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Stahl et al. discovered exosomes in 1983, but the exosomes were initially considered waste products released from the...

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Characterizing Extracellular Vesicles from Biological Fluids
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Published on: February 28, 2025

Human Extracellular Vesicles, Metabolites, and Metabolomics: A Comprehensive Review.

Mohammed A Al-Zubaidi1

  • 1Department of Clinical Laboratory Sciences, College of Pharmacy, Mustansiriyah University, Baghdad, Iraq.

Omics : a Journal of Integrative Biology
|July 14, 2026
PubMed
Summary

Extracellular vesicle (EV) metabolomics offers insights into cell communication and disease. Challenges in EV isolation and analysis are being addressed by advanced techniques and multi-omics integration for clinical applications.

Keywords:
biology systemsbiomarkersextracellular vesiclesextracellular vesicles challengesmetabolomics

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Area of Science:

  • Biochemistry
  • Cell Biology
  • Systems Biology

Background:

  • Metabolomics provides a systems-level view of biochemical pathways and metabolic phenotypes.
  • Extracellular vesicles (EVs) are key mediators of intercellular communication, carrying metabolites reflective of their parent cell.
  • EVs are rich in lipids, proteins, nucleic acids, and metabolites involved in signaling and disease pathogenesis.

Purpose of the Study:

  • To critically synthesize current knowledge in EV metabolomics.
  • To address methodological and analytical challenges in EV metabolomics research.
  • To explore emerging clinical applications and future research directions.

Main Methods:

  • Review of analytical technologies for EV metabolomics.
  • Examination of statistical and computational approaches.
  • Discussion of multi-omics integration and machine learning strategies.

Main Results:

  • EV metabolomics is methodologically and analytically challenging due to vesicle heterogeneity, isolation purity, and detection sensitivity issues.
  • Methodological variability, contamination, and study design limitations impact reproducibility and translation.
  • Multi-omics integration and machine learning show promise for biomarker discovery and biological system interpretation.

Conclusions:

  • Advancements in analytical technology and computational approaches are crucial for overcoming EV metabolomics challenges.
  • Addressing methodological variability and improving study design are essential for reproducible and translatable research.
  • Future research should focus on multi-omics integration and machine learning to advance clinical applications in translational medicine.