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Visualization of head and neck cancer models with a triple fusion reporter gene

机译:用三重融合报告基因可视化头颈癌模型

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The development of experimental animal models for head and neck tumors generally rely on the bioluminescence imaging to achieve the dynamic monitoring of the tumor growth and metastasis due to the complicated anatomical structures. Since the bioluminescence imaging is largely affected by the intracellular luciferase expression level and external D-luciferin concentrations, its imaging accuracy requires further confirmation. Here, a new triple fusion reporter gene, which consists of a herpes simplex virus type 1 thymidine kinase (TK) gene for radioactive imaging, a far-red fluorescent protein (mLumin) gene for fluorescent imaging, and a firefly luciferase gene for bioluminescence imaging, was introduced for in vivo observation of the head and neck tumors through multi-modality imaging. Results show that fluorescence and bioluminescence signals from mLumin and luciferase, respectively, were clearly observed in tumor cells, and TK could activate suicide pathway of the cells in the presence of nucleotide analog-ganciclovir (GCV), demonstrating the effectiveness of individual functions of each gene. Moreover, subcutaneous and metastasis animal models for head and neck tumors using the fusion reporter gene-expressing cell lines were established, allowing multi-modality imaging in vivo. Together, the established tumor models of head and neck cancer based on the newly developed triple fusion reporter gene are ideal for monitoring tumor growth, assessing the drug therapeutic efficacy and verifying the effectiveness of new treatments.
机译:由于复杂的解剖结构,用于头颈部肿瘤的实验动物模型的开发通常依靠生物发光成像来实现对肿瘤生长和转移的动态监测。由于生物发光成像很大程度上受细胞内萤光素酶表达水平和外部D-萤光素浓度的影响,因此其成像精度需要进一步确认。在这里,一个新的三重融合报告基因,由用于放射性成像的单纯疱疹病毒1型胸苷激酶(TK)基因,用于荧光成像的远红荧光蛋白(mLumin)基因和用于生物发光成像的萤火虫荧光素酶基因组成通过多模态成像技术引入,用于体内观察头颈部肿瘤。结果表明,在肿瘤细胞中分别清楚地观察到了来自umumin和荧光素酶的荧光和生物发光信号,并且在核苷酸类似物更昔洛韦(GCV)存在的情况下,TK可以激活细胞的自杀途径,证明了每种功能的有效性基因。此外,使用表达融合报告基因的细胞系建立了头颈部肿瘤的皮下和转移动物模型,从而可以在体内进行多模态成像。总之,基于新开发的三重融合报告基因建立的头颈癌肿瘤模型非常适合监测肿瘤生长,评估药物治疗效果并验证新疗法的有效性。

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