211 Spatial whole transcriptome profiling of human normal liver and HCC uncovers unique insights into metabolic zonation

Background

Understanding the physiology and functions of the liver and cancer requires the knowledge of transcriptional patterns driving biological activities within the functional structures of the tissue, especially the zonated features of the liver metabolic networks.

Methods

Using the powerful and unique capabilities of GeoMx® Digital Spatial Profiler (DSP) with the Whole Transcriptome Atlas (WTA) panel to resolve functional units within FFPE tissues in situ.

Results

We report the spatial analysis of whole transcriptomes across three micro-dissected zones (pericentral zone 3, intermediate zone 2, and periportal zone 1) of human normal liver and in matched hepatocellular carcinoma (HCC) tissues with neoplastic re-partitioning of most of the local histopathological structures. We also show the whole transcriptome expression data from Kupffer cells, portal traits, and interlobular bile ducts from four normal liver samples and HCC. From functional groups within each histological structure, 500 to 3,000 genes were detected. In the liver functional units, 1,000–2,500 genes were detected in zone 1, 2 and 3 separately. By comparing the whole transcriptome profiles of zone 1 and zone 3, we found 32 differentially expressed targets (fold-change > 1.5, p-value < 0.05) which demonstrated a gradient expression pattern along the porto-central axis. The expression patterns of CYP1A2, CYP2E1, CYP3A4 and ALDOB matched well with their respective patterns of protein expression (Human Protein Atlas), recapitulating the well-studied distribution of functional activities along the porto-central axis. Moreover, by combining with Gene Set Enrichment Analysis (GSEA), we found important pathways involved in metabolisms in either the pericentral area or the periportal area. Pathways including biological oxidations (CYP1A2, CYP2E1, CYP3A4, ADH1A, and ADH1B) and lipids metabolism (AKR1C1, AKR1C2, and SLCO1B3) indicated high enrichment in zone 3 and decreased towards zone 1. In contrast, pathways including platelet degranulation (FGA, FGB, FGG), glucose metabolism (ALDOB and PCK1), and amino acids metabolism (HAL, SDS, NNMT, and GLS2) showed high enrichment in zone 1 and decreased towards zone 3.

Conclusions

Our WTA data has revealed clear metabolic zonation in the liver along the porto-central axis. GeoMx DSP technology with WTA is a powerful tool to investigate the underlying mechanisms of liver metabolism, regeneration, tissue structure and HCC.

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