Insights into olfactory ensheathing cell development from a laser‐microdissection and transcriptome‐profiling approach
Bibliographic record
Abstract
Olfactory ensheathing cells (OECs) are neural crest-derived glia that ensheath bundles of olfactory axons from their peripheral origins in the olfactory epithelium to their central targets in the olfactory bulb. We took an unbiased laser microdissection and differential RNA-seq approach, validated by in situ hybridization, to identify candidate molecular mechanisms underlying mouse OEC development and differences with the neural crest-derived Schwann cells developing on other peripheral nerves. We identified 25 novel markers for developing OECs in the olfactory mucosa and/or the olfactory nerve layer surrounding the olfactory bulb, of which 15 were OEC-specific (that is, not expressed by Schwann cells). One pan-OEC-specific gene, Ptprz1, encodes a receptor-like tyrosine phosphatase that blocks oligodendrocyte differentiation. Mutant analysis suggests Ptprz1 may also act as a brake on OEC differentiation, and that its loss disrupts olfactory axon targeting. Overall, our results provide new insights into OEC development and the diversification of neural crest-derived glia.
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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".