Abstract
Dendritic cells (DCs) are professional antigen-presenting cells. While plasmacytoid DCs (pDCs) are poor antigen-presenting cells at steady state, myeloid DCs (mDCs), which include DC1s, DC2s and DC3s, are specialized in T cell priming. To generate unbiased human DC atlases, we integrated DCs from 13 tumor tissues across 40 datasets to create a pDC + mDC-VERSE (DC-VERSE) and an mDC-VERSE single-cell RNA-sequencing compendium. We characterized DC subsets and ‘states’ across these tissues. Most studied tumors contained CD207+ DCs, a subset of CD1c+ DCs, whose expansion inversely correlated with tumor CD8+ resident memory T cells, T cell clonality and the survival of patients treated with immune checkpoint inhibitors. Similarly to CCR7+ mDCs (a common state of DC1s, DC2s and DC3s), we found that CD207+ DCs were a common state of DC2s and DC3s. Spatially resolved single-cell transcriptomic and immunohistofluorescence analyses of human carcinomas demonstrated that lymphocytes and most DCs were enriched within the tumor stroma, while CD207+ DCs were mostly embedded within tumor nests. These DC-VERSEs provide a robust resource available to the scientific community on DCs in health and pathology.
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Data availability
No new data were generated; all analyzed datasets are public (Supplementary Table 1). The DC-VERSE and mDC-VERSE are available for download at https://github.com/gustaveroussy/FG-Lab.
Code availability
The DC-VERSE and the mDC-VERSE code can be found at https://github.com/gustaveroussy/FG-Lab.
Change history
16 December 2025
A Correction to this paper has been published: https://doi.org/10.1038/s41590-025-02402-5
20 January 2026
A Correction to this paper has been published: https://doi.org/10.1038/s41590-026-02428-3
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Acknowledgements
We thank L. Robinson of Insight Editing London for the critical review and editing of the manuscript. We thank Y. Velut for providing the immunohistofluorescence images, as well as the Cell Imaging and Flow Cytometry Platform (CHIC) of the Centre de Recherche des Cordeliers for its help with this study. We thank the Foundation MSD Avenir (https://www.msdavenir.fr/) for its financial contribution to this project. We thank the Marie Lannelongue Hospital and its biobank for their valuable collaboration and support of this study. This work was supported by INSERM, Sorbonne Université, Université de Paris, Ligue Contre le Cancer (Equipe Labellisée), the CARPEM (Cancer Research for Personalized Medicine) program of the Sites Intégrés de Recherche sur le Cancer (SIRIC), and LabEx Immuno-Oncology. F.G. is an EMBO YIP awardee and is supported by Singapore Immunology Network (SIgN) core funding as well as a Singapore National Research Foundation Senior Investigatorship (NRFI) NRF2016NRF-NRFI001-02 and the Foundation Gustave Roussy. C.-A.D. is an INSERM researcher supported by INSERM. L.Z. was supported by the European Union’s Horizon 2020 research and innovation program under grant agreement no. 825410 (ONCOBIOME project), ANR RHU5 ‘ANR-21-5 RHUS-0017’ IMMUNOLIFE, MAdCAM INCA_ 16698 and ERC advanced, funded by the European Research Council (ERC) under grant agreement number 101052444, the ANR-23-RHUS-0010 (LUCA-pi), the European Union’s Horizon 2020 research and innovation program no. 964590 (project acronym: IHMCSA, project title: International Human Microbiome Coordination and Support Action), the European Union’s Horizon Europe research and innovation program under grant agreement no. 101095604 (project acronym: PREVALUNG EU, project title: Personalized lung cancer risk assessment leading to stratified interception), as well as by the SEERAVE Foundation. Other grant supports include Ligue Contre le Cancer and the SIGN’IT ARC Foundation (MICROBIONT-PREDICT, 2021).
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Experiments: K.M., M.G. W.T.K., A.A.P., A.C., C.d.l.C.-F., E.P., L.R., A.B., S.K. and A.-G.G. Data analysis: K.M., M.G., W.T.K., A.A.P., A.C., G.G., C.d.l.C.-F., C.P., Q.B., E.L., A.-A.A., G.D., G.P., G.A.-E., R.J.D. and C.-A.D. Provision of human NSCLC samples: A.-G.G. and L.Z. Provision of human NSCLC FFPE blocks: V.T.d.M. Provision of human ovarian samples: C.d.l.C.-F. and J.M. Provision of breast cancer Visium spatial transcriptomic data: A.S. Generation, provision and segmentation of MERFISH data: L.H., T.W., J.H. and G.E. Establishment of the publicly available online DC-VERSE and mDC-VERSE: K.M. and M.D. Writing of the manuscript: K.M., M.G., W.T.K., A.A.P., F.G. and C.-A.D. Online cellXgene VERSEs: M.D. Intellectual input: C.S.-F., W.H.F. and A.B. Project supervision: F.G. and C.-A.D. Study conceptualization: F.G. and C.-A.D.
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Extended data
Extended Data Fig. 1 Quality control plots of datasets included in the study.
Violin plots displaying nFeature RNA, nCounts RNA, the percentage of mitochondrial genes (percent.mito), the percentage of ribosomal genes (percent.ribo), and the percentage of heat shock protein genes (percent.hs). Related to Fig. 1.
Extended Data Fig. 2 Identification of clusters within the mDC-VERSE and mDC mega clusters’ distribution across juxta-tumoral “healthy” tissues.
a, Phenograph clusters’ (cl.) annotation of the mDC-VERSE. b, Heatmap showing the relative expression levels of Differentially Expressed Regulons (DERs) between phenograph clusters common to Lung (Maier) and Tonsil (Cillo) cancer datasets. c, Quality control metrics for each Phenograph cluster and meaning plot of nFeature_RNA. d, Annotation of cl. #15 on the mDC-VERSE. e-f, CITE-seq data (from Maier et al.) showing expression of signature T and B cell protein markers and DC2 and DC3 protein markers. g, Identification of DC2s and DC3s using CD5 and CD14 protein expression from CITE-seq data (from Maier et al.) within the DC2 + DC3 region of the mDC-VERSE. h, Meaning plots of DC2 and DC3 gene signatures from Dutertre et al. on the mDC-VERSE. i, DEG heatmap between mega-clusters of the mDC-VERSE. j, Mean expression of the moDC signature from Gao et al. overlayed onto the MNP-VERSE (from Mulder et al.) and onto the mDC-VERSE UMAP spaces. k, moDC signature score for each cell of the different mDC-VERSE Phenograph clusters. l,m, Composition of DC mega clusters across juxta-tumoral “healthy” tissues. n, Annotation of cl.3 & cl.4 (corresponding to cDC2As_Brown) from Brown et al. on the mDC-VERSE. Related to Fig. 2.
Extended Data Fig. 3 Comparison of mDC-VERSE DC2 + DC3 populations to Cheng’s cDC2 subsets.
a-b, Projection of a, all mDC subsets and b, DC2 + DC3 populations defined by Cheng et al.’s metadata on the mDC-VERSE using multimodal reference mapping. c, Quality control metrics of predicted mDC-VERSE Phenograph clusters from Cheng et al. data projected by multimodal reference mapping. d, Mean expression of the top 50 genes of mega-clusters from the mDC-VERSE mapped onto the UMAP from Cheng et al. e, Mapping of cDC2_CD1A cells from Cheng et al., enriched in CD207 or LTB DC signatures (from the mDC-VERSE) onto the UMAP from Cheng et al. f, Meaning plots of the mean gene signatures of DC2 + DC3 populations from Cheng et al. shown on the mDC-VERSE. Related to Fig. 2.
Extended Data Fig. 4 Metadata analysis of mDC populations in cancer.
a, Density plots of global colon, liver and lung datasets highlighting changes in DC1s, CCR7 mDCs, CD207 DCs, Prolif. DCs, ISG DCs and LTB DCs between juxta-tumoral and tumoral tissues. b, Percentage of CCR7 mDCs and Prolif. DCs, and DC3/DC2 ratio in datasets which had analysed juxta-tumoral tissue, tumour periphery and tumour core. c,d, Percentage of mDC-VERSE c, phenograph clusters and d, mega clusters in all integrated and query datasets (Obtained through multimodal reference mapping and annotated with cross symbol) between matched juxta-tumoral and cancer tissues. See Supplementary Table 1 for the specified tumour types. P-values were calculated using a Wilcoxon non-parametric paired test. Related to Fig. 3.
Extended Data Fig. 5 Gating strategy to define DC populations and states and evaluation of their phenotype in NSCLC spectral flow cytometry data.
a, Gating strategy from singlets, live, CD45+ cells and projection of each gated population onto the Live_CD45+_UMAP space. b, MNP extracted from the Live_CD45+_UMAP were analysed by UMAP to generate the MNP_UMAP, whose annotation is confirmed by protein expression. c, Gating of pDCs and pre-DCs within CD123+ DCs defined in Fig. 4a-b. d, RNA expression of CD207 and CD1A and protein expression of CD103 overlaid on the mDC-VERSE. e, Fold increase of CD207+ DCs in tumour versus matched juxta-tumoral tissue. Related to Fig. 4.
Extended Data Fig. 6 Subset versus state consideration.
a, Mapping of cMAP scores from Fig. 5b on the mDC-VERSE. b, Overlay of DC “states” identified in Fig. 5c onto the MNP_UMAP space. c, Expression of CADM1 and CD141 by CCR7+ mDCs, CD103+ DCs and CD207+ DCs. d, Gating and phenotype of CD103+ “LTB” and CD1a+CD207+ DCs. e, Expression of CD45, CD1a, CD1c, HLA-DR, HLA-DP, CD88 and CD3/CD16/CD19/CD20 versus CD207 by total live cells (including CD45− non-immune cells) from a NSCLC tumour. f, Mean fluorescence intensity (MFI) of markers expressed by populations of DCs defined in panel d. g, Percentage of DC “states” identified in (Fig. 5c and Fig. 5c) among total CD45+ cells in matched juxta-tumoral tissue versus tumour. h, Gene set enrichment analysis (GSEA) of the CD207 DC signature comparing bulk RNAseq of DC3s at day 3 cultured with GM-CSF + TGF-β with or without OP-9-D4 cells from Kvedaraite et al., 2022. (i) Gating strategy from singlets for the sorting of CD207+ DCs in ovarian cancer. P-values were calculated using a Wilcoxon non-parametric paired test, two-tailed. Related to Fig. 5.
Extended Data Fig. 7 Spatial mapping and characterisation of the pathophysiological involvement of DC populations in human breast and lung cancer patients.
a, Visium spatial transcriptomic profiling of 3 TNBC and 2 ER breast cancer patients from Wu et al. For each patient, the left panel shows the CD207 DC signature score, middle panel shows tissue niches, and the right panel shows haematoxylin and eosin (H&E) staining. b,) Enrichment score of CD207 DC signature across different tissue niches identified in a. c, Meaning plots of EPCAM and PTPRC expression visualised on the UMAP generated with all cells from the Merscope data of the breast cancer patient. Immune cells were extracted, and different cell populations were annotated based on a curated list of genes. mDCs were then extracted to generate an mDC UMAP that identified mDC populations. d, Meaning plots of representative genes used to define the immune populations identified in the Immune cells’ UMAP from panel c. e,f, Merfish analysis of breast cancer and lung cancer cross-sections. e, Visualisation of the expression of DC population-defining transcripts in the segmented Merfish spatial data. f, Spatial distribution of tumour cells (grey) and immune populations within the breast and lung tumour cross-sections analysed by Merfish. g, Single fluorescent images for CD207 (green), CD3 (red), CD8 (yellow) and CD20 (cyan) of the IHF data shown in Fig. 6g. * = p < 0.05, ** = p < 0.005, *** = p < 0.005 and **** = p < 0.0005. Related to Fig. 6.
Extended Data Fig. 8 Characterisation of functional mDC states in human cancer patients.
a, Gating strategy for identifying mDC populations in the ICS experiment (see Fig. 7a-d). b, Percentage of positive cells for co-stimulatory factors in, (Left) CD1c+ DC2/3 CD207 +/−, (Middle) CADM1+ DC1 CCR7+/−,(Right) CD1c+DC2/3 CCR7+/−. P-values were calculated using a Wilcoxon non-parametric paired test, two-tailed. Related to Fig. 7.
Extended Data Fig. 9 Characterisation of the pathophysiological involvement of mDC populations in human cancer patients.
a, Percentage of predicted phenograph clusters from query dataset (Bassez et al.) projected using multimodal reference mapping onto the mDC-VERSE. b, Percentage of predicted DC2 and DC3 mega-clusters by multimodal reference mapping of query data from breast cancer patients categorised by T-cell clonality and treatment status (anti-PD-1 therapeutic monoclonal antibody = Immune Checkpoint Blockade = ICB). c, Percentage of CD207 DCs, CCR7 mDC, ISG DC and DC1 between patients with non-expanded and expanded T-cell clonality in the Bassez et al. data. d, Upper panel shows the correlation between the frequencies among CD45+ cells of DC populations (DC1s, ISG DCs, DC2s, DC3s and CCR7 mDCs) and CD8 TRMs in lung tumours within the Leader et al. scRNAseq data. Lower panel shows the frequencies among PTPRC(CD45)-expressing immune cells of the same DC populations split by CD8 TRMshi and CD8 TRMslo. e, Correlation between the frequencies of CD207+ DCs and CD4+ T-cells from flow cytometry analysis of 8 NSCLC patients. f, Correlation map of DC population signatures (defined in the mDC-VERSE) and of other signatures obtained from Ramos et al. in the BRCA (Breast) and the LUAD (Lung) adenocarcinoma TCGA datasets. g, Kaplan-Meier plots of the overall survival (OS) of patients with different cancers whose tumour was sampled and analysed by bulk RNAseq prior to immune checkpoint blockade (ICB) treatment. Patients were separated based on high or low expression of genes specifically expressed by total DC2s + DC3s, by CD207 DCs, by DC1s or by CCR7 mDCs. Correlations were evaluated using the Pearson correlation (r) with two-tailed p values. P-values were calculated using a Wilcoxon non-parametric paired test, two-tailed. Related to Fig. 7.
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Mulder, K., Gardet, M., Kong, W.T. et al. DC subsets and states unraveled across human juxtatumoral and malignant tissues. Nat Immunol 27, 135–149 (2026). https://doi.org/10.1038/s41590-025-02337-x
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DOI: https://doi.org/10.1038/s41590-025-02337-x
