Profiling Glioma Stem Cell Dynamics via 3D-based Cell Cycle Reporter Assays
Bibliographic record
Abstract
Summary Successful containment of unwanted cell cycle progression in tumours such as glioblastoma (GBM) requires targeted therapeutic approaches which rely on understanding cell cycle dynamics in response to microenvironmental stimuli. Glioma Stem Cells (GSCs) can drive tumour initiation, recurrence, therapy resistance, and are often attributed to the heterogeneity and plasticity of GBM. In vitro models using patient-derived GSCs provide a life relevant tool for exploration of complex molecular mechanisms underlying the aggressive characteristics of GBM. Introduction of 3D tissue culture systems permits the study of spatial complexity of the tumour mass and enables control over diverse conditions within the surrounding microenvironment. This chapter demonstrates detailed methods to study spatio-temporal changes to the cell cycle dynamics using available fluorescent cell cycle reporter systems in combination with bioinformatics-based signal intensity and localization analysis. We present a successful approach that investigates the 3D cell cycle dynamics of GSC populations. This approach utilizes GBM neurosphere and organoid cultures, which are assessed over time and under therapeutic pressure. These models can be further explored, manipulated, and customized to serve specific experimental designs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".