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

• Published in: Nanoscale 2023-05, Vol.15 (17), p.7991-85
• Main Authors: Wang, Yueming, Sheng, Huaixuan, Cong, Meng, Wang, Wenjin, He, Qianru, Li, Huizhu, Li, Shunyao, Zhang, Jian, Chen, Yuzhou, Guo, Shuaicheng, Fang, Lu, Pluchino, Stefano, Biskup, Ewelina, Artemyev, Mikhail, Chen, Fuchun, Li, Yunxia, Chen, Jun, Feng, Sijia, Wo, Yan
• Format: Article
• ISSN: 2040-3364; 2040-3372
• DOI: 10.1039/d3nr00795b

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. In this study, a new non-invasive near-infrared fluorescence imaging strategy based on glucose-conjugated quantum dots (QDs-Glu) labeling to target and track EVs in a sciatic nerve injury rat model in real-time was introduced.