Fig 2: DC3s Infiltrate Human Breast Tumors(A) Representative gating strategy used to define macrophages, CD5+ cDC2s, and CD14+ DC3s and histograms showing the expression of CD163, FceRI, BTLA, and CD11c in human breast cancer primary tumors.(B) Violin plot quantifying cDC1, CD5+cDC2, CD14+ DC3, and CD14+CD88+ macrophage subsets identified in (A) in human breast cancer primary tumors (n = 25; *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, one-way ANOVA test).(C) Pearson correlations of the frequencies of macrophages, cDC1s, CD5+cDC2s, and CD14+ DC3s within HLA-DR+ cells in human breast cancer primary tumors (red, significantly correlated p < 0.05; black, not correlated).(D) HC showing the relative expression of markers used for subset identification in Figure 1 in CD1c+, CD1c+CD14+, and CD14+ cells from invaded lymph nodes draining human breast cancer primary tumors.(E) GSEA of pairwise comparisons of CD1c+CD14+ cells with CD1c+ or CD14+ from invaded lymph nodes draining human breast cancer primary tumors. Gene signatures of blood DC3s compared with cDC2s (DC3 > cDC2) or CD14+ monocytes (DC3 > Mono) and, vice versa, of blood cDC2s (cDC2 > DC3) or CD14+ monocytes (Mono > DC3) compared with DC3s were used (Villani et al., 2017).See also Figure S2 and Table S2.

Fig 3: DC3s Are a Discrete Subset of CD88-CD1c+CD163+ Cells in Human Peripheral Blood(A) Gating strategy used to define mononuclear phagocytes expressing CD14 and/or CD1c. Cells expressing CD14 and/or CD1c were sorted by flow cytometry from 3 healthy donors and pooled before scRNA-seq analysis. To improve the resolution of CD1c+ subsets, the cellular input was enriched in CD1high cells (Figure S1A). Single cells were isolated using a droplet-based approach and sequenced. Dimensionality reduction of scRNA-seq data was performed using dimensionality reduction (t-distributed stochastic neighbor embedding [tSNE]). Clusters A, B, C, and D were identified using the shared nearest neighbor (SNN) clustering algorithm. Each dot represents an individual cell (n = 1,622).(B) Hierarchal clustering of groups A, B, C, and D based on average gene expression (14,933 genes).(C) Absolute number of differentially expressed genes (DEGs) for pairwise comparisons between groups A, B, and D.(D) Heatmaps displaying relative expression of up to 20 DEGs defining each cluster.(E) Violin plots illustrating expression probability distributions across clusters of representative DEGs (226 total DEGs). Feature plots display the average expression of groups of genes (identified in violin plots) in each cell of the tSNE plot defined in (A).(F) Expression distribution across clusters A, B, C, and D of gene signatures identified by Villani et al. (2017) and Yin et al. (2017). (**p < 0.01, ****p < 0.0001, one-way ANOVA test)(G) Identification of 4 subsets within CD14lo to hi CD1clo to hi cells by unsupervised clustering of flow cytometry data using the FlowSOM algorithm. tSNE and unsupervised clustering were performed using the following markers: CD88, CD1c, FceRI, CD14, CD163, BTLA, CD123, and CD5. tSNE plots (right) display the relative expression of each marker among the subsets. Dot plots (below) show the expression of specific markers in clusters 1, 2, and 3 when combined in 2-dimensional analysis.(H) Improved gating strategy for identification of cDC2s, DC3s, and CD14+ monocytes in circulating PBMCs and histograms showing expression of S100A8/9, FceRI, CD5, CD14, CD116, and CD206.(I) Principal-component analysis (PCA) for bulk-sequenced mononuclear phagocyte populations as defined in (H).(J) Cluster dendrogram of the different cell types using the 2,000 most variable genes.(K) Heatmaps comparing the relative expression of markers discriminating clusters in scRNA-seq analysis (A, B, C, and D, left) and in bulk RNA-seq analysis on sorted subsets based on the gating strategy defined in (H) (right).See also Figure S1 and Table S1.

Fig 4: CD1c+ DC Heterogeneity Is Evident in Human BM(A) Flow phenotyping of CD1c+ DCs from HC PB mononuclear cells (PBMCs) (representative example of n = 22), distinct from SIRPA-CD141+ cDC1s, CD123+CD303/4+ pDCs, and CD88+monocytes (Mono). CD14+CD163+BTLA- (orange), CD14-CD163+BTLA- (light orange), CD163-BTLA+CD5- (light red), and CD163-BTLA+CD5+ (red) CD1c+ DC subsets are indicated.(B) 3D representation of CD14, CD5, and BTLA expression (flow cytometry) across the CD1c+ DC population. Heatmap shows expression of CD163.(C) PCA of NanoString gene expression profiling of fluorescence-activated cell sorting (FACS)-purified DC subsets from n = 3 HC PBMCs. CD1c+ DCs were purified based on their expression of CD14, CD5, and BTLA (A).(D) Intracellular flow analysis of in vitro cytokine elaboration (percentage of positive cells) by PB monocytes (black) and CD1c+DC subsets CD14+ (orange), CD14-CD5- (gray), and CD5+ (red) from n = 9 HC donors in response to 14-h stimulation with TLR agonists (CpG, poly(I:C), CL075, and lipopolysaccharide [LPS]). p values were derived from paired two-tailed t tests; *p < 0.05; **p < 0.01; ***p < 0.005. Bars show mean ± SEM, and circles represent individual donors.(E and F) Representative examples of the flow profiling of DC subsets in human spleen (n = 3), dermis (n = 3) (E) and BM (n = 13) (F), gated as in (A). Histograms show CD163 and BTLA expression on CD14+ (orange), CD5+ (red) and CD14-CD5- (gray) CD1c+ DCs.(G) tSNE visualization of the expression of TFs and surface markers across HC PB and BM lineage(lin, CD3,19,20,56,161)-HLA-DR+ cells by CyTOF analysis. Black gates indicate the CD1c+DC population distinct from CD88+monocytes, CLEC9A+cDC1 and CD303+pDC. Red and orange gates indicate expression of lymphocyte- or monocyte-associated antigens, respectively.(H) Hierarchical clustering of single-cell transcriptomes of mature DCs from BM using all protein-coding, non-cell-cycle genes. Marker genes were identified within SC3 with parameters p < 0.01, area under the receiver operating characteristic curve (AUROC) > 0.85; cluster 1, pDCs (GZMB, JCHAIN); cluster 2, monocytes (S100A8, VCAN); cluster 3, CD14+ DC3s (HLA-DPB1); cluster 5, cDC1s (CD59). The top rows show fluorescence intensity of surface antigens (“Antigens”) from index-sorted cells, and “Phenotype” denotes their classification defined by surface markers.See also Figure S1.