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Longitudinal profiling reveals transcriptomic heterogeneity and 12 stable reference genes in healthy urinary extracellular vesicles, highlighting candidates for standardized biomarker normalization.
Urinary extracellular vesicles (uEVs) have emerged as promising non-invasive molecular carriers for biomarker discovery, yet the physiological variability and tissue-associated characteristics of uEV RNA cargo in healthy individuals remain poorly defined. This knowledge gap limits the interpretation, normalization, and clinical translation of uEV-based transcriptomic studies. Here, we performed a longitudinal RNA-sequencing analysis of uEVs from 12 healthy donors, of whom six contributed complete longitudinal sample sets, to establish a molecular reference framework for physiological uEV transcriptomes. We systematically characterized inter- and intra-individual variation in uEV RNA cargo and found substantial transcriptomic heterogeneity despite relatively stable extracellular vesicle secretion levels over time. A conserved set of highly expressed genes was significantly enriched in ribosomal function and oxidative phosphorylation, indicating their role in fundamental cellular maintenance. Importantly, we identified 12 protein-coding genes that showed consistently low expression variance both across individuals and within individuals over time. Cross-dataset analyses using independent external and pan-cancer datasets further supported their potential utility as candidate reference transcripts for uEV RNA studies. Computational tissue deconvolution inferred predominant kidney- and bladder-associated transcriptomic signatures, while cell-type enrichment analysis showed relatively high enrichment scores for smooth muscle cells and mesenchymal stem cells. Together, this study defines the physiological landscape of healthy uEV transcriptomes, delineates key sources of biological variation, and provides candidate reference transcripts and tissue-associated molecular profiles to support the standardization and translational application of uEV-based molecular biomarker research.
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Hu et al. (2026) studied this question.
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