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.

It has recently become possible to assay T-cell specificity with respect to large sets of antigens as well as T-cell receptor sequence in high-throughput single-cell experiments. We propose multiple sequence-data specific deep learning approaches to impute TCR to epitope specificity to reduce the complexity of new experiments. We found that models that treat antigens as categorical variables outperform those which model the TCR and epitope sequence jointly. Moreover, we show that variability in single-cell immune repertoire screens can be mitigated by modeling cell-specific covariates.Antigen recognition is one of the key factors of T-cell-mediated immunity. The ability to accurately predict T-cell activation upon epitope recognition would have transformative effects on many research areas from in infectious disease, autoimmunity, vaccine design, and cancer immunology, but has been thwarted by lack of training data and adequate models. Although tremendous effort has been spent on elucidating the common rules that govern the TCR-pMHC interaction, it still remains elusive. The T-cell receptor (TCR) interacts with peptides immobilized on MHC multimers (pMHC) through its three complementarity determining region (CDR) loops of the ɑ-and β-chain. The hypervariable loops CDR3ɑ and CDR3β are most commonly aligned with the presented epitope 1 and are hypothesized to be the main driver of T-cell specificity 2 . Due to lack of sufficient data, previous models for T-cell specificity were only based on the CDR3β loop 3,4 , 5 .In this study, we exploit a newly developed single-cell technology that enables the simultaneous sequencing of the paired TCR ɑ-and β-chain while determining the T-cell specificity to train multiple deep learning architectures modeling the TCR-pMHC interaction including both chains. The models include single-cell specific covariates accounting for the variability found in such data, thereby fully exploit the multiplicity of observations that can be easily sampled in single-cell screens. We show that models that include both ɑ-and β-chain