巴黎索邦大学Encarnita Mariotti-Ferrandiz研究小组近日取得一项新成果。他们对现有的T细胞受体库分析方法进行了基准测试，结果显示其存在大的系统偏差。 2020年9月7日，《自然—生物技术》发表了这一成果。

在这项研究中，课题组研究人员系统地比较了九个商业和学术TCRseq方法，包括六个互补DNA末端快速扩增(RACE)-聚合酶连锁反应(PCR)和三个多重PCR方法，当应用到相同的T细胞样本的结果。

研究组发现，在准确性和方法本身及方法间可重复性上，对于T细胞受体α(TRA)和T细胞受体β(TRB)TCR链，存在显著差异。大多数方法显示的捕获多样性的能力上，TRA低于TRB。低RNA输入会导致生成非代表性库。5'端RACE-PCR方法的结果彼此一致，但是与基于RNA的多重PCR结果不同。

使用从108个重复中生成的计算机内元库，该课题组人员发现，一种基于基因组DNA的方法和两种基于非唯一分子标识符（UMI）RNA的方法在检测罕见克隆型方面比UMI方法更为灵敏，然而后者的克隆型量化准确度更好。

据了解，监测T细胞受体(TCR)在健康和疾病情况下可以为适应性免疫反应提供关键的见解，但当前TCR测序(TCRseq)方法的准确性尚不清楚。

附：英文原文

Title: Benchmarking of T cell receptor repertoire profiling methods reveals large systematic biases

Author: Pierre Barennes, Valentin Quiniou, Mikhail Shugay, Evgeniy S. Egorov, Alexey N. Davydov, Dmitriy M. Chudakov, Imran Uddin, Mazlina Ismail, Theres Oakes, Benny Chain, Anne Eugster, Karl Kashofer, Peter P. Rainer, Samuel Darko, Amy Ransier, Daniel C. Douek, David Klatzmann, Encarnita Mariotti-Ferrandiz

Issue&Volume: 2020-09-07

Abstract: Monitoring the T cell receptor (TCR) repertoire in health and disease can provide key insights into adaptive immune responses, but the accuracy of current TCR sequencing (TCRseq) methods is unclear. In this study, we systematically compared the results of nine commercial and academic TCRseq methods, including six rapid amplification of complementary DNA ends (RACE)-polymerase chain reaction (PCR) and three multiplex-PCR approaches, when applied to the same T cell sample. We found marked differences in accuracy and intra- and inter-method reproducibility for T cell receptor α (TRA) and T cell receptor β (TRB) TCR chains. Most methods showed a lower ability to capture TRA than TRB diversity. Low RNA input generated non-representative repertoires. Results from the 5′ RACE-PCR methods were consistent among themselves but differed from the RNA-based multiplex-PCR results. Using an in silico meta-repertoire generated from 108 replicates, we found that one genomic DNA-based method and two non-unique molecular identifier (UMI) RNA-based methods were more sensitive than UMI methods in detecting rare clonotypes, despite the better clonotype quantification accuracy of the latter.

DOI: 10.1038/s41587-020-0656-3

Source: https://www.nature.com/articles/s41587-020-0656-3