Advanced Extracellular Vesicle Isolation: A Hybrid Electrokinetic-Tangential Flow Filtration Approach for Improved Yield, Purity, and Scalability.

Abstract

As extracellular vesicles (EVs) become increasingly important in diagnostics and therapeutics, achieving both improved purity and yield during isolation remains a critical challenge. Conventional techniques often suffer from the coisolation of nonvesicular particles and soluble proteins, limiting their clinical and research utility. In response, we introduce ExoTFF, a hybrid isolation technology that sequentially integrates electrokinetic filtration (ExoFilter) with size-exclusion tangential flow filtration (TFF) to deliver unprecedented performance gains through an iterative, synergistic mechanism. In the ExoTFF system, the sample is repeatedly circulated through an electrokinetic mesh filter and TFF until the liquid is removed. This recirculating flow gradually eliminates contaminants, while the electrokinetic filter continuously captures EVs as the sample is purified. Finally, any residual impurities in the TFF unit are completely removed via a dead volume elimination process. The complementary actions of these two distinct separation mechanisms double EV recovery rates and reduce impurity levels by 80% compared to conventional TFF, culminating in an impressive 800% improvement in the purity ratio. In proof-of-concept experiments, ExoTFF processed 10 mL of plasma within 10 min, efficiently depleting albumin and high-density lipoprotein (HDL) while achieving superior EV recovery. To further explore scalability, an automated ExoTFF system processed 500 mL of sample in 50 min, maintaining consistent yield and purity. The ability to sustain performance across different scales highlights ExoTFF's potential for both laboratory research and industrial-level EV production. Beyond biological applications, this platform also offers broad applicability for the isolation of negatively charged nanoparticles, demonstrating its potential impact across multiple nanotechnology-driven fields.