T-bet fate mapping identifies a novel ILC1-ILC2 subset <i>in vivo</i>
Bibliographic record
Abstract
ABSTRACT Innate lymphoid cells (ILC) play a critical role in regulating immune responses at mucosal surfaces. Various subsets exist resembling T cell lineages defined by the expression of specific transcription factors. Thus, T-bet is expressed in ILC1 and Th1 cells. In order to further understand the functional roles of T-bet in ILC, we generated a fate-mapping mouse model that permanently marks cells and their progeny that are expressing, or have ever expressed T-bet. Here we have identified and characterised a novel ILC with characteristics of ILC1 and ILC2 that are “fate-mapped” for T-bet expression and arise early in neonatal life prior to establishment of a mature microbiome. These ILC1-ILC2 cells are critically dependent on T-bet and are able to express type 1 and type 2 cytokines at steady state, but not in the context of inflammation. These findings refine our understanding of ILC lineage regulation and stability and have important implications for the understanding of ILC biology at mucosal surfaces. SUMMARY Innate lymphoid cells (ILC) play a critical role in regulating immune responses at mucosal surfaces. Three distinct ILC groups have been described according to expression of subset defining transcription factors and other markers. In this study we characterize a novel ILC subset with characteristics of group 1 and group 2 ILC in vivo .
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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) | 0.002 | 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".