Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/151486
Title: Plasmonic sensors for extracellular vesicle analysis : from scientific development to translational research
Authors: Chin, Lip Ket
Son, Taehwang
Hong, Jae-Sang
Liu, Ai Qun
Skog, Johan
Castro, Cesar M.
Weissleder, Ralph
Lee, Hakho
Im, Hyungsoon
Keywords: Engineering
Issue Date: 2020
Source: Chin, L. K., Son, T., Hong, J., Liu, A. Q., Skog, J., Castro, C. M., Weissleder, R., Lee, H. & Im, H. (2020). Plasmonic sensors for extracellular vesicle analysis : from scientific development to translational research. ACS Nano, 14(11), 14528-14548. https://dx.doi.org/10.1021/acsnano.0c07581
Journal: ACS Nano
Abstract: Extracellular vesicles (EVs), actively shed from a variety of neoplastic and host cells, are abundant in blood and carry molecular markers from parental cells. For these reasons, EVs have gained much interest as biomarkers of disease. Among a number of different analytical methods that have been developed, surface plasmon resonance (SPR) stands out as one of the ideal techniques given its sensitivity, robustness, and ability to miniaturize. In this Review, we compare different SPR platforms for EV analysis, including conventional SPR, nanoplasmonic sensors, surface-enhanced Raman spectroscopy, and plasmonic-enhanced fluorescence. We discuss different surface chemistries used to capture targeted EVs and molecularly profile their proteins and RNAs. We also highlight these plasmonic platforms' clinical applications, including cancers, neurodegenerative diseases, and cardiovascular diseases. Finally, we discuss the future perspective of plasmonic sensing for EVs and their potentials for commercialization and clinical translation.
URI: https://hdl.handle.net/10356/151486
ISSN: 1936-086X
DOI: 10.1021/acsnano.0c07581
Rights: This document is the Accepted Manuscript version of a Published Work that appeared in final form in ACS Nano, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see https://doi.org/10.1021/acsnano.0c07581
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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