Single-Cell Spatial Analysis Identifies Regulators of Brain Tumor–Initiating Cells
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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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".