A robust human immunophenotyping workflow using CyTOF technology coupled with Maxpar Pathsetter, an automated data analysis software
Bibliographic record
Abstract
Abstract Mass cytometry, which utilizes CyTOF® technology, is a single-cell analysis platform that uses metal-tagged antibodies. An advantage of CyTOF is its ability to resolve more than 40 parameters in a single panel without the need for compensation, making mass cytometry an ideal solution for routine enumeration of immune cells. Developing a robust and highly multiplexed assay requires an optimized panel and analysis pipeline, and the analysis of complex panels is time-consuming and difficult to interpret without expertise in immunology. The Maxpar® Direct Immune Profiling Assay™ coupled with Maxpar Pathsetter™ software is a sample-to-answer solution for human immune profiling using mass cytometry. The Maxpar Direct Immune Profiling Assay kit includes an optimized 30-marker panel contained in a lyophilized single-tube format, protocols for human whole blood and PBMC staining, and data acquisition instructions on a Helios™ system. Maxpar Pathsetter is an automated software that accepts FCS 3.0 files and reports cell counts, percentage calculations, and staining intensity. It also produces graphical elements such as histograms, dot plots, and a Cen-se’™ (t-SNE variant) graph for 36 immune cell populations. We present analytical validation data on repeatability and reproducibility for the assay and on the precision and accuracy of Maxpar Pathsetter software. We demonstrate comparative performance between the lyophilized panel and a liquid panel of the same antibodies. Coupling Maxpar Pathsetter software with the Maxpar Direct Immune Profiling Assay reduces variability in sample preparation and subjectivity in data analysis. 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 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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.011 |
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".