Myelinogenic Plasticity of Oligodendrocyte Precursor Cells following Spinal Cord Contusion Injury
Bibliographic record
Abstract
Spontaneous remyelination occurs after spinal cord injury (SCI), but the extent of myelin repair and identity of the cells responsible remain incompletely understood and contentious. We assessed the cellular origin of new myelin by fate mapping platelet-derived growth factor receptor α (PDGFRα), Olig2+, and P0+ cells following contusion SCI in mice. Oligodendrocyte precursor cells (OPCs; PDGFRα+) produced oligodendrocytes responsible for de novo ensheathment of ∼30% of myelinated spinal axons at injury epicenter 3 months after SCI, demonstrating that these resident cells are a major contributor to oligodendrocyte regeneration. OPCs also produced the majority of myelinating Schwann cells in the injured spinal cord; invasion of peripheral myelinating (P0+) Schwann cells made only a limited contribution. These findings reveal that PDGFRα+ cells perform diverse roles in CNS repair, as multipotential progenitors that generate both classes of myelinating cells. This endogenous repair might be exploited as a therapeutic target for CNS trauma and disease. SIGNIFICANCE STATEMENT Spinal cord injury (SCI) leads to profound functional deficits, though substantial numbers of axons often survive. One possible explanation for these deficits is loss of myelin, creating conduction block at the site of injury. SCI leads to oligodendrocyte death and demyelination, and clinical trials have tested glial transplants to promote myelin repair. However, the degree and duration of myelin loss, and the extent and mechanisms of endogenous repair, have been contentious issues. Here, we use genetic fate mapping to demonstrate that spontaneous myelin repair by endogenous oligodendrocyte precursors is much more robust than previously recognized. These findings are relevant to many types of CNS pathology, raising the possibility that CNS precursors could be manipulated to repair myelin in lieu of glial transplantation.
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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) | 0.000 | 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".