Exploring differential gene expression in nasal-mucus derived small extracellular vesicles: Potential biomarker for respiratory health

Introduction: Respiratory diseases, like chronic rhinosinusitis, allergic rhinitis, and asthma, pose global health concerns, affecting over 500 million people and significantly contributing to the healthcare burden. Traditional diagnostic methods often lack sensitivity and specificity, necessitating the development of novel biomarkers for early and accurate detection of specific disease conditions. Small extracellular vesicles (sEVs), nano-sized vesicles released by cells, have gained attention as carriers of molecular biomarkers due to their role in cell communication and their presence in various biological fluids. This study aims to identify respiratory-specific biomarkers within sEVs derived from nasal mucus, offering a non-invasive approach to diagnosing respiratory conditions.

Methods: We received deidentified nasal mucus and serum samples from healthy individuals and extracted the sEVs using precipitation and size exclusion-based techniques. The sEVs were characterized using dynamic light scattering (DLS), nanoparticle tracking analysis (NTA), and western blotting. We subsequently employed RNA sequencing to identify potential biomarkers within these vesicles. In addition, we performed single-cell RNA sequencing for nasal tissue to achieve a high-resolution view of cellular heterogenicity and to compare cell-type-specific biomarkers. We processed the sequencing data using bioinformatic tools including alignment, normalization and differential gene expressions to identify potential respiratory biomarkers from these health individual samples.

Results: Our analysis showed a higher level of specific mRNA transcripts, associated with inflammation, cellular stress response, and membrane integrity, which are crucial for respiratory diseases in nasal sEVs compared to serum sEVs. The findings suggest that sEVs from nasal mucus are a rich source of respiratory-specific biomarkers, which could be utilized to diagnose and monitor inflammation and olfactory dysfunction in respiratory conditions.

Conclusion: These sEV based biomarkers offer a promising avenue for developing non-invasive diagnostic tools that could improve patient outcomes by enabling earlier and more accurate detection of respiratory diseases. Future research should focus on validating these biomarkers in larger, diseasespecific cohorts.