Learning Human T Cell Behaviors through Generative AI Embeddings of T Cell Receptors

Chen, D. G.; Su, Y.; Heath, J. R.

doi:10.1101/2025.06.16.660016

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bioRxiv · 10.1101/2025.06.16.660016

Learning Human T Cell Behaviors through Generative AI Embeddings of T Cell Receptors

Chen, D. G.·Su, Y.·Heath, J. R.

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Abstract

T cells interact with the world through T cell receptors (TCRs). The extent to which TCRs determine T cell behavior has not been comprehensively characterized. Our Tarpon model leverages advances in generative artificial intelligence to synthesize large-scale (>1M sequences) TCR atlases across human development and diseases into actionable insights. Tarpon creates: 1) bespoke sampling functions generating realistic Ag-specific TCRs, 2) embeddings revealing CD4+ and CD8+ single-positive TCR repertoires as distinct with divergent physiochemical properties, and 3) cross-dataset mappings of T cell states that validate fetal CD4+ versus CD8+ TCR differences in adults and find fetal type I innate T cells to map to MAIT and KIR+ adult CD8+ T cells which we verify via whole transcriptome analysis. Tarpon is a resource as a reference of TCRs across human physiological states and as a computational framework to create interpretable TCR embeddings, via physicochemical associations, that have broad implications for the field.

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Chen, D. G., Su, Y., Heath, J. R.. 2025-06-22. Learning Human T Cell Behaviors through Generative AI Embeddings of T Cell Receptors. https://doi.org/10.1101/2025.06.16.660016

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