The CD8+ T cell tolerance checkpoint triggers a distinct differentiation state defined by protein translation defects.

Peripheral CD8+ T cell tolerance is a checkpoint in both autoimmune disease and anti-cancer immunity. Despite its importance, the relationship between tolerance-induced states and other CD8+ T cell differentiation states remains unclear. Using flow cytometric phenotyping, single-cell RNA sequencing (scRNA-seq), and chromatin accessibility profiling, we demonstrated that in vivo peripheral tolerance to a self-antigen triggered a fundamentally distinct differentiation state separate from exhaustion, memory, and functional effector cells but analogous to cells defectively primed against tumors. Tolerant cells diverged early and progressively from effector cells, adopting a transcriptionally and epigenetically distinct state within 60 h of antigen encounter. Breaching tolerance required the synergistic actions of strong T cell receptor (TCR) signaling and inflammation, which cooperatively induced gene modules that enhanced protein translation. Weak TCR signaling during bystander infection failed to breach tolerance due to the uncoupling of effector gene expression from protein translation. Thus, tolerance engages a distinct differentiation trajectory enforced by protein translation defects.

Identification of human progenitors of exhausted CD8+ T cells associated with elevated IFN-γ response in early phase of viral infection.

T cell exhaustion is a hallmark of hepatitis C virus (HCV) infection and limits protective immunity in chronic viral infections and cancer. Limited knowledge exists of the initial viral and immune dynamics that characterise exhaustion in humans. We studied longitudinal blood samples from a unique cohort of individuals with primary infection using single-cell multi-omics to identify the functions and phenotypes of HCV-specific CD8+ T cells. Early elevated IFN-γ response against the transmitted virus is associated with the rate of immune escape, larger clonal expansion, and early onset of exhaustion. Irrespective of disease outcome, we find heterogeneous subsets of progenitors of exhaustion, based on the level of PD-1 expression and loss of AP-1 transcription factors. Intra-clonal analysis shows distinct trajectories with multiple fates and evolutionary plasticity of precursor cells. These findings challenge the current paradigm on the contribution of CD8+ T cells to HCV disease outcome and provide data for future studies on T cell differentiation in human infections.

Antigen-driven EGR2 expression is required for exhausted CD8+ T cell stability and maintenance.

Chronic stimulation of CD8+ T cells triggers exhaustion, a distinct differentiation state with diminished effector function. Exhausted cells exist in multiple differentiation states, from stem-like progenitors that are the key mediators of the response to checkpoint blockade, through to terminally exhausted cells. Due to its clinical relevance, there is substantial interest in defining the pathways that control differentiation and maintenance of these subsets. Here, we show that chronic antigen induces the anergy-associated transcription factor EGR2 selectively within progenitor exhausted cells in both chronic LCMV and tumours. EGR2 enables terminal exhaustion and stabilizes the exhausted transcriptional state by both direct EGR2-dependent control of key exhaustion-associated genes, and indirect maintenance of the exhausted epigenetic state. We show that EGR2 is a regulator of exhaustion that epigenetically and transcriptionally maintains the differentiation competency of progenitor exhausted cells.

Single-Cell Transcriptome Analysis of T Cells.

Single-cell RNA-seq (scRNA-seq) has provided novel routes to investigate the heterogeneous populations of T cells and is rapidly becoming a common tool for molecular profiling and identification of novel subsets and functions. This chapter offers an experimental and computational workflow for scRNA-seq analysis of T cells. We focus on the analyses of scRNA-seq data derived from plate-based sorted T cells using flow cytometry and full-length transcriptome protocols such as Smart-Seq2. However, the proposed pipeline can be applied to other high-throughput approaches such as UMI-based methods. We describe a detailed bioinformatics pipeline that can be easily reproduced and discuss future directions and current limitations of these methods in the context of T cell biology.