Sca-1+Lin−CD117− Mesenchymal Stem/Stromal Cells Induce the Generation of Novel IRF8-Controlled Regulatory Dendritic Cells through Notch–RBP-J Signaling
Bibliographic record
Abstract
Mesenchymal stem/stromal cells (MSCs) can influence the destiny of hematopoietic stem/progenitor cells (HSCs) and exert broadly immunomodulatory effects on immune cells. However, how MSCs regulate the differentiation of regulatory dendritic cells (regDCs) from HSCs remains incompletely understood. In this study, we show that mouse bone marrow-derived Sca-1(+)Lin(-)CD117(-) MSCs can drive HSCs to differentiate into a novel IFN regulatory factor (IRF)8-controlled regDC population (Sca(+) BM-MSC-driven DC [sBM-DCs]) when cocultured without exogenous cytokines. The Notch pathway plays a critical role in the generation of the sBM-DCs by controlling IRF8 expression in an RBP-J-dependent way. We observed a high level of H3K27me3 methylation and a low level of H3K4me3 methylation at the Irf8 promoter during sBM-DC induction. Importantly, infusion of sBM-DCs could alleviate colitis in mice with inflammatory bowel disease by inhibiting lymphocyte proliferation and increasing the numbers of CD4(+)CD25(+) regulatory T cells. Thus, these data infer a possible mechanism for the development of regDCs and further support the role of MSCs in treating immune disorders.
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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".