Spatially resolved single-cell analysis uncovers protein kinase Cδ-expressing microglia with anti-tumor activity in glioblastoma
Bibliographic record
Abstract
Abstract Glioblastoma (GBM) is a brain tumor that poses a formidable challenge to treatment options available. The tumor microenvironment (TME) in GBM is highly complex, marked by immunosuppression and cellular heterogeneity. Understanding the cellular interactions and their spatial organization within the TME is crucial for developing effective therapeutic strategies. In this study, we integrated single-cell RNA sequencing and spatial transcriptomics in a GBM mouse model to unravel the spatial landscape of the brain TME. We identified a previously unrecognized microglia subtype expressing protein kinase Cδ (PKCδ) associated with potent anti-tumor functions. The presence of PKCδ-expressing microglia was confirmed in resected human GBM specimens. Elevating tumoral PKCδ expression using niacin or adeno-associated virus in mice enhanced the phagocytosis of GBM cells by microglia in culture and increased the lifespan of mice with intracranial GBM. These findings were corroborated in analyses of the TCGA GBM datasets where low PKCδ samples showed negative pathway enrichment for apoptosis, phagocytosis, and immune signaling pathways, as well as lower levels of immune cell infiltration overall. Our study underscores the importance of integrating spatial context to unravel the TME, resulting in the identification of previously unrecognized subsets of microglia with anti-tumor functions. These findings provide valuable insights for advancing innovative immunotherapeutic strategies in GBM.
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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) | 0.000 | 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 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".