Spatial metabolomics reveals glycogen as an actionable target for pulmonary fibrosis

Manipulating mitochondrial pyruvate carrier function causes metabolic remodeling in corneal myofibroblasts that ameliorates fibrosis

Myofibroblasts are key cellular effectors of corneal wound healing from trauma, surgery, or infection. However, their persistent deposition of disorganized extracellular matrix can also cause corneal fibrosis and visual impairment. Recent work showed that the PPARγ agonist Troglitazone can mitigate established corneal fibrosis, and parallel in vitro data suggested this occurred through inhibition of the mitochondrial pyruvate carrier (MPC) rather than PPARγ. In addition to oxidative phosphorylation (Ox-Phos), pyruvate and other mitochondrial metabolites provide carbon for the synthesis of biological macromolecules. However, it is currently unclear how these roles selectively impact fibrosis. Here, we performed bioenergetic, metabolomic, and epigenetic analyses of corneal fibroblasts treated with TGF-β1 to stimulate myofibroblast trans-differentiation, with further addition of Troglitazone or the MPC inhibitor UK5099, to identify MPC-dependencies that may facilitate remodeling and loss of the myofibroblast phenotype. Our results show that a shift in energy metabolism is associated with, but not sufficient to drive cellular remodeling. Metabolites whose abundances were sensitive to MPC inhibition suggest that sustained carbon influx into the Krebs' cycle is prioritized over proline synthesis to fuel collagen deposition. Furthermore, increased abundance of acetyl-CoA and increased histone H3 acetylation suggest that epigenetic mechanisms downstream of metabolic remodeling may reinforce cellular phenotypes. Overall, our results highlight a novel molecular target and metabolic vulnerability that affects myofibroblast persistence in the context of corneal wounding.

Spatial and phenotypic heterogeneity of resident and monocyte-derived macrophages during inflammatory exacerbations leading to pulmonary fibrosis

Introduction

Genetic mutations in critical nodes of pulmonary epithelial function are linked to the pathogenesis of pulmonary fibrosis (PF) and other interstitial lung diseases. The slow progression of these pathologies is often intermitted and accelerated by acute exacerbations, complex non-resolving cycles of inflammation and parenchymal damage, resulting in lung function decline and death. Excess monocyte mobilization during the initial phase of an acute exacerbation, and their long-term persistence in the lung, is linked to poor disease outcome.

Methods

The present work leverages a clinical idiopathic PF dataset and a murine model of acute inflammatory exacerbations triggered by mutation in the alveolar type-2 cell-restricted Surfactant Protein-C [SP-C] gene to spatially and phenotypically define monocyte/macrophage changes in the fibrosing lung.

Results

SP-C mutation triggered heterogeneous CD68+ macrophage activation, with highly active peri-injured cells relative to those sampled from fully remodeled and healthy regions. Ingenuity pathway analysis of sorted CD11b-SigF+CD11c+ alveolar macrophages defined asynchronous activation of extracellular matrix re-organization, cellular mobilization, and Apolipoprotein E (Apoe) signaling in the fibrosing lung. Cell-cell communication analysis of single cell sequencing datasets predicted pro-fibrogenic signaling (fibronectin/Fn1, osteopontin/Spp1, and Tgfb1) emanating from Trem2/TREM2 + interstitial macrophages. These cells also produced a distinct lipid signature from alveolar macrophages and monocytes, characterized by Apoe expression. Mono- and di-allelic genetic deletion of ApoE in SP-C mutant mice had limited impact on inflammation and mortality up to 42 day after injury.

Discussion

Together, these results provide a detailed spatio-temporal picture of resident, interstitial, and monocyte-derived macrophages during SP-C induced inflammatory exacerbations and end-stage clinical PF, and propose ApoE as a biomarker to identify activated macrophages involved in tissue remodeling.

Mass spectrometry imaging in pulmonary disorders

Mass Spectrometry Imaging (MSI) represents a novel and advancing technology that offers unparalleled in situ characterization of tissues. It provides comprehensive insights into the chemical structures, relative abundances, and spatial distributions of a vast array of both identified and unidentified endogenous and exogenous compounds, a capability not paralleled by existing analytical methodologies. Recent scholarly endeavors have increasingly explored the utility of MSI in the adjunct diagnosis and biomarker research of pulmonary disorders, including but not limited to lung cancer. Concurrently, MSI has proven instrumental in elucidating the spatiotemporal dynamics of various pharmacological agents. This review concisely delineates the fundamental principles underpinning MSI, its applications in pulmonary disease diagnosis, biomarker discovery, and drug distribution investigations. Additionally, it presents a forward-looking perspective on the prospective trajectories of MSI technological advancements.

Application of Metabolomics across the Spectrum of Pulmonary and Critical Care Medicine

In recent years, metabolomics, the systematic study of small-molecule metabolites in biological samples, has yielded fresh insights into the molecular determinants of pulmonary diseases and critical illness. The purpose of this article is to orient the reader to this emerging field by discussing the fundamental tenets underlying metabolomics research, the tools and techniques that serve as foundational methodologies, and the various statistical approaches to analysis of metabolomics datasets. We present several examples of metabolomics applied to pulmonary and critical care medicine to illustrate the potential of this avenue of research to deepen our understanding of pathophysiology. We conclude by reviewing recent advances in the field and future research directions that stand to further the goal of personalizing medicine to improve patient care.

Multi Omics Applications in Biological Systems

Traditional methodologies often fall short in addressing the complexity of biological systems. In this regard, system biology omics have brought invaluable tools for conducting comprehensive analysis. Current sequencing capabilities have revolutionized genetics and genomics studies, as well as the characterization of transcriptional profiling and dynamics of several species and sample types. Biological systems experience complex biochemical processes involving thousands of molecules. These processes occur at different levels that can be studied using mass spectrometry-based (MS-based) analysis, enabling high-throughput proteomics, glycoproteomics, glycomics, metabolomics, and lipidomics analysis. Here, we present the most up-to-date techniques utilized in the completion of omics analysis. Additionally, we include some interesting examples of the applicability of multi omics to a variety of biological systems.

Spatially resolved metabolomic dataset of distinct human kidney anatomic regions

Cortex, medulla and papilla are three major human kidney anatomic structures and they harbour unique metabolic functions, but the underlying metabolomic profiles are largely unknown at spatial resolution. Here, we generated a spatially resolved metabolomics dataset on human kidney cortex, medulla and papilla tissues dissected from the same donor. Matrix-Assisted Laser Desorption/Ionization-Imaging Mass Spectrometry (MALDI-IMS) was used to detect metabolite species over mass-to-charge ratios of 50 -1500 for each section at a resolution of 10 × 10 µm2 pixel size. We present raw data matrix of each sample, feature annotations, raw AnnData merged from three samples and processed AnnData files after quality control, dimensional reduction and data integration, which contains a total of 170,459 spatially resolved metabolomes with 562 features detected. This dataset can be either visualized through an interactive browser or further analyzed to study metabolomic heterogeneity across regional human kidney anatomy.

Desorption Electrospray Ionization Mass Spectrometry Imaging Techniques Depict a Reprogramming of Energy and Purine Metabolism in the Core Brain Regions of Chronic Social Defeat Stress Mice

Depression is associated with pathological changes and metabolic abnormalities in multiple brain regions. The simultaneous comprehensive and in situ detection of endogenous molecules in all brain regions is essential for a comprehensive understanding of depression pathology, which is described in this paper. A method based on desorption electrospray ionization mass spectrometry imaging (DESI-MSI) technology was developed to classify mouse brain regions using characteristic lipid molecules and to detect the metabolites in mouse brain tissue samples simultaneously. The results showed that characteristic lipid molecules can be used to clearly distinguish each subdivision of the mouse brain, and the accuracy of this method is higher than that of the conventional staining method. The cerebellar cortex, medial prefrontal cortex, hippocampus, striatum, nucleus accumbens-core, and nucleus accumbens-shell exhibited the most significant differences in the chronic social defeat stress model. An analysis of metabolic pathways revealed that 13 kinds of molecules related to energy metabolism and purine metabolism exhibited significant changes. A DESI-MSI method was developed for the detection of pathological brain sections. We found, for the first time, that there are characteristic changes in the energy metabolism in the cortex and purine metabolism in the striatum, which is highly important for obtaining a deeper and more comprehensive understanding of the pathology of depression and discovering regulatory targets.

Transcriptomic, epigenomic, and spatial metabolomic cell profiling redefines regional human kidney anatomy

A large-scale multimodal atlas that includes major kidney regions is lacking. Here, we employed simultaneous high-throughput single-cell ATAC/RNA sequencing (SHARE-seq) and spatially resolved metabolomics to profile 54 human samples from distinct kidney anatomical regions. We generated transcriptomes of 446,267 cells and chromatin accessibility profiles of 401,875 cells and developed a package to analyze 408,218 spatially resolved metabolomes. We find that the same cell type, including thin limb, thick ascending limb loop of Henle and principal cells, display distinct transcriptomic, chromatin accessibility, and metabolomic signatures, depending on anatomic location. Surveying metabolism-associated gene profiles revealed non-overlapping metabolic signatures between nephron segments and dysregulated lipid metabolism in diseased proximal tubule (PT) cells. Integrating multimodal omics with clinical data identified PLEKHA1 as a disease marker, and its in vitro knockdown increased gene expression in PT differentiation, suggesting possible pathogenic roles. This study highlights previously underrepresented cellular heterogeneity underlying the human kidney anatomy.

Multidimensional Interactive Cascading Nanochips for Detection of Multiple Liver Diseases via Precise Metabolite Profiling

It is challenging to detect and differentiate multiple diseases with high complexity/similarity from the same organ. Metabolic analysis based on nanomatrix-assisted laser desorption/ionization mass spectrometry (NMALDI-MS) is a promising platform for disease diagnosis, while the enhanced property of its core nanomatrix materials has plenty of room for improvement. Herein, a multidimensional interactive cascade nanochip composed of iron oxide nanoparticles (FeNPs)/MXene/gold nanoparticles (AuNPs), IMG, is reported for serum metabolic profiling to achieve high-throughput detection of multiple liver diseases. MXene serves as a multi-binding site and an electron-hole source for ionization during NMALDI-MS analysis. Introduction of AuNPs with surface plasmon resonance (SPR) properties facilitates surface charge accumulation and rapid energy conversion. FeNPs are integrated into the MXene/Au nanocomposite to sharply reduce the thermal conductivity of the nanochip with negligible heat loss for strong thermally-driven desorption, and construct a multi-interaction proton transport pathway with MXene and AuNPs for strong ionization. Analysis of these enhanced serum fingerprint signals detected from the IMG nanochip through a neural network model results in differentiation of multiple liver diseases via a single pass and revelation of potential metabolic biomarkers. The promising method can rapidly and accurately screen various liver diseases, thus allowing timely treatment of liver diseases.

Glycosphingolipids in Osteoarthritis and Cartilage-Regeneration Therapy: Mechanisms and Therapeutic Prospects Based on a Narrative Review of the Literature

Glycosphingolipids (GSLs), a subtype of glycolipids containing sphingosine, are critical components of vertebrate plasma membranes, playing a pivotal role in cellular signaling and interactions. In human articular cartilage in osteoarthritis (OA), GSL expression is known notably to decrease. This review focuses on the roles of gangliosides, a specific type of GSL, in cartilage degeneration and regeneration, emphasizing their regulatory function in signal transduction. The expression of gangliosides, whether endogenous or augmented exogenously, is regulated at the enzymatic level, targeting specific glycosyltransferases. This regulation has significant implications for the composition of cell-surface gangliosides and their impact on signal transduction in chondrocytes and progenitor cells. Different levels of ganglioside expression can influence signaling pathways in various ways, potentially affecting cell properties, including malignancy. Moreover, gene manipulations against gangliosides have been shown to regulate cartilage metabolisms and chondrocyte differentiation in vivo and in vitro. This review highlights the potential of targeting gangliosides in the development of therapeutic strategies for osteoarthritis and cartilage injury and addresses promising directions for future research and treatment.

spatialHeatmap: visualizing spatial bulk and single-cell assays in anatomical images

Visualizing spatial assay data in anatomical images is vital for understanding biological processes in cell, tissue, and organ organizations. Technologies requiring this functionality include traditional one-at-a-time assays, and bulk and single-cell omics experiments, including RNA-seq and proteomics. The spatialHeatmap software provides a series of powerful new methods for these needs, and allows users to work with adequately formatted anatomical images from public collections or custom images. It colors the spatial features (e.g. tissues) annotated in the images according to the measured or predicted abundance levels of biomolecules (e.g. mRNAs) using a color key. This core functionality of the package is called a spatial heatmap plot. Single-cell data can be co-visualized in composite plots that combine spatial heatmaps with embedding plots of high-dimensional data. The resulting spatial context information is essential for gaining insights into the tissue-level organization of single-cell data, or vice versa. Additional core functionalities include the automated identification of biomolecules with spatially selective abundance patterns and clusters of biomolecules sharing similar abundance profiles. To appeal to both non-expert and computational users, spatialHeatmap provides a graphical and a command-line interface, respectively. It is distributed as a free, open-source Bioconductor package (https://bioconductor.org/packages/spatialHeatmap) that users can install on personal computers, shared servers, or cloud systems.

Lysosomal glucose sensing and glycophagy in metabolism

Lysosomes are cellular organelles that function to catabolize both extra- and intracellular cargo, act as a platform for nutrient sensing, and represent a core signaling node integrating bioenergetic cues to changes in cellular metabolism. Although lysosomal amino acid and lipid sensing in metabolism has been well characterized, lysosomal glucose sensing and the role of lysosomes in glucose metabolism is unrefined. This review will highlight the role of the lysosome in glucose metabolism with a focus on lysosomal glucose and glycogen sensing, glycophagy, and lysosomal glucose transport and how these processes impact autophagy and energy metabolism. Additionally, the role of lysosomal glucose metabolism in genetic and metabolic diseases will be briefly discussed.

Detection of proteomic alterations at different stages in a Huntington's disease mouse model via matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) imaging

Huntington's disease (HD) is a progressive and irreversible neurodegenerative disease leading to the inability to carry out daily activities and for which no cure exists. The underlying mechanisms of the disease have not been fully elucidated yet. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) allows the spatial information of proteins to be obtained upon the tissue sections without homogenisation. In this study, we aimed to examine proteomic alterations in the brain tissue of an HD mouse model with MALDI-MSI coupled to LC-MS/MS system. We used 3-, 6- and 12-month-old YAC128 mice representing pre-stage, mild stage and pathological stage of the HD and their non-transgenic littermates, respectively. The intensity levels of 89 proteins were found to be significantly different in YAC128 in comparison to their control mice in the pre-stage, 83 proteins in the mild stage, and 82 proteins in the pathological stage. Among them, Tau, EF2, HSP70, and NogoA proteins were validated with western blot analysis. In conclusion, the results of this study have provided remarkable new information about the spatial proteomic alterations in the HD mouse model, and we suggest that MALDI-MSI is an excellent technique for identifying such regional proteomic changes and could offer new perspectives in examining complex diseases.

Spatial Metabolome Lipidome and Glycome from a Single brain Section

Metabolites, lipids, and glycans are fundamental biomolecules involved in complex biological systems. They are metabolically channeled through a myriad of pathways and molecular processes that define the physiology and pathology of an organism. Here, we present a blueprint for the simultaneous analysis of spatial metabolome, lipidome, and glycome from a single tissue section using mass spectrometry imaging. Complimenting an original experimental protocol, our workflow includes a computational framework called Spatial Augmented Multiomics Interface (Sami) that offers multiomics integration, high dimensionality clustering, spatial anatomical mapping with matched multiomics features, and metabolic pathway enrichment to providing unprecedented insights into the spatial distribution and interaction of these biomolecules in mammalian tissue biology.