MATE-Seq: microfluidic antigen-TCR engagement sequencing
Bibliographic record
Abstract
Adaptive immunity is based on peptide antigen recognition. Our ability to harness the immune system for therapeutic gain relies on the discovery of the T cell receptor (TCR) genes that selectively target antigens from infections, mutated proteins, and foreign agents. Here we present a method that selectively labels peptide antigen-specific CD8+ T cells using magnetic nanoparticles functionalized with peptide-MHC tetramers, isolates these specific cells within an integrated microfluidic device, and directly amplifies the TCR genes for sequencing. Critically, the identity of the peptide recognized by the TCR is preserved, providing the link between peptide and gene. The platform requires inputs on the order of just 100 000 CD8+ T cells, can be multiplexed for simultaneous analysis of multiple peptides, and performs sorting and isolation on chip. We demonstrate 1000-fold sensitivity enhancement of detecting antigen-specific TCRs relative to bulk analysis and simultaneous capture of two virus antigen-specific TCRs from a population of T cells.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".