Spatial metabolomics reveals glycogen as an actionable target for pulmonary fibrosis

Predictive Modeling of ARDS Mortality Integrating Biomarker/Cytokine, Clinical and Metabolomic Data

Acute Respiratory Distress Syndrome (ARDS), characterized by the rapid onset of respiratory failure and mortality rates of ∼40%, remains a significant challenge in critical care medicine. Despite advances in supportive care, accurate prediction of ARDS mortality remains challenging, resulting in delayed delivery of targeted interventions and effective disease management. Traditional critical illness severity scores lack specificity for ARDS, underscoring the need for more precise prognostic tools for ARDS mortality. To address this crucial gap, we employed a multimodal approach to predict ARDS patients utilizing a comprehensive dataset comprised of integrated clinical, metabolomic, and biochemical/cytokine data from ARDS patients (collected within hours of ICU admission) to develop and validate predictive models of ARDS mortality risk. The most robust multimodal data model generated demonstrated superior predictive capability with an area under the curve (AUC) of 0.868 on the test set and 0.959 on the validation set. Notably, this model achieved perfect specificity in identifying non-survivors in the validation cohort, highlighting potential utility in guiding early and targeted interventions in ICU settings. Metabolomic analysis revealed significant alterations in crucial pathways associated with ARDS mortality with tryptophan metabolism, particularly the kynurenine pathway, emerging as the most significantly enriched metabolic route, as well as the NAD+ metabolism/ nicotinamide phosphoribosyltransferase (NAMPT) and glycosaminoglycan biosynthesis pathways. These metabolic derangements were strongly confirmed by lipidomic/metabolomic analysis of lung tissues from a porcine sepsis/ARDS model. Together, these findings demonstrate the promise of integrating multimodal data to improve ARDS prognostication and to provide important insights into the complex metabolic derangements underlying severe ARDS. Identification of metabolic signatures, such as kynurenine and NAD+ metabolism/NAMPT pathways, may serve as a foundation for developing personalized and effective targeted interventions and management strategies for ARDS patients.

Spatial mapping of the brain metabolome lipidome and glycome

Metabolites, lipids, and glycans are fundamental but interconnected classes of biomolecules that form the basis of the metabolic network. These molecules are dynamically channeled through multiple pathways that govern cellular physiology and pathology. Here, we present a framework for the simultaneous spatial analysis of the metabolome, lipidome, and glycome from a single tissue section using mass spectrometry imaging. This workflow integrates a computational platform, the Spatial Augmented Multiomics Interface (Sami), which enables multiomics integration, high-dimensional clustering, spatial anatomical mapping of matched molecular features, and metabolic pathway enrichment. To demonstrate the utility of this approach, we applied Sami to evaluate metabolic diversity across distinct brain regions and to compare wild-type and Ps19 Alzheimer's disease (AD) mouse models. Our findings reveal region-specific metabolic demands in the normal brain and highlight metabolic dysregulation in the Ps19 model, providing insights into the biochemical alterations associated with neurodegeneration.

Discovery and Evaluation of Active Site-Directed, Potent, and Selective Sulfophenyl Acetic Amide-Based Inhibitors for the Laforin Phosphatase

Lafora disease is a rare and fatal progressive myoclonus epilepsy characterized by the accumulation of insoluble glycogen deposits in the brain and peripheral tissues. Mutations in the gene encoding the glycogen phosphatase laforin result in Lafora disease. Currently, there are no laforin-specific chemical probes, limiting our understanding of the roles of laforin in glycogen metabolism and other cellular processes. Here, we identified sulfophenyl acetic amide (SPAA), as a novel nonhydrolyzable phosphotyrosine mimetic for laforin inhibition. Using fragment-based and scaffold-hopping strategies, we discovered several highly potent and selective active site-directed laforin inhibitors. Among them, compound 9c displayed a Ki value of 1.9 ± 0.2 nM and more than 8300-fold preference for laforin. Moreover, these inhibitors efficiently block laforin-mediated glucan dephosphorylation inside the cell and possess favorable pharmacokinetic properties in mice. These chemical probes will enable further investigation of the roles of laforin in normal physiological processes and in diseases.

Maximizing Data Coverage through Eight Sequential Mass Spectrometry Images of a Single Tissue Section

Typical mass spectrometry imaging (MSI) experiments involve the collection of data from only one class of molecules per section. However, it is often necessary to collect data from different classes of analytes from the same biopsy, and generally, serial sections are used for additional analyte classes. However, differences will be observed between the cells present in each section, especially if the sections are not immediately serial with each other. In this study, a method is presented that allows for 8 mass spectrometry images to be collected sequentially from the same tissue section, including metabolites in positive and negative mode, lipids in positive and negative mode, N-linked glycans, O-linked N-acetylglucosamine, small intact proteins, and tryptic peptides. The order of data collection allows for washing to be used that removes analytes already detected and enhances the signal of subsequently imaged analytes. The collection of multiple images from the same tissue section enables facile coregistration of multiple data sets for evaluation of co- and differential localization across molecular classes.

Glycogen drives tumour initiation and progression in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is an aggressive cancer defined by oncogenic drivers and metabolic reprogramming. Here we leverage next-generation spatial screens to identify glycogen as a critical and previously underexplored oncogenic metabolite. High-throughput spatial analysis of human LUAD samples revealed that glycogen accumulation correlates with increased tumour grade and poor survival. Furthermore, we assessed the effect of increasing glycogen levels on LUAD via dietary intervention or via a genetic model. Approaches that increased glycogen levels provided compelling evidence that elevated glycogen substantially accelerates tumour progression, driving the formation of higher-grade tumours, while the genetic ablation of glycogen synthase effectively suppressed tumour growth. To further establish the connection between glycogen and cellular metabolism, we developed a multiplexed spatial technique to simultaneously assess glycogen and cellular metabolites, uncovering a direct relationship between glycogen levels and elevated central carbon metabolites essential for tumour growth. Our findings support the conclusion that glycogen accumulation drives LUAD cancer progression and provide a framework for integrating spatial metabolomics with translational models to uncover metabolic drivers of cancer.

Protocol for high-power, brain-focused microwave fixation to define rodent metabolism

Analysis of metabolites provides key insights into brain physiology and function. Due to post-mortem metabolism, both the euthanasia method and dissection time can make a critical difference. Here, we describe a protocol to euthanize rodents by microwave irradiation. This workflow details steps for animal placement, tissue fixation, and post-fixation processing. This protocol enables the rapid halting of metabolic activity for the accurate assessment of the metabolome in situ for analyses such as mass spectrometry and nuclear magnetic resonance. For complete details on the use and execution of this protocol, please refer to Juras et al.1.

AI-driven framework to map the brain metabolome in three dimensions

High-resolution spatial imaging is transforming our understanding of foundational biology. Spatial metabolomics is an emerging field that enables the dissection of the complex metabolic landscape and heterogeneity from a thin tissue section. Currently, spatial metabolism highlights the remarkable complexity in two-dimensional (2D) space and is poised to be extended into the three-dimensional (3D) world of biology. Here we introduce MetaVision3D, a pipeline driven by computer vision, a branch of artificial intelligence focusing on image workflow, for the transformation of serial 2D MALDI mass spectrometry imaging sections into a high-resolution 3D spatial metabolome. Our framework uses advanced algorithms for image registration, normalization and interpolation to enable the integration of serial 2D tissue sections, thereby generating a comprehensive 3D model of unique diverse metabolites across host tissues at submesoscale. As a proof of principle, MetaVision3D was utilized to generate the mouse brain 3D metabolome atlas of normal and diseased animals (available at https://metavision3d.rc.ufl.edu ) as an interactive online database and web server to further advance brain metabolism and related research.

Brain Glycogen-Its Metabolic Role in Neuronal Health and Neurological Disorders-An Extensive Narrative Review

Background: Brain glycogen is imperative for neuronal health, as it supports energy demands and metabolic processes. This review examines the pathways involved in glycogen storage and utilization in the central nervous system, emphasizing their role in both physiology and pathology. It explores how alterations in glycogen metabolism contribute to neurological disorders, including neurodegenerative diseases, epilepsy, and metabolic conditions while highlighting the bidirectional interaction between neurons and glia in maintaining brain homeostasis. Methods: A comprehensive search of articles published between 2015 and 2025 was conducted using the following databases: ScienceDirect, Scopus, Wiley, Web of Science, Medline, and PubMed. The selection of relevant studies was based on their focus on brain glycogen metabolism and its role in neurological conditions, with studies that did not meet the inclusion criteria being excluded. Results: The metabolic processes of brain glycogen are subject to rigorous regulation by astrocyte-neuron interactions, thereby ensuring metabolic homeostasis and energy availability. The dysregulation of glycogen storage and mobilization has been implicated in the development of synaptic dysfunction, excitotoxicity, and neurodegeneration in a variety of disorders. For instance, aberrant glycogen accumulation in diseases such as Lafora disease has been associated with severe neurodegeneration, while impaired glycogen mobilization has been shown to exacerbate energy deficits in Alzheimer's and epilepsy. Conclusions: Targeting brain glycogen metabolism represents a promising approach for therapeutic intervention in neurological disorders. However, the translation of these strategies to human models remains challenging, particularly with regard to the long-term safety and specificity of glycogen-targeted therapies.

Metabolomics as an emerging tool for the pharmacological and toxicological studies on Aconitum alkaloids

Aconitum (Ranunculaceae) has a long-standing history in traditional Chinese medicine (TCM), where it has been widely used to treat conditions such as rheumatoid arthritis (RA), myocardial infarction, and heart failure. However, the potency of Aconitum alkaloids, the primary active components of Aconitum, also confers substantial toxicity. Therefore, assessing the efficacy and toxicity of these Aconitum alkaloids is crucial for ensuring clinical effectiveness and safety. Metabolomics, a quantitative method for analyzing low-molecular-weight metabolites involved in metabolic pathways, provides a comprehensive view of the metabolic state across multiple systems in vivo. This approach has become a vital investigative tool for facilitating the evaluation of their efficacy and toxicity, identifying potential sensitive biomarkers, and offering a promising avenue for elucidating the pharmacological and toxicological mechanisms underlying TCM. This review focuses on the applications of metabolomics in pharmacological and toxicological studies of Aconitum alkaloids in recent years and highlights the significant role of metabolomics in exploring compatibility detoxification and the mechanisms of TCM processing, aiming to identify more viable methods for characterizing toxic medicinal plants.

Pneumocystis Pneumonia Severity Is Associated with Taxonomic Shifts in the Respiratory Microbiota

Pneumonia caused by Pneumocystis jirovecii infection (PCP) is a potentially life-threatening illness, particularly affecting the immunocompromised. The past two decades have shown an increase in PCP incidence; however, the underlying factors that promote disease severity and fatality have yet to be fully elucidated. Recent evidence suggests that the microbiota of the respiratory tract may play a role in stimulating or repressing pulmonary inflammation, as well as the progression of both bacterial and viral pneumonia. Here, we employed 16S rRNA metataxonomic sequencing to profile the respiratory microbiota of patients with mild-moderate and severe PCP. Our results show that the upper and lower airways of PCP patients have bacterial profiles which have been associated with a pro-inflammatory response. Furthermore, we find that severe PCP is associated with lower bacterial diversity and an increase in Prevotella and a decrease in Neisseria. Functionally, severe PCP was associated with a decrease in metabolic pathways of molecules with anti-inflammatory and antimicrobial properties. To our knowledge, this is the first study showing an association of PCP severity with shifts in the respiratory microbiome and may provide some insight into which patients are more susceptible to the more severe manifestations of the disease.

Spatially-resolved characterization of the metabolic and N-glycan alterations in colorectal cancer using matrix-assisted laser desorption/ionization mass spectrometry imaging

Colorectal cancer is the second leading cause of cancer-related deaths worldwide, and its development typically involves complex metabolic reprogramming. By mapping the spatial distributions of metabolites and N-glycans in heterogeneous colorectal cancer tissues, we can elucidate cancer-associated metabolic and N-glycan changes. Herein, we combine mass spectrometry imaging-based metabolomics and N-glycomics to characterize the spatially resolved reprogramming of metabolites and N-glycans in colorectal cancer tissues. The metabolic characteristics of different regions of colorectal cancer were evaluated through the utilization of orthogonal partial least squares discriminant analysis. In combination with metabolic pathway enrichment analysis, significant alterations were identified in the fatty acid metabolism, arginine and proline metabolism of colorectal cancer. Cancer cell regions exhibited a marked upregulation of saturated fatty acids, monounsaturated fatty acids, polyamines, and histidine. Additionally, we discovered that the high-mannose N-glycans were predominantly distributed in tumor tissue regions, whereas complex N-glycans were more commonly found in the normal tissue regions adjacent to the tumor. Such findings provide new insights into the spatial signatures of metabolites and N-glycans in colorectal cancer, thereby offering a crucial basis for the diagnosis of colorectal cancer and potential vulnerabilities that might be targeted for cancer therapy.

Blockade of TREM2 ameliorates pulmonary inflammation and fibrosis by modulating sphingolipid metabolism

Pulmonary fibrosis is a chronic interstitial lung disease involving systemic inflammation and abnormal collagen deposition. Dysregulations in lipid metabolism, such as macrophage-dependent lipid catabolism, have been recognized as critical factors for the development of pulmonary fibrosis. However, little is known about the signaling pathways involved and the key regulators. Here we found that triggering receptor expressed on myeloid cells 2 (TREM2) plays a pivotal role in regulating the lipid handling capacities of pulmonary macrophages and triggering fibrosis. By integrating analysis of single-cell and bulk RNA sequencing data from patients and mice with pulmonary fibrosis, we revealed that pulmonary macrophages consist of heterogeneous populations with distinct pro-fibrotic properties, and found that both sphingolipid metabolism and the expression of chemotaxis-related genes are elevated in fibrotic lungs. TREM2, a sensor recognizing multiple lipid species, is specifically upregulated in a subset of monocyte-derived macrophages. Blockade of TREM2 by conventional/conditional knock-out or soluble TREM2 administration can attenuate bleomycin-induced pulmonary fibrosis. By utilizing scRNA Seq and lipidomics, we found that Trem2 deficiency downregulates the synthesis of various sphingomyelins, and inhibits the expression of chemokines such as Ccl2. Together, our findings not only reveal the alterations in lipidomic profiles and the atlas of pulmonary macrophages during pulmonary fibrosis, but also suggest that targeting TREM2, the crucial regulator affecting both pulmonary sphingolipid metabolism and the chemokines secretion, can benefit pulmonary fibrosis patients in the future.

Neonatal systemic gene therapy restores cardiorespiratory function in a rat model of Pompe disease

Absence of functional acid-α-glucosidase (GAA) leads to early-onset Pompe disease with cardiorespiratory and neuromuscular failure. A novel Pompe rat model (Gaa -/-) was used to test the hypothesis that neonatal gene therapy with adeno-associated virus serotype 9 (AAV9) restores cardiorespiratory neuromuscular function across the lifespan. Temporal vein administration of AAV9-DES-GAA or sham (saline) injection was done on post-natal day 1; rats were studied at 6-12 months old. Whole-body plethysmography showed that reduced inspiratory tidal volumes in Gaa -/- rats were corrected by AAV-GAA treatment. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI) revealed that AAV-GAA treatment normalized diaphragm muscle glycogen as well as glycans. Neurophysiological recordings of phrenic nerve output and immunohistochemical evaluation of the cervical spinal cord indicated a neurologic benefit of AAV-GAA treatment. In vivo magnetic resonance imaging demonstrated that impaired cardiac volumes in Gaa -/- rats were corrected by AAV-GAA treatment. Biochemical assays showed that AAV treatment increased GAA activity in the heart, diaphragm, quadriceps and spinal cord. We conclude that neonatal AAV9-DES-GAA therapy drives sustained, functional GAA expression and improved cardiorespiratory function in the Gaa -/- rat model of Pompe disease.

Profiling of collagen and extracellular matrix deposition from cell culture using in vitro ExtraCellular matrix mass spectrometry imaging (ivECM-MSI)

While numerous approaches have been reported towards understanding single cell regulation, there is limited understanding of single cell production of extracellular matrix phenotypes. Collagens are major proteins of the extracellular microenvironment extensively used in basic cell culture, tissue engineering, and biomedical applications. However, identifying compositional regulation of collagen remains challenging. Here, we report the development of In vitro ExtraCellular Matrix Mass Spectrometry Imaging (ivECM-MSI) as a tool to rapidly and simultaneously define collagen subtypes from coatings and basic cell culture applications. The tool uses the mass spectrometry imaging platform with reference libraries to produce visual and numerical data types. The method is highly integrated with basic in vitro strategies as it may be used with conventional cell chambers on minimal numbers of cells and with minimal changes to biological experiments. Applications tested include semi-quantitation of collagen composition in culture coatings, time course collagen deposition, deposition altered by gene knockout, and changes induced by drug treatment. This approach provides new access to proteomic information on how cell types respond to and change the extracellular microenvironment and provides a holistic understanding of both the cell and extracellular response.

Advances in mass spectrometry for metabolomics: Strategies, challenges, and innovations in disease biomarker discovery

Mass spectrometry (MS) plays a crucial role in metabolomics, especially in the discovery of disease biomarkers. This review outlines strategies for identifying metabolites, emphasizing precise and detailed use of MS techniques. It explores various methods for quantification, discusses challenges encountered, and examines recent breakthroughs in biomarker discovery. In the field of diagnostics, MS has revolutionized approaches by enabling a deeper understanding of tissue-specific metabolic changes associated with disease. The reliability of results is ensured through robust experimental design and stringent system suitability criteria. In the past, data quality, standardization, and reproducibility were often overlooked despite their significant impact on MS-based metabolomics. Progress in this field heavily depends on continuous training and education. The review also highlights the emergence of innovative MS technologies and methodologies. MS has the potential to transform our understanding of metabolic landscapes, which is crucial for disease biomarker discovery. This article serves as an invaluable resource for researchers in metabolomics, presenting fresh perspectives and advancements that propels the field forward.

Metabolomics-driven approaches for identifying therapeutic targets in drug discovery

Identification of therapeutic targets can directly elucidate the mechanism and effect of drug therapy, which is a central step in drug development. The disconnect between protein targets and phenotypes under complex mechanisms hampers comprehensive target understanding. Metabolomics, as a systems biology tool that captures phenotypic changes induced by exogenous compounds, has emerged as a valuable approach for target identification. A comprehensive overview was provided in this review to illustrate the principles and advantages of metabolomics, delving into the application of metabolomics in target identification. This review outlines various metabolomics-based methods, such as dose-response metabolomics, stable isotope-resolved metabolomics, and multiomics, which identify key enzymes and metabolic pathways affected by exogenous substances through dose-dependent metabolite-drug interactions. Emerging techniques, including single-cell metabolomics, artificial intelligence, and mass spectrometry imaging, are also explored for their potential to enhance target discovery. The review emphasizes metabolomics' critical role in advancing our understanding of disease mechanisms and accelerating targeted drug development, while acknowledging current challenges in the field.

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.

Stem cells, cell therapies, and bioengineering in lung biology and diseases 2023

Repair and regeneration of a diseased lung using stem cells or bioengineered tissues is an exciting therapeutic approach for a variety of lung diseases and critical illnesses. Over the past decade, increasing evidence from preclinical models suggests that mesenchymal stromal cells, which are not normally resident in the lung, can be used to modulate immune responses after injury, but there have been challenges in translating these promising findings to the clinic. In parallel, there has been a surge in bioengineering studies investigating the use of artificial and acellular lung matrices as scaffolds for three-dimensional lung or airway regeneration, with some recent attempts of transplantation in large animal models. The combination of these studies with those involving stem cells, induced pluripotent stem cell derivatives, and/or cell therapies is a promising and rapidly developing research area. These studies have been further paralleled by significant increases in our understanding of the molecular and cellular events by which endogenous lung stem and/or progenitor cells arise during lung development and participate in normal and pathological remodeling after lung injury. For the 2023 Stem Cells, Cell Therapies, and Bioengineering in Lung Biology and Diseases Conference, scientific symposia were chosen to reflect the most cutting-edge advances in these fields. Sessions focused on the integration of "omics" technologies with function, the influence of immune cells on regeneration, and the role of the extracellular matrix in regeneration. The necessity for basic science studies to enhance fundamental understanding of lung regeneration and to design innovative translational studies was reinforced throughout the conference.

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.

MetaVision3D: Automated Framework for the Generation of Spatial Metabolome Atlas in 3D

High-resolution spatial imaging is transforming our understanding of foundational biology. Spatial metabolomics is an emerging field that enables the dissection of the complex metabolic landscape and heterogeneity from a thin tissue section. Currently, spatial metabolism highlights the remarkable complexity in two-dimensional space and is poised to be extended into the three-dimensional world of biology. Here, we introduce MetaVision3D, a novel pipeline driven by computer vision techniques for the transformation of serial 2D MALDI mass spectrometry imaging sections into a high-resolution 3D spatial metabolome. Our framework employs advanced algorithms for image registration, normalization, and interpolation to enable the integration of serial 2D tissue sections, thereby generating a comprehensive 3D model of unique diverse metabolites across host tissues at mesoscale. As a proof of principle, MetaVision3D was utilized to generate the mouse brain 3D metabolome atlas (available at https://metavision3d.rc.ufl.edu/ ) as an interactive online database and web server to further advance brain metabolism and related research.

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.