Directly visualized glioblastoma-derived extracellular vesicles transfer RNA to microglia/macrophages in the brain
Bibliographic record
Abstract
BACKGROUND: To understand the ability of gliomas to manipulate their microenvironment, we visualized the transfer of vesicles and the effects of tumor-released extracellular RNA on the phenotype of microglia in culture and in vivo. METHODS: Extracellular vesicles (EVs) released from primary human glioblastoma (GBM) cells were isolated and microRNAs (miRNAs) were analyzed. Primary mouse microglia were exposed to GBM-EVs, and their uptake and effect on proliferation and levels of specific miRNAs, mRNAs, and proteins were analyzed. For in vivo analysis, mouse glioma cells were implanted in the brains of mice, and EV release and uptake by microglia and monocytes/macrophages were monitored by intravital 2-photon microscopy, immunohistochemistry, and fluorescence activated cell sorting analysis, as well as RNA and protein levels. RESULTS: Microglia avidly took up GBM-EVs, leading to increased proliferation and shifting of their cytokine profile toward immune suppression. High levels of miR-451/miR-21 in GBM-EVs were transferred to microglia with a decrease in the miR-451/miR-21 target c-Myc mRNA. In in vivo analysis, we directly visualized release of EVs from glioma cells and their uptake by microglia and monocytes/macrophages in brain. Dissociated microglia and monocytes/macrophages from tumor-bearing brains revealed increased levels of miR-21 and reduced levels of c-Myc mRNA. CONCLUSIONS: Intravital microscopy confirms the release of EVs from gliomas and their uptake into microglia and monocytes/macrophages within the brain. Our studies also support functional effects of GBM-released EVs following uptake into microglia, associated in part with increased miRNA levels, decreased target mRNAs, and encoded proteins, presumably as a means for the tumor to manipulate its environs.
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 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 | 0.001 |
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".