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Updated: May 9, 2025

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Flow Cytometric Analysis of Extracellular Vesicles from Cell-conditioned Media
Published on: February 12, 2019
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Novel PPT+SEC Workflow for High-Sensitivity Extracellular Vesicle Proteomics from Cell Media
Asia Botto1,2, Chiara De Cesari1,3, Noa Ndimurwanko1,4
1Fondazione Pisana per la Scienza ONLUS, 56017 San Giuliano Terme (PI), Italy.
Journal of Proteome Research
|May 2, 2025
Summary
This study introduces a novel extracellular vesicle (EV) isolation method for proteomics, reducing sample loss and improving protein identification. The streamlined workflow enhances sensitivity for cancer biomarker discovery.
Area of Science:
- Biochemistry
- Cell Biology
- Proteomics
Background:
- Extracellular vesicles (EVs) isolation using size exclusion chromatography (SEC) typically requires a concentration step.
- This concentration step can lead to significant loss of valuable EV material.
- Current methods limit the efficiency of EV proteomics analysis.
Purpose of the Study:
- To develop a novel EV isolation workflow compatible with direct proteomics analysis.
- To minimize EV loss during sample preparation.
- To enhance the sensitivity and scope of EV proteomics.
Main Methods:
- Development of a low-volume EV isolation technique using size exclusion chromatography (SEC).
- Characterization of isolated small EVs via transmission electron microscopy, Western blot, and nanoparticle tracking analysis.
- Proteomics analysis of isolated EVs and benchmarking against an automated UHPLC-SEC platform.
Main Results:
- The novel workflow isolates EVs in 80 microL, eliminating the need for concentration.
- Identified more proteins and EV markers compared to UHPLC-SEC, including 96% of top exosomal proteins from ExoCarta.
- Demonstrated higher sensitivity for pancreatic cancer EV markers in pancreatic cancer cell lines.
Conclusions:
- The developed EV isolation method is efficient and preserves EV integrity for proteomics.
- This approach significantly enhances the identification of EV proteins and biomarkers.
- The workflow shows promise for sensitive detection of cancer-specific EV markers.

