Department of Computer Science, Aalto University, Espoo, Finland.
Translational Immunology Research program and Department of Clinical Chemistry and Hematology, University of Helsinki, Helsinki, Finland.
PLoS Comput Biol. 2021 Mar 25;17(3):e1008814. doi: 10.1371/journal.pcbi.1008814. eCollection 2021 Mar.
Adaptive immune system uses T cell receptors (TCRs) to recognize pathogens and to consequently initiate immune responses. TCRs can be sequenced from individuals and methods analyzing the specificity of the TCRs can help us better understand individuals' immune status in different disorders. For this task, we have developed TCRGP, a novel Gaussian process method that predicts if TCRs recognize specified epitopes. TCRGP can utilize the amino acid sequences of the complementarity determining regions (CDRs) from TCRα and TCRβ chains and learn which CDRs are important in recognizing different epitopes. Our comprehensive evaluation with epitope-specific TCR sequencing data shows that TCRGP achieves on average higher prediction accuracy in terms of AUROC score than existing state-of-the-art methods in epitope-specificity predictions. We also propose a novel analysis approach for combined single-cell RNA and TCRαβ (scRNA+TCRαβ) sequencing data by quantifying epitope-specific TCRs with TCRGP and identify HBV-epitope specific T cells and their transcriptomic states in hepatocellular carcinoma patients.
适应性免疫系统利用 T 细胞受体 (TCRs) 识别病原体，并由此引发免疫反应。可以从个体中对 TCRs 进行测序，而分析 TCRs 特异性的方法可以帮助我们更好地了解个体在不同疾病状态下的免疫状况。为此，我们开发了 TCRGP，这是一种新颖的高斯过程方法，可用于预测 TCR 是否识别特定的表位。TCRGP 可以利用 TCRα 和 TCRβ 链的互补决定区 (CDRs) 的氨基酸序列，并学习哪些 CDRs 对识别不同的表位很重要。我们利用针对特定表位的 TCR 测序数据进行了全面评估，结果表明，TCRGP 在预测针对特定表位的准确性方面，平均而言优于现有的最先进方法。我们还提出了一种新的分析方法，用于对单细胞 RNA 和 TCRαβ（scRNA+TCRαβ）测序数据进行联合分析，通过 TCRGP 对特定表位的 TCR 进行量化，并鉴定乙型肝炎病毒 (HBV)-表位特异性 T 细胞及其在肝癌患者中的转录组状态。
