<i>In vitro</i>‐generated <scp>MART</scp>‐1‐specific <scp>CD</scp>8 T cells display a broader T‐cell receptor repertoire than <i>ex vivo</i> naïve and tumor‐infiltrating lymphocytes
Bibliographic record
Abstract
The differentiation of human hematopoietic stem cells into CD8 T cells can be achieved in vitro with the OP9-DL4 system. We considered that in the absence of medullary thymic epithelial cells, which serve to restrict the breath of the T-cell receptor (TCR) repertoire by expressing tissue-restricted antigens, a distinct repertoire would be generated in vitro. To test this notion, we compared the TCR-Vα/Vβ (TRAV/TRBV) gene usage of major histocompatibility complex-restricted antigen (MART-1)-specific T cells generated in vitro to that from ex vivo naïve T cells and tumor-infiltrating lymphocytes (TILs) using high-throughput DNA sequencing. In contrast to naïve T cells and TILs, which showed the expected narrow TRAV repertoire, in vitro-generated MART-1-specific T cells used almost all TRAV gene families and displayed unique CDR3 lengths. Our work demonstrates that the OP9-DL4 system supports the creation of a broad antigen-specific TCR repertoire, suggesting that T cells generated in vitro may undergo a different set of selection events that otherwise constrains the TCR repertoire of thymus-derived 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 | 0.001 |
| 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".