Enumeration of human antigen–specific naive CD8+ T cells reveals conserved precursor frequencies
Bibliographic record
Abstract
The number of antigen-specific naive CD8(+) T cells is believed to be important in the shaping of adaptive immune responses, and is predictive for the magnitude of priming responses in mouse models. Because of extremely low precursor frequencies, knowledge about these cells comes from indirect techniques and estimations. Here, we present a strategy based on the combination of tetramer staining, magnetic-bead enrichment, and multiparametric cytometry, which permitted direct detection and analysis of CD8(+) T cells reactive for 6 different naive epitopes (MART-1(26-35), HIV-1 Gag p17(77-85), hepatitis C virus [HCV] NS3(1406-1415), HCV Core(132-140), NY-ESO-1(157-165), and cytomegalovirus [CMV] pp65(495-503)). Interestingly, we detected higher than 100-fold differences in precursor frequency across these epitopes (from 0.6 x 10(-6) to 1.3 x 10(-4)), but conserved frequencies among humans. Development of a procedure for direct assessment of T-cell precursor frequency in humans has important implications, with particular relevance to vaccine development and monitoring of tumor and self-reactive 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 | 0.000 | 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".