Instructional role of ligand in γδ T cell lineage commitment (153.42)
Bibliographic record
Abstract
Abstract The processes that influence alphabeta versus gammadelta T cell lineage commitment are not well defined. Evidence suggests that strength and duration of TCR signaling during T cell development can directly influence fate decisions, irrespective of the TCR isotype. Accordingly, T cell progenitors expressing a gammadelta TCR can be directed toward the alphabeta lineage when signaling through the TCR is attenuated. Previously, we showed that KN6 gammadelta TCR transgenic thymocytes can be directed to the alphabeta lineage when the KN6 TCR ligand, T-10/22, is eliminated. Here we investigate the impact of ligand affinity on cell fate as well as whether it regulates fate selection instructively or stochastically. These issues are addressed in vitro using the OP9 stromal cell model where ligand expression can be manipulated. Our data suggest that differences in ligand expression and affinity are important determinants in fate selection. Moreover, ligand appears to be influencing lineage-fate in an instructional manner and is associated with characteristic changes in gene expression. Understanding how modulating TCR signaling affects T cell lineage commitment will provide insight into pathways that control fate decisions of developing alphabeta or gammadelta T 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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".