Maintenance of T Cell Specification and Differentiation Requires Recurrent Notch Receptor–Ligand Interactions
Bibliographic record
Abstract
Notch signaling has been shown to play a pivotal role in inducing T lineage commitment. However, T cell progenitors are known to retain other lineage potential long after the first point at which Notch signaling is required. Thus, additional requirements for Notch signals and the timing of these events relative to intrathymic differentiation remain unknown. Here, we address this issue by culturing subsets of CD4 CD8 double negative (DN) thymocytes on control stromal cells or stromal cells expressing Delta-like 1 (Dll1). All DN subsets were found to require Notch signals to differentiate into CD4+ CD8+ T cells. Using clonal analyses, we show that CD44+ CD25+ (DN2) cells, which appeared committed to the T cell lineage when cultured on Dll1-expressing stromal cells, nonetheless gave rise to natural killer cells with a progenitor frequency similar to that of CD44+ CD25- (DN1) thymocytes when Notch signaling was absent. These data, together with the observation that Dll1 is expressed on stromal cells throughout the thymic cortex, indicates that Notch receptor-ligand interactions are necessary for induction and maintenance of T cell lineage specification at both the DN1 and DN2 stages of T cell development, suggesting that the Notch-induced repression of the B cell fate is temporally separate from Notch-induced commitment to the T 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".