NOTCH1 signaling establishes the medullary thymic epithelial cell progenitor pool during mouse fetal development
Bibliographic record
Abstract
Abstract The cortical and medullary thymic epithelial cell (cTEC and mTEC) lineages are essential for inducing T cell lineage commitment, T cell positive selection and the establishment of self-tolerance, but the mechanisms controlling their fetal specification and differentiation are poorly understood. Here, we show that Notch signaling is required to specify and expand the mTEC lineage. Notch1 is expressed by and active in TEC progenitors. Deletion of Notch1 in TECs resulted in depletion of mTEC progenitors and dramatic reductions in mTECs during fetal stages, consistent with defects in mTEC specification and progenitor expansion. Conversely, forced Notch signaling in all TEC resulted in widespread expression of mTEC progenitor markers and profound defects in TEC differentiation. In addition, lineage-tracing analysis indicated that all mTECs have a history of receiving a Notch signal, consistent with Notch signaling occurring in mTEC progenitors. Interestingly, this lineage analysis also showed that cTECs are divided between Notch lineage-positive and lineage-negative populations, identifying a previously unknown complexity in the cTEC lineage.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".