Development in context: interferon response networks regulate human fetal thymic epithelial cell differentiation
Bibliographic record
Abstract
ABSTRACT The thymus instructs T cell immunity and central tolerance, yet its therapeutic potential remains untapped. The quest to regenerate thymic function for clinical application is lagging while the signals that drive thymic epithelial cell differentiation remain incompletely understood. Here, we elucidate pathways instructing commitment and specialization of the human thymic epithelial stroma through complementary single cell transcriptomic approaches. First, we identify gene regulatory networks that define fetal thymic epithelium in the thus far unexplored context of other anterior foregut-derived organs; then, we characterize lineage trajectories within the thymic epithelial compartment across embryonic, fetal, and early postnatal stages. Activation of interferon response gene regulatory networks distinguished epithelial cells of the thymus from those of all other anterior foregut-derived organs. Interferon signals were processed differentially within thymic cortical and medullary lineages, reflected in distinct NFκB and IRF signatures, respectively. Our study reveals novel, translatable insights into the developmental programs underlying thymic epithelial cell differentiation that may advance the field of regenerative cell therapies. SUMMARY Single cell transcriptomics of anterior foregut-derived organs identifies pathways governing thymic epithelial commitment and specialization.
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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) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".