Single-Cell Analysis of the Human T Regulatory Population Uncovers Functional Heterogeneity and Instability within FOXP3+ Cells
Bibliographic record
Abstract
Natural FOXP3(+)CD4(+)CD25(High) regulatory T cells are critical in immunological self-tolerance. Their characterization in humans is hindered by the failure to discriminate these cells from activated effector T cells in inflammation. To explore the relationship between FOXP3 expression and regulatory function at the clonal level, we used a single-cell cloning strategy of CD25-expressing CD4(+) T cell subsets from healthy human donors. Our approach unveils a functional heterogeneity nested within CD4(+)CD25(High)FOXP3(+) T cells, and typically not revealed by conventional bulk assays. Whereas most cells display the canonical regulatory T (T(reg)) cell characteristics, a significant proportion of FOXP3(+) T cells is compromised in its suppressive function, despite the maintenance of other phenotypic and functional regulatory T hallmark features. In addition, these nonsuppressive FOXP3(+) T cells preferentially emerge from the CD45RO(+) memory pool, and arise as a consequence of a rapid downregulation of FOXP3 expression upon T cell reactivation. Surprisingly, these dysfunctional T(reg) cells with unstable FOXP3 expression do not manifest overt plasticity in terms of inflammatory cytokine secretion. These results open a path to an extensive study of the functional heterogeneity of CD4(+)CD25(High)FOXP3(+) T(reg) cells and warrant caution in the sole use of FOXP3 as a clinical marker for monitoring of immune regulation in humans.
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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".