A single-tube, 44-marker CyTOF assay to assess antigen-specific immunity in whole blood human samples with data analysis solution
Bibliographic record
Abstract
Abstract Monitoring the immune response in the setting of infectious disease and cancer is critical to assess disease status and targets of immune therapy. CyTOF® mass cytometry enables multiplex cellular phenotyping with more than 50 markers, making it ideal for comprehensive immune profiling. CyTOF technology utilizes antibodies tagged with unique monoisotopic metals, resulting in distinct signals that provide a high-resolution multiparametric landscape of a single cell. The Maxpar® Direct™ Immune Profiling Assay™ is a pre-titrated, dried-down, 30-marker antibody cocktail for immune profiling of human whole blood and PBMC by CyTOF. Paired with Maxpar Pathsetter™ software, stained samples are automatically resolved into 37 immune populations including major lineages and their subsets. In this study, we expanded the 30-marker assay to a 44-marker panel including exhaustion markers such as PD-1 and CTLA-4, co-stimulation markers 4-1BB and ICOS, and intracellular cytoplasmic markers IFN-γ, TNF-α, IL-2, perforin and granzyme B to assess cellular function in PMA/ionomycin-stimulated whole blood cultures. We modified the existing Maxpar Pathsetter model to automate the analysis of the expanded panel and report on additional functional parameters such as T cell exhaustion and cytokine production. Next, we applied this panel to whole blood stimulated with CMV peptides to investigate antigen-specific immune responses in a viral infection model in concert with in-depth phenotypic assessment. Collectively, we demonstrate the flexibility of the Maxpar Direct Immune Profiling Assay to incorporate additional surface and intracellular markers to study antigen-specific immunity in the context of whole blood immune profiling. For RUO.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".