Cell therapy with human interleukin 10–producing ILC2s enhances islet function and inhibits allograft rejection
Bibliographic record
Abstract
Group 2 innate lymphoid cells (ILC2s) that produce IL-10 (IL-10 + ILC2s) have demonstrated regulatory and tissue-protective properties in murine studies, but preclinical studies are lacking that explore the potential of human IL-10 + ILC2s as a tolerance-promoting cell therapy for transplantation or autoimmunity. Here, we investigated whether human IL-10 + ILC2s could enhance islet function and prevent allograft rejection in humanized mouse models of islet transplantation. In vitro , human IL-10 + ILC2s did not display cytotoxicity towards allogeneic deceased-donor islets or stem cell-derived islet-like cells, and co-transplantation with IL-10 + ILC2s significantly improved glucose control post-transplantation. Allogeneic IL10 + ILC2s directly inhibited T cell-mediated cytotoxicity against islet-like cells in vitro, and in an antigen-specific transplant rejection model, prevented T cell-mediated rejection of deceased donor islet grafts. Effects were greater with allogeneic IL-10 + ILC2s, as autologous cells did not inhibit T cell IFN-γ production or cytotoxic activity in vitro, and were not sufficient to prevent islet rejection in vivo. Collectively, these studies provide proof-of-principle that human IL-10 + ILC2s have therapeutic potential for islet transplantation and type 1 diabetes, and support their use as an allogeneic regulatory cell therapy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".