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Analysis of Extracellular Vesicles Using Fluorescence Nanoparticle Tracking Analysis.

Pauline Carnell-Morris1, Dionne Tannetta2, Agnieszka Siupa1

  • 1Malvern Instruments Ltd, Amesbury, UK.

Methods in Molecular Biology (Clifton, N.J.)
|August 23, 2017
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Summary

This study details two fluorescence nanoparticle tracking analysis (fl-NTA) protocols for extracellular vesicle (EV) analysis. These methods, using membrane markers and antibody conjugates, address challenges in EV characterization.

Keywords:
ExosomesExtracellular vesiclesFluorescence nanoparticle tracking analysisMicrovesiclesQuantum dots

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Area of Science:

  • Biotechnology
  • Nanotechnology
  • Biochemistry

Background:

  • Extracellular vesicles (EVs) are crucial for intercellular communication.
  • Accurate characterization of EVs is essential for understanding their biological roles.
  • Fluorescence nanoparticle tracking analysis (fl-NTA) offers advanced capabilities for EV analysis.

Purpose of the Study:

  • To present and validate two distinct protocols for EV analysis using fl-NTA.
  • To highlight potential challenges and pitfalls encountered during fl-NTA of EVs.
  • To optimize fl-NTA settings for accurate EV sizing, counting, and phenotyping.

Main Methods:

  • Protocol 1: Utilizes CellMask Orange™ (CMO) for general labeling of plasma-derived EVs.
  • Protocol 2: Employs Qdot-conjugated antibodies for specific identification of syncytiotrophoblast (STB)-derived EVs.
  • Optimization involved using standardized STB-derived microvesicles (STBMV) and exosomes (STBEX) with known placental alkaline phosphatase (PLAP) antigen expression.

Main Results:

  • Demonstrated successful application of CMO for general EV labeling.
  • Showcased specific detection of STB-derived EVs using antibody-conjugated Qdots.
  • Identified critical parameters for optimizing fl-NTA camera settings for reliable EV quantification.

Conclusions:

  • fl-NTA provides a powerful tool for quantitative and phenotypic analysis of EVs.
  • The presented protocols offer robust methods for characterizing EVs from different sources.
  • Careful optimization and consideration of potential pitfalls are crucial for accurate fl-NTA results in EV research.