CCL22-expressing Stem Cell–derived Islet Grafts Recruit Regulatory T Cells in Mice
Bibliographic record
Abstract
BACKGROUND: Cell therapy using human donor or stem cell-derived islets (SC-islets) to replace lost insulin-producing beta (β) cells holds great promise for type 1 diabetes. Recruiting regulatory T cells (Treg) through chemokine signaling could mitigate allo- and autoimmune attack on transplanted β-cells, potentially obviating the need for immunosuppressants. We hypothesized that SC-islets genetically engineered to secrete the chemokine C-C motif chemokine ligand 22 (CCL22) would attract Treg to the site of transplantation and may ultimately prolong graft survival. METHODS: We engineered human embryonic stem cells to express CCL22 and differentiated them into SC-islets. CCL22 + SC-islets were assessed for gene and protein markers of endocrine cells and tested for function in vitro by glucose-stimulated insulin secretion assay, and in vivo by transplanting SC-islets into immune-deficient, streptozotocin-treated diabetic mice. Next, CCL22 bioactivity was confirmed by Transwell Treg migration assay. Treg migration was tracked using bioluminescent imaging of mice with CCL22 + SC-islet grafts and infused with luciferase-expressing Treg. RESULTS: The expression of CCL22 did not adversely impact the differentiation into SC-islets, as confirmed by gene and protein analysis and functional tests in vitro and in vivo. CCL22 + SC-islets induced Treg migration in vitro, with specificity to CCL22 confirmed by a C-C motif chemokine receptor type 4 antagonist. Furthermore, CCL22 + SC-islet grafts recruited human Treg to the transplant site. CONCLUSIONS: CCL22 + SC-islets are functional and capable of attracting Treg. By recruiting Treg, CCL22 + SC-islets may create a tolerogenic immune environment for SC-islets after transplantation.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".