Removal of myeloid cytokines from the cellular environment enhances T-cell development in vitro
Bibliographic record
Abstract
The majority of T-cell development occurs in the thymus. Thymic epithelial cells are specialized cells that express NOTCH ligands and secrete specific cytokines required for normal T-cell lymphopoiesis. It has been demonstrated that OP9 cells derived from macrophage colony-stimulating factor (M-CSF)-deficient mice can support T-cell development when transduced with a NOTCH ligand, Delta-like 1 (Dll1). In this report, we have tested CSF-deficient mouse fibroblasts transduced with Dll1 for their ability to support T-cell differentiation. The data provided here demonstrate that CSF-deficient fibroblasts expressing DLL1 can support T-cell development. Indeed, co-cultures with these fibroblasts produced more T-cell progenitors compared with OP9-DL1 cultures. Addition of myeloid cytokines to OP9-DL1 co-cultures significantly inhibited T-cell development while CSF-deficient DLL1(+) fibroblasts retained partial T-cell differentiation. Taken together, these data imply that their lack of myeloid cytokines allows DLL1(+) fibroblasts to more efficiently generate T-cells. Development of this fibroblast system suggests that there is potential for generating human T-cell precursors via co-culture with human fibroblasts expressing DLL1 or DLL4. These T-cell precursors could be used for treating immunodeficient patients.
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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 | 0.000 | 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) | 0.008 | 0.003 |
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; both teacher heads agree on what is shown here.
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".