The canine T cell receptor repertoire
Bibliographic record
Abstract
BACKGROUND: Tseek is a method of sequencing T cell receptor (TCR) repertoires with minimal bias. This work aimed to develop methods to characterize the TCR repertoire in dogs, identify influences such as genetic lineage and age, and evaluate the use of repertoires to monitor immune status in dogs. METHODS: Two studies were conducted to develop the techniques and characterize the effect of individual, breed, and age. One study analyzed RNA data from individuals (n = 32), 8 from each of 4 breeds, sampled at 2 time points a year apart. The second, lifestage study, used individuals within a single breed (Labrador Retriever) with ages dispersed across a broad range (0.2 to 12 yr, n = 50). Tseek was used to process samples for sequencing, to identify the V, and J segments to annotate the CDR3, which were then analyzed to draw inferences. RESULTS: The TCR repertoires had signatures of breeds, and of the individual, with stability over at least a year. Across the lifestage study, littermate-specific characteristics were not detected, but an age-related effect was observed: older dogs exhibited reduced diversity characterized by a greater abundance of individual-specific high-frequency clones, while puppies had a more diverse repertoire. CONCLUSION: An individual's TCR repertoire includes stable information, indicative of the individual, breed, and age-related decline. The α and β chain repertoires had distinct properties in the breed-specific signatures, indicating differential influences on their selection, despite their pairing in each T cell. Consistent, age-related changes can be seen in the repertoire, but their impact on immune system needs to be delineated.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".