To establish a foundation for analyzing T cell activation and functional status, Szabo et al. utilized single cell RNA-sequencing (scRNAseq) for cataloging the heterogeneity and functional status of resting and stimulated T cells from healthy human blood, lymphoid, and mucosal tissues. Distinct CD4+/CD8+ T cell expression signatures distinguished blood and tissue subsets, while conserved signatures identified CD4+ T cell activation states across all tested tissues. In tumors, scRNAseq identified cytotoxic T cells, cytokine-producing CD8+ T cells, Tregs, and resting CD4+ T cells as predominant phenotypes, however, activated CD4+ T cells were absent.
Contributed by Samuel Goldman
Human T cells coordinate adaptive immunity in diverse anatomic compartments through production of cytokines and effector molecules, but it is unclear how tissue site influences T cell persistence and function. Here, we use single cell RNA-sequencing (scRNA-seq) to define the heterogeneity of human T cells isolated from lungs, lymph nodes, bone marrow and blood, and their functional responses following stimulation. Through analysis of >50,000 resting and activated T cells, we reveal tissue T cell signatures in mucosal and lymphoid sites, and lineage-specific activation states across all sites including distinct effector states for CD8(+) T cells and an interferon-response state for CD4(+) T cells. Comparing scRNA-seq profiles of tumor-associated T cells to our dataset reveals predominant activated CD8(+) compared to CD4(+) T cell states within multiple tumor types. Our results therefore establish a high dimensional reference map of human T cell activation in health for analyzing T cells in disease.
Author Info: (1) Columbia Center for Translational Immunology, Columbia University Irving Medical Center, New York, NY, USA. (2) Department of Systems Biology, Columbia University Irving Medica
Author Info: (1) Columbia Center for Translational Immunology, Columbia University Irving Medical Center, New York, NY, USA. (2) Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA. (3) Columbia Center for Translational Immunology, Columbia University Irving Medical Center, New York, NY, USA. Department of Microbiology and Immunology, Columbia University Irving Medical Center, New York, NY, USA. (4) Columbia Center for Translational Immunology, Columbia University Irving Medical Center, New York, NY, USA. (5) Columbia Center for Translational Immunology, Columbia University Irving Medical Center, New York, NY, USA. Department of Surgery, Columbia University Irving Medical Center, New York, NY, USA. (6) Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA. (7) Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA. (8) Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA. (9) Columbia Center for Translational Immunology, Columbia University Irving Medical Center, New York, NY, USA. (10) Columbia Center for Translational Immunology, Columbia University Irving Medical Center, New York, NY, USA. (11) Columbia Center for Translational Immunology, Columbia University Irving Medical Center, New York, NY, USA. df2396@cumc.columbia.edu. Department of Microbiology and Immunology, Columbia University Irving Medical Center, New York, NY, USA. df2396@cumc.columbia.edu. Department of Surgery, Columbia University Irving Medical Center, New York, NY, USA. df2396@cumc.columbia.edu. (12) Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA. pas2182@cumc.columbia.edu. Department of Biochemistry and Molecular Biophysics, Columbia University Irving Medical Center, New York, NY, USA. pas2182@cumc.columbia.edu.
