Dendritic cells: understanding ontogeny, subsets, functions, and their clinical applications
Bibliographic record
Abstract
Dendritic cells (DCs) play a central role in coordinating immune responses by linking innate and adaptive immunity through their exceptional antigen-presenting capabilities. Recent studies reveal that metabolic reprogramming-especially pathways involving acetyl-coenzyme A (acetyl-CoA)-critically influences DC function in both physiological and pathological contexts. This review consolidates current knowledge on how environmental factors, tumor-derived signals, and intrinsic metabolic pathways collectively regulate DC development, subset differentiation, and functional adaptability. Acetyl-CoA emerges as a dual-function metabolite, serving not only as an energy carrier but also as an epigenetic regulator that controls DC fate via lipid biosynthesis, mitochondrial metabolism, and chromatin modification. In the tumor microenvironment (TME), DCs may experience immune suppression polarization and insufficient T cell activation due to disrupted acetyl-CoA related metabolic pathways. While existing DC-based therapies remain constrained by TME-induced metabolic limitations, emerging approaches that restore acetyl-CoA related metabolic pathways balance show enhanced antitumor efficacy. The review further examines distinct metabolic adaptations among DC subsets and their relevance to autoimmune diseases, infectious immunity, and transplant outcomes. By integrating current research on targeting DC metabolic targets, we outline strategies for developing immunotherapies that target DC metabolic flexibility. Remaining hurdles include tailoring interventions to specific subsets, refining metabolic manipulation techniques, and addressing TME heterogeneity through combination therapies. These findings position acetyl-CoA as a key therapeutic target for recalibrating immunometabolism circuits, with significant implications for DC-focused cancer treatment.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".