Extracellular Vesicle Identification Using Label-Free Surface-Enhanced Raman Spectroscopy: Detection and Signal Analysis Strategies

Autor: Dongkwon Seo, Hyunku Shin, Yeonho Choi
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Materials science
Pharmaceutical Science
Nanotechnology
02 engineering and technology
Review
Exosomes
Spectrum Analysis
Raman
Extracellular vesicles
signal analysis
Analytical Chemistry
Cell Line
lcsh:QD241-441
Pattern identification
03 medical and health sciences
Extracellular Vesicles
Mice
Deep Learning
Electromagnetic Fields
lcsh:Organic chemistry
Cell Line
Tumor
Drug Discovery
Animals
Cluster Analysis
Humans
Physical and Theoretical Chemistry
Least-Squares Analysis
Spectral data
030304 developmental biology
Label free
Titanium
0303 health sciences
Signal processing
Organic Chemistry
Liquid Biopsy
Extracellular vesicle
Surface-enhanced Raman spectroscopy
Surface Plasmon Resonance
021001 nanoscience & nanotechnology
surface-enhanced Raman spectroscopy
Nanostructures
Chemistry (miscellaneous)
Microscopy
Electron
Scanning
Molecular Medicine
Identification (biology)
Gold
0210 nano-technology
Biomarkers
Zdroj: Molecules
Molecules, Vol 25, Iss 5209, p 5209 (2020)
ISSN: 1420-3049
Popis: Extracellular vesicles (EVs) have been widely investigated as promising biomarkers for the liquid biopsy of diseases, owing to their countless roles in biological systems. Furthermore, with the notable progress of exosome research, the use of label-free surface-enhanced Raman spectroscopy (SERS) to identify and distinguish disease-related EVs has emerged. Even in the absence of specific markers for disease-related EVs, label-free SERS enables the identification of unique patterns of disease-related EVs through their molecular fingerprints. In this review, we describe label-free SERS approaches for disease-related EV pattern identification in terms of substrate design and signal analysis strategies. We first describe the general characteristics of EVs and their SERS signals. We then present recent works on applied plasmonic nanostructures to sensitively detect EVs and notable methods to interpret complex spectral data. This review also discusses current challenges and future prospects of label-free SERS-based disease-related EV pattern identification.
Databáze: OpenAIRE
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