Trp2 Peptide-Assembled Nanoparticles with Intrinsically Self-Chelating 64 Cu Properties for PET Imaging Tracking and Dendritic Cell-Based Immunotherapy against Melanoma.
Zhesheng HeHuiju JiaMiaomiao ZhengHuangwei WangWenjiang YangLiang GaoZhiyong ZhangJingquan XueBaixuan XuWenzhi YangGengmei XingXueyun GaoXueyun GaoPublished in: ACS applied bio materials (2021)
Dendritic cell-based immunotherapy, in which the antigen is effectively delivered to dendritic cells and then the dendritic cells stimulated by the antigen migrate to draining lymph nodes (DLNs) to induce the CD8 + T-cell immune response, shows great promise for tumor immunotherapy. In this study, we used coassembled nanoparticles formed by Trp2 antigen and the conjugates of short-chain poly(ethylene glycol) (PEG) and pyropheophorbide-A (PPa) (Trp2/PPa-PEGm) to deliver Trp2 to DCs. Intrinsically self-chelating 64 Cu of coassemblies could be used to sensitively image the migration of DCs in vivo by positron emission tomography (PET) imaging. The coassemblies of the Trp2 antigen were efficiently engulfed by DCs without causing DC cytotoxicity in vitro and induced DC maturation. After injection of DCs labeled by coassemblies of the Trp2 antigen, the homing of DCs to DLNs in vivo could be sensitively observed by PET imaging. The C57BL/6 mice injected with DCs containing the Trp2/PPa-PEGm NP showed antigen-specific immune responses including enhanced interferon-γ (IFN-γ) production, splenocyte proliferation, and percentage of IFN-γ-secreting CD8 + T cells. In addition, C57BL/6 mice inoculated with B16-F10 tumor cells showed delayed tumor growth after immunization with the Trp2/PPa-PEGm NP-labeled DC vaccine and enhanced infiltration of CD8 + T cells in tumors.
Keyphrases
- dendritic cells
- pet imaging
- immune response
- positron emission tomography
- regulatory t cells
- computed tomography
- lymph node
- toll like receptor
- high fat diet induced
- oxidative stress
- type diabetes
- inflammatory response
- insulin resistance
- deep learning
- artificial intelligence
- machine learning
- diabetic rats
- rectal cancer
- skeletal muscle