Mtb HLA-E-tetramer-sorted CD8+ T cells have a diverse TCR repertoire
Bibliographic record
Abstract
HLA-E molecules can present self- and pathogen-derived peptides to both natural killer (NK) cells and T cells. T cells that recognize HLA-E peptides via their T cell receptor (TCR) are termed donor-unrestricted T cells due to restricted allelic variation of HLA-E. The composition and repertoire of HLA-E TCRs is not known so far. We performed TCR sequencing on CD8 + T cells from 21 individuals recognizing HLA-E tetramers (TMs) folded with two Mtb -HLA-E-restricted peptides. We sorted HLA-E Mtb TM + and TM − CD8 + T cells directly ex vivo and performed bulk RNA-sequencing and single-cell TCR sequencing. The identified TCR repertoire was diverse and showed no conservation between and within individuals. TCRs selected from our single-cell TCR sequencing data could be activated upon HLA-E/peptide stimulation, although not robust, reflecting potentially weak interactions between HLA-E peptide complexes and TCRs. Thus, HLA-E- Mtb -specific T cells have a highly diverse TCR repertoire.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.004 | 0.007 |
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; both teacher heads agree on what is shown here.
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".