Pitfalls in the characterization of circulating and tissue-resident human γδ T cells
Bibliographic record
Abstract
Dissection of the role and function of human γδ T cells and their heterogeneous subsets in cancer, inflammation, and auto-immune diseases is a growing and dynamic research field of increasing interest to the scientific community. Therefore, harmonization and standardization of techniques for the characterization of peripheral and tissue-resident γδ T cells is crucial to facilitate comparability between published and emerging research. The application of commercially available reagents to classify γδ T cells, in particular the combination of multiple Abs, is not always trouble-free, posing major demands on researchers entering this field. Occasionally, even entire γδ T cell subsets may remain undetected when certain Abs are combined in flow cytometric analysis with multicolor Ab panels, or might be lost during cell isolation procedures. Here, based on the recent literature and our own experience, we provide an overview of methods commonly employed for the phenotypic and functional characterization of human γδ T cells including advanced polychromatic flow cytometry, mass cytometry, immunohistochemistry, and magnetic cell isolation. We highlight potential pitfalls and discuss how to circumvent these obstacles.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".