A Simple and Efficient Method to Detect Nuclear Factor Activation in Human Neutrophils by Flow Cytometry
Bibliographic record
Abstract
Neutrophils are the most abundant leukocytes in peripheral blood. These cells are the first to appear at sites of inflammation and infection, thus becoming the first line of defense against invading microorganisms. Neutrophils possess important antimicrobial functions such as phagocytosis, release of lytic enzymes, and production of reactive oxygen species. In addition to these important defense functions, neutrophils perform other tasks in response to infection such as production of proinflammatory cytokines and inhibition of apoptosis. Cytokines recruit other leukocytes that help clear the infection, and inhibition of apoptosis allows the neutrophil to live longer at the site of infection. These functions are regulated at the level of transcription. However, because neutrophils are short-lived cells, the study of transcriptionally regulated responses in these cells cannot be performed with conventional reporter gene methods since there are no efficient techniques for neutrophil transfection. Here, we present a simple and efficient method that allows detection and quantification of nuclear factors in isolated and immunolabeled nuclei by flow cytometry. We describe techniques to isolate pure neutrophils from human peripheral blood, stimulate these cells with anti-receptor antibodies, isolate and immunolabel nuclei, and analyze nuclei by flow cytometry. The method has been successfully used to detect NF-κB and Elk-1 nuclear factors in nuclei from neutrophils and other cell types. Thus, this method represents an option for analyzing activation of transcription factors in isolated nuclei from a variety of cell types.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".