Induction of T-cell development by Delta-like 4-expressing fibroblasts
Bibliographic record
Abstract
The thymus provides a unique environment for the induction of T-cell lineage commitment and differentiation, which is predicted by specific Notch ligand-receptor interactions on epithelial cells and lymphoid progenitors, respectively. Accordingly, a bone marrow-derived stromal cell line (OP9) ectopically expressing the Notch ligand Delta-like 1 (Dll1) or Dll4 (OP9-DL1 and OP9-DL4, respectively) gains the ability to recapitulate thymus-like function, supporting T-cell differentiation of both mouse and human progenitors. In this study, we extend these findings by demonstrating that, unlike the NIH3T3 cell line, mouse primary fibroblasts made to express Dll4 (mFibro-DL4) acquire the capacity to promote and support T-cell development from hematopoietic stem cells (HSCs) into TCRαβ(+), CD4(+) and CD8(+) T-lineage cells. However, mFibro-DL4 cells showed a lower efficiency of T-cell generation than OP9-DL4 cells did. Nevertheless, progenitor T-cells (CD117(+) CD44(+) CD25(+)) generated in HSC/mFibro-DL4 co-cultures, when intravenously transferred into immunodeficient (Rag2(-/-) γc(-/-)) mice, home to the thymus, undergo differentiation, and give rise to mature T-cells that go on to populate the periphery. Surprisingly, primary human fibroblast cells expressing Dll4 showed very low efficiency in supporting human T-lineage differentiation, which could not be improved by blocking myelopoiesis. Nevertheless, mFibro-DL4 cells could support human T-cell lineage differentiation. Our results provide a functional framework for the induction of T-cell development using easily accessible fibroblasts made to express Dll4. These cells, which are amenable for in vitro applications, can be further utilized in the design of individualized systems for T-cell production, with implications for the treatment of immunodeficiencies.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".