The glia club: Validation of polarization biomarkers for human microglia (HMC3) using quantitative real time RT-qPCR
Bibliographic record
Abstract
Abstract Microglia are the primary immune cells of the brain and play critical roles in neurodevelopment, neuroprotection, the maintenance of homeostasis, and neurotoxicity. Classification of microglia polarization, however, remains contentious. Identifying suitable biomarkers for gene expression analysis of microglia polarization is crucial for characterizing the biological significance of microglia in health and disease. In this study, we use human microglia clone 3 (HMC3) cells to validate and test suitable internal controls ( GAPDH , PKM , 18S, ACTB, PGK1, TKT1, TPI1 ), homeostatic ( CD68, TGF-β, IBA1, BIN1, RGS10 ), proinflammatory ( IL-6, CXCL10, CCL5, SAA, IL-1β ) and anti-inflammatory ( CCL2, SOCS3, IL-10, CD200R1, ARG1 ) markers along with their transcriptional profiles upon interferon-gamma (IFN-γ) stimulation to characterize the microglia polarization spectrum. Our study is the first to present a comprehensive list of biomarkers with detailed methodology on gene selection, primer design, RT-qPCR parameters, and transcript abundance in baseline and polarized HMC3 cells. Our study will increase the rigor of gene expression analysis and target selection in a widely used brain macrophage.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".