VDisk: Microfluidic Cartridge for Multimodal High-Yield, High-Purity Isolation of Extracellular Vesicles from up to 1 mL of Plasma
Mahmodi Arjmand, E.; Grether, G.; Bustos-Quevedo, G.; Atanga, J.; Sanchez-Martin, P.; Van Deun, J.; Hutzenlaub, T.; Paust, N.; Nazarenko, I.; Lueddecke, J.
Show abstract
Blood-derived extracellular vesicles (EVs) hold strong diagnostic potential, yet conventional methods such as ultracentrifugation (UC) and size-exclusion chromatography (SEC) struggle to efficiently separate EVs from lipoproteins and plasma proteins due to overlapping biophysical properties. Manual workflows further introduce operator-dependent variability, limiting reproducibility and hindering clinical translation. This study presents the Vesicle Disk (VDisk), an EV purification platform using centrifugal microfluidics that combines cation exchange chromatography (CEX), sequential filtration (SeqF), and multimodal chromatography (MMC) for efficient, label-free EV isolation from up to 1 mL plasma. Various VDisk configurations with different filter membranes and plasma volumes (0.1- 1.0 mL) were benchmarked against SEC for recovery, purity, reproducibility and robustness. VDisk achieved up to 84.3% EV recovery, surpassing SEC (60.0%), with excellent reproducibility (CV < 5%) and consistent performance under both fasting and postprandial sampling conditions. VDisk offers application-specific flexibility: For applications requiring high EV concentrations, processing up to 1 mL of plasma yields a two to three times higher EV concentration compared to SEC. For purity-critical applications, processing 0.1-0.5 mL of plasma achieves approximately two times higher EV/total protein ratio. These results establish VDisk as an automated, robust, scalable, and adaptable alternative to existing EV isolation methods, suitable for both research and clinical applications.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Asymmetric depth-filtration - a versatile and scalable approach for isolation and purification of extracellular vesicles 98%
- Interferometric nanoparticle tracking analysis enables label-free discrimination of extracellular vesicles from large lipoproteins 96%
- Physical association of low density lipoprotein particles and extracellular vesicles unveiled by single particle analysis 96%
Similar papers in this journal
- Advanced Extracellular Vesicle Isolation: A Hybrid Electrokinetic-Tangential Flow Filtration Approach for Improved Yield, Purity, and Scalability 97%
- High-Efficiency Capture and Proteomic Analysis of Plasma-Derived Extracellular Vesicles through Affinity Purification 95%
- Real-time luminescence assay for cytoplasmic cargo delivery of extracellular vesicles 92%
Similar papers in this journal
- Scalable, high-throughput isolation of extracellular vesicles using electrokinetic-assisted mesh filtration 97%
- Quantitative fluorescent nanoparticle tracking analysis and nano-flow cytometry enable advanced characterization of single extracellular vesicles 96%
- Investigating the Consistency of Extracellular Vesicle Production from Breast Cancer Subtypes Using CELLine Adherent Bioreactors 96%
Similar papers in this journal
- Assessing Extracellular Vesicle Turnover In vivo Using Highly Sensitive Phosphatidylserine-Binding Reagents 94%
- Novel endogenous engineering platform for robust loading and delivery of functional mRNA by extracellular vesicles 93%
- Extracellular vesicles mediate the intercellular exchange of nanoparticles 92%
Similar papers in this journal
- Digital Profiling of Tumor Extracellular Vesicle-associated RNAs Directly from Unprocessed Blood Plasma 94%
- Single extracellular vesicle imaging and computational analysis identifiesinherent architectural heterogeneity 94%
- Massively parallel encapsulation of single cells with structured microparticles and secretion-based flow sorting 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.