An efficient human whole blood workflow using CyTOF technology: a lyophilized 30-plex antibody panel coupled with automated data analysis
Bibliographic record
Abstract
Abstract CyTOF® mass cytometry is a highly multiplexed, single-cell analysis platform that uses metal-tagged antibodies. A significant advantage of this technology is its ability to resolve more than 30 markers in a single panel without the need for compensation. Nonetheless, a highly multiplexed panel requires optimization of antibody titers, full panel verification, sample preparation, data acquisition, and analysis. Preparation of a staining cocktail from 30 individual tubes may be prone to errors. Not only is data analysis time-consuming for complex panels, it is difficult to interpret without expertise in immunology. We have developed a lyophilized 30-plex immunphenotyping panel contained in a single tube, an efficient workflow, and an automated software solution for human whole blood analysis using mass cytometry. The panel focuses on T cell lineage while also capturing other relevant immune populations. Blood is added directly to the lyophilized antibody tube, followed by RBC lysis, wash, and fixation steps and finally data acquisition of stained samples on the Helios™ system. The dedicated software tool accepts FCS3.0 files and in under 10 minutes automatically generates reports on the number of live cells, percentage of specific cell populations, staining intensities, histograms, 2D dot plots, and tSNE graphs. The auto-calculated frequencies of populations are comparable to manual gating, with a validated correlation coefficient over 0.9. The panel and software tool enable researchers to streamline immunophenotyping of whole blood while accurately and reproducibly monitoring changes in immune cell subsets in patient samples.
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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 | 0.003 | 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.009 |
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".