Single-Cell Transcriptomics Reveals Discrete Steps in Regulatory T Cell Development in the Human Thymus
Bibliographic record
Abstract
regulatory T (Treg) cells control immunological tolerance. Treg cells are generated in the thymus (tTreg) or in the periphery. Their superior lineage fidelity makes tTregs the preferred cell type for adoptive cell therapy (ACT). How human tTreg cells develop is incompletely understood. By combining single-cell transcriptomics and flow cytometry, we in this study delineated three major Treg developmental stages in the human thymus. At the first stage, which we propose to name pre-Treg I, cells still express lineage-inappropriate genes and exhibit signs of TCR signaling, presumably reflecting recognition of self-antigen. The subsequent pre-Treg II stage is marked by the sharp appearance of transcription factor FOXO1 and features induction of KLF2 and CCR7, in apparent preparation for thymic exit. The pre-Treg II stage can further be refined based on the sequential acquisition of surface markers CD31 and GPA33. The expression of CD45RA, finally, completes the phenotype also found on mature recent thymic emigrant Treg cells. Remarkably, the thymus contains a substantial fraction of recirculating mature effector Treg cells, distinguishable by expression of inflammatory chemokine receptors and absence of CCR7. The developmental origin of these cells is unclear and warrants caution when using thymic tissue as a source of stable cells for ACT. We show that cells in the major developmental stages can be distinguished using the surface markers CD1a, CD27, CCR7, and CD39, allowing for their viable isolation. These insights help identify fully mature tTreg cells for ACT and can serve as a basis for further mechanistic studies into tTreg development.
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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 | 0.001 | 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".