Quantitative V gene–targeted T cell receptor sequencing as a biomarker in type 1 diabetes
Bibliographic record
Abstract
Developing biomarkers to quantitatively monitor disease-specific T cell activity is crucial for assessing type 1 diabetes (T1D) progression and evaluating immunotherapies. This study presents an approach using V gene-targeted sequencing to quantify T cell receptor (TCR) clonotypes as biomarkers for pathogenic T cells in T1D. We identified "public" TCR clonotypes shared among multiple nonobese diabetic (NOD) mice and human organ donors, with a subset expressed exclusively by islet-antigen-reactive T cells in those with T1D. Employing V gene-targeted sequencing of only TCRs containing TRAV16/16D allowed quantitative detection of the public islet-antigen-reactive TCR clonotypes in peripheral blood of NOD mice. Frequencies of these public TCR clonotypes distinguished prediabetic NOD mice from those protected from diabetes. In human islets, public TCR clonotypes identical to preproinsulin-specific clones were exclusively found in T1D donors. This quantifiable TCR sequencing approach uncovered public, disease-specific clonotypes in T1D, providing biomarker candidates to monitor pathogenic T cell frequencies in blood for assessing disease activity and therapeutic response.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".