Linking the T cell receptor to the single cell transcriptome in antigen‐specific human T cells
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
Heterogeneity of T cells is a hallmark of a successful adaptive immune response, harnessing the vast diversity of antigen-specific T cells into a coordinated evolution of effector and memory outcomes. The T cell receptor (TCR) repertoire is highly diverse to account for the highly heterogeneous antigenic world. During the response to a virus multiple individual clones of antigen specific CD8+ (Ag-specific) T cells can be identified against a single epitope and multiple epitopes are recognised. Advances in single-cell technologies have provided the potential to study Ag-specific T cell heterogeneity at both surface phenotype and transcriptome levels, thereby allowing investigation of the diversity within the same apparent sub-population. We propose a new method (VDJPuzzle) to reconstruct the native TCRαβ from single cell RNA-seq data of Ag-specific T cells and then to link these with the gene expression profile of individual cells. We applied this method using rare Ag-specific T cells isolated from peripheral blood of a subject who cleared hepatitis C virus infection. We successfully reconstructed productive TCRαβ in 56 of a total of 63 cells (89%), with double α and double β in 18, and 7% respectively, and double TCRαβ in 2 cells. The method was validated via standard single cell PCR sequencing of the TCR. We demonstrate that single-cell transcriptome analysis can successfully distinguish Ag-specific T cell populations sorted directly from resting memory cells in peripheral blood and sorted after ex vivo stimulation. This approach allows a detailed analysis of the TCR diversity and its relationship with the transcriptional profile of different clones.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it