Department of Genomics of Adaptive Immunity, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 117997 Moscow, Russia.
Department of Molecular Technologies, Pirogov Russian National Research Medical University, 117997 Moscow, Russia.
Proc Natl Acad Sci U S A. 2018 Dec 11;115(50):12704-12709. doi: 10.1073/pnas.1809642115. Epub 2018 Nov 20.
T cell receptor (TCR) repertoire data contain information about infections that could be used in disease diagnostics and vaccine development, but extracting that information remains a major challenge. Here we developed a statistical framework to detect TCR clone proliferation and contraction from longitudinal repertoire data. We applied this framework to data from three pairs of identical twins immunized with the yellow fever vaccine. We identified 600 to 1,700 responding TCRs in each donor and validated them using three independent assays. While the responding TCRs were mostly private, albeit with higher overlap between twins, they could be well-predicted using a classifier based on sequence similarity. Our method can also be applied to samples obtained postinfection, making it suitable for systematic discovery of new infection-specific TCRs in the clinic.
T 细胞受体 (TCR) 库数据包含有关感染的信息，可用于疾病诊断和疫苗开发，但提取这些信息仍然是一个主要挑战。在这里，我们开发了一个统计框架，用于从纵向库数据中检测 TCR 克隆的增殖和收缩。我们将该框架应用于三对用黄热病疫苗免疫的同卵双胞胎的数据。我们在每个供体中鉴定了 600 到 1700 个反应性 TCR，并使用三种独立的检测方法对其进行了验证。虽然反应性 TCR 主要是特异性的，但双胞胎之间的重叠率更高，但可以使用基于序列相似性的分类器很好地预测它们。我们的方法也可以应用于感染后获得的样本，使其适合在临床上系统地发现新的感染特异性 TCR。
