Multi-objective optimization reveals time- and dose-dependent inflammatory cytokine-mediated regulation of human stem cell derived T-cell development
Bibliographic record
Abstract
Abstract The generation of T-cells from stem cells in vitro could provide an alternative source of cells for immunotherapies. T-cell development from hematopoietic stem and progenitor cells (HSPCs) is tightly regulated through Notch pathway activation by Delta-like (DL) ligands 1 and 4. Other molecules, such as stem cell factor (SCF) and interleukin (IL)-7, play a supportive role in regulating the survival, differentiation, and proliferation of developing T-cells. Numerous other signaling molecules influence T-lineage development in vivo, but little work has been done to understand and optimize their use for T-cell production. Using a defined engineered thymic niche system, we undertook a multi-stage statistical learning-based optimization campaign and identified IL-3 and tumor necrosis factor α (TNFα) as a stage- and dose-specific enhancers of cell proliferation and T-lineage differentiation. We used this information to construct an efficient three-stage process for generating conventional TCRαβ + CD8 + T-cells expressing a diverse TCR repertoire from blood stem cells. Our work provides new insight into T-cell development and a robust system 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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.004 | 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".