Clonally Expanded Virus-Specific CD8 T Cells Acquire Diverse Transcriptional Phenotypes During Acute, Chronic, and Latent Infections

Abstract

CD8+ T cells play a crucial role in the control and resolution of viral infections and can adopt a wide range of phenotypes and effector functions depending on the inflammatory context and the duration and extent of antigen exposure. Similarly, viral infections can exert diverse selective pressures on populations of clonally related T cells. Technical limitations have nevertheless made it challenging to investigate the relationship between clonal selection and transcriptional phenotypes of virus-specific T cells. We therefore performed single-cell T cell receptor (TCR) repertoire and transcriptome sequencing of virus-specific CD8 T cells in murine models of acute, chronic and latent infection. We observed clear infection-specific populations corresponding to memory, effector, exhausted, and inflationary phenotypes. We further uncovered a mouse-specific and polyclonal T cell response, despite all T cells sharing specificity to a single viral epitope, which was accompanied by stereotypic TCR germline gene usage in all three infection types. Persistent antigen exposure during chronic and latent viral infections resulted in a higher proportion of clonally expanded T cells relative to acute infection. We furthermore observed a relationship between transcriptional heterogeneity and clonal expansion for all three infections, with highly expanded clones having distinct transcriptional phenotypes relative to less expanded clones. Together our work relates clonal selection to gene expression in the context of viral infection and further provides a dataset and accompanying software for the immunological community.

Single-cell immune repertoire sequencing of B and T cells in murine models of infection and autoimmunity

“…On an individual-chain level, similar TCR β V genes were expressed across all infection conditions (Figure S3A, S3B), although on a clonal level, no group-specific pattern of TRAV-TRBV pairings could be observed (Figure S3C). This is in contrast to previously reported results where TCR repertoires against a single peptide demonstrated stereotypic germline gene usage (Kuhn et al 2022), suggesting that polyclonal repertoires encoding diverse TCR specificities are less dominated by certain combinations of germline genes.…”

“…The abundance of certain TCR V germline genes or their pairings have been previously observed in virus-specific T cells in several infection conditions (Qu et al 2016;Izraelson et al 2018;Welten et al 2020;Yermanos et al 2020;Khatun et al 2021;Kuhn et al 2022). Additionally, a previous study reported preferential usage of TRAV-TRAJ gene combination in Th1 and Tfh cells, indicating a contribution of the TCR α chain to lineage commitment (Khatun et al 2021).…”

“…Here, we recovered GEX data for 36,590 cells with an average of 1,001 median genes per cell (Figure S4A). We then performed unsupervised clustering and UMAP on cells from both experiments in addition to our recently published GP33-specific CD8+ T cells 28 dpi (Kuhn et al 2022). This resulted in 10 clusters, of which all with the exception of cluster 10 were represented by cells from all three experiments (Figure 3B, 3C).…”

“…We recently demonstrated that gp 33-41 -specific (GP33) CD8+ T cells adopted distinct transcriptional and selection phenotypes 28 days post acute, chronic, and latent viral infection (Kuhn et al 2022).…”

ePlatypus: an ecosystem for computational analysis of immunogenomics data

“…To demonstrate several use cases of the ePlatypus computational ecosystem, we integrated and analyzed multiple single-cell transcriptomes and immune receptor repertoires across different disease conditions, viral infections, and vaccination studies. We directly downloaded murine T cell repertoires from previously published datasets containing both CD4 and CD8 T cells from conditions such as acute and chronic viral infections (Khatun et al, 2021;Kuhn et al, 2022;Merkenschlager et al, 2021;Shlesinger et al, 2022), homeostatic aging and experimental autoimmune encephalomyelitis (Shlesinger et al, 2022) (Table S2). Following transcriptional integration with Harmony (Korsunsky et al, 2019), which aims to reduce batch effects across different datasets, we visualized all cells using uniform manifold approximation projection (UMAP) (Figure S11A,S11B).…”