128 Enabling sensitive and reproducible functional profiling for immuno-oncology research on the CyTOF XT PRO mass cytometer
Bibliographic record
Abstract
Background Reproducible immune profiling is crucial for translational and clinical research and for developing accurate prognoses and effective treatments. Reproducibly identifying low-expressing targets and rare functional populations is challenging with fluorescence flow cytometry due to spectral spillover, autofluorescence and unmixing errors, issues not present with mass cytometry. Further, multiple workflows in mass cytometry provide flexibility and minimize technical variation, including the abilities to freeze metal-tagged antibody cocktails, barcode samples and freeze stained samples for future acquisition. The goal of this study was to assess the consistency and robustness of mass cytometry across various staining and acquisition workflows.Methods Human whole blood and PBMC samples were stained with antibody panels containing up to 50 surface and intracellular markers for phenotyping and functional profiling via checkpoint markers and cytokines. Stained samples were frozen and acquired on a later date using three CyTOF™ XT PRO and three CyTOF XT systems to assess repeatability and reproducibility, and to ensure data quality was not compromised when samples were acquired at 2x and 4x speeds with the CyTOF XT PRO system.Results Both systems were highly repeatable and reproducible across multiple instruments. Cell population frequencies and signal intensities were not significantly different between the two CyTOF systems. Both systems had high sensitivity and dynamic range for abundant and low-abundance markers to accurately identify T helper cell populations. Furthermore, opt-SNE analysis revealed that the enhanced throughput of the CyTOF XT PRO system retained clear visualization of major immune subsets and striking functional diversity in high-dimensional space.Conclusions Overall, these studies demonstrate that CyTOF XT and CyTOF XT PRO systems generate highly repeatable and reproducible data. Moreover, the CyTOF XT PRO system enables acquisition at up to 4x increased event rate without compromising data quality. Automated acquisition by the CyTOF XT PRO system enables researchers to accurately and reproducibly streamline human immunophenotyping and functional profiling, leading to accelerated biological insights and discoveries.For Research Use Only. Not for use in diagnostic procedures.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.014 |
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".