Issue 15, 2022

Real-time in vivo imaging reveals specific nanoparticle target binding in a syngeneic glioma mouse model

Abstract

Nanomaterial-based drug delivery is a promising strategy for glioma treatment. However, the detailed dynamics of nanoparticles in solid glioma are still a mystery, including their intratumoral infiltration depth, penetration, retention time, and distribution. Revealing these processes in detail requires repeated intravital imaging of the corresponding brain tumor regions over time during glioma growth. Hereby, we established a syngeneic orthotopic cerebral glioma mouse model by combining the chronic cranial window and two-photon microscopy. Thus, we were able to investigate the dynamics of the nanoparticles during long-term glioma growth. Three hours after the intravenous (i.v.) injection of integrin αVβ3 binding conjugated silicon nanoparticles (SNPs-PEG-RGD-FITC), green nanoparticles had already infiltrated the brain glioma, and then more nanoparticles penetrated into the solid brain tumor and were retained for at least 8 days. However, the amount of control SNPs-PEG-FITC that infiltrated into the solid brain tumor was very low. Moreover, we found that SNPs-PEG-RGD-FITC were not only located in the tumor border but could also infiltrate into the core region of the solid tumor. In vitro assay also confirmed the high binding affinity between GL-261-Tdtomato cells and SNPs-PEG-RGD-FITC. Our results indicate that SNPs-PEG-RGD-FITC has high penetration and retention in a solid glioma and our model provides novel ideas for the investigation of nanoparticle dynamics in brain tumors.

Graphical abstract: Real-time in vivo imaging reveals specific nanoparticle target binding in a syngeneic glioma mouse model

Supplementary files

Article information

Article type
Communication
Submitted
17 Nov 2021
Accepted
08 Feb 2022
First published
09 Feb 2022

Nanoscale, 2022,14, 5678-5688

Real-time in vivo imaging reveals specific nanoparticle target binding in a syngeneic glioma mouse model

W. Zhang, X. Chen, D. Ding, G. Zhang, Z. Zhu, X. Yang, M. Li, L. Liang, X. Shi, T. Wang and R. Gao, Nanoscale, 2022, 14, 5678 DOI: 10.1039/D1NR07591H

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