Metabolic study of aristolochic acid I-exposed mice liver by atmospheric pressure matrix-assisted laser desorption/ionization mass spectrometry imaging and machine learning

Mass spectrometry imaging-based metabolomics highlights spatial metabolic alterations in three types of liver injuries

Liver's distinctive function renders it highly susceptible to diverse damage sources. Characterizing the metabolic profiles and spatial signatures in different liver injuries is imperative for early diagnosis and etiology-oriented treatment. In this comparative study, we conducted whole-body spatial metabolomics on zebrafish with liver injury induced by ethanol (EtOH), acetaminophen (APAP), and thioacetamide (TAA). The two specific levels, the whole-body and liver-specific metabolic profiles, as well as their regional distributions, were systematically mapped in situ by mass spectrometry imaging, which is distinct from conventional LC-MS and GC-MS methods. We found that liver injury regions exhibited more pronounced metabolic reprogramming than the entire organism, leading to significant alterations in eight fatty acids, three phospholipids, and four low-molecular-weight metabolites. More importantly, fatty acids as well as small molecule metabolites including glutamine, glutamate, taurine and malic acid displayed contrasting changes between alcoholic liver disease (ALD) and non-alcoholic fatty liver disease (NAFLD). In addition, phospholipids, including Lyso PC (16:0) and Lyso PE (18:0), demonstrated notable down-regulation in all damaged liver, whereas PC (34:1) underwent upregulation. This study not only deepens insights into distinct potential biomarkers for liver injuries, but also underscores spatial metabolomics as a powerful tool to elucidate possible pathogenic mechanisms in other metabolic diseases.

Spatial metabolomics and its application in the liver

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.

Multi-MSIProcessor: Data Visualizing and Analysis Software for Spatial Metabolomics Research

Mass spectrometry imaging (MSI) has emerged as a revolutionary analytical strategy in biomedical research for molecular visualization. By linking the characterization of functional metabolites with tissue architecture, it is now possible to reveal unknown biological functions of tissues. However, due to the complexity and high dimensionality of MSI data, mining bioinformatics-related peaks from batch MSI data sets and achieving complete spatially resolved metabolomics analysis remain a great challenge. Here, we propose novel MSI data processing software, Multi-MSIProcessor (MMP), which integrates the data read-in, MSI visualization, processed data preservation, and biomarker discovery functions. The MMP focuses on the AFADESI-MSI data platform but also supports mzXML and imzmL data input formats for compatibility with data generated by other MSI platforms such as MALDI/SIMS-MSI. MMP enables deep mining of batch MSI data and has flexible adaptability with the source code opened that welcomes new functions and personalized analysis strategies. Using multiple clinical biosamples with complex heterogeneity, we demonstrated that MMP can rapidly establish complete MSI analysis workflows, assess batch sample data quality, screen and annotate differential MS peaks, and obtain abnormal metabolic pathways. MMP provides a novel platform for spatial metabolomics analysis of multiple samples that could meet the diverse analysis requirements of scholars.

Nanoparticle-based applications by atmospheric pressure matrix assisted desorption/ionization mass spectrometry

Recently, the development of atmospheric pressure matrix assisted desorption/ionization mass spectrometry (AP MALDI MS) has made contributions not only to biomolecule analysis but also to spatial distribution. This has positioned AP MALDI as a powerful tool in multiple domains, thanks to its comprehensive advantages compared to conventional MALDI MS. These developments have addressed challenges associated with previous AP MALDI analysis systems, such as optimization of apparatus settings, synthesis of novel matrices, preconcentration and isolation strategies before analysis. Herein, applications in different fields using AP MALDI MS were described, including peptide and protein analysis, metabolite analysis, pharmaceutical analysis, and mass spectrometry imaging.

Development of mass spectrometry imaging techniques and its latest applications

Mass spectrometry imaging (MSI) is a novel molecular imaging technology that collects molecular information from the surface of samples in situ. The spatial distribution and relative content of various compounds can be visualized simultaneously with high spatial resolution. The prominent advantages of MSI promote the active development of ionization technology and its broader applications in diverse fields. This article first gives a brief introduction to the vital parts of the processes during MSI. On this basis, provides a comprehensive overview of the most relevant MS-based imaging techniques from their mechanisms, pros and cons, and applications. In addition, a critical issue in MSI, matrix effects is also discussed. Then, the representative applications of MSI in biological, forensic, and environmental fields in the past 5 years have been summarized, with a focus on various types of analytes (e.g., proteins, lipids, polymers, etc.) Finally, the challenges and further perspectives of MSI are proposed and concluded.

In situ localization of lipids on mouse kidney tissues with acute cadmium toxicity using atmospheric pressure-MALDI mass spectrometry imaging

Cadmium-induced nephrotoxicity has been one of the major concerns for public health over the past century. Lipid peroxidation is a principal mechanism in its pathological process. Atmospheric pressure-MALDI mass spectrometry imaging (AP-MALDI MSI) enables direct mapping of lipids in the biological tissue sections. Considering the spatial visualization of lipids on mouse kidney tissues with acute cadmium toxicity is lacking, this study dedicates to filling the gap by using AP-MALDI MSI. Of the tested matrices, the optimized matrix for labeling lipids was 2,5-dihydroxyacetophenone (DHAP). A set of lipids including phosphatidylcholines (PC), phosphatidylglycerol (PG), lysophosphatidylcholine (LPC), sphingomyelin (SM), phosphatidic acid (PA), triglyceride (TG), phosphatidylethanolamine (PE) and phosphatidylinositol (PI), etc. were identified and visualized. Accordingly, PC, PG, LPC, SM, PA and TG were down-regulated while PE and PI were up-regulated in the renal cortex or medulla regions in kidney tissues of the mouse with acute cadmium toxicity. Such in situ locations of lipids on mouse kidney tissues with acute cadmium toxicity could help discover tissue-specific nephrotoxic biomarkers and provide new insights into its renal toxicological mechanism.