosa and laboratory processing of samples. J Vis Exp. 2018; https://doi.org/10.3791/56413. 56413.

  29. Li K, Naviaux JC, Monk JM, Wang L, Naviaux RK. Improved dried blood spot-based metabolomics: a targeted, broad-spectrum, single-injection method. Metabolites. 2020;10:82.

    CAS  Article  Google Scholar 

  30. Zukunft S, Sorgenfrei M, Prehn C, Möller G, Adamski J. Targeted metabolomics of dried blood spot extracts. Chromatographia. 2013;76:1295–305.

    CAS  Article  Google Scholar 

  31. Trifonova OP, Maslov DL, Balashova EE, Lokhov PG. Evaluation of dried blood spot sampling for clinical metabolomics: effects of different papers and sample storage stability. Available from: URL (Accessed n Date Accessed Year).

  32. Lee Y-R, Lee J, Kang H-G. Presoaking dried blood spot with water improves efficiency for small-molecule analysis. BioTechniques. 2019;67:219–28.

    CAS  Article  Google Scholar 

  33. Farne H, Groves HT, Gill SK, Stokes I, McCulloch S, Karoly E, et al. Comparative metabolomic sampling of upper and lower airways by four different methods to identify biochemicals that may support bacterial growth. Front Cell Infect Microbiol. 2018;8:432–432.

    CAS  Article  Google Scholar 

  34. Hao L, Wang J, Page D, Asthana S, Zetterberg H, Carlsson C, et al. Comparative evaluation of ms-based metabolomics software and its application to preclinical Alzheimer’s disease. Sci Rep. 2018;8:9291.

    Article  Google Scholar 

  35. Sapozhnikova Y, Nuñez A, Johnston J. Screening of chemicals migrating from plastic food contact materials for oven and microwave applications by liquid and gas chromatography - Orbitrap mass spectrometry. J Chromatogr A. 2021;1651:462261.

    CAS  Article  Google Scholar 

  36. Sardar SW, Choi Y, Park N, Jeon J. Occurrence and concentration of chemical additives in consumer products in korea. Int. J. Environ. Res. Public Health 2019;16:5075. https://doi.org/10.3390/ijerph16245075.

  37. Brunner AM, Bertelkamp C, Dingemans MML, Kolkman A, Wols B, Harmsen D, et al. Integration of target analyses, non-target screening and effect-based monitoring to assess OMP related water quality changes in drinking water treatment. Sci Total Environ. 2020;705:135779.

    CAS  Article  Google Scholar 

  38. Sumner LW, Amberg A, Barrett D, Beale MH, Beger R, Daykin CA, et al. Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI). Metabolomics: Off J Metabolomic Soc. 2007;3:211–21.

    CAS  Article  Google Scholar 

  39. Rappaport SM, Barupal DK, Wishart D, Vineis P, Scalbert A. The blood exposome and its role in discovering causes of disease. Environ Health Perspect. 2014;122:769–74.

    Article  Google Scholar 

  40. Blaženović I, Kind T, Ji J, Fiehn O. Software tools and approaches for compound identification of LC-MS/MS data in metabolomics. Metabolites 2018;8:31. https://doi.org/10.3390/metabo8020031.

Download references

Acknowledgements

We thank the human subjects who contributed to this research with their time and effort. The graphical abstract was created using BioRender.com.

Funding

Research reported in this publication was supported by the National Institute of Environmental Health Sciences of the National Institutes of Health under award number P30ES010126, via pilot grant from the University of North Carolina Center for Environmental Health and Susceptibility, and R21ES032928. We also thank the instrumentation support from the Chemistry and Analytical Core (CAC) of the UNC Superfund Research Program (P42ES031007).

Author information

Authors and Affiliations

Authors

Contributions

MER and KL designed the work that led to the submission. MER, NK, CR, YH, and CL acquired samples and data. YH, CL, KL, and MER played an important role in interpreting the results. YH drafted the manuscript. All authors revised the manuscript, approved the final version, and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Corresponding authors

Correspondence to Kun Lu or Meghan E. Rebuli.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Hsiao, YC., Liu, CW., Robinette, C. et al. Development of LC-HRMS untargeted analysis methods for nasal epithelial lining fluid exposomics. J Expo Sci Environ Epidemiol (2022). https://doi.org/10.1038/s41370-022-00448-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41370-022-00448-3

Keywords

  • Metabolomics
  • Exposomics
  • LC-MS
  • Exposure biomarker
  • Nasal epithelial lining fluid
  • Untargeted analysis

Search

Quick links