Cord-Blood-Derived Mesenchymal Stromal Cells Downmodulate CD4 <sup>+</sup> T-Cell Activation by Inducing IL-10-Producing Th1 Cells
Bibliographic record
Abstract
The mechanisms by which mesenchymal stromal cells (MSCs) induce immunomodulation are still poorly understood. In the current work, we show by a combination of polymerase chain reaction (PCR) array, flow cytometry, and multiplex cytokine data analysis that during the inhibition of an alloantigen-driven CD4+ T-cell response, MSCs induce a fraction of CD4+ T-cells to coexpress interferon-γ (IFNγ) and interleukin-10 (IL-10). This CD4+ IFNγ+ IL-10+ cell population shares properties with recently described T-cells originating from switched Th1 cells that start producing IL-10 and acquire a regulatory function. Here we report that IL-10-producing Th1 cells accumulated with time during T-cell stimulation in the presence of MSCs. Moreover, MSCs caused stimulated T-cells to downregulate the IFNγ receptor (IFNγR) without affecting IL-10 receptor expression. Further, the inhibitory effect of MSCs could be reversed by an anti-IFNγR-blocking antibody, indicating that IFNγ is one of the major players in MSC-induced T-cell suppression. Stimulated (and, to a lesser extent, resting) CD4+ T-cells treated with MSCs were able to inhibit the proliferation of autologous CD4+ T-cells, demonstrating their acquired regulatory properties. Altogether, our results suggest that the generation of IL-10-producing Th1 cells is one of the mechanisms by which MSCs can downmodulate an immune response.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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) | 0.002 | 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".