Predicting antigen specificity of single T cells based on TCR CDR3 regions. Issue 8 (11th August 2020)

• Record Type: Journal Article
• Authors: Fischer, David S; Wu, Yihan; Schubert, Benjamin; Theis, Fabian J
• Is Part Of: Molecular systems biology. Volume 16:Issue 8(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-08-11
• Subjects: antigen specificity -- multimodal -- single cell -- supervised learning -- T‐cell receptors; Molecular biology -- Periodicals; Systems biology -- Periodicals; 572.8
• DOI: 10.15252/msb.20199416
• Languages: English
• ISSNs: 1744-4292

Abstract

Abstract: 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. Synopsis: TcellMatch is a deep‐learning based algorithm that predicts the antigen specificity of single T cells based on multimodal single‐cell experiments that measure pMHC binding and T‐cell receptor sequences among other properties. pMHC measurements are predicted in a large single‐cell data set with > 100, 000 cells, additionally using TCR‐antigen pairs from IEDB and VDJdb. Benchmarking categorical models of antigens with antigen‐embedding models indicates that categorical models are often preferable. The study highlights the need to measure TCR specificity for a larger repertoire of antigens to generalize models to unseen antigens. Abstract : TcellMatch is a deep‐learning based algorithm that predicts the antigen specificity of single T cells based on multimodal single‐cell experiments that measure pMHC binding and T‐cell receptor sequences among other properties.