Exploratory trajectory inference reveals convergent lineages for CD8 T cells in chronic LCMV infection

In chronic infections, CD8 T cells acquire a state termed exhaustion characterized by impaired effector functions and expression of co-inhibitory receptors in response to continuous TCR stimulation. The pool of exhausted T cells has been shown to harbor multiple functionally distinct populations with memory-like and effector-like features, though differentiation and lineage relations between these remain unclear. Here, we present a high-resolution scRNAseq time-series analysis from the beginning of infection to established exhaustion in CD8 T cells together with a novel exploratory trajectory inference approach to discover unusual differentiation trajectories. Specifically, we used simulation-based trajectory inference to identify start and end states and connections between them. We identified a branch region early during chronic infection where cells separate into an exhausted and a memory-like lineage. Adoptive transfer confirmed fate predictions for exhaustion. However, these experiments indicated that CD8 T cells with predicted memory-like fate differentiated into both memory-like and exhaustion states. We developed an approach for exploratory trajectory inference based on unsupervised analysis of simulated Markov chains to identify novel differentiation trajectories explaining this behavior. It allowed us to identify a convergent differentiation trajectory traversing memory-like states and ending in the exhausted population, corroborating the results from the adoptive transfer experiment and complementing details on intermediate states of the full differentiation trajectory. In this work, exploratory trajectory inference enabled the detection of convergent differentiation trajectories during chronic LCMV infection. We expect it to be applicable in other scRNAseq-based studies aiming at comprehensive characterization of differentiation trajectories with bifurcating and convergent topologies.