A preanalytical quality indicator framework for automated extracellular vesicle separation

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.