Reawakening inflammation in the chronically injured spinal cord using lipopolysaccharide induces diverse microglial states
Bibliographic record
Abstract
BACKGROUND: Rehabilitative training is an effective method to promote recovery following spinal cord injury (SCI), with lower training efficacy observed in the chronic stage. The increased training efficacy during the subacute period is associated with a shift towards a more adaptive or proreparative state induced by the SCI. A potential link is SCI-induced inflammation, which is elevated in the subacute period, and, as injection of lipopolysaccharide (LPS) alongside training improves recovery in chronic SCI, suggesting LPS could reopen a window of plasticity late after injury. Microglia may play a role in LPS-mediated plasticity as they react to LPS and are implicated in facilitating recovery following SCI. However, it is unknown how microglia change in response to LPS following SCI to promote neuroplasticity. MAIN BODY: Here we used single-cell RNA sequencing to examine microglial responses in subacute and chronic SCI with and without an LPS injection. We show that subacute SCI is characterized by a disease-associated microglial (DAM) signature, while chronic SCI is highly heterogeneous, with both injury-induced and homeostatic states. DAM states exhibit predicted metabolic pathway activity and neuronal interactions that are associated with potential mediators of plasticity. With LPS injection, microglia shifted away from the homeostatic signature to a primed, translation-associated state and increased DAM in degenerated tracts caudal to the injury. CONCLUSION: Microglial states following an inflammatory stimulus in chronic injury incompletely recapitulate the subacute injury environment, showing both overlapping and distinct microglial signatures across time and with LPS injection. Our results contribute to an understanding of how microglia and LPS-induced neuroinflammation contribute to plasticity following SCI.
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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 | 0.001 |
| 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".