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.
Bibliographical noteFunding Information:
Funding: This article was supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI) funded by the Ministry of Health & Welfare, Republic of Korea (Grant No. HI14C3477) and the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (Grant No. NRF-2017R1E1A1A01075147).
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.
- extracellular vesicles
- signal analysis
- surface-enhanced Raman spectroscopy
ASJC Scopus subject areas
- Analytical Chemistry
- Chemistry (miscellaneous)
- Molecular Medicine
- Pharmaceutical Science
- Drug Discovery
- Physical and Theoretical Chemistry
- Organic Chemistry