Suppr超能文献

基于单细胞RNA测序数据的T细胞受体构建方法评估

Evaluation of T Cell Receptor Construction Methods from scRNA-Seq Data.

作者信息

Tian Ruonan, Yu Zhejian, Xue Ziwei, Wu Jiaxin, Wu Lize, Cai Shuo, Gao Bing, He Bing, Zhao Yu, Yao Jianhua, Lu Linrong, Liu Wanlu

机构信息

Department of Rheumatology and Immunology of the Second Affiliated Hospital, and Centre of Biomedical Systems and Informatics of Zhejiang University-University of Edinburgh Institute, Zhejiang University School of Medicine, Hangzhou 310003, China.

Future Health Laboratory, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314100, China.

出版信息

Genomics Proteomics Bioinformatics. 2025 Jan 15;22(6). doi: 10.1093/gpbjnl/qzae086.

Abstract

T cell receptors (TCRs) serve key roles in the adaptive immune system by enabling recognition and response to pathogens and irregular cells. Various methods have been developed for TCR construction from single-cell RNA sequencing (scRNA-seq) datasets, each with its unique characteristics. Yet, a comprehensive evaluation of their relative performance under different conditions remains elusive. In this study, we conducted a benchmark analysis utilizing experimental single-cell immune profiling datasets. Additionally, we introduced a novel simulator, YASIM-scTCR (Yet Another SIMulator for single-cell TCR), capable of generating scTCR-seq reads containing diverse TCR-derived sequences with different sequencing depths and read lengths. Our results consistently showed that TRUST4 and MiXCR outperformed others across multiple datasets, while DeRR demonstrated considerable accuracy. We also discovered that the sequencing depth inherently imposes a critical constraint on successful TCR construction from scRNA-seq data. In summary, we present a benchmark study to aid researchers in choosing the appropriate method for reconstructing TCRs from scRNA-seq data.

摘要

T细胞受体(TCR)通过实现对病原体和异常细胞的识别与反应,在适应性免疫系统中发挥关键作用。已开发出多种从单细胞RNA测序(scRNA-seq)数据集中构建TCR的方法,每种方法都有其独特的特点。然而,对它们在不同条件下的相对性能进行全面评估仍然难以实现。在本研究中,我们利用实验性单细胞免疫分析数据集进行了基准分析。此外,我们还引入了一种新型模拟器YASIM-scTCR(单细胞TCR的又一模拟器),它能够生成包含不同测序深度和读长的多种TCR衍生序列的scTCR-seq reads。我们的结果一致表明,在多个数据集中,TRUST4和MiXCR的表现优于其他方法,而DeRR也显示出相当高的准确性。我们还发现,测序深度对从scRNA-seq数据成功构建TCR存在固有的关键限制。总之,我们开展了一项基准研究,以帮助研究人员选择从scRNA-seq数据重建TCR的合适方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8403/11846667/15dfa8988ce1/qzae086f1.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验