A preanalytical quality indicator framework for automated extracellular vesicle separation

Abstract

Abstract Objectives Clinical translation of extracellular vesicle (EV)-based diagnostics is constrained by preanalytical variability in separation workflows. We developed a preanalytical quality indicator framework for automated EV separation, anchored to IFCC, EFLM, and MISEV2023 standards. This proof-of-concept study establishes analytical prerequisites; validation in disease cohorts is required before clinical implementation. Methods Paired serum and plasma from 10 healthy controls were processed by EXODUS, Summit, and manual SEC. Endpoints included NTA yield, particle-to-protein ratio (PPR), intra-matrix reproducibility (CV%), and tetraspanin preservation (CD9, CD63, CD81) by SP-IRIS. Results EXODUS achieved the highest particle concentration and PPR, exceeding SEC-based methods by 5- to 10-fold. Summit showed the lowest process variation (CV% <20 %) in serum (18.9 %) and plasma (16.5 %), satisfying IFCC-EFLM robustness criteria. Manual SEC showed the highest variability (serum: 55.3 %; plasma: 45.5 %). SP-IRIS revealed comparable tetraspanin detection across EXODUS and Summit, with qEV showing lower intensity. Summit confirmed superior scalability (4–48 samples/run, 10–15 min hands-on time). Conclusions This proof-of-concept framework establishes analytical prerequisites. No single platform optimizes all parameters; selection should be driven by intended application: EXODUS for sensitivity-limited discovery, Summit for analytical standardization, manual SEC for morphological reference. Validation in disease cohorts is required before clinical implementation.