A single cell atlas of the human liver tumor microenvironment

Article17 December 2020Open Access Transparent process A single cell atlas of the human liver tumor microenvironment Hassan Massalha Hassan Massalha orcid.org/0000-0002-9923-6878 Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel Search for more papers by this author Keren Bahar Halpern Keren Bahar Halpern Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel Search for more papers by this author Samir Abu-Gazala Samir Abu-Gazala Department of General Surgery, Hadassah Hebrew University Medical Center, Jerusalem, Israel Transplant Division, Department of Surgery, Hospital of the University of Pennsylvania, Philadelphia, PA, USA Search for more papers by this author Tamar Jana Tamar Jana Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel Search for more papers by this author Efi E Massasa Efi E Massasa Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel Search for more papers by this author Andreas E Moor Andreas E Moor orcid.org/0000-0001-8715-8449 Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland Search for more papers by this author Lisa Buchauer Lisa Buchauer orcid.org/0000-0002-4722-8390 Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel Search for more papers by this author Milena Rozenberg Milena Rozenberg Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel Search for more papers by this author Eli Pikarsky Eli Pikarsky The Lautenberg Center for Immunology, Institute for Medical Research Israel-Canada, Hebrew University Medical School, Jerusalem, Israel Search for more papers by this author Ido Amit Ido Amit Department of Immunology, Weizmann Institute of Science, Rehovot, Israel Search for more papers by this author Gideon Zamir Gideon Zamir Department of General Surgery, Hadassah Hebrew University Medical Center, Jerusalem, Israel Search for more papers by this author Shalev Itzkovitz Corresponding Author Shalev Itzkovitz [email protected] orcid.org/0000-0003-0685-2522 Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel Search for more papers by this author Hassan Massalha Hassan Massalha orcid.org/0000-0002-9923-6878 Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel Search for more papers by this author Keren Bahar Halpern Keren Bahar Halpern Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel Search for more papers by this author Samir Abu-Gazala Samir Abu-Gazala Department of General Surgery, Hadassah Hebrew University Medical Center, Jerusalem, Israel Transplant Division, Department of Surgery, Hospital of the University of Pennsylvania, Philadelphia, PA, USA Search for more papers by this author Tamar Jana Tamar Jana Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel Search for more papers by this author Efi E Massasa Efi E Massasa Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel Search for more papers by this author Andreas E Moor Andreas E Moor orcid.org/0000-0001-8715-8449 Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland Search for more papers by this author Lisa Buchauer Lisa Buchauer orcid.org/0000-0002-4722-8390 Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel Search for more papers by this author Milena Rozenberg Milena Rozenberg Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel Search for more papers by this author Eli Pikarsky Eli Pikarsky The Lautenberg Center for Immunology, Institute for Medical Research Israel-Canada, Hebrew University Medical School, Jerusalem, Israel Search for more papers by this author Ido Amit Ido Amit Department of Immunology, Weizmann Institute of Science, Rehovot, Israel Search for more papers by this author Gideon Zamir Gideon Zamir Department of General Surgery, Hadassah Hebrew University Medical Center, Jerusalem, Israel Search for more papers by this author Shalev Itzkovitz Corresponding Author Shalev Itzkovitz [email protected] orcid.org/0000-0003-0685-2522 Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel Search for more papers by this author Author Information Hassan Massalha1, Keren Bahar Halpern1, Samir Abu-Gazala2,3, Tamar Jana1, Efi E Massasa1, Andreas E Moor4, Lisa Buchauer1, Milena Rozenberg1, Eli Pikarsky5, Ido Amit6, Gideon Zamir2 and Shalev Itzkovitz *,1 1Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel 2Department of General Surgery, Hadassah Hebrew University Medical Center, Jerusalem, Israel 3Transplant Division, Department of Surgery, Hospital of the University of Pennsylvania, Philadelphia, PA, USA 4Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland 5The Lautenberg Center for Immunology, Institute for Medical Research Israel-Canada, Hebrew University Medical School, Jerusalem, Israel 6Department of Immunology, Weizmann Institute of Science, Rehovot, Israel *Corresponding author. Tel: +972 89343104; E-mail: [email protected] Molecular Systems Biology (2020)16:e9682https://doi.org/10.15252/msb.20209682 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Malignant cell growth is fueled by interactions between tumor cells and the stromal cells composing the tumor microenvironment. The human liver is a major site of tumors and metastases, but molecular identities and intercellular interactions of different cell types have not been resolved in these pathologies. Here, we apply single cell RNA-sequencing and spatial analysis of malignant and adjacent non-malignant liver tissues from five patients with cholangiocarcinoma or liver metastases. We find that stromal cells exhibit recurring, patient-independent expression programs, and reconstruct a ligand–receptor map that highlights recurring tumor–stroma interactions. By combining transcriptomics of laser-capture microdissected regions, we reconstruct a zonation atlas of hepatocytes in the non-malignant sites and characterize the spatial distribution of each cell type across the tumor microenvironment. Our analysis provides a resource for understanding human liver malignancies and may expose potential points of interventions. SYNOPSIS Single cell transcriptomics and spatial methods are used to generate a cell atlas of the human liver tumor microenvironment, exposing recurring tumor-stroma interactions and zonation patterns in the healthy and malignant tissue. A single cell atlas of the malignant and adjacent non-malignant human liver is presented. Recurring stromal cell gene expression signatures are found in liver metastases and cholangiocarcinomas. Tumor and stromal cells communicate through a conserved ligand-receptor interaction network. Spatial transcriptomics reveal zonated expression patterns in the malignant and non-malignant liver. Introduction Cancer is a heterogeneous disease, exhibiting both interpatient and intrapatient variability (Marusyk et al, 2012; Meacham & Morrison, 2013; Patel et al, 2014; Alizadeh et al, 2015). Tumor cells do not operate in isolation, but rather closely interact with a complex milieu of supporting stromal cells that form the tumor microenvironment (TME) (Polyak et al, 2009; Hanahan & Weinberg, 2011; Lambrechts et al, 2018). These cells include, among others, a range of immune cells, cancer-associated fibroblasts (CAFs), and endothelial cells. Interactions between the tumor and stromal cells are critical for cancer cell survival (Meacham & Morrison, 2013). Stromal cells supply the cancer cells with growth factors, facilitate immune evasion, and modulate the composition of the extracellular matrix. Given the diversity of cell types that form the TME, it is essential to apply single cell approaches to resolve their molecular identities (Tirosh et al, 2016; Puram et al, 2017; Lambrechts et al, 2018). The liver is a major site of both primary tumors and metastases (Llovet et al, 2016). Tumors of liver origin include hepatocellular carcinomas (Guichard et al, 2012), cholangiocarcinomas [tumors originating from liver cholangiocytes (Patel, 2011; Sia et al, 2013)], and hepatoblastomas. Liver metastases often originate in colorectal and pancreatic tumors and are the main cause of mortality in these cancer patients (Weinberg, 2013). Single cell atlases have provided important insight into the development (Camp et al, 2017; Segal et al, 2019; Popescu et al, 2019), physiology (MacParland et al, 2018; Aizarani et al, 2019), and pathology (Zhang et al, 2019, 2020; Ramachandran et al, 2019; Sharma et al, 2020) of the human liver. Here, we reconstruct a cell atlas of the malignant human liver in patients with liver metastases or cholangiocarcinomas. Our analysis highlights recurring stromal cell type signatures and interaction modalities with the carcinoma cells. By combining spatial information, we reconstruct zonation patterns of hepatocytes in the non-malignant tissue sites and identify distinct spatial distributions of cell types across the TME. Results A cell atlas of the human liver tumor microenvironment To assemble a cell atlas of the human liver TME, we analyzed tissues from six patients who underwent liver resection (Fig 1A, Appendix Fig S1). Three Patients underwent hepatic resection for colorectal metastases, two for intrahepatic cholangiocarcinoma, and one for a cyst at a benign stage (Dataset EV1). We dissociated the tissues into single cells and measured their transcriptomes using MARS-seq (Jaitin et al, 2014; Materials and Methods). In parallel, we preserved tissues for spatial analysis using laser-capture microdissection (LCM) (Moor et al, 2017, 2018) and single molecule fluorescence in situ hybridization (smFISH) (Bahar Halpern et al, 2015). Figure 1. Single cell atlas of the malignant human liver Experimental scheme, tumor, and adjacent non-tumor liver samples from surgeries were dissociated for scRNA-seq, frozen for LCM, and fixed for smFISH. tSNE plot colored by normalized sum of pan-carcinoma markers taken form Puram et al (2017). “n”—indicates the number of cells per group. tSNE plot colored by the 17 Seurat clusters including hepatocytes, endothelial cells (liver sinusoidal endothelial cells—LSEC, non-tumor liver vascular endothelial cells—LVEC, and tumor liver vascular endothelial cells—LVECt), mesenchymal cells (Stellate cells, cancer-associated fibroblasts—CAFs, Pericytes, vascular smooth muscle cells—vSMC), immune cells (Kupffer cells, scar-associated macrophages—SAMs, tissue monocytes 1—TM1, cDC1, cDC2, T cells, and B cells), proliferating cells, and carcinoma cells. Heatmap showing the normalized expression of marker genes for the different clusters (Materials and Methods). Expression is normalized by the maximal expression among all cell types. Download figure Download PowerPoint Our single cell atlas included 7,947 cells, 4,140 from the malignant sites and 3,807 from the non-malignant sites (Fig 1B). The non-malignant sites did not show histological signs of fibrosis, with the exception of the cholangiocarcinoma patient p2 (Materials and Methods, Dataset EV1). The cells formed 17 clusters, which we annotated based on known marker genes and a recent cell atlas of cirrhotic human livers (Ramachandran et al, 2019) (Fig 1C). Notably, the stromal clusters included a mixture of cells from different patients (Appendix Fig S1), demonstrating recurring stromal signatures. Cells from the non-malignant liver sites included clusters of hepatocytes and several non-parenchymal cell populations—hepatic stellate cells, vascular smooth muscle cells (vSMC), Kupffer cells, T cells, B cells, liver sinusoidal endothelial cells (LSEC), liver vascular endothelial cells (LVEC), and cholangiocytes, the latter clustering with the carcinoma cells. Cells from the malignant liver sites included carcinoma cells, marked by KRT8, KRT18, and EPCAM (Puram et al, 2017; Fig 1B) and diverse TME cell populations, including fibroblasts, endothelial cells, and immune cells (Fig 1C). Carcinoma cells exhibited distinct gene expression differences between the cholangiocarcinoma patients and the metastatic patients (Appendix Fig S1E). Genes elevated in cholangiocarcinomas included higher expression of the cholangiocyte gene Beta-defensin 1 (DEFB1) (Harada et al, 2004) and FGFR2. Genes elevated in colorectal cancer metastasis included higher expression of Cadherin 17 (CDH17) (Panarelli et al, 2012) and the adhesion molecules CEACAM5 and CEACAM6, previously shown to correlate with metastasis colonization (Powell et al, 2018). We extracted global gene expression signatures and unique markers for each of these cell types (Fig 1D, Datasets EV2 and EV3). We validated the expression of a panel of 12 marker genes using smFISH (Appendix Fig S2). TME cell types exhibit recurring expression signatures A common question in single cell analysis is whether the reconstructed cell atlases are stable with regard to the numbers of cells per sample and the numbers of samples (Mereu et al, 2020). This question is particularly important in cancer, due to the profound levels of interpatient heterogeneity (Marusyk et al, 2012; Meacham & Morrison, 2013; Patel et al, 2014; Alizadeh et al, 2015). We assessed the stability of the expression signatures obtained from our atlas with regard to the number of sampled patients and the number of sampled cells. To this end, we reconstructed the mean gene expression signatures for each of the 17 cell type clusters, based on subsamples of the six patients, and equally sized subsamples of cells from all patients as controls. We compared these mean expression signatures of subsets of the data with those obtained from the full atlas. We found that the gain in correlations, when adding new patients, strongly curtailed for most cell types beyond three patients and converged on the correlations obtained when subsampling cells rather than patients (Appendix Fig S3). An exception was the carcinoma cluster, where gene expression signatures changed with each new added patient (Appendix Fig S3). Our analysis thus demonstrates that, while carcinoma cells exhibit high interpatient variability, the liver TME exhibits recurring gene expression signatures that are more uniform between patients. Differences in TME gene expression between the malignant and non-malignant sites Our single cell analysis of matching malignant and non-malignant sites within the same patients enabled identification of gene expression differences in distinct cell populations that compose the TME (Fig 2). Genes elevated in tumor endothelial cells compared to the non-tumor endothelial cells included the von Willebrand factor VWA1, encoding a glycoprotein previously shown to facilitate tumor cell extravasation (Terraube et al, 2007), as well as SOX17 (Yang et al, 2012) and INSR (Nowak-Sliwinska et al, 2019), both shown to promote tumor angiogenesis (Fig 2A). The immune cell populations in the malignant liver predominantly included scar-associated macrophages (SAMs) (Ramachandran et al, 2019; Fig 2B). These cells express the marker genes CD9 and TREM2, a tumor suppressor in hepatocellular carcinoma (Tang et al, 2019), as well as the markers CAPG and GPNMB. GSEA analysis of Subramanian et al (2005) SAM genes resulted in a significant enrichment of apical junction genes and the complement system. Their recurring signatures included lipid-associated genes, such as PLIN2 and LPL, overlapping the recently identified SPP1+ lipid-associated macrophages (LAMs) in mouse fatty livers (Remmerie et al, 2020). Liver mononuclear phagocyte populations from the non-malignant liver sites were composed of Kupffer cells, expressing C1QB, MARCO, CD5L, and CD163 (Appendix Fig S4). T cells from the malignant sites were predominantly composed of Tregs, marked by CTLA4 and FOXP3, whereas T-cell populations from the non-malignant sites were predominantly composed of cytotoxic T cells, expressing CCL5, GZMK, and NKG7 (Fig 2C). These divisions within the immune cell populations suggest a recruitment of immune-suppressive subsets of T cells and macrophages, as previously demonstrated for other tumors (Lambrechts et al, 2018; Binnewies et al, 2018). Additional immune cell types in the TME included conventional dendritic cells (cDC1 and cDC2), tissue monocytes (TM1), expressing FCN1 and S100A12 (Ramachandran et al, 2019), and B cells (Fig 1C, Appendix Fig S4). Figure 2. Expression signatures of tumor endothelial and immune cells A–D. Top-left—tSNE plot of the Seurat clusters, boxes demarcate compared clusters. Dashed boxes and labels indicate the cell clusters that are compared in panels A–C. (A) Volcano plot of differential gene expression (DGE) between liver vascular endothelial cells in the tumor and non-tumor samples. (B) Volcano plot of DGE between mononuclear phagocytes in the malignant and non-malignant samples. (C) Volcano plot of DGE between T cells in the tumor and non-tumor samples. (D) DGE analysis between tumor mononuclear phagocytes classified by cancer type (cholangiocarcinoma in dark purple and metastasis in light purple). Wilcoxon rank-sum tests were used to generate P-values, Benjamini–Hochberg multiple hypotheses correction was used to compute q-values. Labeled dots in all panels are gene names of selected differentially expressed genes between the compared two clusters. Download figure Download PowerPoint We further assessed the differences in the expression signatures of endothelial cells, mononuclear phagocytes, and T cells between the tumor sites of the cholangiocarcinoma patients and the metastases patients (Fig 2D). Endothelial cells and T cells did not exhibit differential expression between these two etiologies. In contrast, mononuclear phagocytes exhibited up-regulation of chemokines such as CCL4, CCL4L2, and CCL3L3 in the cholangiocarcinoma samples and extracellular remodeling genes such as MMP19, MMP12, and HS3ST2 in the metastatic patients. Diversity of the human liver mesenchymal cells Our atlas included four mesenchymal cell clusters (Fig 3, Appendix Fig S5A). Hepatic stellate cells, marked by the retinol binding protein 1 (RBP1), and vascular smooth muscle cells, marked by Myosin-11 (MYH11) (Ramachandran et al, 2019), were abundant in the non-malignant liver sites (Fig 3A, Appendix Fig S1C). Mesenchymal cells in the malignant liver sites included two clusters. Cancer-associated fibroblasts (CAFs), expressing extracellular matrix (ECM) genes such as COL1A1, LUM, and BGN, formed the larger cluster. A second cluster included cells expressing classic markers of pericytes, periendothelial mesenchymal cells with important roles in regulating vascular integrity (Armulik et al, 2011). These markers included RGS5 and CSPG4, encoding the neuron-glial antigen 2 protein (NG2) (Armulik et al, 2011). We found that some previously suggested markers of pericytes, such as DES (Nehls et al, 1992) and ANPEP (Kumar et al, 2017), were not specifically expressed in pericytes in the malignant human liver context (Dataset EV3). Importantly, pericytes were almost absent from the non-malignant sites (Appendix Fig S1C). We used smFISH to demonstrate that the RGS5+ cells are indeed adjacent to endothelial cells, marked by PDGFB, as expected from pericytes (Fig 3B and C). In contrast, cells expressing the CAFs marker COL1A1 resided farther away from the endothelial cells (Fig 3B and D, and Appendix Fig S5A). Figure 3. Mesenchymal heterogeneity in the liver malignant sites A. Key marker genes for the four mesenchymal clusters (RBP1, COL1A1, RGS5, and MYH11). Light gray dots denote cells originating from non-tumor samples. Dark gray dots denote cells originating from the tumor samples. B. Left—Representative smFISH image of patient p1 stained for RGS5 and COL1A1 showing distinct spatial localization of CAFs and pericytes. Scale bar 10 µm. Dashed lines mark the shortest distance of cells (2a) and (2b) from the cell (1). Middle—zoom-in of (1) from left panel, showing a blood vessel like structure formed by endothelial cells marked by PDGFB (magenta) wrapped by pericytes marked by RGS5 (green). Dashed lines are two consecutive cell layers of endothelial cells and pericytes. Scale bar 2.5 µm. Right—zoom-in of (2a and b) from the left panel, showing two distant CAFs expressing high COL1A1 signal but not RGS5. DAPI used for nuclei staining. Scale bar 2.5 µm. C, D. Violin plot of the distance from blood vessels of low/high RGS5 expressing cells (n = 358 and n = 360, respectively) and low/high COL1A1 expressing cells (n = 359 and n = 359, respectively). “p” is the P-value determined by Wilcoxon rank-sum test. Empty circles are the medians over all repeats. E. Schematic representation of the top-ranked interaction (bona-fide) detected by NicheNet (Materials and Methods). Results are sorted by the prior interaction potential between pericytes and tumor LVECt cells. F. Pathway enrichment analysis for all bona-fide genes (Dataset EV4) using Enrichr tool. Images in this figure are representative images out of eight independent experiments over four patients. Download figure Download PowerPoint Paracrine and juxtacrine interactions between endothelial cells and their attached pericytes have been shown to be important for proper vascularization (Annika et al, 2005). Our single cell atlas enabled unbiased identification of signaling pathways that could affect gene expression between the physically interacting endothelial cells and pericytes. To this end, we applied NicheNet (Browaeys et al, 2019), a computational method that predicts ligand–receptor interactions based on induction of downstream target genes (Fig 3E, Dataset EV4). We identified signaling from endothelial cells to pericytes via JAG1,2-NOTCH3, and PDGFB-PDGFRB (Fig 3E, Appendix Fig S5B), and signaling from pericytes to endothelial cells through SLIT2-ROBO1,4 (Fig 3E, Appendix Fig S5C) and ANGPT2-TEK (Fig 3E). The ligands and receptors mediating the endothelial cell–pericyte cross-talk were enriched for juxtacrine signaling pathways, angiogenesis, and chemokine and cytokine signaling (Fig 3F). Recurring interactions between the carcinoma cells and the tumor microenvironment Tumor growth is highly dependent on the cross-talk between the tumor cells and the stromal cells in the TME. Stromal cells provide important growth factors and signaling molecules that enhance tumor growth and survival. In turn, the tumor cells secrete ligands sensed by the stromal cells, which facilitate their recruitment (Zhou et al, 2017). Our single cell atlas facilitated an analysis of the molecular cross-talk between tumor cells and each of the stromal cell types. To this end, we parsed a database of ligand–receptor interactions (Ramilowski et al, 2015) and identified pairs, for which the interacting proteins were specific to the carcinoma cell cluster on the one hand and to the supporting stromal cell clusters on the other (Zhou et al, 2017; Halpern et al, 2018; Materials and Methods). We found that CAFs and SAMs were interaction hubs, representing 49.3% of all carcinoma–TME interactions (Fig 4A). We focused on specific ligand–receptor interactions that recurred in at least three of the five patients with malignant cancer (Fig 4B, Dataset EV5). The resulting tumor interactome network highlighted several recurring modules, including a large matrix remodeling module, modules centered around ERBB, HGF-MET, TGFbeta, FGF, IGF, and VEGFA, a lipid trafficking module, and a WNT planar cell polarity module (Fig 4B). Figure 4. Human liver interactome delineates tumor–stroma cross-talk Summary of the total number of ligand–receptor interactions among clusters with at least 20% tumor cells. Interactions of tumor TME cell types with the carcinoma cluster marked by red box. Network of recurring ligand–receptor interactions between carcinoma cells and stromal cells from the malignant sites. Node colors denote the cell type cluster in which the ligands/receptors are enriched. Gray arrows color indicates the interaction Zscore (Materials and Methods). Recurring modules are shaded. Included are all recurring interactions that significantly appeared in at least three patients. Dot-plot of selected genes highlight shared interaction motifs between different clusters colored by max normalized expression for each genes across all clusters in (A). For each gene, dot size represents the fraction of positive cells for each cluster. Recurring interaction motifs between CAFs and tumor immune cells. Top—CAFs and SAMs comodulate carcinoma–stroma interaction. CAFs produce DCN that modulates the interaction between the CAF-SAMs-expressed ligand HGF and the carcinoma-expressed receptor MET. Bottom—CAFs produce CTHRC1 that modulates the interaction between the WNT5A ligand, expressed by CAFs, SAMS, and TM1 and the carcinoma-expressed receptor FZD5. Strategy for computing interaction scores for each tumor. A score is computed as the sum of the products of all ligands and matching receptors of the recurring interaction network. These are compared to the average score obtained when randomizing the real interaction network in a manner that preserves the number of outgoing and incoming interactions of each ligand and receptor (Scorerand). The ratio of the real and randomized scores constitutes a network score. Network score increases with increasing tumor stage. Analysis for 383 TCGA sample of liver hepatocellular carcinoma (LIHC) and cholangiocarcinoma (CHOL). P-value determined by Wilcoxon rank-sum test. Empty circles are the medians over all repeats. Dashed lines are the median over the tumor stage. Download figure Download PowerPoint The largest module consisted of matrix remodeling proteins. The malignant ECM has a unique composition that is shaped by ECM assembly and degrading proteins, collectively known as the tumor matrisome (Naba et al, 2016; Varol & Sagi, 2018). Features of the ECM such as stiffness and porosity facilitate both optimal cellular contacts, maximize accessibility of growth factors and control immune cell exclusion from cancer cells (Binnewies et al, 2018). Within the matrix remodeling module, we found that CAFs produced most of the collagens and laminins, interacting with integrin receptors on the tumor cells (Fig 4B). Our scRNA-seq analysis further enabled identifying the secreting stromal cell type for each of the matrisome components (Naba et al, 2016; Appendix Fig S6). The WNT Planar cell polarity (WNT-PCP) pathway has been suggested to promote metastases and cancer cell invasion (Wang, 2009). PCP signaling is activated by non-canonical Wnt morphogens, such as WNT5A, which we found to be expressed by both CAFs and immune cells (Fig 4B–D). CTHRC1, a secreted collagen triple helix filament that forms a complex that stabilizes WNT binding to its tumor-expressed receptor-FZD (Yamamoto et al, 2008), was specifically expressed by CAFs. Thus, immune cells and CAFs jointly modulate WNT-PCP tumor signaling. We observed a similar cooperation of immune cells and CAFs within the MET signaling module. MET signaling is a major driver in hepatic tumors and metastases (De Silva et al, 2017). We found that HGF, the main activating ligand of MET, was expressed by both SAMs and CAFs (Fig 4B–D). DCN, encoding the decorin protein, is expressed by CAFs and in turn inhibits HGF-MET binding (Goldoni et al, 2009). Our interaction map further revealed an additional role of DCN as an interactor of the carcinoma-specific receptor EGFR (Fig 4B). In summary, our tumor interactome analysis revealed the details of the molecular cross-talk between the tumor and stromal cell types. Recurring interaction network connectivity correlates with liver tumor severity The recurring interactions between the carcinoma cells and cells in the TME suggest that elevated expression of these ligands and their matching receptors could convey a selective advantage to cells in the liver TME. To assess this hypothesis, we examined a cohort of 383 bulk-sequenced liver tumors from the TCGA database and computed a network score based on our recurring network connectivity (Fig 4E and F). For each tumor, we first computed a score that consists of the summed products of the expression levels of each ligand and matching receptor and normalized it by computing a randomized score based on degree-preserving random networks (Materials and Methods, Fig 4E). This normalization is important, since a high score may simply reflect elevated expression of the ligands and receptors, often oncogenes, rather than the coordinated expression of ligands and their matching receptors. We found that the network score significantly increased along the liver tumor stages (Fig 4F). Thus, our interaction score correlates with tumor severity. Spatial transcriptomics identifies zonation patterns of hepatocytes Cells in tissues and solid tumors reside in zones that often exhibit variability in oxygen levels, nutrient availability, and morphogen concentrations. These can in turn generate spatial heterogeneity of gene expression (Moor & Itzkovitz, 2017) and result in distinct spatial representation of different cell types. The liver is a spatially heterogeneous organ, composed of repeating anatomical units termed lobules, which are polarized by centripetal bloo