Ependymal cells undergo astrocyte‐like reactivity in response to neuroinflammation
Bibliographic record
Abstract
Abstract Ependymal cells form a specialized brain–cerebrospinal fluid (CSF) interface and regulate local CSF microcirculation. It is becoming increasingly recognized that ependymal cells assume a reactive state in response to aging and disease, including conditions involving hypoxia, hydrocephalus, neurodegeneration, and neuroinflammation. Yet what transcriptional signatures govern these reactive states and whether this reactivity shares any similarities with classical descriptions of glial reactivity (i.e., in astrocytes) remain largely unexplored. Using single‐cell transcriptomics, we interrogated this phenomenon by directly comparing the reactive ependymal cell transcriptome to the reactive astrocyte transcriptome using a well‐established model of autoimmune‐mediated neuroinflammation (MOG 35‐55 EAE). In doing so, we unveiled core glial reactivity‐associated genes that defined the reactive ependymal cell and astrocyte response to MOG 35‐55 EAE. Interestingly, known reactive astrocyte genes from other CNS injury/disease contexts were also up‐regulated by MOG 35‐55 EAE ependymal cells, suggesting that this state may be conserved in response to a variety of pathologies. We were also able to recapitulate features of the reactive ependymal cell state acutely using a classic neuroinflammatory cocktail (IFNγ/LPS) both in vitro and in vivo. Taken together, by comparing reactive ependymal cells and astrocytes, we identified a conserved signature underlying glial reactivity that was present in several neuroinflammatory contexts. Future work will explore the mechanisms driving ependymal reactivity and assess downstream functional consequences. image
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 | 0.000 |
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".