Multiplexed detection and isolation of viable low-frequency cytokine-secreting human B cells using cytokine secretion assay and flow cytometry (CSA-Flow)
Bibliographic record
Abstract
Abstract The ability to functionally characterize cytokine-secreting immune cells has broad implications in both health and a range of immune-mediated and auto-immune diseases. Low-frequency cytokine-defined immune-cell subsets can play key immune-regulatory roles, yet their detailed study is often hampered by limited clinical sample availability. Commonly used techniques including intracellular cytokine staining require cell fixation, precluding subsequent functional interrogation. The cytokine-secretion assay (CSA) can overcome this limitation, though has mostly been used for detection of relatively high-frequency, single-cytokine secreting cells. We examined how adaptation of the CSA in combination with multiparametric flow-cytometry (CSA-Flow) may enable simultaneous isolation of multiple, low-frequency, cytokine-secreting cells. Focusing on human B cells (traditionally recognized as harder to assay than T cells), we show that single-capture CSA-Flow allows for isolation of highly-purified populations of both low-frequency (IL-10+; GM-CSF+) and high-frequency (TNF+) cytokine-defined B cells. Simultaneous detection and isolation of up to three viable and highly-purified cytokine-secreting B-cell subpopulations is feasible, albeit with some signal loss, with fractions subsequently amenable to gene expression analysis and in vitro cell culture. This multiplexing CSA-Flow approach will be of interest in many human cellular immunology contexts aiming to functionally characterize cytokine-secreting immune cells, especially when sample volumes and cell numbers are limited.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".