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Extracellular vesicles (EVs) are cell-derived particles that carry proteins, RNAs, lipids, metabolites, and other molecules. EVs play vital roles in intercellular communication and are implicated in disease and cancer, positioning them as a promising resource for non-invasive diagnostics and therapeutics. However, there are technical challenges in rigor and reproducibility for analysis of EVs. To address these challenges, we have been working on the development of super-SILAC based EVs as universal internal standardStandards (UIS) for robust reproducible quantification of EV proteins. In this chapter, we have discussed different EV isolation methods and updated our recent progress in the proteome characterization of a mixture of EVs derived from different cancer cell lines and SILAC-labeled HeLa EVs. Our data suggest that it is highly promising to use super-SILAC EVs as UIS for reliable accurate quantification of EVs across large cohorts of clinical samples. Lastly, we have provided perspectives for future directions in EV proteomicsProteomics research toward clinical application.

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Internal Standard for Quantification of Extracellular Vesicles in Biomarker Studies

  • 章节

  • 发表日期：

    2025年

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  • Sehong Min,
  • Tong Zhang,
  • Komal Abhange,
  • Reta Birhanu Kitata,
  • Matthew J. Gaffrey,
  • David M. Lubman,
  • Tujin Shi

摘要

Extracellular vesicles (EVs) are cell-derived particles that carry proteins, RNAs, lipids, metabolites, and other molecules. EVs play vital roles in intercellular communication and are implicated in disease and cancer, positioning them as a promising resource for non-invasive diagnostics and therapeutics. However, there are technical challenges in rigor and reproducibility for analysis of EVs. To address these challenges, we have been working on the development of super-SILAC based EVs as universal internal standardStandards (UIS) for robust reproducible quantification of EV proteins. In this chapter, we have discussed different EV isolation methods and updated our recent progress in the proteome characterization of a mixture of EVs derived from different cancer cell lines and SILAC-labeled HeLa EVs. Our data suggest that it is highly promising to use super-SILAC EVs as UIS for reliable accurate quantification of EVs across large cohorts of clinical samples. Lastly, we have provided perspectives for future directions in EV proteomicsProteomics research toward clinical application.