Inferring differentiation trajectories from T cell clonotype distributions

Authors

Guryleva, M. V., Danielsson, M., Tibbitt, C. A., Coquet, J. M., Murrell, B.

Abstract

Single-cell RNA sequencing (scRNA-seq) enables reconstruction of cellular differentiation trajectories, but most trajectory inference methods rely largely on gene expression and overlook lineage relationships. In T lymphocytes, the T cell receptor (TCR) provides a unique, endogenous barcode that is stably inherited during clonal expansion. Here we introduce PhyloTrajectory, a framework that leverages TCR-based clonotype frequencies together with scRNA-seq to infer T cell differentiation dynamics. By modeling clonotype frequency evolution as a continuous stochastic process, PhyloTrajectory reconstructs differentiation tree topologies from clonotype frequencies across cellular subsets. We investigate the ability to recover the underlying tree topology using simulated data, and apply our approach to CD4 T cells in murine models of allergic inflammation and viral infection. We further demonstrate that PhyloTrajectory is useful in the context of exogenously introduced barcodes. Overall, this shows that a stochastic model of clonal expansion can be used to infer cell state transitions from clonotype frequencies.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 04 Jul 2026.

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