4.8 Review

Advanced Nanotechnologies for Extracellular Vesicle-Based Liquid Biopsy

Journal

ADVANCED SCIENCE
Volume 8, Issue 20, Pages -

Publisher

WILEY
DOI: 10.1002/advs.202102789

Keywords

cancer; disease diagnosis; extracellular vesicles; liquid biopsy; nanotechnologies

Funding

  1. National Key R&D Program of China [2019YFA0709300]
  2. National Natural Science Foundation of China [21875269, 82073390, 81702314]
  3. National Program for Special Support of Eminent Professionals, Beijing Science and Technology Nova Program [Z191100001119128]
  4. Beijing Municipal Science and Technology Project [Z191100006619081]
  5. Youth Innovation Promotion Association CAS [2017036]

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Extracellular vesicles (EVs) are recognized as a new source of biomarkers in liquid biopsy for their widespread presence in body fluids and ability to carry cargoes from disease-related cells. Significant advancements have been made in isolating, detecting, and analyzing EVs using nanotechnologies, including sensors with high sensitivity and machine learning applications. Future opportunities for next-generation nanotechnologies in EV detection are also highlighted.
Extracellular vesicles (EVs) are emerging as a new source of biomarkers in liquid biopsy because of their wide presence in most body fluids and their ability to load cargoes from disease-related cells. Owing to the crucial role of EVs in disease diagnosis and treatment, significant efforts have been made to isolate, detect, and analyze EVs with high efficiency. A recent overview of advanced EV detection nanotechnologies is discussed here. First, several key challenges in EV-based liquid biopsies are introduced. Then, the related pivotal advances in nanotechnologies for EV isolation based on physical features, chemical affinity, and the combination of nanostructures and chemical affinity are summarized. Next, a summary of high-sensitivity sensors for EV detection and advanced approaches for single EV detection are provided. Later, EV analysis is introduced in practical clinical scenarios, and the application of machine learning in this field is highlighted. Finally, future opportunities for the development of next-generation nanotechnologies for EV detection are presented.

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