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Extracellular vesicles (EVs) represent small, membrane-enclosed particles that are derived from parent cells and are secreted into the extracellular space. Once secreted, EVs can then travel and communicate with nearby or distant cells. Due to their inherent stability and biocompatibility, these particles can effectively transfer RNAs, proteins, and chemicals/metabolites from parent cells to target cells, impacting cellular and pathological processes. EVs have been shown to respond to disease-causing agents and impact target cells. Given that disease-causing agents span environmental contaminants, pathogens, social stressors, drugs, and other agents, the translation of EV methods into public health is now a critical research gap. This paper reviews approaches to translate EVs into exposure science, toxicology, and public health applications, highlighting blood as an example due to its common use within clinical, epidemiological, and toxicological studies. Approaches are reviewed surrounding the isolation and characterization of EVs and molecular markers that can be used to inform EV cell-of-origin. Molecular cargo contained within EVs are then discussed, including an original analysis of blood EV data from Vesiclepedia. Methods to evaluate functional consequences and target tissues of EVs are also reviewed. Lastly, the expanded integration of these approaches into future public health applications is discussed, including the use of EVs as promising biomarkers of exposure, effect, and disease. IMPACT STATEMENT:• Extracellular vesicles (EVs) represent small, cell-derived structures consisting of molecules that can serve as biomarkers of exposure, effect, and disease. •This review lays a novel foundation for integrating EVs, a rapidly advancing molecular biological tool, into the field of public health research including epidemiological, toxicological, and clinical investigations.• This article represents an important advancement in public health and exposure science as it is among the first to translate EVs into this field.

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Inhalation exposure to cigarette smoke and e-cigarette aerosol is known to alter the respiratory immune system, particularly cytokine signaling. In assessments of health impacts of tobacco product use, cytokines are often measured using a variety of sample types, from serum to airway mucosa. However, it is currently unclear whether and how well cytokine levels from different sample types and the airway locations they represent are correlated, making comparing studies that utilize differing sample types challenging. To address this challenge, we compared baseline cytokine signatures in upper and lower airways and systemic samples and evaluated how groups of coexpressed cytokines change with tobacco product use. Matched nasal lavage fluid (NLF), nasal epithelial lining fluid (NELF), sputum, and circulating serum samples were collected from 14 nonsmokers, 13 cigarette smokers, and 17 e-cigarette users and analyzed for levels of 22 cytokines. Individual cytokine signatures were first compared across each sample type, followed by identification of cytokine clusters within each sample type. Identified clusters were then evaluated for potential alterations following tobacco product use using eigenvector analyses. Individual cytokine signatures in the respiratory tract were significantly correlated (NLF, NELF, and sputum) compared with randomly permutated signatures, whereas serum was not significantly different from random permutations. Cytokine clusters that were similar across airway sample types were modified by tobacco product use, particularly e-cigarettes, indicating a degree of uniformity in terms of how cytokine host defense and immune cell recruitment responses cooperate in the upper and lower airways. Overall, cluster-based analyses were found to be especially useful in small cohort assessments, providing higher sensitivity than individual signatures to detect biologically meaningful differences between tobacco use groups. This novel cluster analysis approach revealed that eigencytokine patterns in noninvasive upper airway samples simulate cytokine patterns in lower airways.