Cell therapy with human IL-10-producing ILC2s limits xenogeneic graft-versus-host disease by inhibiting pathogenic T cell responses
Bibliographic record
Abstract
Interleukin-10 (IL-10)-producing group 2 innate lymphoid cells (ILC2 10 ) regulate inflammatory immune responses, yet their therapeutic potential remains largely unexplored. Here, we demonstrate that cell therapy with human ILC2 10 inhibits pathogenic T cell responses in humanized mouse models of graft-versus-host disease (GVHD), resulting in reduced GVHD severity and improved overall survival without limiting the graft-versus-leukemia effect. ILC2 10 conferred superior protection from GVHD than IL-10 −/low ILC2s, and blocking IL-10 and IL-4 abrogated ILC2 10 protective effects, indicating that these cytokines are important for the protective effects of ILC2 10 . Notably, ILC2 10 provided comparable protection from GVHD to regulatory T cells without impairing T cell engraftment, instead decreasing intestinal T cell infiltration and suppressing CD4 + Th1 and CD8 + Tc1 cells. CITE-seq of expanded ILC2s revealed CD49d and CD86 are markers that allow for enrichment of ILC2 10 from conventional ILC2s and tracking of ILC2 10 in patient studies. Altogether, these findings demonstrate the potential of ILC2 10 in cell therapies for GVHD and other immune-mediated diseases.
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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) | 0.002 | 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".