Optimization, Characterization, and Comparison of Two Luciferase-Expressing Mouse Glioblastoma Models.
Louis T RodgersJulia Annette SchulzBryan J MaloneyAnika M S HartzBjörn BauerPublished in: Cancers (2024)
Glioblastoma (GBM) is the most aggressive brain cancer. To model GBM in research, orthotopic brain tumor models, including syngeneic models like GL261 and genetically engineered mouse models like TRP, are used. In longitudinal studies, tumor growth and the treatment response are typically tracked with in vivo imaging, including bioluminescence imaging (BLI), which is quick, cost-effective, and easily quantifiable. However, BLI requires luciferase-tagged cells, and recent studies indicate that the luciferase gene can elicit an immune response, leading to tumor rejection and experimental variation. We sought to optimize the engraftment of two luciferase-expressing GBM models, GL261 Red-FLuc and TRP-mCherry-FLuc, showing differences in tumor take, with GL261 Red-FLuc cells requiring immunocompromised mice for 100% engraftment. Immunohistochemistry and MRI revealed distinct tumor characteristics: GL261 Red-FLuc tumors were well-demarcated with densely packed cells, high mitotic activity, and vascularization. In contrast, TRP-mCherry-FLuc tumors were large, invasive, and necrotic, with perivascular invasion. Quantifying the tumor volume using the HALO ® AI analysis platform yielded results comparable to manual measurements, providing a standardized and efficient approach for the reliable, high-throughput analysis of luciferase-expressing tumors. Our study highlights the importance of considering tumor engraftment when using luciferase-expressing GBM models, providing insights for preclinical research design.
Keyphrases
- induced apoptosis
- high throughput
- immune response
- cell cycle arrest
- magnetic resonance imaging
- high resolution
- magnetic resonance
- mouse model
- stem cells
- type diabetes
- computed tomography
- endoplasmic reticulum stress
- single cell
- mass spectrometry
- intensive care unit
- dna methylation
- resting state
- artificial intelligence
- transcription factor
- mesenchymal stem cells
- copy number
- young adults
- cell cycle
- brain injury
- machine learning
- papillary thyroid
- deep learning
- toll like receptor
- data analysis