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Cultivating Ex Vivo Patient-Derived Glioma Organoids Using a Tissue Chopper.
Marah Alsalkini1, Veronika Cibulková1, Maria Breun2
1Section Experimental Neurosurgery, Department of Neurosurgery, University Hospital Würzburg.
Journal of Visualized Experiments : Jove
|February 5, 2024
Summary
Generating patient-derived organoids (PDOs) for glioblastoma (GBM) research is faster and yields more organoids using an automated tissue chopper. This efficient method improves GBM modeling for drug screening.
Area of Science:
- Neuro-oncology
- Biotechnology
- Cancer Research
Background:
- Glioblastoma (GBM), IDH-wild type, CNS WHO grade 4, is an aggressive primary brain tumor with poor prognosis.
- Realistic ex vivo models are crucial for understanding GBM and developing treatments.
- Patient-derived organoid (PDO) models capture GBM heterogeneity but manual generation is inefficient.
Purpose of the Study:
- To present and evaluate an automated tissue chopper method for efficient GBM PDO production.
- To compare the time efficiency and PDO yield of automated versus manual dissection methods.
- To assess the quality of PDOs generated by both methods.
Main Methods:
- Tumor samples from GBM and astrocytoma patients were processed manually and using an automated tissue chopper at different angles.
- Organoids were cultured on an orbital shaker, and morphological changes were observed.
- Proliferation (Ki67) and apoptosis (CC3) were assessed via immunofluorescence after 6 weeks.
Main Results:
- The automated tissue chopper reduced manufacturing time by approximately 70%.
- Significantly higher PDO counts were observed from week 2 onwards with the automated method (e.g., week 4: 1195 vs. 784).
- Both methods yielded PDOs with comparable rates of proliferation and apoptosis.
Conclusions:
- Automated tissue choppers provide a more efficient and higher-yield method for GBM PDO production.
- This optimized PDO generation technique enhances potential for GBM drug and immunotherapy screening.
- The automated method addresses limitations of manual dissection, improving GBM modeling capabilities.
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