Single-Cell Spatial Analysis Identifies Regulators of Brain Tumor–Initiating Cells
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Glioblastomas (GBM) are aggressive brain tumors with extensive intratumoral heterogeneity that contributes to treatment resistance. Spatial characterization of GBMs could provide insights into the role of the brain tumor microenvironment in regulating intratumoral heterogeneity. Here, we performed spatial transcriptomic and single-cell analyses of the mouse and human GBM microenvironment to dissect the impact of distinct anatomical regions of brains on GBM. In a syngeneic GBM mouse model, spatial transcriptomics revealed that numerous extracellular matrix (ECM) molecules, including biglycan, were elevated in areas infiltrated with brain tumor-initiating cells (BTIC). Single-cell RNA sequencing and single-cell assay for transposase-accessible chromatin using sequencing showed that ECM molecules were differentially expressed by GBM cells based on their differentiation and cellular programming phenotypes. Exogeneous biglycan or overexpression of biglycan resulted in a higher proliferation rate of BTICs, which was associated mechanistically with low-density lipoprotein receptor-related protein 6 (LRP6) binding and activation of the Wnt/β-catenin pathway. Biglycan-overexpressing BTICs developed into larger tumors and displayed mesenchymal phenotypes when implanted intracranially in mice. This study points to the spatial heterogeneity of ECM molecules in GBM and suggests that the biglycan-LRP6 axis could be a therapeutic target to curb tumor growth. SIGNIFICANCE: Characterization of the spatial heterogeneity of glioblastoma identifies regulators of brain tumor-initiating cells and tumor growth that could serve as candidates for therapeutic interventions to improve the prognosis of patients.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it