Characterization of In vitro Generated Human Polarized Macrophages
Bibliographic record
Abstract
Objective: Contact with invading pathogens and/or tissue injury leads to the polarization of macrophages into either a M1 or a M2 state which is further divided into M2a, M2b and M2c subsets. The human macrophage subsets have been poorly characterized. The present study was undertaken to characterize macrophage polarization using a non-exhaustive panel of surface markers with respect to M1, M2a, M2b and M2c macrophages and production of pro- and anti-inflammatory cytokines in response to various toll-like receptors (TLR), ligands.Methods: We generated various macrophage subsets by treating monocyte-derived macrophages (MDMs) with IFNγ (M1), IL-4 (M2a), LPS and IL-1β (M2b) or IL-10 (M2c) followed by stimulation with toll-like receptor (TLR)- 2, TLR-3 and TLR-4 agonists and analysis of surface marker and cytokines expression was carried out by flow cytometry and ELISA, respectively.Results: M2a subset was characterized by CD14low, CD163low and TLR4low phenotype and produced high levels of IL-10. M2b subset was characterized by CD14high, CD80high and CD200Rlow phenotype and produced IL-6 prior to stimulation. M2c subset displayed a CD86low, CD163high phenotype and produced high levels of IL-10. M1 subset was characterized by CD80high, CD86high, CD163low and TLR4high phenotype and produced high levels of proinflammatory IFN-g, IL-12, TNFα and IL-23 following stimulation.Conclusion: This study characterizes all four polarization states in human macrophages. Each polarization state demonstrated a unique cell surface marker profile and cytokine profile. These phenotypic markers can be used to characterize macrophage populations in tissue inflammatory disease conditions in vivo to further understand disease pathogenesis.
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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) | 0.000 | 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) | 0.002 | 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".