Immune Responses to Human Induced Pluripotent Stem Cells and their Derived Myogenic Progenitors Are Mediated by Different Mechanisms in Humanized Mice
Bibliographic record
Abstract
ABSTRACT It is still unclear if immune responses will compromise the large scale utilization of cell therapies derived from human induced pluripotent stem cells (hiPSCs). To answer this question, we used humanized mouse models and evaluated the engraftment in skeletal muscle of myoblasts derived either directly from a muscle biopsy or differentiated from hiPSCs or fibroblasts. Our results showed that while allogeneic grafts were rejected, engraftment of autologous cells was tolerated, indicating reprogramming and differentiation procedures are not immunogenic. We also demonstrated that hiPSC-derived myogenic progenitors, in opposition to hiPSCs, are not targeted by natural killer (NK) cells both in vitro and in vivo . Yet, adoptive transfer of NK cells can prevent the formation of hiPSC-derived teratoma. Overall, our findings suggest that hiPSC-derived muscular therapies will be tolerated in presence of a competent human immune system and highlight the risk of forming a teratoma if using partially differentiated autologous human cells. Highlights hiPSC-derived myofibers are tolerated in autologous humanized mouse models Infiltration of autologous T cells is not predictive of successful skeletal muscle engraftment Adoptive transfer of NK cells prevents the formation of hiPSCs derived teratomas NK cells are unable to reject established teratomas
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".