Merocytic dendritic cell: a new subset of conventional dendritic cells
Bibliographic record
Abstract
Abstract Conventional dendritic cells (cDC) are potent antigen-presenting cells that induce the activation of naïve T cells in response to pathogens. cDC activity is mediated primarily by two cDC subsets, namely cDC1 and cDC2, each bearing unique properties. Recently, another DC subset, termed merocytic dendritic cells (mcDC), was defined. In contrast to both cDC1 and cDC2, mcDC are able to reverse T cell anergy, even in non-inflammatory conditions, properties that could be exploited to potentiate cancer treatments. Here, we further characterize mcDC to determine their relationship to cDCs. First, we demonstrate that mcDC express key cDC traits, namely they express the cDC-restricted transcription factor, Zbtb46, and are very potent inducers of mixed lymphocyte reactions. Second, transcriptomic studies reveal that mcDC are more closely related to cDC1 than to cDC2. In contrast, similar to cDC2, mcDC are dependent on IRF4, but not IRF8 and BATF3, two major transcription factors required for cDC1 differentiation. Third, investigating mcDC population dynamics in reconstitution kinetics studies and in parabiotic mice, we demonstrate that, as for cDC1 and cDC2, mcDCs are terminally differentiated cells. Altogether, these data demonstrate that mcDC compose novel cDC subset. Defining the properties of mcDC in mice may help identify a functionally equivalent subset in humans leading to the development of novel 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".