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Multimodal Analytical Platform on a Multiplexed Surface Plasmon Resonance Imaging Chip for the Analysis of Extracellular Vesicle Subsets
Published on: March 17, 2023
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A microfluidic platform for multi-marker profiling of extracellular vesicles from single-cell-derived clones
Biorxiv : the Preprint Server for Biology
|March 27, 2026
Summary
This study introduces a microfluidic platform for analyzing extracellular vesicles (EVs) from single cells. The technology reveals significant heterogeneity in EV marker expression, linking EV signatures to specific cell clones.
Area of Science:
- Biotechnology
- Cell Biology
- Nanotechnology
Background:
- Extracellular vesicles (EVs) are crucial intercellular communicators, but bulk analysis methods obscure their inherent heterogeneity.
- Understanding single-cell-derived EV profiles is vital for accurate biological interpretation and therapeutic applications.
Purpose of the Study:
- To develop a microfluidic platform for high-throughput, multi-marker profiling of EVs from single-cell-derived clones.
- To investigate the heterogeneity of EV marker expression and its correlation with cellular characteristics.
Main Methods:
- A semi-open microfluidic platform with 17,305 wells was designed to capture EVs from single-cell clones.
- EVs were immunolabeled for tetraspanin markers (CD9, CD63, CD81) and EpCAM, then analyzed via fluorescence microscopy and automated image analysis.
- Single-cell-derived PC3 clones were utilized to assess EV heterogeneity and marker co-expression.
Main Results:
- The platform enabled multi-marker profiling of EVs at a near single-EV level, linked to specific clonal lineages.
- Substantial heterogeneity in EV tetraspanin marker co-expression was observed, with four distinct profiles identified.
- EpCAM-positive EV fraction correlated with PC3 cell proliferation, unlike free EpCAM.
Conclusions:
- The developed platform effectively dissects cellular and EV heterogeneity at the single-cell lineage level.
- This approach provides a practical method for linking EV signatures to their single-cell origins.
- Findings highlight the importance of single-cell-based analysis for understanding EV biology.

