DL4-μbeads induce T cell lineage differentiation from stem cells in a stromal cell-free system
Bibliographic record
Abstract
Abstract T cells are pivotal effectors of the immune system and can be harnessed as therapeutics for regenerative medicine and cancer immunotherapy. An unmet challenge in the field is the development of a clinically relevant system that is readily scalable to generate large numbers of T-lineage cells from hematopoietic stem/progenitor cells (HSPCs). Here, we report a stromal cell-free, microbead-based approach that supports the efficient in vitro development of both human progenitor T (proT) cells and T-lineage cells from CD34 + cells sourced from cord blood, GCSF-mobilized peripheral blood, and pluripotent stem cells (PSCs). DL4-μbeads, along with lymphopoietic cytokines, induce an ordered sequence of differentiation from CD34 + cells to CD34 + CD7 + CD5 + proT cells to CD3 + αβ T cells. Single-cell RNA sequencing of human PSC-derived proT cells reveals a transcriptional profile similar to the earliest thymocytes found in the embryonic and fetal thymus. Furthermore, the adoptive transfer of CD34 + CD7 + proT cells into immunodeficient mice demonstrates efficient thymic engraftment and functional maturation of peripheral T cells. DL4-μbeads provide a simple and robust platform to both study human T cell development and facilitate the development of engineered T cell therapies from renewable sources.
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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 | 0.001 |
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".