Human Regulatory Dendritic Cells Develop From Monocytes in Response to Signals From Regulatory and Helper T Cells
Bibliographic record
Abstract
Dendritic cells (DCs) are powerful antigen presenting cells, derived from bone marrow progenitors (cDCs) and monocytes (moDCs), that can shape the immune response by priming either proinflammatory or tolerogenic immune effector cells. The cellular mechanisms responsible for the generation of DCs that will prime a proinflammatory or tolerogenic response are poorly understood. Here we describe a novel mechanism by which tolerogenic DCs are formed from monocytes. When human monocytes were cultured with CD4+FoxP3+ natural regulatory T cells (Tregs) and T helper cells (Th) from healthy donor blood, they differentiated into regulatory DCs (DCReg), capable of generating induced Tregs from naïve T cells. DCReg exhibited morphology, surface phenotype, cytokine secretion, and transcriptome that were distinct from other moDCs including those derived from monocytes cultured with Th or with GM-CSF/IL-4, as well as macrophages (Mo). Direct cell contact between monocytes, Tregs and Th, along with Treg-derived CTLA-4, IL-10 and TGF-beta, was required for the phenotypic differentiation of DCReg, although only IL-10 was required for imprinting the Treg-inducing capacity of DCReg. High ratios of Treg:Th, along with monocytes and DCReg similar in function and phenotype to those induced in vitro, were present in situ in human colorectal cancer specimens. Thus, through the combined actions of Tregs and Th, monocytes differentiate into DCs with regulatory properties, forming a positive feedback loop to reinforce Treg initiated immune regulation. This mechanism may contribute to immune tolerance in tissues such as tumors, which contain an abundance of Tregs, Th and monocytes.
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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) | 0.002 | 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".