Divergent clonal differentiation trajectories of T cell exhaustion

Abstract

SUMMARYT cells activated by chronic antigen exposure in the setting of viral infections or cancer can adopt an exhausted T cell (Tex) state, characterized by reduced effector function and proliferative capacity, and the upregulation of inhibitory receptors. However, whether all antigen-specific T cell clones follow the same molecular and cellular Tex differentiation trajectory remains unclear. Here, we generate a single-cell multi-omic atlas of T cell exhaustion that redefines the phenotypic diversity and molecular regulation of Tex phenotypes. Longitudinal analysis during chronic viral infection identifies an early effector phenotype that is epigenetically primed for Tex differentiation and two late-stage Tex cell states with either a terminal exhaustion or a killer cell lectin-like receptor (KLR)-expressing cytotoxic gene signature. We define clonal trajectories of antigen-specific T cells using paired single-cell RNA and T cell receptor sequencing and reveal distinct differentiation trajectories resulting in terminal Tex-biased, KLR Tex-biased, or divergent clones that differentiate into both phenotypes. Comparison of Tex phenotypes among shared T cell clones that traffic to multiple organs reveals that clonal differentiation trajectories are maintained across tissues. Finally, we show that differences in clonal differentiation trajectory are driven by TCR signal strength, whereby high-affinity T cell clones preferentially adopt a terminal Tex fate, while low-affinity clones adopt an effector-like KLR Tex fate that is detectable long-term but depleted in high antigen settings. These findings reveal clonal heterogeneity in the T cell response to chronic antigen and genomic programs that underlie Tex fates and persistence.HighlightsA single-cell atlas of T cell exhaustion identifies novel early effector and KLR Tex states.Clonal T cell analysis defines divergent differentiation trajectories during chronic viral infection leading to terminal and KLR Tex fates.The heterogeneity of the Tex pool arises from three primary differentiation patterns and are differentially persistent in the setting of high antigen.Clonal Tex differentiation patterns are conserved across organ sites and driven by TCR signal strength.

Genome-wide CRISPR screens of T cell exhaustion identify chromatin remodeling factors that limit T cell persistence

“…Finally, we analyzed chromatin accessibility at transcription factor (TF) binding sites using chromVAR (Schep et al, 2017), which showed that TF motifs previously associated with terminal exhaustion, including Batf, Fos, Jun, and Nr4a motifs were highly accessible in vitro at day 10. Moreover, we observed progressive loss of accessibility at naive and progenitor exhaustion-associated Lef1 and Tcf7 motifs, early increased accessibility of NF-κB and Nfat motifs, and later increased accessibility of AP-1 and Nr4a motifs, mirroring the progression of TF activity observed in T cell exhaustion in vivo (Figure 1G) (Lynn et al, 2019; Miller et al, 2019; Beltra et al, 2020; Daniel et al, 2021). In summary, these results demonstrate that the in vitro T cell exhaustion assay displayed hallmark functional and genomic features of in vivo T cell exhaustion, including expression of inhibitory receptors, impaired proliferation, cytokine secretion, and tumor killing, and global chromatin remodeling of dysfunctional T cell gene loci.…”

“…Cluster 1 cells expressed high levels of Klf2 and S1pr1 (T effector memory; T EM ), Cluster 2 expressed high levels of interferon stimulated genes (ISGs) including Mx1 (T ISG ), Cluster 3 expressed high levels of Tnfrsf9 (encoding 41BB) and Cd160 (T-41BB), Cluster 4 expressed high levels of progenitor exhaustion genes including Pdcd1 , Tcf7 and Slamf6 (T EX Prog ) , Cluster 5 expressed the highest levels of inhibitory receptors Pdcd1 , Lag3 , and Havcr2 (T EX Term), and Cluster 6 consisted primary of cycling cells, marked by Mki67 and confirmed by cell cycle analysis (T-Cycling; Figure S9C-D). To further refine the cluster identities, we generated gene signatures from previously published CD8 + T cell types present in acute or chronic LCMV infection in vivo (Figure S9E-F) (Daniel et al, 2021). We used the top 100 marker genes for each LCMV T cell cluster to score each single cell in our Perturb-seq dataset according to the average expression of these signature gene sets.…”

“…(F) Comparison of gene sets upregulated by perturbation of cBAF subunits, INO80 subunits, or Pdcd1 , Gata3 , or Arid2 . (G) Enrichments of upregulated and downregulated gene sets in LCMV expression data (Daniel et al, 2021). Module scores of each gene set were computed for each single cell in the LCMV dataset, averaged by cluster, and then z-scored to obtain the indicated enrichment z-scores.…”

“…* p < 0.05, *** p < 0.001. (Daniel et al, 2021). Module scores of each gene set were computed for each single cell in the LCMV dataset, averaged by cluster, and then z-scored to obtain the indicated enrichment z-scores.…”

“…(E) Expanded reference LCMV dataset with single cell profiles colored by LCMV cluster. Data from (Daniel et al, 2021). (F) Expanded LCMV dataset with single cell profiles colored by LCMV infection (Acute corresponds to Armstrong infection while Chronic corresponds to Clone 13) and time point (Day 8 or Day 21 post infection).…”