A Multiplex Flow Cytometric Approach to Define Molecularly Distinct Extracellular Vesicle Subsets

Abstract

Extracellular vesicles (EVs) are molecularly very heterogeneous, and their characterization at the single-particle level is technically challenging. Existing approaches, such as nanoparticle tracking analysis, fluorescence microscopy, and nano-flow cytometry, provide important insights but often lack the flexibility to detect multiple molecular markers simultaneously. Here, we describe an optimized workflow for multiparametric EV phenotyping using a spectral flow cytometry instrument with enhanced small particle detection capacity. EVs were isolated from murine melanoma and melanocyte cell lines via size-exclusion chromatography and labeled with a fluorogenic membrane probe that enables robust, single EV detection. In this study, we systematically optimized staining conditions, EV concentrations, and fluorophore combinations for a 5-color antibody panel on single EVs. We show that single-particle flow cytometry can reliably detect and resolve multiple EV surface markers simultaneously. Data analysis by unsupervised clustering further enabled unbiased identification of distinct EV subsets, providing a practical approach for EV phenotyping in both research and clinical contexts.

Keywords

Extracellular vesicles / Flow cytometry / Size exclusion chromatography / Melanoma / Single EV analysis

Statement of the Use of Generative AI and AI-Assisted Technologies in the Writing Process

During the preparation of this manuscript, the authors used ChatGPT (OpenAI) to assist with debugging and refining R code to improve the clarity, and legibility of the figures. The AI tool did not generate original data or interpretations. All results, analyses, and conclusions were developed and verified by the authors, who take full responsibility for the content of the published article.

Acknowledgments

The authors thank the FlowCore and Live Cell Imaging (LIMA) Core Facilities at the Medical Faculty Mannheim, Heidelberg University, for their support. We and the FlowCore specifically acknowledge the FI-EMS Core Facility Strategic Extension funded by the Federal Ministry of Education and Research (BMBF) and the Ministry of Science, Research and the Arts Baden-Württemberg within the framework of the Excellence Strategy of the Federal and State Governments of Germany for funding the Cytek^@Northern Lights^TM. The authors also acknowledge the data storage service SDS@hd, supported by the Ministry of Science, Research and the Arts Baden-Württemberg (MWK).

Author Contributions

Conceptualization, L.C.D.; Methodology, P.L., S.U. and K.B.; Formal Analysis, P.L. and E.R.; Investigation, P.L., S.H. (Sarah Hofmann), S.H. (Shruthi Hemanna) and A.A.; Data Curation, P.L.; Writing—Original Draft Preparation, P.L. and L.C.D.; Writing—Review & Editing, P.L., S.H. (Sarah Hofmann), S.H. (Shruthi Hemanna), E.R., A.A., K.B. and L.C.D.; Visualization, P.L. and L.C.D.; Supervision, S.H. (Sarah Hofmann) and L.C.D.; Funding Acquisition, L.C.D.

Ethics Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Raw flow cytometry data used in this work will be made accessible to readers upon reasonable request.

Funding

This work was funded by Deutsche Forschungsgemeinschaft (DFG)—project number 492531042 and by the Wilhelm-Sander Stiftung—project number R2024.157.1 (to L.C.D.).

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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