MODL-26. A PROTOCOL TO ADAPT GLIOBLASTOMA IN VITRO CULTURE SYSTEMS INTO ORGANOID MODELS USING NEUROCULT™ NS-A PROLIFERATION MEDIUM
Bibliographic record
Abstract
Abstract Glioblastoma (GBM) is a malignant and aggressive brain tumor that is difficult to treat due to its heterogeneity. Traditional in vitro models (i.e. monolayers and tumorspheres) do not recapitulate this heterogeneity, leading to difficulty assessing new therapeutic strategies. Culturing GBM cells as organoids may provide a better method to retain the phenotype of the parent tumor. Here, we present a protocol for generating GBM organoids using NeuroCult™ NS-A Proliferation Medium, a medium commonly used for tumorsphere cultures. Two methods were assessed for the initial stage of formation, each suitable depending on throughput and cell source. In the first method, tumorspheres were dissociated and single cells were mixed with Growth Factor Reduced Matrigel® at a 1:4 ratio to form 15 μL droplets with 1000 cells per droplet. In the second method, 9000 cells from dissociated tumorspheres were seeded per well in a 96-well U-bottom plate and cultured for 5 days to form aggregates. On day 6, the aggregates were embedded in 15 μL Matrigel® droplets. In both methods, polymerized droplets were then cultured in suspension in stationary 6-well plates for 3 days, then maintained on an orbital shaker with full-medium changes every 2 - 3 days. Organoids were cryosectioned and processed for immunofluorescence at 5 - 6 weeks. In vivo, GBM tumors display an architecture containing regions of high and low nutrient and oxygen availability around a necrotic core. GBM organoids generated using this protocol exhibited distinct areas of proliferation near the organoid rim (Ki67+) and hypoxia (CA9+) near the core, mimicking this in vivo architecture (n = 3 GBM cell lines). Organoids grew to 2 - 4 mm in diameter and expressed glioma markers including TUJ1, Nestin, SOX2, OLIG2, and GFAP. These data show that NeuroCult™ NS-A Proliferation Medium can be used to adapt traditional GBM culture systems into organoids.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.014 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".