Lyoplate-based multiparameter flow cytometry for the analysis of T cell subsets in human immuno-monitoring studies
Bibliographic record
Abstract
In recent years immuno-monitoring studies are becoming increasingly popular due to the relevant role the immune system plays in many pathologies and in treatment responses. Human translational research is hampered by limited amounts of samples, intrinsic human variability and practical issues involving multi-centre sample collection and analysis. Therefore, human immuno-monitoring studies need to be standardized. Multicolour flow cytometry (MFC) provides a powerful tool to unravel the complexity of the immune system. However standardization of this technique is still in progress, due to differences in sample quality, reagents, antibodies and fluorchromes used, as well as instrument settings. Part of this variability could be overcome by using lyophilized reagents in a 96 well plate format for cell stimulation and staining. In this pilot study we assess how lyoplate based-MFC performs compared to traditional liquid reagent-based MFC, mirroring larger human immuno-monitoring cohorts. Peripheral blood mononuclear cells were collected from healthy volunteers at two time points. Frozen samples were thawed, stimulated and stained using either liquid or lyophilized reagents. The 10 colour flow cytometry antibody cocktail used allowed the analysis of different T cell subsets (CD8 T cells, Th cells and Treg cells) and their cytokine production (IFNγ, IL17A, IL10). Quantitative and qualitative differences between liquid and lyophilized reagents were evaluated, as well as intra- and inter-assay variability. Data from this study will assess the feasibility of standardized and high-throughput immuno-monitoring studies to discover pathology associated signatures and biomarkers predictive of therapy response.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".