Metal-conjugated neutravidin for MHC multimer assays using mass cytometry (TECH3P.939)
Bibliographic record
Abstract
Abstract Antigen-specific T cells in blood can be detected through the use of soluble MHC-peptide ligands that engage αβTCR. The fluorescent tetramer assay, developed in part by John Altman et al. (1) of the Vaccine Research Center, has become a standard tool for immunologists. Evan Newell et al. ( 2) adapted this peptide-MHC tetramer technology to mass cytometry for the purpose of screening of up to 109 different peptide-MHC tetramers in a single human blood sample, as well as analyzing another 23 markers of T-cell phenotype and function using a recombinant form of streptavidin conjugated to metal tags (Maxpar® kits, Fluidigm CA). We will describe the workflow for enumeration and identification of CMV-specific CD8+ T-cells with Neutravidin-metal reagent complexed with HLA-A*0201 CMV pp65 biotinylated monomer (MBL International, MA) simultaneously with metal-labeled surface markers, cisplatin dead-cell identifier, and the use of metal barcoding of several samples into one. There are 198 different biotinylated monomers, commercially available from MBL International, which can be combined with up to 35 isotope-tagged Neutravidin reagents to design a highly multiparametric assay. 1. Altman, J. D. et al. Phenotypic analysis of antigen-specific T lymphocytes. Science 274: 94-96 (1996) 2. Newell, E. W. et al. Combinatorial tetramer staining and mass cytometry analysis facilitate t-cell epitope mapping and characterization. Nat. Biotechnol. 31, 623-629 (2013).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.033 | 0.035 |
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".