Development and Function of Myeloid-Derived Suppressor Cells Generated From Mouse Embryonic and Hematopoietic Stem Cells
Bibliographic record
Abstract
Emerging evidence suggests that myeloid-derived suppressor cells (MDSCs) have great potential as a novel immune intervention modality in the fields of transplantation and autoimmune diseases. Thus far, efforts to develop MDSC-based therapeutic strategies have been hampered by the lack of a reliable source of MDSCs. Here we show that functional MDSCs can be efficiently generated from mouse embryonic stem (ES) cells and bone marrow hematopoietic stem (HS) cells. In vitro-derived MDSCs encompass two homogenous subpopulations: CD115(+)Ly-6C(+) and CD115(+)Ly-6C(-) cells. The CD115(+)Ly-6C(+) subset is equivalent to the monocytic Gr-1(+)CD115(+)F4/80(+) MDSCs found in tumor-bearing mice. In contrast, the CD115(+)Ly-6C(-) cells, a previously unreported population of MDSCs, resemble the granulocyte/macrophage progenitors developmentally. In vitro, ES- and HS-MDSCs exhibit robust suppression against T-cell proliferation induced by polyclonal stimuli or alloantigens via multiple mechanisms involving nitric oxide synthase-mediated NO production and interleukin (IL)-10. Impressively, they display even stronger suppressive activity and significantly enhance ability to induce CD4(+)CD25(+)Foxp3(+) regulatory T-cell development compared with tumor-derived MDSCs. Furthermore, adoptive transfer of ES-MDSCs can effectively prevent alloreactive T-cell-mediated lethal graft-versus-host disease, leading to nearly 82% long-term survival among treated mice. The successful in vitro generation of MDSCs may represent a critical step toward potential clinical application of MDSCs.
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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".