Single-cell transcriptomic and T cell antigen receptor analysis of human cytomegalovirus (hCMV)-specific memory T cells reveals effectors and pre-effectors of CD8 + - and CD4 + -cytotoxic T cells.
Raunak KarSomdeb ChattopadhyayAnjali SharmaKirti SharmaShreya SinhaGopalakrishnan Aneeshkumar ArimbasseriVeena S PatilPublished in: Immunology (2024)
Latent human cytomegalovirus (hCMV) infection can pose a serious threat of reactivation and disease occurrence in immune-compromised individuals. Although T cells are at the core of the protective immune response to hCMV infection, a detailed characterization of different T cell subsets involved in hCMV immunity is lacking. Here, in an unbiased manner, we characterized over 8000 hCMV-reactive peripheral memory T cells isolated from seropositive human donors, at a single-cell resolution by analysing their single-cell transcriptomes paired with the T cell antigen receptor (TCR) repertoires. The hCMV-reactive T cells were highly heterogeneous and consisted of different developmental and functional memory T cell subsets such as, long-term memory precursors and effectors, T helper-17, T regulatory cells (T REGs ) and cytotoxic T lymphocytes (CTLs) of both CD4 and CD8 origin. The hCMV-specific T REGs , in addition to being enriched for molecules known for their suppressive functions, showed enrichment for the interferon response signature gene sets. The hCMV-specific CTLs were of two types, the pre-effector- and effector-like. The co-clustering of hCMV-specific CD4-CTLs and CD8-CTLs in both pre-effector as well as effector clusters suggest shared transcriptomic signatures between them. The huge TCR clonal expansion of cytotoxic clusters suggests a dominant role in the protective immune response to CMV. The study uncovers the heterogeneity in the hCMV-specific memory T cells revealing many functional subsets with potential implications in better understanding of hCMV-specific T cell immunity. The data presented can serve as a knowledge base for designing vaccines and therapeutics.
Keyphrases
- deep learning
- single cell
- artificial intelligence
- rna seq
- regulatory t cells
- endothelial cells
- dendritic cells
- machine learning
- type iii
- healthcare
- risk assessment
- high throughput
- nk cells
- big data
- gene expression
- induced pluripotent stem cells
- pluripotent stem cells
- epstein barr virus
- cell death
- cell proliferation
- oxidative stress