Fig 2: Validated flow cytometry gating strategy for murine myeloid cells, related to Figure 2(A) CITE-seq data from the murine myeloid cells in Figure 2A were exported as an FCS file and an in silico gating strategy identified in FlowJo software.(B) Application of the in silico gating strategy with a 21-color flow cytometry panel. Myeloid cells were pre-gated as live CD45+ lineage cells (Ly6G-CD19-NK1.1-B220-CD3-). Data are representative of 3 experiments with 3–6 mice per experiment.(C) cDC1s, cDC2s, migratory cDCs (Mig. cDCs), peritoneal macs (Peri. Macs), KCs, and non-KC macs (non-KCs) were FACS-purified using gating strategy in (B), mRNA was isolated and qPCR performed to examine expression of indicated genes defining each population to validate their identity. Data are representative of 2 experiments with n = 3–6.(D) Putative peritoneal macs were FACS-purified using gating strategy in (B) and expression of Gata6 was examined by qPCR compared with other hepatic myeloid populations. Data are from a single experiment with n = 6.(E) Peritoneal macs as a % of total macs recovered from the liver using different digestion techniques (in vivo, ex vivo, or capsule) or in supernatants in which livers were washed following removal from the mouse but prior to digestion (wash). Data are from a single experiment with n = 4. *p < 0.05, **p < 0.01 one-way ANOVA with Bonferroni post-test compared with wash data.(F) Expression of CD14 and CD207 within the non-KC mac population from (B) (left) and % of CD207+ and CD207- populations among total macs in livers digested using the ex vivo or in vivo protocols or in dissected and digested liver capsule (right). Data are representative of two experiments with n = 4–5 mice per experiment. ****p < 0.0001 mixed effects analysis with Tukey’s multiple comparison test.(G) Expression of VSIG4, F4/80, GLUL, and DAPI by confocal microscopy. Insets represent zones featured in Figures 2E, 2G, and S3I.(H) Molecular Cartography of indicated genes and cell types. Insets represent zones featured in Figures 2F, 2H, and S3J.(I) Expression of VSIG4, F4/80, GLUL, and DAPI by confocal microscopy at the central vein. Scale bars, 50 µm.(J) Molecular Cartography of indicated genes and cell types at central vein.(K) Expression of F4/80, EPCAM, CCR2, GPNMB, and DAPI by confocal microscopy at a portal vein (top) or F4/80 or GPNMB alone (bottom). Scale bars, 25 µm.(L) Quantification of % of Gpnmb & Trem2 counts over Adgre1 counts in indicated regions of tissue as assessed using Molecular Cartography data. Each dot represents an individual region. *p < 0.05, ****p > 0.0001 one-way ANOVA with Bonferroni post-test.(M) Expression of DESMIN and F4/80 at the liver capsule and underlying parenchyma (left) or EPCAM, DESMIN and F4/80 at the bile duct by confocal microscopy. PV, portal vein; CV, central vein; HA, hepatic artery; BD, bile duct. Arrows indicate specific cell types, where color corresponds to cell type/markers. All images are representative of 2–6 mice.

Fig 3: Trimodal measurement of transcription, epitopes, and accessibility.(a) Workflow diagram for the major steps in transcription, epitopes, and accessibility (TEA-seq). (b) Scatterplot comparing unique single-cell assays for transposase-accessible chromatin (scATAC-seq) fragments and scRNA-seq unique molecule indexes (UMIs) for each TEA-seq cell barcode. In (a, b, d, and e), n = 227,390 barcodes are displayed in total; 29,264 passing QC criteria are represented by purple points. (c) Scatterplot comparing unique scATAC-seq fragments and antibody-derived tags UMIs for each cell barcode. (d) scATAC-seq QC scatterplot comparing unique scATAC-seq fragments and fraction of reads in peaks scores for each cell barcode. n = 34,757 total cells are displayed (those with >1,000 unique ATAC fragments); 29,264 passing QC criteria are represented by purple points. (e) Uniform manifold approximation and projection (UMAP) projections generated using each of the three modalities separately. Only cells passing QC (n = 7,939 barcodes) are presented in (e, f). (f) A joint UMAP projection generated using three-way weighted nearest neighbors that leverages all three of the measured modalities. (g) Scatterplot showing the peak-to-RNA correlations (x-axis) and peak-to-protein (y-axis) correlation values for each peak (points) that was found to be correlated with the CCR2 gene or CD192 antibody in TEA-seq data. Histograms at the margins show the distribution of scores for RNA correlations (top) and protein correlations (right). Dashed line shows 1:1 correspondence. Peaks not found to be correlated in each method were assigned a score of 0. (h) Genome tracks showing links between peaks (red hashes) and the CCR2 gene based on protein expression (above peaks) and gene expression (below peaks). Correlations are represented by arcs colored based on the correlation score (color scale for both panels to the left). The bottom panel shows the gene neighborhood around CCR2. All coordinates are from the Hg38 genome assembly. (i), as in (g), for the CD38 gene and correlated peaks. (j), as in (h), for the CD38 gene locus. Figure 4—source data 1.Single cell quality metrics for TEA-seq samples. Figure 4—source data 2.Cell type labels and UMAP coordinates for TEA-seq samples. Figure 4—source data 3.Peak to gene and peak to protein link correlations.