A CD4+ T cell reference map delineates subtype-specific adaptation during acute and chronic viral infections
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Evaluation Summary:
This paper uses single-cell genomics to examine the heterogeneity of virus-specific CD4 T cells over time in both acute and chronic viral infection. Further, the authors build a comprehensive atlas of the transcriptional evolution of virus-specific CD4 T cell responses that could be used as a reference tool to interpret other datasets. This work characterizes how the antiviral CD4 T cell transcriptional landscape changes with time and will be of broad interest to those that study acute and chronic CD4 T cell responses.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 agreed to share their name with the authors.)
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Abstract
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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Author Response
Reviewer #2 (Public Review):
The main strength of the paper is the parallel profiling of virus-specific CD4 T cells in different stages of acute and persistent infection, and the ease of publicly accessing the data and source code. These data extend previous studies, such as Khatun et al. JEM 2020 and Cicucci et al. Immunity 2019, by revealing single-cell transcriptome information on virus-specific CD4 T cells at different stages of infection.
The main drawback is the paper's advertised use as a 'comprehensive atlas of virus-specific CD4 T cells'. This study includes virus-specific T cells from a single organ (spleen) during infections with two clones of a single virus (LCMV). Therefore, its use as a reference atlas does not extend to other viruses or T cells from organs other than spleen during LCMV infection. If …
Author Response
Reviewer #2 (Public Review):
The main strength of the paper is the parallel profiling of virus-specific CD4 T cells in different stages of acute and persistent infection, and the ease of publicly accessing the data and source code. These data extend previous studies, such as Khatun et al. JEM 2020 and Cicucci et al. Immunity 2019, by revealing single-cell transcriptome information on virus-specific CD4 T cells at different stages of infection.
The main drawback is the paper's advertised use as a 'comprehensive atlas of virus-specific CD4 T cells'. This study includes virus-specific T cells from a single organ (spleen) during infections with two clones of a single virus (LCMV). Therefore, its use as a reference atlas does not extend to other viruses or T cells from organs other than spleen during LCMV infection. If such samples were integrated with the splenic LCMV atlas, either new unique populations would be found and therefore not meaningfully annotated or they would be force-integrated with one of the splenic subsets, producing a potentially misleading and crude annotation. In this sense, the authors did not construct an atlas but rather a dataset on LCMV-specific splenic CD4 T cells which, like other datasets, can be compared with other single-cell sequencing datasets.
The methodology description does not include convincing evidence that the integration was successful in minimizing batch effects and retaining biological heterogeneity, virtually no data is presented in support of this point. Therefore, the scope of the work should be refined and the methodology significantly improved before this paper becomes acceptable for publication.
We thank the referee for recognizing the strengths of the study, as well as for advising where we had been insufficiently clear in describing the methodology – in particular with regards to data integration and generalizability of our bioinformatics tool. As detailed below, we provide new evidence supporting the quality of data integration, the robustness and replicability of the T cell states defined in our reference map, and its ability to make accurate predictions across multiple tissues (spleen, liver, lung, lymph nodes) and beyond the LCMV infection model. In addition, we conducted several additional analyses demonstrating the robustness of our predictions, and generated a new scRNA-seq dataset of tumor-specific CD4+ T cells, showing how our LCMV-derived reference map can help identify and characterize a novel cell state uniquely acquired by tumor-infiltrating CD4+ T cells.
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Evaluation Summary:
This paper uses single-cell genomics to examine the heterogeneity of virus-specific CD4 T cells over time in both acute and chronic viral infection. Further, the authors build a comprehensive atlas of the transcriptional evolution of virus-specific CD4 T cell responses that could be used as a reference tool to interpret other datasets. This work characterizes how the antiviral CD4 T cell transcriptional landscape changes with time and will be of broad interest to those that study acute and chronic CD4 T cell responses.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 agreed to share their name with the authors.)
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Reviewer #1 (Public Review):
This study by Andreatta et al. defines the transcriptional state of virus-specific CD4 T cells in both acute and chronic LCMV infection over time. They identify 6 distinct cell states in both infections: Th1, Tfh, T central memory precursors (Tcmp), Th1 memory, Tfh memory and T central memory (Tcm) and characterize how the proportions and gene expression of each of these states are altered across time. In acute infection, Th1 effector function is downregulated as the cells transition to early memory cells, but the loss of Tfh effector function appears to be delayed until later memory timepoints. Tcmp and Tcm are transcriptionally distinct from the other states and have memory markers at early timepoints. During chronic viral infection, Th1 effector cells are lost with time, but Tfh function appears to be …
Reviewer #1 (Public Review):
This study by Andreatta et al. defines the transcriptional state of virus-specific CD4 T cells in both acute and chronic LCMV infection over time. They identify 6 distinct cell states in both infections: Th1, Tfh, T central memory precursors (Tcmp), Th1 memory, Tfh memory and T central memory (Tcm) and characterize how the proportions and gene expression of each of these states are altered across time. In acute infection, Th1 effector function is downregulated as the cells transition to early memory cells, but the loss of Tfh effector function appears to be delayed until later memory timepoints. Tcmp and Tcm are transcriptionally distinct from the other states and have memory markers at early timepoints. During chronic viral infection, Th1 effector cells are lost with time, but Tfh function appears to be maintained. Further, using single cell TCR sequencing analysis, the authors determine that CD4 T cell responses are private amongst different individuals and that most TCR clonotypes can differentiate into all subtypes and are independent of the TCR. Lastly, the authors use their data to create a reference atlas as a new computational resource to interpret and subset CD4 T cells from other single-cell datasets.
In this study the computational methods and analysis are strong and the conclusions are well supported. As attributed in the text, some of the changes observed confirm those that have been documented in other studies, but this reference atlas now assembles the data from these timepoints and serves as a useful tool for the field to analyze other CD4 datasets. A few points listed below, however, would clarify and enhance the study.
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Reviewer #2 (Public Review):
The main strength of the paper is the parallel profiling of virus-specific CD4 T cells in different stages of acute and persistent infection, and the ease of publicly accessing the data and source code. These data extend previous studies, such as Khatun et al. JEM 2020 and Cicucci et al. Immunity 2019, by revealing single-cell transcriptome information on virus-specific CD4 T cells at different stages of infection.
The main drawback is the paper's advertised use as a 'comprehensive atlas of virus-specific CD4 T cells'. This study includes virus-specific T cells from a single organ (spleen) during infections with two clones of a single virus (LCMV). Therefore, its use as a reference atlas does not extend to other viruses or T cells from organs other than spleen during LCMV infection. If such samples were …
Reviewer #2 (Public Review):
The main strength of the paper is the parallel profiling of virus-specific CD4 T cells in different stages of acute and persistent infection, and the ease of publicly accessing the data and source code. These data extend previous studies, such as Khatun et al. JEM 2020 and Cicucci et al. Immunity 2019, by revealing single-cell transcriptome information on virus-specific CD4 T cells at different stages of infection.
The main drawback is the paper's advertised use as a 'comprehensive atlas of virus-specific CD4 T cells'. This study includes virus-specific T cells from a single organ (spleen) during infections with two clones of a single virus (LCMV). Therefore, its use as a reference atlas does not extend to other viruses or T cells from organs other than spleen during LCMV infection. If such samples were integrated with the splenic LCMV atlas, either new unique populations would be found and therefore not meaningfully annotated or they would be force-integrated with one of the splenic subsets, producing a potentially misleading and crude annotation. In this sense, the authors did not construct an atlas but rather a dataset on LCMV-specific splenic CD4 T cells which, like other datasets, can be compared with other single-cell sequencing datasets.
The methodology description does not include convincing evidence that the integration was successful in minimizing batch effects and retaining biological heterogeneity, virtually no data is presented in support of this point. Therefore, the scope of the work should be refined and the methodology significantly improved.
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