Direct Reprogramming of Glioblastoma Cells into Neurons Using Small Molecules
Bibliographic record
Abstract
Glioblastoma multiforme, a type of deadly brain cancer, originates most commonly from astrocytes found in the brain. Current multimodal treatments for glioblastoma minimally increase life expectancy, but significant advancements in prognosis have not been made in the past few decades. Here we investigate cellular reprogramming for inhibiting the aggressive proliferation of glioblastoma cells. Cellular reprogramming converts one differentiated cell type into another type based on the principles of regenerative medicine. In this study, we used cellular reprogramming to investigate whether small molecule mediated reprogramming could convert glioblastoma cells into neurons. We investigated a novel method for reprogramming U87MG human glioblastoma cells into terminally differentiated neurons using a small molecule cocktail consisting of forskolin, ISX9, CHIR99021 I-BET 151, and DAPT. Treating U87MG glioblastoma cells with this cocktail successfully reprogrammed the malignant cells into early neurons over 13 days. The reprogrammed cells displayed morphological and immunofluorescent characteristics associated with neuronal phenotypes. Genetic analysis revealed that the chemical cocktail upregulates the Ngn2, Ascl1, Brn2, and MAP2 genes, resulting in neuronal reprogramming. Furthermore, these cells displayed decreased viability and lacked the ability to form high numbers of tumor-like spheroids. Overall, this study validates the use of a novel small molecule cocktail for reprogramming glioblastoma into nonproliferating neurons.
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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".