Spatial metabolomics and its application in the liver

Abstract

Hepatocytes work in highly structured, repetitive hepatic lobules. Blood flow across the radial axis of the lobule generates oxygen, nutrient, and hormone gradients, which result in zoned spatial variability and functional diversity. This large heterogeneity suggests that hepatocytes in different lobule zones may have distinct gene expression profiles, metabolic features, regenerative capacity, and susceptibility to damage. Here, we describe the principles of liver zonation, introduce metabolomic approaches to study the spatial heterogeneity of the liver, and highlight the possibility of exploring the spatial metabolic profile, leading to a deeper understanding of the tissue metabolic organization. Spatial metabolomics can also reveal intercellular heterogeneity and its contribution to liver disease. These approaches facilitate the global characterization of liver metabolic function with high spatial resolution along physiological and pathological time scales. This review summarizes the state of the art for spatially resolved metabolomic analysis and the challenges that hinder the achievement of metabolome coverage at the single-cell level. We also discuss several major contributions to the understanding of liver spatial metabolism and conclude with our opinion on the future developments and applications of these exciting new technologies.

Graphical abstract

FIGURE 1

Liver architecture and metabolic zonation. (A) The mammalian liver is a highly complex 3D structure organized in a honeycomb-like arrangement made of liver lobules as repetitive functional structural units. Liver lobules can be further divided into 3 zones: the region surrounding the portal triads, composed of the portal vein (PV), hepatic artery, and the bile duct, is called the periportal (zone 1); hepatocytes adjacent to the central vein (CV) are known as the pericentral (zone 3); and the regions in between are referred to as the midlobular (zone 2). (B) Currently described spatial metabolic zonation of liver metabolic processes and liver immune cells, and the molecular determinants of liver zonation are schematically summarized.

FIGURE 2

Mass spectrometry imaging (MSI) modalities and typical workflow. (A) Schematic drawing of the main ion sources used in MSI. From left to right; secondary ion mass spectrometry (SIMS), desorption electrospray ionization (DESI), and matrix-assisted laser desorption ionization (MALDI). In SIMS, a primary ion beam is applied to sputter secondary analyte ions off the sample. DESI uses an electrically charged stream of solvent, which is sprayed over the tissue surface to desorb analyte ions. To aid ionization, MALDI requires the coating of the sample with an energy-absorbing matrix. A laser is then rastered over the sample to desorb and release analyte ions. (B) A typical workflow for MALDI-MSI. (1) Frozen tissue specimens are cryosectioned, mounted onto glass slides, and (2) coated with an energy-absorbing matrix such as 2,5-dihydroxybenzoic acid using a robotic sprayer. (3) Once loaded on the instrument, a laser beam is rastered across the tissue on a pixel-by-pixel basis, producing a mass spectrum per (x, y) coordinate or pixel. For any m/z value, a heatmap can be generated by mapping the ion intensity values across the coordinates analyzed. (4) The resulting MSI data set is customarily interrogated in the context of the specimen histology (top). Spatial segmentation can be carried out by clustering pixels based on their spectral similarity (bottom left). MSI can also be integrated with other molecular imaging modalities such as immunofluorescence, imaging mass cytometry, and spatial transcriptomics (bottom right). Created with BioRender.com.