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Updated: May 6, 2026

Flow Cytometric Analysis of Extracellular Vesicles from Cell-conditioned Media
Published on: February 12, 2019
sEV-ICP-TOF-MS for Multiparametric Single-EV Analysis via Nanoparticle-Conjugated Antibodies and Isotope Ratio
Minseop Kim1, Seung Min Ha1, Eunyong Ha1
1Department of Chemistry, Hanyang University, Seoul 04763, Republic of Korea.
Abstract:
Extracellular vesicles (EVs) play a vital role in cell-to-cell communication and hold great promise for diagnostics and therapy. However, their small size, heterogeneity, and interference with other nanoparticles make them difficult to detect and characterize using traditional methods. In this study, we introduce a single-EV inductively coupled plasma time-of-flight mass spectrometry (sEV-ICP-TOF-MS) technique that integrates mass cytometry with nanoparticle-conjugated antibodies and isotope ratio filtering to improve EV detection and quantification. The application of nanoparticle-conjugated antibodies significantly increased the detection sensitivity compared to metal isotope-conjugated antibodies, allowing for reliable identification of EVs with low-abundance surface markers. The implementation of isotope ratio filtering enhanced signal specificity by distinguishing EV signals from background noise based on the distinct natural isotopic abundance patterns of the nanoparticles. Quantitative analysis of calibration curves revealed that applying isotope ratio filtering enhanced the limit of detection (LOD) by more than 36-fold, reducing it to 2.07 × 104 particles/mL, resulting in an extension of linear range by 2 orders of magnitude toward lower concentrations. Multiparametric profiling of surface proteins uncovered a bimodal distribution of CD9+CD63+ coexpression, indicating further heterogeneity in CD9+CD63+ EV subpopulations. This novel sEV-ICP-TOF-MS technique enhances both the sensitivity and specificity of single-EV detection and enables high-resolution, multiparametric EV profiling. It presents a scalable approach with strong potential for biomarker discovery, disease diagnostics, and advancing our understanding of EV biology.
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