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Updated: Dec 12, 2025

Using Nanoplasmon-Enhanced Scattering and Low-Magnification Microscope Imaging to Quantify Tumor-Derived Exosomes
Published on: May 24, 2019
The proteomic analysis of breast cell line exosomes reveals disease patterns and potential biomarkers
Yousef Risha1, Zoran Minic2, Shahrokh M Ghobadloo3
1Department of Chemistry and Biomolecular Sciences, University of Ottawa, Ottawa, Canada.
Abstract:
Cancer cells release small extracellular vesicles, exosomes, that have been shown to contribute to various aspects of cancer development and progression. Differential analysis of exosomal proteomes from cancerous and non-tumorigenic breast cell lines can provide valuable information related to breast cancer progression and metastasis. Moreover, such a comparison can be explored to find potentially new protein biomarkers for early disease detection. In this study, exosomal proteomes of MDA-MB-231, a metastatic breast cancer cell line, and MCF-10A, a non-cancerous epithelial breast cell line, were identified by nano-liquid chromatography coupled to tandem mass spectrometry. We also tested three exosomes isolation methods (ExoQuick, Ultracentrifugation (UC), and Ultrafiltration-Ultracentrifugation) and detergents (n-dodecyl β-D-maltoside, Triton X-100, and Digitonin) for solubilization of exosomal proteins and enhanced detection by mass spectrometry. A total of 1,107 exosomal proteins were identified in both cell lines, 726 of which were unique to the MDA-MB-231 breast cancer cell line. Among them, 87 proteins were predicted to be relevant to breast cancer and 16 proteins to cancer metastasis. Three exosomal membrane/surface proteins, glucose transporter 1 (GLUT-1), glypican 1 (GPC-1), and disintegrin and metalloproteinase domain-containing protein 10 (ADAM10), were identified as potential breast cancer biomarkers and validated with Western blotting and high-resolution flow cytometry. We demonstrated that exosomes are a rich source of breast cancer-related proteins and surface biomarkers that may be used for disease diagnosis and prognosis.
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