Multi‐site reproducibility of a human immunophenotyping assay in whole blood and peripheral blood mononuclear cells preparations using CyTOF technology coupled with Maxpar Pathsetter, an automated data analysis system
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
High-dimensional mass cytometry data potentially enable a comprehensive characterization of immune cells. In order to positively affect clinical trials and translational clinical research, this advanced technology needs to demonstrate a high reproducibility of results across multiple sites for both peripheral blood mononuclear cells (PBMC) and whole blood preparations. A dry 30-marker broad immunophenotyping panel and customized automated analysis software were recently engineered and are commercially available as the Fluidigm® Maxpar® Direct™ Immune Profiling Assay™. In this study, seven sites received whole blood and six sites received PBMC samples from single donors over a 2-week interval. Each site labeled replicate samples and acquired data on Helios™ instruments using an assay-specific acquisition template. All acquired sample files were then automatically analyzed by Maxpar Pathsetter™ software. A cleanup step eliminated debris, dead cells, aggregates, and normalization beads. The second step automatically enumerated 37 immune cell populations and performed label intensity assessments on all 30 markers. The inter-site reproducibility of the 37 quantified cell populations had consistent population frequencies, with an average %CV of 14.4% for whole blood and 17.7% for PBMC. The dry reagent coupled with automated data analysis is not only convenient but also provides a high degree of reproducibility within and among multiple test sites resulting in a comprehensive yet practical solution for deep immune phenotyping.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 it