OMIP‐117: 40‐Parameter/37‐Color Spectral Cytometry Panel for Robust Immunoprofiling of Human Lymphoid Subsets in Cancer Patients
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
The analysis of immune cell compartments in cancer patients is crucial to predict treatment efficacy and relapse. We introduce a robust 40-parameter, 37-channel spectral cytometry panel designed to profile human lymphoid subsets and CAR-T cell expansion, with the capability to assess exhaustion status by profiling immune checkpoints and activating receptors in cancer patients. Developed for the 5-laser Cytek Aurora, the panel optimizes fluorophore selection and uses three pairs of mutually exclusive markers assigned to a single fluorescent parameter to simplify setup and ensure robust data, adopting a conservative design choice to keep similarity indices below 0.85; though higher overlaps can still yield high-quality data when best practices are applied. The panel enables detailed analysis of well-defined lymphoid subsets using a conventional gating strategy, as well as detection of unconventional subsets with variable expression patterns by unsupervised algorithm-based analysis. The effectiveness of the panel is demonstrated through a dataset simulating the progression of multiple myeloma, from pre-malignant disease to a highly aggressive stage.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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