Inflammatory Cytokines Regulate T-Cell Development from Blood Progenitor Cells in a Stage and Dose-Specifc Manner
Bibliographic record
Abstract
ABSTRACT T-cell development from hematopoietic stem and progenitor cells (HSPCs) is tightly regulated through Notch pathway activation by the Notch ligands Delta-like (DL) 1 and 4 and Jagged-2. Other molecules, such as stem cell factor (SCF), FMS-like tyrosine kinase 3 ligand (Flt3L) and interleukin (IL)-7, play a supportive role in regulating the survival, differentiation, and proliferation of developing progenitor (pro)T-cells. Numerous other signaling molecules are known to instruct T-lineage development in vivo , but little work has been done to optimize their use for T-cell production in vitro . Using a defined T-lineage differentiation assay consisting of plates coated with the Notch ligand DL4 and adhesion molecule VCAM-1, we performed a cytokine screen that identified IL-3 and tumor necrosis factor α (TNFα) as enhancers of proT-cell differentiation and expansion. Mechanistically, we found that TNFα induced T-lineage differentiation through the positive regulation of T-lineage genes GATA3, TCF7 , and BCL11b . TNFα also synergized with IL-3 to induce proliferation by upregulating the expression of the IL-3 receptor on CD34 + HSPCs, yielding 753.2 (532.4-1026.9; 5-95 percentile)-fold expansion of total cells after 14 days compared to 8.9 (4.3-21.5)-fold expansion in conditions without IL-3 and TNFα. We then optimized cytokine concentrations for T-cell maturation. Focusing on T-cell maturation, we used quantitative models to optimize dynamically changing cytokine requirements and used these to construct a three-stage assay for generating CD3 + CD4 + CD8 + and CD3 + CD4 − CD8 + T-cells. Our work provides new insight into T-cell development and a robust in vitro assay for generating T-cells to enable clinical therapies for treating cancer and immune disorders.
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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) | 0.002 | 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 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".