Merocytic Dendritic Cells Compose a Conventional Dendritic Cell Subset with Low Metabolic Activity
Bibliographic record
Abstract
Abstract Conventional dendritic cells (cDCs) are arguably the most potent APCs that induce the activation of naive T cells in response to pathogens. In addition, at steady-state, cDCs help maintain immune tolerance. Two subsets of cDCs have been extensively characterized, namely cDC1 and cDC2, each contributing differently to immune responses. Recently, another dendritic cell (DC) subset, termed merocytic DCs (mcDCs), was defined. In contrast to both cDC1 and cDC2, mcDCs reverse T cell anergy, properties that could be exploited to potentiate cancer treatments. Yet, whether mcDCs represent an unconventional DC or a cDC subset remains to be defined. In this article, we further characterize mcDCs and find that they bear true characteristics of cDC subsets. Indeed, as for cDCs, mcDCs express the cDC-restricted transcription factor Zbtb46 and display very potent APC activity. In addition, mcDC population dynamics parallels that of cDC1 and cDC2 in both reconstitution kinetic studies and parabiotic mice. We next investigated their relatedness to cDC1 and cDC2 and demonstrate that mcDCs are not dependent on cDC1-related Irf8 and Batf3 transcription factors, are dependent on Irf4, a cDC2-specific transcription factor, and express a unique transcriptomic signature. Finally, we find that cDC1, cDC2, and mcDCs all present with different metabolic phenotypes, in which mcDCs exhibit the lowest glucose uptake activity and mcDC survival is the least affected by glycolysis inhibition. Defining the properties of mcDCs in mice may help identify a functionally equivalent subset in humans leading to the development of innovative cancer immunotherapies.
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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".