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Tonic type I interferon signaling optimizes the antiviral function of plasmacytoid dendritic cells

Nature Immunology (2025)Cite this article

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Abstract

Plasmacytoid dendritic cells (pDCs) mount powerful antiviral type I interferon (IFN-I) responses, yet only a fraction of pDCs produces high levels of IFN-I. Here we report that peripheral pDCs in naive mice comprise three subsets (termed A, B and C) that represent progressive differentiation stages. This heterogeneity was generated by tonic IFN-I signaling elicited in part by the cGAS/STING and TLR9 DNA-sensing pathways. A small ‘IFN-I-naive’ subset (pDC-A) could give rise to other subsets; it was expanded in STING deficiency or after the IFN-I receptor blockade, but was abolished by exogenous IFN-I. In response to RNA viruses, pDC-A showed increased Bcl2-dependent survival and superior IFN-I responses, but was susceptible to virus infection. Conversely, the majority of pDCs comprised the ‘IFN-I-primed’ subsets (pDC-B/C) that showed lower IFN-I responses and poor survival, but did not support virus replication. Thus, tonic IFN-I signaling decreases the cytokine-producing capacity and survival of pDCs but increases their virus resistance, facilitating optimal antiviral responses.

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Fig. 1: Mature splenic pDCs show transcriptional heterogeneity.
Fig. 2: Mature splenic pDCs comprise three transcriptionally and phenotypically distinct subsets.
Fig. 3: Prospective identification of pDC subsets and their differentiation trajectory.
Fig. 4: pDC heterogeneity is generated by tonic IFN-I signaling that partially depends on cGAS/STING and TLR9 signaling.
Fig. 5: pDC heterogeneity correlates with distinct cytokine-producing capacity.
Fig. 6: pDC heterogeneity and function are dynamically modulated by IFN-I signaling.
Fig. 7: pDC heterogeneity correlates with distinct responses and susceptibility to virus infection.
Fig. 8: Differential survival of pDC subsets.

Data availability

All sequencing data have been deposited in the NCBI Gene Expression Omnibus database under accession number GSE252191. All other data are presented in the paper or are available from the corresponding authors upon request. Source data are provided with this paper.

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Acknowledgements

We thank S. Hao (New York Genome Center) for help with CITE-seq, K. Khanna for VSV-GFP, I. Aifantis and D. Papaioannou for Venetoclax, D. Littman and T. Najar for OT-II mice and S. Weiss (University of Pennsylvania) for M-CoV-GFP. This study was supported by the NIH grants no. HL145997 and no. CA009161 (J.N.P.), no. AI072571 (B.R.), no. AI158808 (J.I.) and no. AI128949 (B.R., S.P.W., D.L.F.), and the German Research Foundation grant no. EI1185/1-1 (A.E.). We acknowledge the use of resources provided by NYU Genome Technology Center (GTC; RRID:SCR_017929), Applied Bioinformatics Laboratories (RRID:SCR_019178), High Performance Computing Facility (HPCF), Cytometry and Cell Sorting Laboratory (CSL; RRID:SCR_019179) and the Experimental Pathology Research Laboratory (RRID:SCR_017928), and shared resources partially supported by NIH grant no. P30CA016087 at the Laura and Isaac Perlmutter Cancer Center.

Author information

Author notes

  1. These authors contributed equally: Joseph N. Pucella, Raul A. Maqueda-Alfaro.

Authors and Affiliations

  1. Department of Pathology, New York University Grossman School of Medicine, New York, NY, USA

    Joseph N. Pucella, Anna Eichinger, Eduardo Esteva, Annesa Das, Oriana A. Perez, Jue Feng, Ellie Ivanova, Sergei B. Koralov, Chetna Soni & Boris Reizis

  2. Inflammatory Bowel Disease Center, Division of Gastroenterology, Department of Medicine, New York University Grossman School of Medicine, New York, NY, USA

    Joseph N. Pucella

  3. Department of Pharmacology, University of California San Diego School of Medicine, San Diego, CA, USA

    Raul A. Maqueda-Alfaro, Hai Ni, Fernando Bandeira Sulczewski & Juliana Idoyaga

  4. Department of Pediatrics, Dr. von Hauner Children’s Hospital, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany

    Anna Eichinger

  5. Applied Bioinformatics Laboratories, New York University Grossman School of Medicine, New York, NY, USA

    Eduardo Esteva, Ai C. Ra, Alireza Khodadadi-Jamayran & Igor Dolgalev

  6. Technology Innovation Lab, New York Genome Center, New York, NY, USA

    Marlon Stoeckius & Peter Smibert

  7. Department of Microbiology, New York University Grossman School of Medicine, New York, NY, USA

    Stela Sota & Ken Cadwell

  8. Division of Gastroenterology, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

    Ken Cadwell

  9. Division of Biostatistics, Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA

    Judy Zhong

  10. Department of Immunology, University of Washington School of Medicine, Seattle, WA, USA

    Daniel B. Stetson

  11. Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, NY, USA

    Stuart P. Weisberg

  12. Department of Microbiology and Immunology, Columbia University Irving Medical Center, New York, NY, USA

    Donna L. Farber

  13. Department of Molecular Biology, University of California San Diego School of Biological Sciences, San Diego, CA, USA

    Juliana Idoyaga

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  1. Joseph N. Pucella
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Contributions

J.N.P., R.A.M.-A., H.N., F.B.S., A.E., O.A.P., A.D. and J.F. performed experiments and analyzed results. J.N.P., E.E., A.C.R., A.K.-J. and I.D. developed analytical methods and analyzed results. J.Z. advised on the statistical analysis of results. M.S. and P.S. developed experimental technology. E.I., S.S., K.C., S.B.K. D.B.S., S.P.W. and D.L.F. provided research materials. C.S. provided conceptual input. B.R. and J.I. supervised the project and analyzed the results. J.N.P. and B.R. conceived the project and wrote the paper with input from all authors.

Corresponding authors

Correspondence to Joseph N. Pucella, Juliana Idoyaga or Boris Reizis.

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Competing interests

M.S. and P.S. are inventors on a patent related to the CITE-seq method and are or have been employed by 10x Genomics, Inc., but were not affiliated with it or any other commercial entity at the time of the work described in this paper. B.R. is an advisor for Related Sciences and a co-founder of Danger Bio. J.I. serves on the advisory board of Immunitas Therapeutics. None of these affiliations are related to the present work. The other authors declare no competing interests.

Peer review

Peer review information

Nature Immunology thanks Michael Chopin, Marc Dalod, Tsuneyasu Kaisho and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: L. A. Dempsey, in collaboration with the Nature Immunology team.

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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Initial analysis of the single-cell transcriptome of splenic pDCs.

Sorted pDCs from pooled spleens of naive mice were subjected to multiome analysis by single-nuclei RNA-seq and ATAC-seq (Fig. 1), and the former was analyzed. (a) UMAP plot of cells based on mRNA transcript reads, indicating the original 9 clusters. (b) Heatmap of top 10 marker genes of each cluster (rows) across individual cells (columns). (c) Correlation heatmap of the original 9 clusters. (d) Correlation heatmap of the reduced clusters (Mt clusters in Fig. 1a).

Source data

Extended Data Fig. 2 Initial analysis of the single-cell chromatin of splenic pDCs.

Sorted pDCs from pooled spleens of naive mice were subjected to multiome analysis by single-nuclei RNA-seq and ATAC-seq (Fig. 1), and the latter was analyzed. (a) UMAP plot of cells based on ATAC-Seq reads, indicating the original 9 clusters. (b) Correlation heatmap of the original 9 clusters and after cluster reduction, with the corresponding Leiden clustering resolutions indicated. The heatmap on the right corresponds to the four Ma clusters in Fig. 1e. (c) The projection of Ma clusters onto the UMAP plot based on RNA-Seq from Fig. 1a. Shown are combined projections of all clusters (left panel) and feature plots of individual Mt clusters (right panel). (d) The fraction of Ma clusters within the total dataset and within each Mt cluster.

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Extended Data Fig. 3 Additional analysis of CITE-Seq of splenic pDCs.

Sorted pDCs from spleens of three individual naive mice were hashtagged and analyzed by CITE-Seq for single-cell transcriptome and phenotype (antibody-derived tags, ADT). (a) UMAP plot and clustering of the dataset prior to the tDC cluster removal (clusters are numbered and colored randomly). (b-c) Violin plots showing transcript levels of key pDC (b) and tDC (c) identity genes. (d) Heatmap of top 10 marker gene expression based on mRNA transcripts or ADT reads for the clusters in panel A. (e) Heatmap of ADT for markers expressed in pDCs across the three Ct clusters defined in Fig. 2b. (f) RNA velocity analysis. Shown are velocity vectors projected onto the KnetL and UMAP plots from Fig. 2b, with the three Ct clusters indicated.

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Extended Data Fig. 4 Verification of transcriptional heterogeneity in murine splenic pDCs.

A previously reported scRNA-seq dataset of splenic pDCs from naive mice (GSE114313) was reanalyzed to assess steady-state heterogeneity. (a) UMAP plot, with clusters from the KNetL clustering in the next panel indicated by colors. (b) KNetL plot and clustering. (c) Violin plots showing transcript levels of key pDC identity genes by KNetL clusters. (d) Heatmap of marker gene expression for KNetL clusters. (e) Feature plots of key marker gene transcripts on the KNetL plot from panel B; cluster with prominent ISG expression (cluster 2) is highlighted. (f) The expression of MHC cl II transcript shown on the feature plot (with the MHC cl. II-low cluster 5 highlighted) and violin plot.

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Extended Data Fig. 5 The analysis of transcriptional heterogeneity in human pDCs.

A previously reported scRNA-seq dataset of pDCs from peripheral blood of three healthy human donors (GSE189120) was reanalyzed to assess steady-state heterogeneity. (a) KNetL and UMAP plots, with KNetL-based human transcriptome (Ht) clusters indicated. (b) Violin plots showing transcript levels of key pDC identity genes by KNetL clusters. (c) KNetL plots of Ht clusters, split up by individual donor. (d) Heatmap of marker gene expression for KNetL clusters.

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Extended Data Fig. 6 Prospective identification and isolation of murine pDCs.

(a-b) Gating strategy for the analysis of pDC subsets in the spleen (panel a) and bone marrow (panel b). (c) Gating strategy for the recovery of pDC subsets in the hosts of adoptive transfer experiments in Fig. 3k-m. (d) Gating strategy for the sorting (top panel) and post-sort purity (bottom panel) of pDC subsets isolated for functional experiments in Figs. 7–8.

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Extended Data Fig. 7 Modulation of pDC heterogeneity in bone marrow-derived pDCs by IFN-I blockade.

(a) Total BM cells were harvested from naïve wild-type mice and cultured in the presence of Flt3L for 12 days to induce DC differentiation. Anti-IFNAR1 mAb was added throughout the culture (Blockade), during the first 6 days (D6 washoff) or during the last 6 days (D6 blockade). (b) The dynamics of Sca1 expression on pDCs between days 6 and 12 of culture, as determined by flow cytometry. Cultures from Ifnar1−/− mice were used as controls (dashed line). Data represent averages of biological replicates (cultures from individual mice) from one experiment. (c) The fraction of Sca1+ pDCs on days 6 and 9 of culture. Bars represent means ± SD, symbols represent biological replicates (cultures from individual mice) from one experiment. Statistical significance was analyzed using one-way ANOVA followed by Tukey’s test. * p < 0.05; ** p < 0.005; *** p < 0.0005; **** p < 0.00005. Illustration in a created using BioRender.com.

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Supplementary information

Reporting Summary

Supplementary Table 1

Marker genes of cell clusters in the single-nucleus RNA-seq of mouse pDCs.

Supplementary Table 2

Marker peaks of cell clusters in the single-nucleus ATAC-seq of mouse pDCs.

Supplementary Table 3

Marker genes of cell clusters in the CITE-seq of mouse pDCs.

Supplementary Table 4

Gene expression in prospectively isolated subsets of mouse pDCs.

Source data

Source Data for Figs. 1–8 and Extended Data Figs. 1–7

Statistical source data.

Source Data Fig. 2

Raw image files (JPEG) for Fig. 2g,h.

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Pucella, J.N., Maqueda-Alfaro, R.A., Ni, H. et al. Tonic type I interferon signaling optimizes the antiviral function of plasmacytoid dendritic cells. Nat Immunol (2025). https://doi.org/10.1038/s41590-025-02279-4

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