Senescent-like microglia limit remyelination through the senescence associated secretory phenotype
Bibliographic record
Abstract
The capacity to regenerate myelin in the central nervous system diminishes with age. This decline is particularly evident in multiple sclerosis (MS), a chronic demyelinating disease. Whether cellular senescence, a hallmark of aging, contributes to remyelination impairment remains unknown. Here, we show that senescent cells accumulate within demyelinated lesions after injury, and treatments with senolytics enhances remyelination in young and middle-aged mice but not aged mice. In young mice, we observe the upregulation of senescence-associated transcripts, primarily in microglia and macrophages, after demyelination, followed by a reduction during remyelination. However, in aged mice, senescence-associated factors persist within lesions, correlating with inefficient remyelination. Proteomic analysis of the senescence-associated secretory phenotype (SASP) reveals elevated levels of CCL11/Eotaxin-1 in lesions of aged mice, which is found to inhibit oligodendrocyte maturation. These results suggest therapeutic targeting of SASP components, such as CCL11, may improve remyelination in aging and MS. The impact of senescent cells on remyelination is unknown. Here, the authors show that treatment with senolytics following demyelination enhances remyelination in young, but not aged mice, and these effects are mediated by senescence-associated secretory phenotype factors including CCL11.
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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".