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.
This is a preview of subscription content, access via your institution
Access options
Subscribe to this journal
Receive 6 print issues and online access
$259.00 per year
only $43.17 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Data availability
Data are available from the corresponding author upon reasonable request.
References
Colbeck I, Lazaridis M. Human exposure to pollutants via dermal absorption and inhalation. Dordrecht; London: Springer, © 2010.: Dordrecht London, 2010.
Trivedi V, Apala DR, Iyer VN. Occupational asthma: diagnostic challenges and management dilemmas. Current Opinion Pulmonary Medicine. 2017;23:177–83.
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.
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.
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.
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.
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.
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.
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.
Smolders R, Schramm K-W, Nickmilder M, Schoeters G. Applicability of non-invasively collected matrices for human biomonitoring. Environ Health. 2009;8:8.
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.
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.
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.
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).
Want EJ. LC-MS untargeted analysis. Methods Mol Biol. 2018;1738:99–116.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Zukunft S, Sorgenfrei M, Prehn C, Möller G, Adamski J. Targeted metabolomics of dried blood spot extracts. Chromatographia. 2013;76:1295–305.
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).
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
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
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
About this article
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 32, 847–854 (2022). https://doi.org/10.1038/s41370-022-00448-3
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41370-022-00448-3
Keywords
This article is cited by
-
Evaluation of neurological behavior alterations and metabolic changes in mice under chronic glyphosate exposure
Archives of Toxicology (2024)
-
Exposure forecasting – ExpoCast – for data-poor chemicals in commerce and the environment
Journal of Exposure Science & Environmental Epidemiology (2022)