Optimised Untargeted Metabolomics Workflow for Human Urinary Extracellular Vesicles

Abstract

Extracellular vesicles (EVs) have been a key focus in biomarker discovery, with urinary EVs (uEVs), primarily derived from cells of the urogenital tract, providing valuable insights into kidney and urinary tract health and disease. However, progress in uEV-based metabolomics remains limited by variability in EV isolation and extraction approaches. Here, we systematically evaluated and optimised experimental conditions for untargeted metabolite profiling of human uEVs. We compared three different EV isolation methods, namely precipitation, size-exclusion chromatography, and pH-adjustment with resin separation, and found that precipitation yielded the highest particle count. However, the pH-adjustment with resin separation method produced the highest number of small EVs (30–150 nm), aligning with the primary focus of EV research. Transmission electron microscopy analysis confirmed the presence of well-structured exosomes in these isolates. Moreover, this EV isolation method generated the broadest metabolite coverage. To identify the most effective metabolite extraction conditions, we compared two established protocols (Liu et al. 2023 and Hinzman et al. 2022) with an in-house-developed method. Application of the protocol of Liu et al. led to the identification of the highest number of metabolites. Considering EV purity, contamination risks and metabolite yield, the combination of the pH-adjustment with resin separation method for uEV isolation with the metabolite extraction protocol of Liu et al. was the optimal approach for metabolomics analysis of the uEV cargo. This study provides an experimentally validated workflow for robust untargeted metabolomics analysis of human uEVs and supports the development of more standardised approaches for EV-based biomarker discovery.