Single-nucleus profiling identifies accelerated oligodendrocyte precursor cell senescence in a mouse model of Down Syndrome
Bibliographic record
Abstract
Abstract Down Syndrome (DS), the most common genetic cause of intellectual disability, is associated with lifelong cognitive disability. However, the mechanisms by which triplication of human chromosome 21 genes drive neuroinflammation and cognitive dysfunction are poorly understood. Here, using the Ts65Dn mouse model of DS, we performed an integrated single-nucleus RNA and ATAC-seq analysis of the cortex. We identify cell type-specific transcriptional and chromatin-associated changes in the Ts65Dn cortex, including regulators of neuroinflammation, transcription and translation, myelination, and mitochondrial function. We discover enrichment of a senescence-associated transcriptional signature in Ts65Dn oligodendrocyte precursor cells (OPCs) and epigenetic changes consistent with a loss of heterochromatin. We find that senescence is restricted to a subset of cortical OPCs concentrated in deep cortical layers. Treatment of Ts65Dn mice with a senescence-reducing flavonoid rescues cortical OPC proliferation, restores microglial homeostasis, and improves contextual fear memory. Together, these findings suggest that cortical OPC senescence may be an important driver of neuropathology in DS.
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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 | 0.001 | 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 | 0.001 |
| 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".