Year: 2025
Type: article
Abstract: The use of fatty acids in extracellular vesicles (EVs) has been largely understudied, despite their selective characteristics which hold potential as disease biomarkers. EVs are nanosized vesicles secreted by human cells and play key roles in intercellular communication. Although lipid profiling of EVs has shown promise in differentiating between healthy and diseased individuals, fatty acids specifically remain underexplored. Due to their structural simplicity and high selectivity, this study focuses on optimizing the isolation of urinary EVs and analyzing their fatty acid content using gas chromatography-mass spectrometry (GC-MS). The goal is to identify fatty acid profiles in EVs based on the locality of urine samples (control and luminal space) and evaluate their potential as practical indicators for disease detection. Optimal EV isolation was achieved using a 2-cycle ultracentrifugation process, yielding vesicles with an average size of 131.87 nm and significantly higher protein concentration (2.970 mg/mL, p < 0.05). A total of 14 fatty acid methyl esters (FAMEs) were detected, with 12 shared across all localities and four (C13, C15, C18, and C30) found to vary by sample origin. Tetracosanoic acid methyl ester was the most abundant (84.05%), while tetradecanoic acid methyl ester was the least (11.79%). These results support urinary EV fatty acid profiling as a promising non-invasive biomarker strategy.
Institution: University of Technology Malaysia
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: bronze
Funder: Universiti Teknologi Malaysia
Awards: G1001717021, G6084887513
OpenAlex ID: W4410945010