Single-cell multimodal glioma analyses reveal epigenetic regulators of cellular plasticity and environmental stress response
Bibliographic record
Abstract
ABSTRACT Glioma intratumoral heterogeneity enables adaptation to challenging microenvironments and contributes to universal therapeutic resistance. Here, we integrated 914 single-cell DNA methylomes, 55,284 single-cell transcriptomes, and bulk multi-omic profiles across 11 adult IDH-mutant or IDH-wild-type gliomas to delineate sources of intratumoral heterogeneity. We found that local DNA methylation instability, or epimutation burden, was elevated in more aggressive tumors, reflected intratumoral variability, linked with transcriptional disruption, and associated with environmental stress response. We show that the activation of cell-state specific transcription factors is impacted by epimutations and that loosened epigenetic control may facilitate cellular plasticity. Our analyses support that somatic copy number alterations (SCNAs) promote epigenetic instability and that SCNAs largely precede epigenetic and transcriptomic diversification during glioma evolution. We confirmed the link between genetic and epigenetic instability by analyzing larger cohorts of bulk longitudinally collected and spatially separated DNA methylation data. Increased DNA methylation instability was associated with accelerated disease progression, and recurrently selected DNA methylation changes were enriched for environmental stress response pathways. Our work provides an integrative framework to better understand glioma evolution and highlights the importance of epigenetic heterogeneity in shaping therapeutic response.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".