T‐cell activation–induced marker assays in health and disease
Bibliographic record
Abstract
Activation-induced marker (AIM) assays have proven to be an accessible and rapid means of antigen-specific T-cell detection. The method typically involves short-term incubation of whole blood or peripheral blood mononuclear cells with antigens of interest, where autologous antigen-presenting cells process and present peptides in complex with major histocompatibility complex (MHC) molecules. Recognition of peptide-MHC complexes by T-cell receptors then induces upregulation of activation markers on the T cells that can be detected by flow cytometry. In this review, we highlight the most widely used activation markers for assays in the literature while identifying nuances and potential downfalls associated with the technique. We provide a summary of how AIM assays have been used in both discovery science and clinical studies, including studies of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) immunity. This review primarily focuses on AIM assays using human blood or peripheral blood mononuclear cell samples, with some considerations noted for tissue-derived T cells and nonhuman samples. AIM assays are a powerful tool that enables detailed analysis of antigen-specific T-cell frequency, phenotype and function without needing to know the precise antigenic peptides and their MHC restriction elements, enabling a wider analysis of immunity generated following infection and/or vaccination.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".