Intracellular Staining for Analysis of the Expression and Phosphorylation of Signal Transducers and Activators of Transcription (STATs) in NK Cells
Bibliographic record
Abstract
Cytokines stimulate biological responses by activating intracellular signaling pathways. We have been adapting flow cytometric techniques to measure the levels of expression and activation of signaling molecules within mixed populations containing NK cells and to characterize their differences within NK cell subpopulations. Approaches for evaluating the total levels of the signal transducers and activators of transcription STAT1 and STAT4, of STAT1 in cells expressing IFNgamma, and of the type 1 interferon (type 1 IFN) activation by phosphorylation, i.e., induction of pSTAT1 and pSTAT4, have been developed. The results of experiments using these techniques have demonstrated that an unusual feature of NK cells is high basal expression of STAT4 but reduced STAT1 levels. The condition predisposes for pSTAT4 activation by type 1 IFNs. The work has also shown, however, that total STAT1 levels are induced during viral infections as a result of IFN exposure, and that this change acts to promote the activation of STAT1 but limit both the activation of STAT4 and IFNgamma expression. The intracellular staining approaches used for the studies described here have utility in characterizing other mechanisms regulating cytokine-mediated signaling, and defining additional pathways shaping cellular responses to cytokines.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".