A Stable Cranial Neural Crest Cell Line from Mouse
Bibliographic record
Abstract
Cranial neural crest cells give rise to ectomesenchymal derivatives such as cranial bones, cartilage, smooth muscle, dentin, as well as melanocytes, corneal endothelial cells, and neurons and glial cells of the peripheral nervous system. Previous studies have suggested that although multipotent stem-like cells may exist during the course of cranial neural crest development, they are transient, undergoing lineage restriction early in embryonic development. We have developed culture conditions that allow cranial neural crest cells to be grown as multipotent stem-like cells. With these methods, we obtained 2 independent cell lines, O9-1 and i10-1, which were derived from mass cultures of Wnt1-Cre; R26R-GFP-expressing cells. These cell lines can be propagated and passaged indefinitely, and can differentiate into osteoblasts, chondrocytes, smooth muscle cells, and glial cells. Whole-genome expression profiling of O9-1 cells revealed that this line stably expresses stem cell markers (CD44, Sca-1, and Bmi1) and neural crest markers (AP-2α, Twist1, Sox9, Myc, Ets1, Dlx1, Dlx2, Crabp1, Epha2, and Itgb1). O9-1 cells are capable of contributing to cranial mesenchymal (osteoblast and smooth muscle) neural crest fates when injected into E13.5 mouse cranial tissue explants and chicken embryos. These results suggest that O9-1 cells represent multipotent mesenchymal cranial neural crest cells. The O9-1 cell line should serve as a useful tool for investigating the molecular properties of differentiating cranial neural crest cells.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.010 |
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".