Using Nanoplasmon-Enhanced Scattering and Low-Magnification Microscope Imaging to Quantify Tumor-Derived Exosomes

Meihua Wan, Pouya Amrollahi, Dali Sun, Christopher Lyon, Ye Hu

Research output: Contribution to journalArticle

Abstract

Infected or malignant cells frequently secrete more exosomes, leading to elevated levels of disease-associated exosomes in the circulation. These exosomes have the potential to serve as biomarkers for disease diagnosis and to monitor disease progression and treatment response. However, most exosome analysis procedures require exosome isolation and purification steps, which are usually time-consuming and labor-intensive, and thus of limited utility in clinical settings. This report describes a rapid procedure to analyze specific biomarkers on the outer membrane of exosomes without requiring separate isolation and purification steps. In this method, exosomes are captured on the surface of a slide by exosome-specific antibodies and then hybridized with nanoparticle-conjugated antibody probes specific to a disease. After hybridization, the abundance of the target exosome population is determined by analyzing low-magnification dark-field microscope (LMDFM) images of the bound nanoparticles. This approach can be easily adopted for research and clinical use to analyze membrane-associated exosome biomarkers linked to disease.

Original languageEnglish (US)
JournalJournal of visualized experiments : JoVE
Issue number147
DOIs
StatePublished - May 24 2019
Externally publishedYes

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Exosomes
Tumors
Microscopes
Scattering
Imaging techniques
Biomarkers
Neoplasms
Antibodies
Purification
Nanoparticles
Membranes
Personnel
Health Services Needs and Demand
Disease Progression

ASJC Scopus subject areas

  • Neuroscience(all)
  • Chemical Engineering(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)

Cite this

Using Nanoplasmon-Enhanced Scattering and Low-Magnification Microscope Imaging to Quantify Tumor-Derived Exosomes. / Wan, Meihua; Amrollahi, Pouya; Sun, Dali; Lyon, Christopher; Hu, Ye.

In: Journal of visualized experiments : JoVE, No. 147, 24.05.2019.

Research output: Contribution to journalArticle

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