T-Cell Differentiation from Hematopoietic Progenitor Cells Using 3D Thymic-like Hydrogels
Bibliographic record
Abstract
Chimeric antigen receptor T-cell immunotherapy represents a breakthrough in treating specific cancer types. However, a critical limitation in its application is the current shortage of donor cells. Attempts to recreate the thymic microenvironment, facilitating the differentiation of therapeutic T-cells from stem cells, hold significant promise for clinical advancements. Nonetheless, replicating crucial elements of T-cell development—dependent on the thymus’s three-dimensional (3D) architecture and its complex matrix composition, including stiffness and intricate cell–cell and cell–matrix interactions—remains unattainable with existing thymic models. To address this, we engineered biomaterials integrated with key thymic components such as Delta-like 4, vascular cell adhesion molecule 1, and proteins derived from collagen. These components are instrumental in directing T-cell development while inhibiting alternative lineage differentiation. Our work led to the identification of a hydrogel formulation that results in the production of both CD4+CD8b+ progenitor (Pro) T-cells and mature, functional CD3+CD8b+ T-cells. The resultant T-cells are not only functional, with the capability for cytokine production, but also mark the establishment of the first hydrogel-based platform for producing T-cells from induced pluripotent stem cell-derived hematopoietic stem and Pro cells. This system uniquely supports essential cell-to-cell and cell-to-extracellular matrix interactions within a 3D context.
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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 | 0.000 |
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".