Flt3-Ligand and Granulocyte Colony-Stimulating Factor Mobilize Distinct Human Dendritic Cell Subsets In Vivo
Bibliographic record
Abstract
Dendritic cells (DCs) have a unique ability to stimulate naive T cells. Recent evidence suggests that distinct DC subsets direct different classes of immune responses in vitro and in vivo. In humans, the monocyte-derived CD11c+ DCs induce T cells to produce Th1 cytokines in vitro, whereas the CD11c- plasmacytoid T cell-derived DCs elicit the production of Th2 cytokines. In this paper we report that administration of either Flt3-ligand (FL) or G-CSF to healthy human volunteers dramatically increases distinct DC subsets, or DC precursors, in the blood. FL increases both the CD11c+ DC subset (48-fold) and the CD11c- IL-3R+ DC precursors (13-fold). In contrast, G-CSF only increases the CD11c- precursors (>7-fold). Freshly sorted CD11c+ but not CD11c- cells stimulate CD4+ T cells in an allogeneic MLR, whereas only the CD11c- cells can be induced to secrete high levels of IFN-alpha, in response to influenza virus. CD11c+ and CD11c- cells can mature in vitro with GM-CSF + TNF-alpha or with IL-3 + CD40 ligand, respectively. These two subsets up-regulate MHC class II costimulatory molecules as well as the DC maturation marker DC-lysosome-associated membrane protein, and they stimulate naive, allogeneic CD4+ T cells efficiently. These two DC subsets elicit distinct cytokine profiles in CD4+ T cells, with the CD11c- subset inducing higher levels of the Th2 cytokine IL-10. The differential mobilization of distinct DC subsets or DC precursors by in vivo administration of FL and G-CSF offers a novel strategy to manipulate immune responses in humans.
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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 | 0.000 |
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".