Enhanced human hematopoietic stem and progenitor cell engraftment by blocking donor T cell–mediated TNFα signaling
Bibliographic record
Abstract
Allogeneic hematopoietic stem cell transplantation (HSCT) is a curative therapy, but the large number of HSCs required limits its widespread use. Host conditioning and donor cell composition are known to affect HSCT outcomes. However, the specific role that the posttransplantation signaling environment plays in donor HSC fate is poorly understood. To mimic clinical HSCT, we injected human umbilical cord blood (UCB) cells at different doses and compositions into immunodeficient NOD/SCID/IL-2Rgc-null (NSG) mice. Surprisingly, higher UCB cell doses inversely correlated with stem and progenitor cell engraftment. This observation was attributable to increased donor cell-derived inflammatory signals. Donor T cell-derived tumor necrosis factor-α (TNFα) was specifically found to directly impair the survival and division of transplanted HSCs and progenitor cells. Neutralizing donor T cell-derived TNFα in vivo increased short-term stem and progenitor cell engraftment, accelerated hematopoietic recovery, and altered donor immune cell compositions. This direct effect of TNFα on transplanted cells could be decoupled from the indirect effect of alleviating graft-versus-host disease (GVHD) by interleukin-6 (IL-6) blockade. Our study demonstrates that donor immune cell-derived inflammatory signals directly influence HSC fate, and provides new clinically relevant strategies to improve engraftment efficiency during HSCT.
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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 | 0.001 |
| 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".