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Transcription factors TCF4 and KLF4 respectively control the development of the DC2A and DC2B lineages

Nature Immunology volume 26, pages 1275–1286 (2025)Cite this article

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Abstract

Conventional dendritic cells (cDCs) are a heterogeneous population of professional antigen-presenting cells that bridge innate and adaptive immunity. Many studies in mice have identified various populations of cDCs whose inter-relationships and discrete identities, as well as their link to plasmacytoid DCs (pDCs), have not been cohesively addressed. Here, by combining single-cell sequencing, transcription factor fate-mapping models, conditional knockout models and adoptive transfer, we show that Klf4 expression clearly separates cDC lineage from the pDC lineage, and defined two pre-DC2 subsets: Siglec-H+CD115− pre-DC2s and Siglec-HloCD115+ pre-DC2s. While Siglec-H+CD115− pre-DC2s represent the pDC-like cells that give rise to CD7+CD11blo DC2As in a TCF4-dependent manner, Siglec-HloCD115+ pre-DC2s give rise to CD7−CD11bhi DC2Bs in a KLF4-dependent manner. These data reveal the transcriptional basis of two pre-DC2 subsets and present a firm framework for mouse cDC classification, paving the way for a better understanding of these cells in tissues and in disease.

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Fig. 1: Pre-DC2s contain cells with a pDC-like phenotype.
Fig. 2: KLF4 expression distinguishes cDCs from pDCs.
Fig. 3: Pre-DC2s contain two subpopulations.
Fig. 4: Siglec-H+pre-DC2s can be aligned to pDC-like cells and tDCs.
Fig. 5: Predicted transcriptional regulation of DC2 development.
Fig. 6: KLF4 and TCF4 control the development of pre-DC2 populations.
Fig. 7: Siglec-H+ pre-DC2s produce type I IFN upon CpG stimulation.

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Data availability

The scRNA-seq datasets used in this study are from Liu et al.11.

Code availability

This study did not report original code.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (NSFC) (grants 32270916 and 32070880 to Z. Liu) and Shanghai Jiao Tong University 2030 Initiative (grant WH510363001-16 to Z. Liu). We thank the flow cytometry team, sequencing core and imaging core at Shanghai Institute of Immunology, and the Core Facility of Basic Medical Sciences, Shanghai Jiao Tong University School of Medicine, for their support.

Author information

Author notes

  1. These authors jointly supervised this work: Zhaoyuan Liu, Florent Ginhoux.

Authors and Affiliations

  1. Shanghai Institute of Immunology, Department of Immunology and Microbiology, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Yiwen Zhu, Peiliang Cai, Shuangyan Zhang, Haiting Wang, Fei Gao, Yingying Zeng, Jiawen Qian, Bing Su, Zhaoyuan Liu & Florent Ginhoux

  2. National Key Laboratory of Immunity and Inflammation, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou, China

    Ziyi Li

  3. Gustave Roussy Cancer Campus, Villejuif, France

    Wan Ting Kong & Florent Ginhoux

  4. Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Bing Su & Zhaoyuan Liu

  5. Shanghai Key Laboratory for Tumor Microenvironment and Inflammation, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Zhaoyuan Liu

  6. Singapore Immunology Network, Agency for Science, Technology and Research, Singapore, Singapore

    Florent Ginhoux

  7. Translational Immunology Institute, SingHealth Duke-NUS Academic Medical Centre, Singapore, Singapore

    Florent Ginhoux

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Contributions

Y. Zhu, H.W., S.Z., P.C., F. Gao, W.T.K. and J.Q. conducted the experiments; Z. Li, Y. Zeng, Z. Liu and F. Ginhoux analyzed the data; Z. Liu and F. Ginhoux wrote the paper; B.S. provided intellectual input; Z. Liu and F. Ginhoux conceptualized and supervised the project.

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Correspondence to Zhaoyuan Liu or Florent Ginhoux.

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Nature Immunology thanks the anonymous reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Stephanie Houston in collaboration with the Nature Immunology team.

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Extended data

Extended Data Fig. 1 Pre-DC2s contain cells with a pDC-like phenotype.

a, Dot plot showing the expression of top 10 most differentationally expressed genes across clusters. Colors indicate the average expression of each gene. Spot sizes represent the proportion of gene-expressing cells. b, UMAP plots showing clusters in Siglec-H+ fraction in BM and spleen, data from Rodrigues et al., 2018. c, Stacked violin plots showing the expression of selected genes across clusters. d, Volcano plot showing the DEGs between BM pDC-like cells and pre-pDCs. Genes in brown are upregulated in pDC-like cells; genes in pink are upregulated in pre-pDCs. Horizontal dashed line indicates p-value = 0.05; vertical dashed lines indicate fold change = 1.5 or 0.67 (log2FC = 0.3 or -0.3). e, Stacked violin plot showing the expression of selected genes identified in (d). f, Venn plots showing the overlap of top 25 genes in pre-DC2s (upper panel) or pDCs (lower panel) in BM, blood, and spleen. g, Stacked violin plot showing the expression of genes identified in (f).

Extended Data Fig. 2 KLF4 expression distinguishes cDCs from pDCs.

a, Schemic showing the construct of Klf4EGFP-CreERT2 model, an IRES-EGFP-2A-CreERT2-Wpre-pA cassette were inserted into the 3’ UTR region of Klf4 gene. b, t-distributed Stochastic Neighbor Embedding (tSNE) plots and histograms showing KLF4-GFP expression in the blood of Klf4EGFP-CreERT2 mice. c,d, Flow cytometry plots showing the gating strategy for monocyte and DC subsets in the blood (c), and spleen (d) of Klf4EGFP-CreERT2 mice.

Extended Data Fig. 3 KLF4 expression distinguishes cDCs from pDCs.

a, Flow cytometry plots showing the gating strategy for monocyte and DC subsets in the BM of Klf4EGFP-CreERT2 mice. b, tSNE plots showing KLF4-GFP+ and GFP− populations in Lin−FLT3+ cells separately and merged from BM of Klf4EGFP-CreERT2 mice. c, Quantification of cells generated per 105 FLT3+KLF4-GFP+ or FLT3+c-Kit-KLF4-GFP− cells in in vitro culture (n = 4), data are presented as mean ± s.d. d, Histogram showing the expression of KLF4-GFP and LGALS3 in populations in the BM of Klf4EGFP-CreERT2 mice. e, Flow cytometry plots showing the expression of KLF4-GFP and LGALS3.

Extended Data Fig. 4 Pre-DC2s contain two subpopulations.

a,b, Gating strategy for pre-DC2 subsets in the blood (a) and spleen (b) of Klf4EGFP-CreERT2 mice. c, Giemsa staining image showing the morphology of indicated cells sorted from the BM of Klf4EGFP-CreERT2 mice. Scale bar = 10 μm. d, Quantification of cells generated per 105 Siglec-H+ pre-DC2 or CD115+ pre-DC2 in in vitro culture (n = 4), data are presented as mean ± s.d. e, Gating strategy for DC populations in the spleen of Klf4EGFP-CreERT2 mice. f, Histogram plots showing the expression of CD7-EGFP and CD45RB in DC and monocyte populations in the spleen of Cd7EGFP mice. g, Quantification of cells generated per 105 Siglec-H+ pre-DC2 or CD115+ pre-DC2 in in vivo adoptive transfer (n = 3), data are presented as mean ± s.d.

Extended Data Fig. 5 Siglec-H+ pre-DC2s can be aligned to pDC-like cells and tDCs.

a, Overlay of pre-DC2 subsets defined with our gating strategy to the gating of pDC-like cells in Rodrigues et al., 2018. b, Overlaying pre-DC2 subsets defined with our gating strategy to theirs. c, Overlay of CD11chi or CD11clo tDCs defined by Leylek et al., 2019 to our gating strategy. d, Percentages of proliferating cell indicated by Fucci expression in populations in the BM, blood, and spleen of Fucci mice (n = 4), data are presented as mean ± s.d.

Extended Data Fig. 6 Siglec-H+ pre-DC2s can be aligned to pDC-like cells and tDCs.

a, Overlay of pre-DC2 subsets defined with our gating strategy to pre-DC2A and pre-DC2B defined by Minutti et al., 2024 to our gating strategy, b, Overlay of tDC, DC2B, early DC2A, and DC2A defined by Minutti et al., 2024 to our gating strategy.

Extended Data Fig. 7 Siglec-H+ pre-DC2s can be aligned to pDC-like cells and tDCs.

a, Overlay of Siglec-H+Zbtb46-GFP+Ly6D+ cells defined by Lutz et al., 2022 to our gating strategy. b, Overlay of Siglec-H+B220+ pDCs and pre-pDCs defined with our gating strategy to populations defined by Lutz et al., 2022. c, Flow cytometry plot showing the overlay of pre-pDCs (cyan) defined with our gating strategy with B220loCCR9lo cells (red) defined by Lutz et al., 2022. d, Schemic showing the relationship between populations in DC2 lineage.

Extended Data Fig. 8 KLF4 and TCF4 control the development of pre-DC2 populations.

a, Schemic showing the construct of Tcf4tdTomato-LSL-DTA model, an tdTomato-LoxP-stop-LoxP-IRES-DTA-Wpre-pA cassette was inserted into the 3’ UTR region of Tcf4 gene. b, Flow cytometry (left panel) and histogram (right panel) showing the pDCs and cDCs in Klf4fl/fl and Klf4fl/fl;Vav1iCre spleen (n = 3). c, Schemic showing the construct of Tcf4flox model. d, Flow cytometry (left panel) and histogram (right panel) showing the pDCs and cDCs in Tcf4fl/fl and Tcf4fl/fl;Vav1iCre spleen (n = 5). Data in this figure are presented as mean ± s.d.; statistics were calculated by unpaired two-sided Student’s t-test; ns, not significant, *P < 0.05, ****P < 0.0001.

Extended Data Fig. 9 Proposed model for DC lineage development.

Schemic showing the proposed model for DC lineage development.

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Zhu, Y., Cai, P., Li, Z. et al. Transcription factors TCF4 and KLF4 respectively control the development of the DC2A and DC2B lineages. Nat Immunol 26, 1275–1286 (2025). https://doi.org/10.1038/s41590-025-02208-5

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