Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Department of Statistical Science, Southern Methodist University, Dallas, TX, USA.
Nat Methods. 2021 Jan;18(1):92-99. doi: 10.1038/s41592-020-01020-3. Epub 2021 Jan 6.
Many experimental and bioinformatics approaches have been developed to characterize the human T cell receptor (TCR) repertoire. However, the unknown functional relevance of TCR profiling hinders unbiased interpretation of the biology of T cells. To address this inadequacy, we developed tessa, a tool to integrate TCRs with gene expression of T cells to estimate the effect that TCRs confer on the phenotypes of T cells. Tessa leveraged techniques combining single-cell RNA-sequencing with TCR sequencing. We validated tessa and showed its superiority over existing approaches that investigate only the TCR sequences. With tessa, we demonstrated that TCR similarity constrains the phenotypes of T cells to be similar and dictates a gradient in antigen targeting efficiency of T cell clonotypes with convergent TCRs. We showed this constraint could predict a functional dichotomization of T cells postimmunotherapy treatment and is weakened in tumor contexts.
许多实验和生物信息学方法已经被开发出来用于描述人类 T 细胞受体 (TCR) 库。然而，TCR 分析的未知功能相关性阻碍了对 T 细胞生物学的无偏解释。为了解决这个不足，我们开发了 tessa，这是一种将 TCR 与 T 细胞的基因表达整合起来以估计 TCR 对 T 细胞表型的影响的工具。tessa 利用了将单细胞 RNA-seq 与 TCR 测序相结合的技术。我们验证了 tessa，并展示了它优于仅研究 TCR 序列的现有方法的优势。通过 tessa，我们证明了 TCR 的相似性限制了 T 细胞的表型使其趋于相似，并决定了具有趋同 TCR 的 T 细胞克隆型的抗原靶向效率呈梯度变化。我们表明，这种限制可以预测免疫治疗后 T 细胞的功能二分法，并且在肿瘤环境中会减弱。
