A critical assessment for the value of markers to gate-out undesired events in HLA-peptide multimer staining protocols
Bibliographic record
Abstract
BACKGROUND: The introduction of antibody markers to identify undesired cell populations in flow-cytometry based assays, so called DUMP channel markers, has become a practice in an increasing number of labs performing HLA-peptide multimer assays. However, the impact of the introduction of a DUMP channel in multimer assays has so far not been systematically investigated across a broad variety of protocols. METHODS: The Cancer Research Institute's Cancer Immunotherapy Consortium (CRI-CIC) conducted a multimer proficiency panel with a specific focus on the impact of DUMP channel use. The panel design allowed individual laboratories to use their own protocol for thawing, staining, gating, and data analysis. Each experiment was performed twice and in parallel, with and without the application of a dump channel strategy. RESULTS: The introduction of a DUMP channel is an effective measure to reduce the amount of non-specific MULTIMER binding to T cells. Beneficial effects for the use of a DUMP channel were observed across a wide range of individual laboratories and for all tested donor-antigen combinations. In 48% of experiments we observed a reduction of the background MULTIMER-binding. In this subgroup of experiments the median background reduction observed after introduction of a DUMP channel was 0.053%. CONCLUSIONS: We conclude that appropriate use of a DUMP channel can significantly reduce background staining across a large fraction of protocols and improve the ability to accurately detect and quantify the frequency of antigen-specific T cells by multimer reagents. Thus, use of a DUMP channel may become crucial for detecting low frequency antigen-specific immune responses. Further recommendations on assay performance and data presentation guidelines for publication of MULTIMER experimental data are provided.
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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 | 0.041 | 0.056 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".