使用大规模的T细胞谱图概况推断SARS-CoV-2暴露生物标志物的推断
Elizaveta K Vlasova1,2, Alexandra I Nekrasova3, Alexander Y Komkov4,5,6
1Institute of Translational Medicine, Pirogov Russian National Research Medical University, Ostrovityanova Ulitsa 1 Bldg. 1, Moscow, 117513, Russia.
Genome medicine
|February 12, 2026
概括
一个新的机器学习分类器通过分析T细胞受体 (TCR) 库来准确识别COVID-19暴露. 这种方法使用自适应性免疫受体测序 (AIRR-seq) 数据来评估大量人群的免疫状态.
科学领域:
- 免疫学 免疫学 免疫学
- 传染病流行病学 传染病流行病学
- 生物信息学是一种生物信息学.
背景情况:
- 随着COVID-19的爆发,人们越来越需要一种方法来追踪传染病的传播,这种方法是利用人口免疫特征来追踪传染病的传播.
- 适应性免疫受体谱系测序 (AIRR-seq) 识别T细胞受体 (TCR) 生物标志物,用于病原体特异性和免疫记忆.
- AIRR-seq检测感染印记,并有助于研究个体SARS-CoV-2反应.
研究的目的:
- 开发和应用一种机器学习方法,从TCR的特征中推断SARS-CoV-2的暴露.
- 使用AIRR-seq数据创建一个强大的COVID-19暴露评估分类器.
主要方法:
- 应用机器学习对两大AIRR-seq数据集 (>1,200个目录) 来自健康和COVID-19康复的捐赠者.
- 利用一种新的批量效应校正方法,将不同批量和协议的数据结合起来.
- 通过标准化,人类白细胞抗原 (HLA) 类型,以及对TCRα和β链序列的分析,确保了高质量的数据.
主要成果:
- 鉴定了由SARS-CoV-2暴露引起的特定TCR曲目特征.
- 开发了一个强大的,高度准确的分类器来预测COVID-19暴露.
- 证明了机器学习在分析大规模AIRR-seq数据集中的实用性.
结论:
- 开发的分类器适用于通过各种协议生成的单个TCR目录.
- 这种方法可以在大型捐赠者队列中基于AIRR-seq的免疫状态评估.
- 促进了AIRR-seq在传染病监测和免疫学研究中的更广泛应用.
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