Helios+ and Helios− Cells Coexist within the Natural FOXP3+ T Regulatory Cell Subset in Humans
Bibliographic record
Abstract
FOXP3-expressing T regulatory cells (Tregs) can be divided into two distinct subsets: naturally occurring Tregs (nTregs) that develop in the thymus, and induced Tregs (iTregs) that differentiate in peripheral tissues upon exposure to Ag in a tolerogenic environment. Recently it has been proposed that expression of Helios, an Ikaros family transcription factor, may specifically identify nTregs, allowing specific tracking of Tregs from different origins in health and disease. Surprisingly, we found that Helios(-) cells can be readily identified within naive (CD45RA(+)CD31(+)CCR7(+)CD62L(+)) FOXP3(+) Tregs, a finding inconsistent with the notion that lack of Helios expression identifies Ag-experienced iTregs that should express memory markers. To investigate the phenotype and function of naive Helios(+) and Helios(-) Tregs within the nTreg population, we isolated single-cell clones from each subset. We found that both Helios(+) and Helios(-) nTreg clones have a similar suppressive capacity, as well as expression of FOXP3 and cell surface proteins, including CD39 and CTLA-4. Helios(-) nTregs, however, produced significantly more CCL3 and IFN-γ compared with Helios(+) nTregs. Despite increased cytokine/chemokine production, Helios(-) FOXP3(+) nTreg clones were demethylated at the FOXP3 Treg-specific demethylated region, indicative of Treg lineage stability. When cultured under Th1-polarizing conditions, Helios(+) and Helios(-) nTreg clones had an equal ability to produce IFN-γ. Collectively, these data show that a lack of Helios expression does not exclusively identify human iTregs, and, to our knowledge, the data provide the first evidence for the coexistence of Helios(+) and Helios(-) nTregs in human peripheral blood.
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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) | 0.004 | 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".