4.8 Article

A CD4+ T cell reference map delineates subtype-specific adaptation during acute and chronic viral infections

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

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资金

  1. NCI Office of Science and Technology Resources
  2. University of Rochester
  3. Intramural Research Program of the National Cancer Institute, Center for Cancer Research (CCR), National Institutes of Health
  4. Swiss National Science Foundation [180010]
  5. Frederick National Laboratory for Cancer Research [HHSN261200800001E]

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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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