Sequential and interwoven requirements for HEB and Id3 in fetal gamma-delta T cell commitment and functional programming
Bibliographic record
Abstract
Abstract Two major developmental steps are necessary for the generation of innate IL-17 producing γδ T cells. First, T cell precursors in the fetal thymus commit to either the αβ or the γδ T cell lineage. This is followed by a second stage of differentiation into subsets that can produce either IL-17 (γδT17) or IFNγ (γδT1) cells. Both developmental events are dependent on TCR signal strength, but how this is interpreted at the molecular level has been unclear. Id3, which antagonizes the activity of HEB transcription factors, is upregulated in proportion to TCR signal strength, providing an important link between TCR signaling and gene expression. We previously found that γδ T cells in mice lacking HEB were impaired in IL-17 production. To better understand the molecular basis of this defect, we conducted single cell RNA-sequencing on fetal thymic γδ T cells. In WT cells, the ratio of HEB to Id3 was highest in populations undergoing γδ T cell commitment, whereas later populations were dominated by Id3. Comparing scRNA-seq datasets from WT and HEB-deficient γδ T cells revealed a profound shift in TCRγ and TCRδ chain expression. This was accompanied by a decrease in key regulators of early γδ T cell development. Id3 expression was also severely decreased. Accordingly, Id3-deficient mice were impaired in their ability to generate γδT17 cells. However, γδ T cells from Id3-deficient mice showed a decrease in second stage γδT17 regulators, rather than the first stage regulators defective in HEB-deficient mice. Therefore, distinct ratios of HEB and Id3 are required during the γδ T cell commitment, and HEB is instrumental in inducing Id3 to enable functional programming of γδT17 cells in the fetal thymus. Supported by grants from CIHR (201610PJT), NSERC (RGPIN-2020-05596) and NIH (1P01AI102853-06)
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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 | 0.001 |
| 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".