Abstract 6600: Rapid generation of TCR-engineered T lymphocytes by linking the single cell transcriptome to its corresponding T cell receptor in antigen specific T cells
Bibliographic record
Abstract
Abstract Adoptive cell transfer of T-cell receptor (TCR)-engineered T cells has emerged as a powerful and potentially curative therapy for cancers and infectious diseases. However, how to rapidly identify and obtain therapeutic TCRs remains a major challenge in this field. Here, we simultaneously performed deep single-cell RNA and TCR V(D)J sequencing on 879 single T cells from in vitro stimulated CMV pp65NLV-specific CD8+ T cells. The correlation analysis of single-cell transcriptome and TCR V(D)J sequences revealed a differential distribution of T cell transcriptomic profiles among TCR clones. Two distinct cell types were identified in the T cell population. TYPE I T cell subtype, which represents the gene signatures of T cell activation and cytotoxicity, mainly includes TCR clone 1, 2 & 4, while TYPE II subtype, which represents the gene signatures of naïve status, mainly includes TCR clone 3 and other low frequency clones. Based on their temporal phases of T cell activation, the selected TCR sequences from clone 1 (TCRZWX1) and clone 3 (TCRZWX3) were functionally validated by constructing TCR-engineered T cells in vitro. The two clones showed distinct cell stages corresponding to their transcriptome patterns. TCRZWX1 transgenic T cells displayed effector functions including secretion of pro-inflammatory cytokines, IFN-γ and TNF-α, and cytotoxicity in response to CMV pp65NLV peptide. However, TCRZWX3 transgenic T cells displayed non-detectable levels of IFN-γ, which might correlate with a low affinity TCR. Our results provide a novel approach using correlative analysis of the single-cell transcriptome and TCR V(D)J sequences for rapidly identifying potential therapeutic TCRs for T cell therapy. Citation Format: Linnan Zhu, Fei Wang, Qumiao Xu, Hai-Xi Sun, Ziyi Li, Yanling Liang, Zhenkun Zhuang, Ying Gu, Cheng-chi Chao. Rapid generation of TCR-engineered T lymphocytes by linking the single cell transcriptome to its corresponding T cell receptor in antigen specific T cells [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 6600.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".