Differentiation of Langerhans Cells from Monocytes and Their Specific Function in Inducing IL-22–Specific Th Cells
Bibliographic record
Abstract
Abstract Human mucosal tissues and skin contain two distinct types of dendritic cell (DC) subsets, epidermal Langerhans cells (LCs) and dermal DCs, which can be distinguished by the expression of C-type lectin receptors, Langerin and DC-SIGN, respectively. Although peripheral blood monocytes differentiate into these distinct subsets, monocyte-derived LCs (moLCs) induced by coculture with GM-CSF, IL-4, and TGF-β1 coexpress both Langerin and DC-SIGN, suggesting that the environmental cues remain unclear. In this study, we show that LC differentiation is TGF-β1 dependent and that cofactors such as IL-4 and TNF-α promote TGF-β1–dependent LC differentiation into Langerin+DC-SIGN− moLCs but continuous exposure to IL-4 blocks differentiation. Steroids such as dexamethasone greatly enhanced TNF-α–induced moLC differentiation and blocked DC-SIGN expression. Consistent with primary LCs, dexamethasone-treated moLCs express CD1a, whereas monocyte-derived DCs (moDCs) express CD1b, CD1c, and CD1d. moDCs but not moLCs produced inflammatory cytokines after stimulation with CD1b and CD1d ligands mycolic acid and α-galactosylceramide, respectively. Strikingly, CD1a triggering with squalene on moLCs but not moDCs induced strong IL-22-producing CD4+ helper T cell responses. As IL-22 is an important cytokine in the maintenance of skin homeostasis, these data suggest that CD1a on LCs is involved in maintaining the immune barrier in the skin.
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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 | 0.000 |
| 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".