Temporal Analysis of Gene Expression in the Murine Schwann Cell Lineage and the Acutely Injured Postnatal Nerve
Bibliographic record
Abstract
Schwann cells (SCs) arise from neural crest cells (NCCs) that first give rise to SC precursors (SCPs), followed by immature SCs, pro-myelinating SCs, and finally, non-myelinating or myelinating SCs. After nerve injury, mature SCs 'de-differentiate', downregulating their myelination program while transiently re-activating early glial lineage genes. To better understand molecular parallels between developing and de-differentiated SCs, we characterized the expression profiles of a panel of 12 transcription factors from the onset of NCC migration through postnatal stages, as well as after acute nerve injury. Using Sox10 as a pan-glial marker in co-expression studies, the earliest transcription factors expressed in E9.0 Sox10+ NCCs were Sox9, Pax3, AP2α and Nfatc4. E10.5 Sox10+ NCCs coalescing in the dorsal root ganglia differed slightly, expressing Sox9, Pax3, AP2α and Etv5. E12.5 SCPs continued to express Sox10, Sox9, AP2α and Pax3, as well as initiating Sox2 and Egr1 expression. E14.5 immature SCs were similar to SCPs, except that they lost Pax3 expression. By E18.5, AP2α, Sox2 and Egr1 expression was turned off in the nerve, while Jun, Oct6 and Yy1 expression was initiated in pro-myelinating Sox9+/Sox10+ SCs. Early postnatal and adult SCs continued to express Sox9, Jun, Oct6 and Yy1 and initiated Nfatc4 and Egr2 expression. Notably, at all stages, expression of each marker was observed only in a subset of Sox10+ SCs, highlighting the heterogeneity of the SC pool. Following acute nerve injury, Egr1, Jun, Oct6, and Sox2 expression was upregulated, Egr2 expression was downregulated, while Sox9, Yy1, and Nfatc4 expression was maintained at similar frequencies. Notably, de-differentiated SCs in the injured nerve did not display a transcription factor profile corresponding to a specific stage in the SC lineage. Taken together, we demonstrate that uninjured and injured SCs are heterogeneous and distinct from one another, and de-differentiation recapitulates transcriptional aspects of several different embryonic stages.
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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 | 0.000 |
| 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".