Ontogenic timing, T cell receptor signal strength, and Notch signaling direct γδ T cell functional differentiation in vivo
Bibliographic record
Abstract
γδ T cells form an integral arm of the immune system and are critical during protective and destructive immunity. However, how γδ T cells are functionally programmed in vivo remains unclear. Here, we employ RBPJ-inducible and KN6-transgenic mice to assess the roles of ontogenic timing, T cell receptor (TCR) signal strength, and Notch signaling. We find skewing of Vγ1 + cells toward the PLZF + Lin28b + lineage at the fetal stage. Generation of interleukin-17 (IL-17)-producing γδ T cells is favored during, although not exclusive to, the fetal stage. Surprisingly, Notch signaling is dispensable for peripheral γδ T cell IL-17 production. Strong TCR signals, together with Notch, promote IL-4 differentiation. Conversely, less strong TCR signals promote Notch-independent IL-17 differentiation. Single-cell transcriptomic analysis reveals differential programming instilled by TCR signal strength and Notch for specific subsets. Thus, our results precisely define the roles of ontogenic timing, TCR signal strength, and Notch signaling in γδ T cell functional programming in vivo .
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".