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



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.


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.


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.


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.


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.

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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.


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


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).

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Authors and Affiliations



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.

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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).

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


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