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iEVIP: a dual-inspired intelligent platform for smart response extracellular vesicle isolation and single-vesicle profiling

Year: 2026

Type: article

Abstract: Extracellular vesicles (EVs) are promising non-invasive biomarkers for early cancer detection, yet clinical translation is limited by membrane fouling during isolation and vesicle aggregation during single-vesicle imaging. Here, iEVIP (intelligent extracellular vesicle isolation and profiling), an integrated dual-inspired platform, combines intelligent pulsatile filtration, blood-smear-inspired nanoscale organization and machine-learning-based classification. Real-time transmembrane-pressure monitoring triggers back-aspiration pulses upon membrane fouling, promoting membrane regeneration and stable EV recovery from plasma. A wettability-assisted nano-smear array reduces aggregation and fluorescence overlap, enabling high-throughput multiplexed single-EV imaging. iEVIP achieves 95.32% classification accuracy for cell-line-derived EVs and up to 80.37% in exploratory clinical cohorts using random forest algorithm. By bridging macroscopic engineering principles with nanoscale bioanalysis, iEVIP provides a scalable framework for high-throughput single-EV analysis and exploratory clinical sample classification. However, the limited cohort size and absence of cross-center external validation constrain generalizability, and larger independent cohorts are needed to establish diagnostic robustness and clinical applicability. Extracellular vesicle analysis is hindered by membrane fouling during isolation and vesicle aggregation during imaging. Here, authors introduce iEVIP, a platform combining pulsatile filtration, nanoscale sample organization and machine learning for high-throughput single-vesicle analysis.

Source: Nature Communications

DOI: 10.1038/s41467-026-77038-6