RORγt-expressing dendritic cells are functionally versatile and evolutionarily conserved antigen-presenting cells
Bibliographic record
Abstract
Conventional dendritic cells (cDCs) are potent antigen-presenting cells (APCs) that integrate signals from their environment allowing them to direct situation-adapted immunity. Thereby they harbor great potential for being targeted in vaccination, autoimmunity, and cancer. Here, we use fate mapping, functional analyses, and comparative cross-species transcriptomics to show that RORγt + DCs are a conserved, functionally versatile, and transcriptionally distinct type of DCs. RORγt + DCs entail various populations described in different contexts including Janus cells/RORγt-expressing extrathymic Aire-expressing cells (eTACs), subtypes of Thetis cells, RORγt + -DC (R-DC) like cells, cDC2C and ACY3 + DCs. We show that in response to inflammatory triggers, RORγt + DCs can migrate to lymph nodes and in the spleen can activate naïve CD4 + T cells. These findings expand the functional repertoire of RORγt + DCs beyond the known role of eTACs and Thetis cells in inducing T cell tolerance to self-antigens and intestinal microbes in mice. We further show that RORγt + DCs with proinflammatory features accumulate in autoimmune neuroinflammation in mice and men. Thus, our work establishes RORγt + DCs as immune sentinel cells that exhibit a broad functional spectrum ranging from inducing peripheral T cell tolerance to T cell activation depending on signals they integrate from their environment.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".