Detection and phenotyping of extracellular vesicles by size exclusion chromatography coupled with on-line fluorescence detection

Diána Kitka, Judith Mihály, Jean Luc Fraikin, Tamás Beke-Somfai, Zoltán Varga

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

New methods for quantifying extracellular vesicles (EVs) in complex biofluids are critically needed. We report the development of a new technology combining size exclusion chromatography (SEC), a commonly used EV purification technique, with fluorescence detection of specifically labelled EVs. The resulting platform, Flu-SEC, demonstrates a linear response to concentration of specific EVs and could form the basis of a system with phenotyping capability. Flu-SEC was validated using red blood cell derived EVs (REVs), which provide an ideal EV model with monodisperse size distribution and high EV concentration. Microfluidic Resistive Pulse Sensing (MRPS) was used to accurately determine the size distribution and concentration of REVs. Anti-CD235a antibody, specific to glycophorin A, and the more general wheat germ agglutinin (WGA), were selected to label REVs. The results show the quantitative power of Flu-SEC: a highly linear fluorescence response over a wide range of concentrations. Moreover, the Flu-SEC technique reports the ratio of EV-bound and free-antibody molecules, an important metric for determining optimal labelling conditions for other applications. Flu-SEC represents an orthogonal tool to single-particle fluorescent methods such as flow cytometry and fluorescent NTA, for the quantification and phenotyping of EVs.

Original languageEnglish (US)
Article number19868
JournalScientific reports
Volume9
Issue number1
DOIs
StatePublished - Dec 1 2019
Externally publishedYes

Bibliographical note

Funding Information:
This work was supported by the National Research, Development and Innovation Office NKFIH, Hungary under grant numbers PD 121326 and NVKP_16-1-2016-0007 as well as by the Momentum programme (LP2016-2) and the BIONANO project (GINOP-2.3.2-15-2016-00017). Z.V. was supported by the János Bolyai Research Fellowship. K.D. was supported by the ÚNKP-19-3 New National Excellence Program of the Ministry for Innovation and Technology.

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