Issue 17, 2023

Spatio-temporally deciphering peripheral nerve regeneration in vivo after extracellular vesicle therapy under NIR-II fluorescence imaging

Abstract

Extracellular vesicles (EVs) show potential as a therapeutic tool for peripheral nerve injury (PNI), promoting neurological regeneration. However, there are limited data on the in vivo spatio-temporal trafficking and biodistribution of EVs. In this study, we introduce a new non-invasive near-infrared fluorescence imaging strategy based on glucose-conjugated quantum dot (QDs-Glu) labeling to target and track EVs in a sciatic nerve injury rat model in real-time. Our results demonstrate that the injected EVs migrated from the uninjured site to the injured site of the nerve, with an increase in fluorescence signals detected from 4 to 7 days post-injection, indicating the release of contents from the EVs with therapeutic effects. Immunofluorescence and behavioral tests revealed that the EV therapy promoted nerve regeneration and functional recovery at 28 days post-injection. We also found a relationship between functional recovery and the NIR-II fluorescence intensity change pattern, providing novel evidence for the therapeutic effects of EV therapy using real-time NIR-II imaging at the live animal level. This approach initiates a new path for monitoring EVs in treating PNI under in vivo NIR-II imaging, enhancing our understanding of the efficacy of EV therapy on peripheral nerve regeneration and its mechanisms.

Graphical abstract: Spatio-temporally deciphering peripheral nerve regeneration in vivo after extracellular vesicle therapy under NIR-II fluorescence imaging

Supplementary files

Article information

Article type
Paper
Submitted
20 Feb 2023
Accepted
29 Mar 2023
First published
17 Apr 2023

Nanoscale, 2023,15, 7991-8005

Spatio-temporally deciphering peripheral nerve regeneration in vivo after extracellular vesicle therapy under NIR-II fluorescence imaging

Y. Wang, H. Sheng, M. Cong, W. Wang, Q. He, H. Li, S. Li, J. Zhang, Y. Chen, S. Guo, L. Fang, S. Pluchino, E. Biskup, M. Artemyev, F. Chen, Y. Li, J. Chen, S. Feng and Y. Wo, Nanoscale, 2023, 15, 7991 DOI: 10.1039/D3NR00795B

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