Improved methods for fluorescent labeling and detection of single extracellular vesicles using nanoparticle tracking analysis

Growing interest in extracellular vesicles (EV) has necessitated development of protocols to improve EV characterization as a precursor for myriad downstream investigations. Identifying expression of EV surface epitopes can aid in determining EV enrichment and allow for comparisons of sample phenoty...

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Bibliographic Details
Published inScientific reports Vol. 9; no. 1; pp. 12295 - 13
Main Authors Thane, Kristen E., Davis, Airiel M., Hoffman, Andrew M.
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 23.08.2019
Nature Publishing Group
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Summary:Growing interest in extracellular vesicles (EV) has necessitated development of protocols to improve EV characterization as a precursor for myriad downstream investigations. Identifying expression of EV surface epitopes can aid in determining EV enrichment and allow for comparisons of sample phenotypes. This study was designed to test a rigorous method of indirect fluorescent immunolabeling of single EV with subsequent evaluation using nanoparticle tracking analysis (NTA) to simultaneously determine EV concentration, particle size distribution, and surface immunophenotype. In this study, EV were isolated from canine and human cell cultures for immunolabeling and characterized using NTA, transmission electron microscopy, and Western blotting. Indirect fluorescent immunolabeling utilizing quantum dots (Qd) resulted in reproducible detection of individual fluorescently labeled EV using NTA. Methods were proposed to evaluate the success of immunolabeling based on paired particle detection in NTA light scatter and fluorescent modes. Bead-assisted depletion and size-exclusion chromatography improved specificity of Qd labeling. The described method for indirect immunolabeling of EV and single vesicle detection using NTA offers an improved method for estimating the fraction of EV that express a specific epitope, while approximating population size distribution and concentration.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-019-48181-6