Department of Pathology, University of Cambridge, Cambridge, UK.
Department of Health Data Science, Institute of Population Health, University of Liverpool, Liverpool, UK.
Cell Rep Med. 2021 Feb 16;2(2):100192. doi: 10.1016/j.xcrm.2021.100192. Epub 2021 Jan 16.
The identification of SARS-CoV-2-specific T cell receptor (TCR) sequences is critical for understanding T cell responses to SARS-CoV-2. Accordingly, we reanalyze publicly available data from SARS-CoV-2-recovered patients who had low-severity disease (n = 17) and SARS-CoV-2 infection-naive (control) individuals (n = 39). Applying a machine learning approach to TCR beta (TRB) repertoire data, we can classify patient/control samples with a training sensitivity, specificity, and accuracy of 88.2%, 100%, and 96.4% and a testing sensitivity, specificity, and accuracy of 82.4%, 97.4%, and 92.9%, respectively. Interestingly, the same machine learning approach cannot separate SARS-CoV-2 recovered from SARS-CoV-2 infection-naive individual samples on the basis of B cell receptor (immunoglobulin heavy chain; IGH) repertoire data, suggesting that the T cell response to SARS-CoV-2 may be more stereotyped and longer lived. Following validation in larger cohorts, our method may be useful in detecting protective immunity acquired through natural infection or in determining the longevity of vaccine-induced immunity.
鉴定 SARS-CoV-2 特异性 T 细胞受体 (TCR) 序列对于了解 T 细胞对 SARS-CoV-2 的反应至关重要。因此，我们重新分析了来自 SARS-CoV-2 康复患者（轻症，n=17）和 SARS-CoV-2 感染未感染者（对照组，n=39）的公开可用数据。我们应用机器学习方法对 TCR beta (TRB) 库数据进行分析，可以将患者/对照样本的训练灵敏度、特异性和准确度分别分类为 88.2%、100%和 96.4%，测试灵敏度、特异性和准确度分别为 82.4%、97.4%和 92.9%。有趣的是，相同的机器学习方法不能根据 B 细胞受体（免疫球蛋白重链；IGH）库数据将 SARS-CoV-2 从 SARS-CoV-2 感染未感染者样本中分离出来，这表明 T 细胞对 SARS-CoV-2 的反应可能更刻板，寿命更长。在更大的队列中验证后，我们的方法可能有助于检测通过自然感染获得的保护性免疫，或确定疫苗诱导免疫的持久性。
