Integrating Automated Liquid Handling in the Separation Workflow of Extracellular Vesicles Enhances Specificity and Reproducibility

Overview

Journal J Nanobiotechnology

Publisher Biomed Central

Specialty Biotechnology

Date 2023 May 19

PMID 37208684

Authors

Sofie Van Dorpe

Lien Lippens

Robin Boiy

Claudio Pinheiro

Glenn Vergauwen

Pekka Rappu

Ilkka Miinalainen

Philippe Tummers

Hannelore Denys

Olivier de Wever

An Hendrix

Affiliations

Soon will be listed here.

Abstract

Background: Extracellular vesicles (EV) are extensively studied in human body fluids as potential biomarkers for numerous diseases. Major impediments of EV-based biomarker discovery include the specificity and reproducibility of EV sample preparation as well as intensive manual labor. We present an automated liquid handling workstation for the density-based separation of EV from human body fluids and compare its performance to manual handling by (in)experienced researchers.

Results: Automated versus manual density-based separation of trackable recombinant extracellular vesicles (rEV) spiked in PBS significantly reduces variability in rEV recovery as quantified by fluorescent nanoparticle tracking analysis and ELISA. To validate automated density-based EV separation from complex body fluids, including blood plasma and urine, we assess reproducibility, recovery, and specificity by mass spectrometry-based proteomics and transmission electron microscopy. Method reproducibility is the highest in the automated procedure independent of the matrix used. While retaining (in urine) or enhancing (in plasma) EV recovery compared to manual liquid handling, automation significantly reduces the presence of body fluid specific abundant proteins in EV preparations, including apolipoproteins in plasma and Tamm-Horsfall protein in urine.

Conclusions: In conclusion, automated liquid handling ensures cost-effective EV separation from human body fluids with high reproducibility, specificity, and reduced hands-on time with the potential to enable larger-scale biomarker studies.

Citing Articles

Filter-Aided Extracellular Vesicle Enrichment (FAEVEr) for Proteomics.

Pauwels J, Van de Steene T, Van de Velde J, De Muyer F, De Pauw D, Baeke F Mol Cell Proteomics. 2025; 24(2):100907.

PMID: 39842778 PMC: 11872570. DOI: 10.1016/j.mcpro.2025.100907.

Uromodulin and the study of urinary extracellular vesicles.

Harding M, Yavuz H, Gathmann A, Upson S, Swiatecka-Urban A, Erdbrugger U J Extracell Biol. 2024; 3(11):e70022.

PMID: 39582686 PMC: 11583080. DOI: 10.1002/jex2.70022.

Identification and validation of extracellular vesicle reference genes for the normalization of RT-qPCR data.

Pinheiro C, Guilbert N, Lippens L, Roux Q, Boiy R, Fischer S J Extracell Vesicles. 2024; 13(4):e12421.

PMID: 38545822 PMC: 10974686. DOI: 10.1002/jev2.12421.

References

Kalra H, Adda C, Liem M, Ang C, Mechler A, Simpson R . Comparative proteomics evaluation of plasma exosome isolation techniques and assessment of the stability of exosomes in normal human blood plasma. Proteomics. 2013; 13(22):3354-64. DOI: 10.1002/pmic.201300282. View

Kuypers S, Smisdom N, Pintelon I, Timmermans J, Ameloot M, Michiels L . Unsupervised Machine Learning-Based Clustering of Nanosized Fluorescent Extracellular Vesicles. Small. 2021; 17(5):e2006786. DOI: 10.1002/smll.202006786. View

Hendrix A . The nature of blood(y) extracellular vesicles. Nat Rev Mol Cell Biol. 2021; 22(4):243. DOI: 10.1038/s41580-021-00348-8. View

Mussack V, Wittmann G, Pfaffl M . Comparing small urinary extracellular vesicle purification methods with a view to RNA sequencing-Enabling robust and non-invasive biomarker research. Biomol Detect Quantif. 2019; 17:100089. PMC: 6554496. DOI: 10.1016/j.bdq.2019.100089. View

Vergauwen G, Dhondt B, Van Deun J, De Smedt E, Berx G, Timmerman E . Confounding factors of ultrafiltration and protein analysis in extracellular vesicle research. Sci Rep. 2017; 7(1):2704. PMC: 5457435. DOI: 10.1038/s41598-017-02599-y. View

Yang Y, Zhang L, Jin K, He M, Wei W, Chen X . Self-adaptive virtual microchannel for continuous enrichment and separation of nanoparticles. Sci Adv. 2022; 8(30):eabn8440. PMC: 9337757. DOI: 10.1126/sciadv.abn8440. View

Geeurickx E, Lippens L, Rappu P, De Geest B, de Wever O, Hendrix A . Recombinant extracellular vesicles as biological reference material for method development, data normalization and assessment of (pre-)analytical variables. Nat Protoc. 2021; 16(2):603-633. DOI: 10.1038/s41596-020-00446-5. View

Thery C, Witwer K, Aikawa E, Alcaraz M, Anderson J, Andriantsitohaina R . Minimal information for studies of extracellular vesicles 2018 (MISEV2018): a position statement of the International Society for Extracellular Vesicles and update of the MISEV2014 guidelines. J Extracell Vesicles. 2019; 7(1):1535750. PMC: 6322352. DOI: 10.1080/20013078.2018.1535750. View

Dhondt B, Lumen N, de Wever O, Hendrix A . Preparation of Multi-omics Grade Extracellular Vesicles by Density-Based Fractionation of Urine. STAR Protoc. 2020; 1(2):100073. PMC: 7580105. DOI: 10.1016/j.xpro.2020.100073. View

10.

Van Deun J, Mestdagh P, Sormunen R, Cocquyt V, Vermaelen K, Vandesompele J . The impact of disparate isolation methods for extracellular vesicles on downstream RNA profiling. J Extracell Vesicles. 2014; 3. PMC: 4169610. DOI: 10.3402/jev.v3.24858. View

11.

Park J, Choi D, Choi D, Kim H, Kang J, Jung J . Identification and characterization of proteins isolated from microvesicles derived from human lung cancer pleural effusions. Proteomics. 2013; 13(14):2125-34. DOI: 10.1002/pmic.201200323. View

12.

Poste G . Bring on the biomarkers. Nature. 2011; 469(7329):156-7. DOI: 10.1038/469156a. View

13.

Pathan M, Keerthikumar S, Chisanga D, Alessandro R, Ang C, Askenase P . A novel community driven software for functional enrichment analysis of extracellular vesicles data. J Extracell Vesicles. 2017; 6(1):1321455. PMC: 5505018. DOI: 10.1080/20013078.2017.1321455. View

14.

Ji H, Greening D, Barnes T, Lim J, Tauro B, Rai A . Proteome profiling of exosomes derived from human primary and metastatic colorectal cancer cells reveal differential expression of key metastatic factors and signal transduction components. Proteomics. 2013; 13(10-11):1672-86. DOI: 10.1002/pmic.201200562. View

15.

Coumans F, Brisson A, Buzas E, Dignat-George F, Drees E, El-Andaloussi S . Methodological Guidelines to Study Extracellular Vesicles. Circ Res. 2017; 120(10):1632-1648. DOI: 10.1161/CIRCRESAHA.117.309417. View

16.

Choi D, Choi D, Hong B, Jang S, Kim D, Lee J . Quantitative proteomics of extracellular vesicles derived from human primary and metastatic colorectal cancer cells. J Extracell Vesicles. 2013; 1. PMC: 3760640. DOI: 10.3402/jev.v1i0.18704. View

17.

Hong B, Cho J, Kim H, Choi E, Rho S, Kim J . Colorectal cancer cell-derived microvesicles are enriched in cell cycle-related mRNAs that promote proliferation of endothelial cells. BMC Genomics. 2009; 10:556. PMC: 2788585. DOI: 10.1186/1471-2164-10-556. View

18.

Geeurickx E, Tulkens J, Dhondt B, Van Deun J, Lippens L, Vergauwen G . The generation and use of recombinant extracellular vesicles as biological reference material. Nat Commun. 2019; 10(1):3288. PMC: 6650486. DOI: 10.1038/s41467-019-11182-0. View

19.

Onodi Z, Pelyhe C, Nagy C, Brenner G, Almasi L, Kittel A . Isolation of High-Purity Extracellular Vesicles by the Combination of Iodixanol Density Gradient Ultracentrifugation and Bind-Elute Chromatography From Blood Plasma. Front Physiol. 2018; 9:1479. PMC: 6206048. DOI: 10.3389/fphys.2018.01479. View

20.

Gorgens A, Corso G, Hagey D, Jawad Wiklander R, Gustafsson M, Felldin U . Identification of storage conditions stabilizing extracellular vesicles preparations. J Extracell Vesicles. 2022; 11(6):e12238. PMC: 9206228. DOI: 10.1002/jev2.12238. View