TransTCR: Integrating TCRs and Transcriptomes Through Optimal Transport for Antigen Specificity Prediction

Recent advances in single-cell immune profiling enable simultaneous measurement of T cell receptors (TCRs) and transcriptomes, offering unprecedented opportunities to characterize adaptive immunity at single-cell resolution. However, the substantial heterogeneity between these modalities poses challenges for effective integration, and existing approaches often rely on simplistic alignment assumptions that may overlook modality-specific biological signals. To address this challenge, we introduce TransTCR, a multimodal learning framework that integrates optimal transport (OT) with contrastive learning to align TCR and transcriptomic representations. Specifically, TransTCR first extracts informative features from each modality using pretrained foundation models, then performs OT-based projection into a shared latent space to achieve distribution-aware alignment. A bidirectional contrastive objective further refines instance-level correspondence by maximizing agreement between the paired TCR-RNA profiles. Extensive experiments demonstrate that TransTCR substantially outperforms existing single-modality and multimodal baselines across antigen specificity recognition tasks, achieving state-of-the-art performance in intra-donor antigen specificity prediction, inter-donor antigen specificity prediction, and clustering evaluations. Overall, TransTCR provides a powerful computational tool for integrating and analyzing multimodal T cell data, facilitating deeper insight into adaptive immune responses.