An animal component-free, serum-free culture method for generation of human dendritic cells
Bibliographic record
Abstract
Abstract Dendritic cells (DCs) are central regulators of adaptive immune responses that potentially have major applications in the immunotherapy of cancer and autoimmune disorders. DCs are rare in peripheral blood (PB) and are therefore often generated in vitro from PB monocytes. We developed an animal component-free (ACF) and serum-free medium, ImmunoCult-ACF DC, that supports the generation of DCs from monocytes in culture. CD14+ monocytes were isolated using EasySep immunomagnetic separation and cultured for 5 days in ImmunoCult-ACF DC medium and cytokines (GM-CSF and IL-4) to promote their differentiation into CD14−CD83− immature DCs. The immature DCs were then stimulated for 2 days with the ImmunoCult-DC Maturation Supplement, a combination of cytokines and pro-inflammatory mediators, to promote their maturation to CD14− CD83+ DCs (93 ± 5% CD83+, n=23). The yield of mature DCs was 42 ± 23% (n=23), similar to the yield in a control serum-free medium (49 ± 24%, n=16). Mature DCs produced high levels of IL-12, on average 371 pg/mL (range: 27–1756, n=9 in cultures initiated with 106 monocytes). Mature DCs loaded with CMV, EBV and Flu virus peptides efficiently stimulated the proliferation of autologous CD8+ T cells in 7 day co-cultures. An 18-fold increase in T cell numbers (range: 5–31, n=4) was observed when compared to control culture conditions containing only T cells without mature DCs. In conclusion, functional DCs can be efficiently generated by differentiation of monocytes in a completely animal (including human) component-free and serum-free medium. This medium and culture method will enable further research into the development and application of DCs for cellular therapy.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".