An information-theoretic framework for inferring T cell dynamics from single-cell RNA and T cell receptor sequencing

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Abstract
Single-cell T cell receptor (TCR) repertoire sequencing enables simultaneous profiling of gene expression and TCR sequences, providing a coregistered measure of T cell phenotypic diversity and clonal dynamics. However, principled probabilistic frameworks for joint modeling of clonotype and phenotype have remained limited. Here we introduce TCRi, a hierarchical Bayesian information-theoretic framework that infers the joint probability distribution between genetic clonotype and transcriptional phenotype from paired single-cell transcriptomic and TCR sequencing data. TCRi treats phenotype assignment as a probabilistic inference problem: a variational autoencoder embeds single-cell expression into a structured latent space, and a hierarchical Bayesian model learns clonotype-specific phenotype distributions through principled information-sharing across clonotypes, patients and covariates. From these distributions, TCRi computes information-theoretic quantities: clonotypic entropy, phenotypic entropy, phenotypic flux and mutual information between clonotype and phenotype. When TCRi was applied to four real-world datasets comprising 167,118 CD8 T cells from patients with cancer treated with anti-PD-(L)1 immune checkpoint blockade, mutual information emerged as a robust discriminator of therapeutic response.
Funding Information
  • National Cancer Institute
  • Center for Scientific Review
  • Stand Up To Cancer
  • U.S. Department of War
  • National Institutes of Health

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