Investigating immune tolerance: characterization of immunoregulatory DN T cells
Bibliographic record
Abstract
Abstract Autoimmunity results from defects in immune tolerance pathways that impair the capacity of immune cells to distinguish between self and non-self. One of these immune tolerance processes involves immunoregulatory TCRαβ+ CD4− CD8− double negative T cells (DN T). Indeed, DN T cells protect from autoimmune pathologies and graft rejection in mice. Similarly, high DN T cell numbers correlate with a low incidence of chronic graft-versus-host disease in humans, an autoimmune-like pathology. While the thymic differentiation process of DN T cells was recently described, the function of these unconventional T cells is still poorly documented. To characterize DN T cells, we performed bulk RNA sequencing on CD4, CD8, γδ and DN T cells isolated from mouse spleen. Comparison of the transcriptome profiles revealed that DN T cells express a unique signature. Moreover, following in vitro stimulation, we find that DN T cells also present a distinct cytokine secretion pattern. Interestingly, we observed a lower induction of CD69, LAG-3 and PD-1 on DN T cells in comparison to CD4 and CD8 T cells, whereas LCK phosphorylation is unabated. DN T cell proliferation in mixed lymphocyte reactions is also lower than that of CD4 and CD8 T cells. Overall, our transcriptomic and cytokine analyses clearly identify DN T cells as a unique T cell subset. Moreover, while DN T cells do not express an exhaustion profile, they are more refractory to in vitro T cell stimulation than conventional T cells. A better characterization of DN T cell function will help conceive novel immunotherapies to treat autoimmune diseases. Supported by the Canadian Institutes of Health Research - PJT 159603. S.P. was supported by a postdoctoral CIHR fellowship
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.000 |
| 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".