STEM-20MAPPING DYNAMICS OF CELL DIVISION AND GENE EXPRESSION AT THE SINGLE CELL LEVEL TO UNDERSTAND GLIOMA CELL STATES
Bibliographic record
Abstract
GBM tumors are recognized as being heterogeneous in nature, consisting of many different cell types and cell states. This presents a major hurdle during the treatment of tumors as radiation and chemotherapeutic agents preferentially target proliferative cells. Notably, quiescent and slowly-dividing cancer stem cells are thought to provide a reserve of cells that drive cancer relapse following treatment, making them more malignant than their rapidly dividing counter-parts. To determine whether quiescent and slow dividing stem cells are a common feature within patient GBMs we profiled the dynamics of cell proliferation. GBM tumors surgically resected from patients were used to generate Patient Derived Xenograft (PDX) models in NOD SCID mice. During the two months that it takes for xenografts to establish, mice were feed with regimes of thymidine analogues (BrdU, CldU or IdU) in their drinking water, effectively tagging dividing cells, to profile the history of each glioma cell. Adapting the regimes that mice were feed thymidine analogues facilitated the characterization of cancer cell proliferation. We observe diverse proliferation dynamics within each PDX mouse, representing rapidly dividing, slowly dividing and quiescent cells. To then establish a single cell gene expression map of proliferating versus non-proliferating glioma cell states we have generated a custom Glioma Specific Epigenetic Expression (GSEE) qPCR array. The GSEE qPCR array has been utilized for the analysis of gene expression at the single cell level in patient derived glioma cells. We have subsequently generated a core network of epigenetic modifier genes that reliably represent glioma cell state. This has enabled the mapping of glioma cell states with respect to dynamics of cell division, revealing novel therapeutic targets.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".