Expanding the capabilities of the Maxpar Direct Immune Profiling Assay with additional markers, and customization of the analysis model in Maxpar Pathsetter
Bibliographic record
Abstract
Abstract Mass cytometry, which utilizes CyTOF® technology, is a single-cell analysis platform that uses metal-tagged antibodies. CyTOF can resolve more than 50 parameters in a single panel without the need for compensation, making it an ideal solution for routine enumeration of immune cells. The Maxpar® Direct™ Immune Profiling System is a sample-to-answer solution for human immune profiling using mass cytometry. The Maxpar®Direct™ Immune Profiling Assay™ is an optimized 30-marker panel contained in a dry single-tube format for human whole blood or PBMC staining, and samples are acquired on the Helios™ system. Maxpar Pathsetter™ is an automated software that reports cell counts, percentage calculations, and staining intensity. It also produces graphical elements such as dot plots and a Cen-se′™ graph for 37 immune cell populations. The panel can be tailored by adding markers to open channels, the Maxpar Pathsetter model can then be customized to measure expression markers on existing classified populations or identify additional immune cell subsets. We present data where the Maxpar Direct Immune Profiling Assay is used as a core immunophenotyping panel and additional markers are added to create a nearly 50 marker panel. Added markers are used to identify MDSCs, further classify existing cell populations, and measure immuno-oncology related markers including OX40, TIM-3, Fas, PD-1, PD-L1, ICOS, and TIGIT. We demonstrate how the Maxpar Pathsetter model is modified to incorporate the added markers. The ability to customize the Maxpar Pathsetter model and expand the Maxpar Direct Immune Profiling Assay allows for flexibility of the system. It allows researchers to have a streamlined solution for broad immune profiling using mass cytometry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".