Delayed expansion of a restricted T cell repertoire by low-density TCR ligands
Bibliographic record
Abstract
The role of TCR ligand density (i.e. the number of antigen-MHC complexes) in modulating the diversity of a T cell response selected from a pool of naive precursors remains largely undefined. By measuring early-activation markers up-regulation and proliferation following stimulation with staphylococcal enterotoxin A (SEA), we demonstrate that decreasing the ligand dose below an optimal concentration leads to the delayed activation of a restricted set of TCRVbeta-bearing T cells, with the specific, non-stochastic exclusion of some TCRVbeta+ T cells from the activated pool. Our results suggest that the failure of these TCRVbeta-bearing T cells to reach the activation threshold at sub-optimal ligand concentration is due to the inefficiency of TCR engagement, as measured by TCR internalization, and does not correlate with the relative precursor frequency in the non-immune repertoire. Moreover, even at SEA concentrations that lead to the simultaneous proliferation of all SEA-reactive T cells, we observe marked differences in the ability to secrete cytokines among the different responsive TCRVbeta-bearing T cells. Altogether, our results indicate that the development of a T cell response to a scarce display of ligand significantly narrows TCR repertoire diversity by mechanisms that involve focusing of the repertoire on the expansion of those T cells with the highest avidity of TCR engagement.
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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 | 0.000 |
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".