Multiparametric Signature of Glioblastoma Differentiation Revealed by Imaging of Cellular Epigenetic Landscapes
Bibliographic record
Abstract
Abstract The resistance of Glioblastoma (GBM) to conventional cytotoxic drugs has prompted novel therapeutic strategies, including differentiating tumor propagating cells (TPCs) into less tumorigenic cells using small molecule inducers of TPC differentiation. However, high-throughput screening for such molecules is hampered by the lack of robust markers of GBM differentiation. To obtain a signature of differentiated TPCs, we developed “Microscopic Imaging of Epigenetic Landscapes” (MIEL), which captures patterns of nuclear staining for epigenetic marks to derive feature-fingerprints of individual cells. We confirmed MIEL’s ability to accurately distinguish multiple cell fates and identified a multiparametric epigenetic signature of differentiated TPCs. Critically, we validated epigenetic imaging-based signature using global gene expression thus providing the proof of principle for the MIEL’s ability to select and prioritize small molecules, which induce TPC differentiation.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 0.000 |
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".