Lentivirally Transduced Recipient-derived Dendritic Cells Serve to Ex Vivo Expand Functional FcRγ-sufficient Double-negative Regulatory T Cells
Bibliographic record
Abstract
alphabetaTCR(+)CD4(-)CD8(-) double-negative (DN) T regulatory (Treg) cells have recently been shown to suppress antigen-specific immune responses mediated by CD8(+) and CD4(+) T cells in mice and humans. In this study, we developed a system to expand DN Treg cells for transplantation therapy that exclusively uses recipient-derived immune cells and confers a high degree of safety as the protocol does not involve the direct injection of lentiviral vectors. Recipient-derived dendritic cells (DCs) were transduced with lentiviral vectors that express major histocompatibility complex class I L(d) antigen (LV-L(d)), which is expressed by the donor graft but is allogeneic to the graft recipient. LV-L(d)-transduced mature DCs (mDCs) were able to expand effectively both FcRgamma(-/-) and FcRgamma(+/+) DN T cells. After expansion with LV-L(d)-transduced mDCs, only the FcRgamma(+/+) DN Treg cells maintained their ability to suppress CD8(+) T cells in vitro. In addition, adoptive transfer of the FcRgamma(+/+) ex vivo expanded DN Treg cells significantly prolonged the survival of L(d+) skin grafts. This study is the first description of successful ex vivo expansion of antigen-specific DN Treg cells using genetically modified syngeneic DCs for adoptive immunotherapy and demonstrates that although FcRgamma(-/-) DN T cells can be expanded, they do not gain regulatory ability.
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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) | 0.001 | 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".