Determination of Interleukin-17A and Interferon-γ Production in γδ, CD4+, and CD8+ T Cells Isolated from Murine Lymphoid Organs, Perivascular Adipose Tissue, Kidney, and Lung
Bibliographic record
Abstract
, and γδ T-cell subtypes are programmed to produce interleukin (IL)-17 or interferon-γ (IFNγ), and naïve T cells can be induced to produce IL-17 via the IL-23 receptor. Importantly, both IL-17 and IFNγ have been demonstrated to contribute to hypertension. Therefore, profiling cytokine-producing T-cell subtypes in tissues relevant to hypertension provides useful information regarding immune activation. Here, we describe a protocol to obtain single-cell suspensions from the spleen, mesenteric lymph nodes, mesenteric vessels and PVAT, lungs, and kidneys, and profile IL-17A- and IFNγ-producing T cells using flow cytometry. This protocol is different from cytokine assays such as ELISA or ELISpot in that no prior cell sorting is required, and various T-cell subsets can be identified and individually assessed for cytokine production simultaneously within an individual sample. This is advantageous as sample processing is kept to a minimum, yet many tissues and T-cell subsets can be screened for cytokine production in a single experiment. In brief, single-cell suspensions are activated in vitro with phorbol 12-myristate 13-acetate (PMA) and ionomycin, and Golgi cytokine export is inhibited with monensin. Cells are then stained for viability and extracellular marker expression. They are then fixed and permeabilized with paraformaldehyde and saponin. Finally, antibodies against IL-17 and IFNγ are incubated with the cell suspensions to report cytokine production. T-cell cytokine production and marker expression is then determined by running samples on a flow cytometer. While other groups have published methods to perform T-cell intracellular cytokine staining for flow cytometry, this protocol is the first to describe a highly reproducible method to activate, phenotype, and determine cytokine production by CD4, CD8, and γδ T cells isolated from PVAT. Additionally, this protocol can be easily modified to investigate other intracellular and extracellular markers of interest, allowing for efficient T-cell phenotyping.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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) | 0.000 | 0.000 |
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 teacher head, 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".