A serum-free medium for differentiation of monocytes to macrophages
Bibliographic record
Abstract
Abstract Macrophages play an important role in defense against pathogens and in tissue homeostasis. They are classified into two main groups depending on how they are activated: 1) M1 (activated by IFN-γ and LPS) and 2) M2, which include M2a (activated by IL-4 or IL-13), M2b (immune complexes with IL-1β or LPS) and M2c (IL-10) subsets. It is difficult to control macrophage activation and polarization in cultures containing serum, in which variable amounts of M1- and M2-specific cytokines and other factors may be present. Here we describe a serum-free culture system that selectively supports the differentiation of monocytes into either M1 or M2a macrophages. Monocytes were isolated using EasySep immunomagnetic cell separation and cultured for 4 or 6 days in serum-free ImmunoCult medium with 50 ng/mL M-CSF. The cells were then stimulated by adding 10 ng/mL LPS plus 50 ng/mL IFN-γ for M1 or 10 ng/mL IL-4 for M2a macrophage polarization and cultured for 2 more days. The yields of M1 and M2a macrophage were 51±18% and 58±14% (mean±SD), respectively. M1 macrophages expressed high levels of CD80 and CCR7 (96±3% and 62±15%, n=57), but very low levels of CD206 and CD209. In contrast, M2a macrophages expressed CD206 and CD209 (90±10% and 92±13%, n=59), but were negative or low for CD80 and CCR7. M1 macrophages produced TNF-α and IL-12 (mean±SEM: 2821±396 and 656±86, pg/mL, n=24). M2a macrophages produced small amounts of IL-10 (29±6 pg/mL, n=21). Both M1 and M2a macrophages were functional as demonstrated in a phagocytosis assay using fluorescently-labeled E.coli. By selecting appropriate stimuli this culture method can be easily adapted to generate other macrophage subsets as well and should prove useful for the study of macrophage biology.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.008 |
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".