Reproducible detection of antigen-specific T cells and Tregs via standardized and automated activation-induced marker assay workflows
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
Activation-induced marker (AIM) assays are a promising tool to track antigen-specific T cells, but methodological heterogeneity between research groups hinders their clinical utility. To evaluate AIM assay reproducibility, we conducted a multi-site study of SARS-CoV-2 and cytomegalovirus AIMs. We found inherent variability in AIM assays and optimized approaches to enhance reproducibility, including a standardized workflow to minimize technical variability and a generalizable Box-Cox transformation-based statistical method to optimize calculation of AIM stimulation responses. We further standardized AIM data analysis through the development of automated flow cytometric gating software and demonstrated its superior reproducibility compared to manual analysis. We also characterized antigen-responsive regulatory T cells (Tregs) as CD134 + CD137 + cells among CD4 + FOXP3 + HELIOS + cells. The combined methodology results in a high degree of reproducibility within and between research groups, providing a comprehensive foundation from which standardized AIM assays can be implemented across diverse scientific and clinical settings.
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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 | 0.008 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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