Analytical performance of a standardized single‐platform MHC tetramer assay for the identification and enumeration of CMV‐specific CD8+ T lymphocytes
Bibliographic record
Abstract
Major histocompatibility complex (MHC) multimers that identify antigen-specific T cells, coupled with flow cytometry, have made a major impact on immunological research. HLA Class I multimers detect T cells directed against viral, tumor, and transplantation antigens with exquisite sensitivity. This technique has become an important standard for the quantification of a T cell immune response. The utility of this method in multicenter studies, however, is dependant on reproducibility between laboratories. As part of a clinical study using a standardized two-tube three-color single-platform method, we monitored and characterized performance across multiple sites using tetramers against the T cell receptors (TCR) specific for MHC Class I, A*0101--VTEHDTLLY, A*0201--NLVPMVATV and B*0702--TPRVTGGGAM CMV peptides. We studied the analytical performance of this method, focusing on reducing background, maximizing signal intensity, and ensuring that sufficient cells are enumerated to provide meaningful statistics. Inter and intra-assay performance were assessed, which included inherent variability introduced by shipping, type of flow cytometer used, protocol adherence, and analytical interpretation across a range of multiple sample levels and specificities under routine laboratory testing conditions. Using the described protocol, it is possible to obtain intra- and interlab CV's of <20%, with a functional sensitivity for absolute tetramer counts of 1 cell/microL and 0.2% tetramer+ percent for A*0101, A*0201, and B*0702 alleles. The standardized single-platform MHC tetramer assay is simple, rapid, reproducible, and useful for assessing CMV-specific T cells, and will allow for reasonable comparisons of clinical evaluations across multiple centers at clinically relevant thresholds (2.0-10.0 cells/microL).
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".