Immunofluorescence analysis of human eosinophils
Bibliographic record
Abstract
A prominent inflammatory cell type in allergic diseases is the eosinophil, a granulated white blood cell that releases pro-inflammatory cytokines. Eosinophil-derived cytokines, including interleukin-9 (IL-9) and interleukin-13 (IL-13), can skew the immune response towards an allergic phenotype. Unfortunately, it is challenging to immunolabel and collect quantifiable images of eosinophils given their innate autofluorescence and ability to nonspecifically bind to antibodies. Hence, it is important to optimize permeabilization, blocking, and imaging conditions for eosinophils. Here, we show enhanced protocols to ensure that measured immunofluorescence represents specific immunolabelling. To test this, eosinophils were purified from human blood, adhered to glass coverslips, stimulated with or without platelet-activating factor (PAF), fixed with paraformaldehyde, and then permeabilized with Triton X-100 or saponin. Cells were then blocked with goat serum or human serum and incubated with antibodies labelling cytokines (IL-9 and IL-13) and secretory organelles (CD63 for crystalloid granules and transferrin receptor [TfnRc] for recycling endosomes). Carefully selected isotype controls were used throughout, and cells were imaged using Deltavision super-resolution microscopy. Intensities of fluorescent probes were quantified using Volocity software. Our findings show that permeabilization with saponin, blockage with human serum, and using concentrations of antibodies up to 10 μg/ml allowed us to detect marked differences in fluorescence intensities between isotypes and test antibodies. With the achievement of sufficient qualitative and quantitative measures of increased test probe intensity compared to respective isotypes, these results indicate that our protocol allows for optimal immunolabelling of eosinophils. Using this protocol, future studies may provide further insights into trafficking mechanisms within this important inflammatory cell type.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".