T-cell infiltrates and microglia adopt long-term gene signature changes leading to age-specific responses to traumatic brain injury in mice
Bibliographic record
Abstract
Abstract Aged traumatic brain injury (TBI) patients suffer increased mortality and long-term neurocognitive/neuropsychiatric morbidity than younger patients. Microglia, the resident macrophages of the brain, are complicit in both. We hypothesized that aged microglia would fail to return to a homeostatic state after TBI and adopt a long-term, injury-associated state within aged brains compared to young brains after TBI. Young and aged male C57BL/6 mice underwent TBI via controlled cortical impact vs. sham injury and were sacrificed four months post-TBI. We utilized single-cell RNA sequencing to examine age-associated cellular responses after TBI. Brains were harvested with CD45+ cells isolated via florescence-activated cell sorting. cDNA libraries were prepared via the 10x Genomics Chromium Single Cell 3’ Reagent Kit, followed by sequencing on a HiSeq 4000 instrument and computation analyses. Post-injury, aged mice demonstrated a proportional decrease in homeostatic microglia, and greater increased infiltrating T cells compared to young-adult mice. Of note, aged mice post-injury had a subpopulation of age-specific, immune-inflammatory microglia resembling gene profiles of neurodegenerative disease-associated microglia with enriched pathways involving in leukocyte recruitment. Contrastingly, post-injury, aged mice demonstrate a heterogenous T-cell infiltration with gene profiles corresponding to CD8 effector memory, CD8 native-like, CD4, and double-negative T cells and enriched pathways such as macromolecule synthesis. Taken together, our data showed that age-specific gene signature changes in the T-cell infiltrates and the microglial subpopulation contribute to increased vulnerability of the aged brain to TBI. Supported by R01 GM130662
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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 | 0.001 |
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".