Astrocytic Ryk signaling coordinates scarring and wound healing after spinal cord injury
Bibliographic record
Abstract
Wound healing after spinal cord injury involves highly coordinated interactions among multiple cell types, which is poorly understood. Astrocytes play a central role in creating a border against the non-neural lesion core. To do so, astrocytes undergo dramatic morphological changes by first thickening the processes and then elongating and overlap them. We show here show that the expression of a cell-surface receptor, Ryk, is induced in astrocytes after injury in both rodent and human spinal cord. Astrocyte-specific knockout of Ryk dramatically elongated the reactive astrocytes and accelerated the formation of the border and reduced the size of the scar. Astrocyte-specific knockout of Ryk also accelerated the injury responses of multiple cell types, including the resolution of neuroinflammation. Single cell transcriptomics analyses revealed a broad range of changes cell signaling among astrocytes, microglia, fibroblasts, endothelial cell, etc, after astrocyte-specific Ryk knockout, suggesting that Ryk not only regulates the injury response of astrocytes but may also regulate signals which coordinate the responses of multiple cell types. The elongation is mediated by NrCAM, a cell adhesion molecule induced by astrocyte-specific conditional knockout of Ryk after spinal cord injury. Our findings suggest a promising therapeutic target to accelerate wound healing and promote neuronal survival and enhance functional recovery.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".