Characterization of extracellular vesicles from model cell systems and biofluids for non-invasive diagnostic of cancer and infectious diseases

Non-invasive, real-time health monitoring is limited to a few parameters, and there is a growing need for improved diagnostic approaches, particularly for cancer. Liquid biopsy, which involves identifying disease biomarkers in bodily fluids like blood or urine, shows promise for early diagnostics. Extracellular Vesicles (EVs) released by cells contain various analytes, including proteins, lipids, metabolites, and RNA species, making them potential disease biomarkers for liquid biopsy. Sweat, a continuously released biofluid, offers the advantage of providing "real-time" data for non-invasive analysis. However, its potential diagnostic value is relatively unexplored.

This project aims to characterize the molecular composition of EVs in sweat and model cell systems to identify potential tumour biomarkers. The objectives include developing methods for isolating EVs from both large and small amounts of human sweat, studying the nucleic acids and protein composition of EVs-enriched sweat samples and advancing Raman spectroscopy (RS)-based methods for characterizing EVs obtained from cancer cells. Proteomics, Time-Gated Raman Spectroscopy (TG-RS) and Surface Enhanced Time-Gated Raman Spectroscopy (TG-SERS) were used for characterizing the changes in molecular composition of renal cell carcinoma (RCC)-derived EVs samples caused by hypoxia treatment.

Our results allow to conclude about the possibility to use the sweat EVs as a non-invasive source of biomarkers of human and bacterial origin. In addition, the use of commercially available alginate patches for sweat collection provides enough biological material for the molecular composition analysis, eliminating the need for intensive physical exercise, and allows the selective collection of proteins of human origin.