Back

Donor Macrophage Depletion Permits Post-Transplant Tolerance Induction in a Murine Islet Transplant Model

Dilts, M.; Fay, O.; Yu, Y.; Jordan, C. Z.; Luo, X.

2026-01-09 immunology
10.64898/2026.01.08.698403 bioRxiv
Show abstract

Current strategies for experimental tolerance induction for allogeneic transplantation typically require recipient preparation days to weeks prior to transplantation, making them not applicable to deceased donor transplantation. Developing tolerance strategies feasible for deceased donor transplantation would greatly increase the pool of eligible patients for tolerance induction. Here, we aimed to induce tolerance with post-transplant only interventions in a murine pancreatic islet transplant model. We demonstrated that transplant tolerance induction by recipient infusions of ethylcarbodiimide-treated donor splenocytes (ECDI-SPs) could be reliably delayed to the post-transplant timeframe provided that donor islets were depleted of intra-islet macrophages prior to transplantation. Mechanistically, islet production of CCL3, CCL4, and CCL5 (RANTES) was significantly reduced by intra-islet macrophage depletion. On POD+1, islet allograft depleted of donor intra-islet macrophages exhibited significantly reduced infiltration of recipient innate immune cells, including monocytes, macrophages, and neutrophils. Interestingly, perioperative inhibition of CCR5, the receptor for CCL3, CCL4 and CCL5, also reduced POD+1 innate immune cell infiltration, and similarly permitted tolerance induction by post-transplant donor ECDI-SP infusions. This study thus demonstrates the efficacy of a strategy that would allow transplant tolerance induction by post-transplant-only interventions, thereby expanding the applicability of tolerance induction regimens to additional clinically relevant settings.

Published in American Journal of Transplantation (predicted rank #1) · training set

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.