期刊
ELIFE
卷 11, 期 -, 页码 -出版社
eLIFE SCIENCES PUBL LTD
DOI: 10.7554/eLife.76339
关键词
CD4 T cells; viral infection; gene expression; single-cell transcriptomics; exhaustion; single-cell data science; Mouse
类别
资金
- NCI Office of Science and Technology Resources
- University of Rochester
- Intramural Research Program of the National Cancer Institute, Center for Cancer Research (CCR), National Institutes of Health
- Swiss National Science Foundation [180010]
- Frederick National Laboratory for Cancer Research [HHSN261200800001E]
This study utilized single-cell transcriptomics to characterize the diversity of CD4(+) T cells responding to viral infections and built a comprehensive map of their transcriptional states. The research revealed the progressive changes in CD4(+) T cell subtypes during acute infections and identified distinct programs associated with chronic infections. Additionally, the study demonstrated the private nature of virus-specific CD4(+) T cell responses across individuals and their differentiation into Tfh and Th1 subtypes. Moreover, the CD4(+) T cell map developed in this study can be used to interpret cell states in other single-cell datasets.
CD4(+) T cells are critical orchestrators of immune responses against a large variety of pathogens, including viruses. While multiple CD4(+) T cell subtypes and their key transcriptional regulators have been identified, there is a lack of consistent definition for CD4(+) T cell transcriptional states. In addition, the progressive changes affecting CD4(+) T cell subtypes during and after immune responses remain poorly defined. Using single-cell transcriptomics, we characterized the diversity of CD4(+) T cells responding to self-resolving and chronic viral infections in mice. We built a comprehensive map of virus-specific CD4(+) T cells and their evolution over time, and identified six major cell states consistently observed in acute and chronic infections. During the course of acute infections, T cell composition progressively changed from effector to memory states, with subtype-specific gene modules and kinetics. Conversely, in persistent infections T cells acquired distinct, chronicity-associated programs. By single-cell T cell receptor (TCR) analysis, we characterized the clonal structure of virus-specific CD4(+) T cells across individuals. Virus-specific CD4(+) T cell responses were essentially private across individuals and most T cells differentiated into both Tfh and Th1 subtypes irrespective of their TCR. Finally, we showed that our CD4(+) T cell map can be used as a reference to accurately interpret cell states in external single-cell datasets across tissues and disease models. Overall, this study describes a previously unappreciated level of adaptation of the transcriptional states of CD4(+) T cells responding to viruses and provides a new computational resource for CD4(+) T cell analysis.
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