Human proT-cells generated in vitro facilitate hematopoietic stem cell-derived T-lymphopoiesis in vivo and restore thymic architecture
Bibliographic record
Abstract
Hematopoietic stem cell transplantation (HSCT) is followed by a period of immune deficiency due to a paucity in T-cell reconstitution. Underlying causes are a severely dysfunctional thymus and an impaired production of thymus-seeding progenitors in the host. Here, we addressed whether in vitro-derived human progenitor T (proT)-cells could not only represent a source of thymus-seeding progenitors, but also able to influence the recovery of the thymic microenvironment. We examined whether co-transplantation of in vitro-derived human proT-cells with hematopoietic stem cells (HSCs) was able to facilitate HSC-derived T-lymphopoiesis posttransplant. A competitive transfer approach was used to define the optimal proT subset capable of reconstituting immunodeficient mice. Although the 2 subsets tested (proT1, CD34(+)CD7(+)CD5(-); proT2, CD34(+)CD7(+)CD5(+)) showed thymus engrafting function, proT2-cells exhibited superior engrafting capacity. Based on this, when proT2-cells were coinjected with HSCs, a significantly improved and accelerated HSC-derived T-lymphopoiesis was observed. Furthermore, we uncovered a potential mechanism by which receptor activator of nuclear factor κb (RANK) ligand-expressing proT2-cells induce changes in both the function and architecture of the thymus microenvironment, which favors the recruitment of bone marrow-derived lymphoid progenitors. Our findings provide further support for the use of Notch-expanded progenitors in cell-based therapies to aid in the recovery of T-cells in patients undergoing HSCT.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".