Figure 1

Visualization and pattern assessment of hepatic immune and non-immune cells: (A) Livers as well as hematoxylin–Eosin staining of liver specimens collected from the control group (Ctrl) as well as from animals being on a WD prior to tumor development (Pre-T) or after the development of HCC (Post-T). IHC pictures were generated using VectraPolaris 1.0 acquisition software, image size of 16.76 mm × 30.18 mm with the resolution of 25 µm/pixel (40×). (B) SingleR annotated cell types after all cells were classified by the ImmGen and MouseRNAseq reference databases. After annotations were complete and compared, the top 20 marker genes of each cell type (immune and non-immune) were passed to PCA to optimize visualization, indicated by separate clustering of immune and non-immune cells due to the use of these genes. (C–D) UMAP of pooled samples split to show group specific non-immune cells (C) and immune cells (D). All SingleR annotated cell types and scSorter identified subsets were included in data visualization. (E) Pie graphs displaying non-immune cell components in each sample (hepatocytes accounted for over 95% of non-immune cells in each sample, while fibroblasts and endothelial cells primarily comprised the rest. (F) IPA analysis of the hepatocyte population portraying disease-related functions and activation z-scores (blue and orange bar) based on DESeq2 results after filtering on a p-value < 0.01 and z-scores > 2. Carcinogenesis events are shown using vertical lines. (G) Pie graphs showing the composition of immune cells annotated in each sample by ImmGen and MouseRNAseq databases accessed through SingleR. Panels E & G are based on the percentage of all cells in each compartment (innate and adaptive immune cells and non-immune cells) normalized in each for a total of 100%. (H) Ratio of immune cell subsets within each population was identified using scSorter (H only includes cells classified as specific subsets of interest from scSorter to normalize each subset of cells to 100%, while removing any “Unknown” cells that were not classifiable to ensure accuracy). (I) Multilayered immunological patterns during health and diseases, manifesting super-patterns and inferior patterns were quantitatively analyzed by focusing on the ratios/proportion of immune cells interacting with each other in a network.