Coexpression of TIGIT and FCRL3 Identifies Helios+ Human Memory Regulatory T Cells
Bibliographic record
Abstract
Two distinct subsets of CD4(+)Foxp3(+) regulatory T (Treg) cells have been described based on the differential expression of Helios, a transcription factor of the Ikaros family. Efforts to understand the origin and biological roles of these Treg populations in regulating immune responses have, however, been hindered by the lack of reliable surface markers to distinguish and isolate them for subsequent functional studies. Using a single-cell cloning strategy coupled with microarray analysis of different Treg functional subsets in humans, we identify the mRNA and protein expression of TIGIT and FCRL3 as a novel surface marker combination that distinguishes Helios(+)FOXP3(+) from Helios(-)FOXP3(+) memory cells. Unlike conventional markers that are modulated on conventional T cells upon activation, we show that the TIGIT/FCRL3 combination allows reliable identification of Helios(+) Treg cells even in highly activated conditions in vitro as well as in PBMCs of autoimmune patients. We also demonstrate that the Helios(-)FOXP3(+) Treg subpopulation harbors a larger proportion of nonsuppressive clones compared with the Helios(+)FOXP3(+) cell subset, which is highly enriched for suppressive clones. Moreover, we find that Helios(-) cells are exclusively responsible for the productions of the inflammatory cytokines IFN-γ, IL-2, and IL-17 in FOXP3(+) cells ex vivo, highlighting important functional differences between Helios(+) and Helios(-) Treg cells. Thus, we identify novel surface markers for the consistent identification and isolation of Helios(+) and Helios(-) memory Treg cells in health and disease, and we further reveal functional differences between these two populations. These new markers should facilitate further elucidation of the functional roles of Helios-based Treg heterogeneity.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".