Intragraft regulatory T cells in the modern era: what can high-dimensional methods tell us about pathways to allograft acceptance?
Bibliographic record
Abstract
Replacement of diseased organs with transplanted healthy donor ones remains the best and often only treatment option for end-stage organ disease. Immunosuppressants have decreased the incidence of acute rejection, but long-term survival remains limited. The broad action of current immunosuppressive drugs results in global immune impairment, increasing the risk of cancer and infections. Hence, achievement of allograft tolerance, in which graft function is maintained in the absence of global immunosuppression, has long been the aim of transplant clinicians and scientists. Regulatory T cells (Treg) are a specialized subset of immune cells that control a diverse array of immune responses, can prevent allograft rejection in animals, and have recently been explored in early phase clinical trials as an adoptive cellular therapy in transplant recipients. It has been established that allograft residency by Tregs can promote graft acceptance, but whether intragraft Treg functional diversification and spatial organization contribute to this process is largely unknown. In this review, we will explore what is known regarding the properties of intragraft Tregs during allograft acceptance and rejection. We will summarize recent advances in understanding Treg tissue residency through spatial, transcriptomic and high-dimensional cytometric methods in both animal and human studies. Our discussion will explore properties of intragraft Tregs in mediating operational tolerance to commonly transplanted solid organs. Finally, given recent developments in Treg cellular therapy, we will review emerging knowledge of whether and how these adoptively transferred cells enter allografts in humans. An understanding of the properties of intragraft Tregs will help lay the foundation for future therapies that will promote immune tolerance.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".