DLL4 and VCAM1 enhance the emergence of T cell-competent hematopoietic progenitors from human pluripotent stem cells
Bibliographic record
Abstract
ABSTRACT T cells are key mediators of the adaptive immune response and show tremendous efficacy as cellular therapeutics. However, obtaining primary T cells from human donors is expensive and variable. Pluripotent stem cells (PSCs) have the potential to serve as a consistent and renewable source of T cells, but differentiating PSCs into hematopoietic progenitors with T cell potential remains a significant challenge. Here, we developed an efficient serum- and feeder-free protocol for differentiating human PSCs into hematopoietic progenitors and T cells. This defined method allowed us to study the impact of individual recombinant proteins on blood emergence and lineage potential. We demonstrate that the presence of DLL4 and VCAM1 during the endothelial-to-hematopoietic transition (EHT) enhances downstream progenitor T cell output by >80-fold. Using single cell transcriptomics, we showed that these two proteins synergise to drive strong notch signalling in nascent hematopoietic stem and progenitor cells and that VCAM1 additionally drives a pro-inflammatory transcriptional program. Finally, we applied this differentiation method to study the impact of cytokine concentration dynamics on T cell maturation. We established optimised media formulations that enabled efficient and chemically defined differentiation of CD8αβ+, CD4-, CD3+, TCRαβ+ T cells from PSCs.
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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".