It has recently become possible to simultaneously assay T‐cell specificity with respect to large sets of antigens and the T‐cell receptor sequence in high‐throughput single‐cell experiments. Leveraging this new type of data, we propose and benchmark a collection of deep learning architectures to model T‐cell specificity in single cells. In agreement with previous results, we found that models that treat antigens as categorical outcome variables outperform those that model the TCR and antigen sequence jointly. Moreover, we show that variability in single‐cell immune repertoire screens can be mitigated by modeling cell‐specific covariates. Lastly, we demonstrate that the number of bound pMHC complexes can be predicted in a continuous fashion providing a gateway to disentangle cell‐to‐dextramer binding strength and receptor‐to‐pMHC affinity. We provide these models in the Python package TcellMatch to allow imputation of antigen specificities in single‐cell RNA‐seq studies on T cells without the need for MHC staining.

中文翻译：

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基于 TCR CDR3 区域预测单个 T 细胞的抗原特异性。

最近，在高通量单细胞实验中同时测定 T 细胞对大量抗原和 T 细胞受体序列的特异性已成为可能。利用这种新型数据，我们提出了一系列深度学习架构并对其进行了基准测试，以对单细胞中的 T 细胞特异性进行建模。与之前的结果一致，我们发现将抗原视为分类结果变量的模型优于联合模拟 TCR 和抗原序列的模型。此外，我们表明，可以通过对细胞特异性协变量进行建模来减轻单细胞免疫库筛选的变异性。最后，我们证明结合的 pMHC 复合物的数量可以以连续的方式预测，为解开细胞与右旋聚体的结合强度和受体与 pMHC 亲和力提供了途径。我们在 Python 包 TcellMatch 中提供这些模型，以便在 T 细胞的单细胞 RNA-seq 研究中估算抗原特异性，而无需 MHC 染色。