Hands-Free Proteomic Profiling of Urinary Extracellular Vesicles with a High-Throughput Automated Workflow

Extracellular vesicles (EVs) have emerged as a promising source of disease biomarkers for noninvasive early stage diagnoses, but a bottleneck in EV sample processing restricts their immense potential in clinical applications. Existing methods are limited by a low EV yield and integrity, slow processing speeds, low sample capacity, and poor recovery efficiency. We aimed to address these issues with a high-throughput automated workflow for EV isolation, EV lysis, protein extraction, and protein denaturation. The automation can process clinical urine samples in parallel, resulting in protein-covered beads ready for various analytical methods, including immunoassays, protein quantitation assays, and mass spectrometry. Compared to the standard manual lysis method for contamination levels, efficiency, and consistency of EV isolation, the automated protocol shows reproducible and robust proteomic quantitation with less than a 10% median coefficient of variation. When we applied the method to clinical samples, we identified a total 3,793 unique proteins and 40,380 unique peptides, with 992 significantly upregulated proteins in kidney cancer patients versus healthy controls. These upregulated proteins were found to be involved in several important kidney cancer metabolic pathways also identified with a manual control. This hands-free workflow represents a practical EV extraction and profiling approach that can benefit both clinical and research applications, streamlining biomarker discovery, tumor monitoring, and early cancer diagnoses.

Automated summary

Extracellular vesicles (EVs) have been identified as a promising source of disease biomarkers. However, the current methods for processing EV samples have limitations in terms of yield, integrity, speed, capacity, and recovery efficiency. In this study, we developed a high-throughput automated workflow that enables efficient isolation, lysis, and extraction of proteins from EVs in clinical samples. The automated protocol shows reproducible and robust proteomic quantitation, and has been successfully applied to identify upregulated proteins in kidney cancer patients compared to healthy controls. This hands-free workflow has the potential to streamline biomarker discovery, tumor monitoring, and early cancer diagnoses in both clinical and research settings.