Deep profiling of single T cell receptor repertoire and phenotype with targeted RNA-seq (TECH2P.927)
Bibliographic record
Abstract
Abstract Analysis of TCR repertoires is useful for monitoring T cell responses. Integrating TCR sequencing with expression of targeted genes at the single cell level allows comprehensive analysis of T cell function and specificity. RT-PCR reactions of single T cells were performed using 76 primers from VJC regions and 136 primers for targeted T cell genes. A series of nested PCR reactions incorporated with barcodes and adapters for pooled amplicons sequencing. A bioinformatics pipeline was used data analysis. 2880 single T cells were collected and tested. TCR sequences were obtained in up to 90% of the wells. Based on the CDR3 region, multiple dominant TCR clones were identified. 52 of 68 target genes were validated with sorted target-specific T cells. FOXP3, IL10, PRF1, IL13, and RUNX3 were highly expressed in memory activated cells. The specific CD8+ T cells had high frequency of CCR9, LAG3, CD8, CD62L, and IFNG expression. TGFB, TNFA, IL12, FOXP3, PD1, TBET, CTLA4, MKI67, RUNX1, and GATA3 were major biomarkers in the specific CD4 cells. The gene expression patterns were often associated with TCR repertoire CDR3 variations and/or the TCRβ and TCRα chain usage although the same TCRα/β sequences may have different target genes expressed. We expanded a method enabling sequencing of TCR repertoire and multiple functional genes in single, sorted, T cells through targeted RNA-seq technology. This approach could reveal important insights into T cell functions and clonal development.
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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 | 0.000 | 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) | 0.004 | 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".