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. 2016 Sep 27:5:31242.
doi: 10.3402/jev.v5.31242. eCollection 2016.

A standardized method to determine the concentration of extracellular vesicles using tunable resistive pulse sensing

Robert Vogel  1   2 , Frank A W Coumans  3 , Raluca G Maltesen  4 , Anita N Böing  3 , Katherine E Bonnington  5 , Marike L Broekman  6 , Murray F Broom  2 , Edit I Buzás  7 , Gunna Christiansen  8 , Najat Hajji  3 , Søren R Kristensen  4 , Meta J Kuehn  5 , Sigrid M Lund  4 , Sybren L N Maas  6 , Rienk Nieuwland  3 , Xabier Osteikoetxea  7 , Rosalie Schnoor  6 , Benjamin J Scicluna  9   10 , Mitch Shambrook  10 , Jeroen de Vrij  6 , Stephen I Mann  2 , Andrew F Hill  9   10 , Shona Pedersen  11
Affiliations

A standardized method to determine the concentration of extracellular vesicles using tunable resistive pulse sensing

Robert Vogel et al. J Extracell Vesicles. .

Abstract

Background: Understanding the pathogenic role of extracellular vesicles (EVs) in disease and their potential diagnostic and therapeutic utility is extremely reliant on in-depth quantification, measurement and identification of EV sub-populations. Quantification of EVs has presented several challenges, predominantly due to the small size of vesicles such as exosomes and the availability of various technologies to measure nanosized particles, each technology having its own limitations.

Materials and methods: A standardized methodology to measure the concentration of extracellular vesicles (EVs) has been developed and tested. The method is based on measuring the EV concentration as a function of a defined size range. Blood plasma EVs are isolated and purified using size exclusion columns (qEV) and consecutively measured with tunable resistive pulse sensing (TRPS). Six independent research groups measured liposome and EV samples with the aim to evaluate the developed methodology. Each group measured identical samples using up to 5 nanopores with 3 repeat measurements per pore. Descriptive statistics and unsupervised multivariate data analysis with principal component analysis (PCA) were used to evaluate reproducibility across the groups and to explore and visualise possible patterns and outliers in EV and liposome data sets.

Results: PCA revealed good reproducibility within and between laboratories, with few minor outlying samples. Measured mean liposome (not filtered with qEV) and EV (filtered with qEV) concentrations had coefficients of variance of 23.9% and 52.5%, respectively. The increased variance of the EV concentration measurements could be attributed to the use of qEVs and the polydisperse nature of EVs.

Conclusion: The results of this study demonstrate the feasibility of this standardized methodology to facilitate comparable and reproducible EV concentration measurements.

Keywords: Coulter counter; EV; colloids; concentration; exosomes; extracellular vesicles; microparticles; micropores; nanoparticles; nanopores; resistive pulse sensing.

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Conflict of interest statement

and funding RV and MFB are contractors at Izon Science and their contributions to this paper were made as part of their contracts. AFH is funded by grants from the Australian National Health and Medical Research Council (grants 628946 and 400202; www.nhmrc.gov.au) and an Australian Research Council (www.arc.gov.au) Future Fellowship (grant FT100100560).

Figures

Fig. 1
Fig. 1
Typical size exclusion column (qEV) elution profiles of serum extracellular vesicles (EVs) depending on loading.
Fig. 2
Fig. 2
Transmission electron microscopy (TEM) images of plasma EVs (a) before and (b) after qEV purification and (c) CD9+ immunogold-labelled EVs after qEV purification.
Fig. 3
Fig. 3
Blockade rates and mean full width half maximum durations (insets) of CPC200 calibration particles (a) with and (b) without coating of the pore. Calibration particles were recorded before the serum (in black) and after the serum (in red), in order to assess pore modification processes.
Fig. 4
Fig. 4
(a) Typical size distribution of liposomes and (b) histogram of mean diameters of the complete liposome data set, including a total of 68 measurements.
Fig. 5
Fig. 5
(a) Concentration scatter and box plots and (b) concentration distribution for the complete liposome data set of 68 measurements. Protocol and “green zone” violations are marked with white circles within the scatter plot in (a).
Fig. 6
Fig. 6
Performance similarity across the 6 groups was qualitatively visualized in the principal component analysis (PCA) score plot (a). Samples cluster into 2 groups on principal component 1 (PC1), corresponding to data from the EV (+PC1) and calibration (−PC1) data. Data were colour coded according to the 6 groups. (b) PCA loadings plot was used to identify which parameters separated the sample and calibration data. FWHM=full width half maximum duration (as opposed to “duration,” which is the total blockade duration), RMS=root mean square, MRP=mid-range particle size, dI/I=relative blockade magnitude.
Fig. 7
Fig. 7
(a) Concentration scatter and box plots and (b) concentration distribution for the complete EV data set of 74 measurements. Protocol and green zone violations are marked with white circles within the scatter plot in (a).
Fig. 8
Fig. 8
(a) Typical EV size distributions with different lower detection limits. (b) Positive correlation between measured concentration and relative blockade magnitude of calibration is displayed; the circled measurements in red and green represent the size distributions.
See this image and copyright information in PMC

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