Using mass spectrometry imaging to map fluxes quantitatively in the tumor ecosystem

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.

Metabolic interplays between the tumour and the host shape the tumour macroenvironment

Metabolic reprogramming of cancer cells and the tumour microenvironment are pivotal characteristics of cancers, and studying these processes offer insights and avenues for cancer diagnostics and therapeutics. Recent advancements have underscored the impact of host systemic features, termed macroenvironment, on facilitating cancer progression. During tumorigenesis, these inherent features of the host, such as germline genetics, immune profile and the metabolic status, influence how the body responds to cancer. In parallel, as cancer grows, it induces systemic effects beyond the primary tumour site and affects the macroenvironment, for example, through inflammation, the metabolic end-stage syndrome of cachexia, and metabolic dysregulation. Therefore, understanding the intricate metabolic interplay between the tumour and the host is a growing frontier in advancing cancer diagnosis and therapy. In this Review, we explore the specific contribution of the metabolic fitness of the host to cancer initiation, progression and response to therapy. We then delineate the complex metabolic crosstalk between the tumour, the microenvironment and the host, which promotes disease progression to metastasis and cachexia. The metabolic relationships among the host, cancer pathogenesis and the consequent responsive systemic manifestations during cancer progression provide new perspectives for mechanistic cancer therapy and improved management of patients with cancer.

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.

Harnessing the Power of Metabolomics for Precision Oncology: Current Advances and Future Directions

Metabolic reprogramming is a hallmark of cancer, with cancer cells acquiring many unique metabolic traits to support malignant growth, and extensive intra- and inter-tumour metabolic heterogeneity. Understanding these metabolic characteristics presents opportunities in precision medicine for both diagnosis and therapy. However, despite its potential, metabolic phenotyping has lagged behind genetic, transcriptomic, and immunohistochemical profiling in clinical applications. This is partly due to the lack of a single experimental technique capable of profiling the entire metabolome, necessitating the use of multiple technologies and approaches to capture the full range of cancer metabolic plasticity. This review examines the repertoire of tools available for profiling cancer metabolism, demonstrating their applications in preclinical and clinical settings. It also presents case studies illustrating how metabolomic profiling has been integrated with other omics technologies to gain insights into tumour biology and guide treatment strategies. This information aims to assist researchers in selecting the most effective tools for their studies and highlights the importance of combining different metabolic profiling techniques to comprehensively understand tumour metabolism.

Optimized MALDI2-Mass Spectrometry Imaging for Stable Isotope Tracing of Tissue-Specific Metabolic Pathways in Mice

Spatial stable isotope tracing metabolic imaging is a cutting-edge technique designed to investigate tissue-specific metabolic functions and heterogeneity. Traditional matrix-assisted laser desorption ionization-mass spectrometry imaging (MALDI-MSI) techniques often struggle with low coverage of low-molecular-weight (LMW) metabolites, which are often crucial for spatial metabolic studies. To address this, we developed a high-coverage spatial isotope tracing metabolic method that incorporates optimized matrix selection, sample preparation protocols, and enhanced post-ionization (MALDI2) techniques. We employed this approach to mouse kidney, brain, and breast tumors to visualize the spatial dynamics of metabolic flow. Our results revealed diverse regional distributions of nine labeled intermediates derived from 13C6-glucose across glycolysis, glycogen metabolism, and the tricarboxylic acid (TCA) cycle in kidney tissues. In brain sections, we successfully mapped six intermediates from the TCA cycle and glutamate-glutamine (Glu-Gln) cycle simultaneously in distinct neurological regions. Furthermore, in breast cancer tumor tissues, our approach facilitated the mapping of nine metabolic intermediates in multiple pathways, including glycolysis, the pentose phosphate pathway (PPP), and the TCA cycle, illustrating metabolic heterogeneity within the tumor microenvironment. This methodology enhances metabolite coverage, enabling more comprehensive imaging of isotope-labeled metabolites and opening new avenues for exploring the metabolic landscape in various biological contexts.

To image or not to image: Use of imaging mass spectrometry in biomedical lipidomics

Lipid imaging mass spectrometry (LIMS) allows for establishing the bidimensional distribution of lipid species within a tissue section. One of the main advantages is the generation of spatial information on lipid species distribution at a spatial (lateral) resolution bordering on single-cell resolution with no need to isolate cells. Thus, LIMS images demonstrate, with a level of detail never described before, that lipid profiles are highly sensitive to cell type and pathophysiological state. The wealth and relevance of the information conveyed by LIMS makes up for the lack of a separation stage before sample injection into the mass analyzer, which can somehow be circumvented by other means. Hence, the possibility of describing the lipidome at the cellular level while preserving the microenvironment offers an incomparable opportunity to investigate physiological and pathological contexts. However, to fully grasp the biological implications of the lipid profiles, it is essential to contextualize LIMS data within the broader multiscale 'omic' landscape, entailing genomics, epigenomics, and proteomics, each offering a unique window into the regulatory layers of the cell. In this line, the number of techniques that can be combined with LIMS to delve into the molecular mechanisms underlying differential lipid profiles is continuously increasing. Herein, we aim to describe the key features of LIMS analyses, from sample preparation to data interpretation, as well as the current methodologies to enrich and complete the final outcome. While the field is rapidly advancing, we consider there is solid evidence to foresee the incorporation of LIMS into clinical environments.

In Situ Isotope Tracing at Single-Cell Resolution Using Mass Spectrometry Imaging

Mass spectrometry imaging (MSI) allows for label-free spatial molecular interrogation of tissues. With advances in the field over recent years, the spatial resolution at which MSI data can be recorded has reached the single-cell level. This makes MSI complementary to other single-cell omics technologies. As metabolism is a highly dynamic process, capturing the metabolic turnover adds a valuable layer of information. Here, we describe how to set up in situ stable isotope tracing followed by MSI-enabled spatial metabolomics to perform dynamic metabolomics at the single-cell level.

Biofluorescence imaging-guided spatial metabolic tracing: In vivo tracking of metabolic activity in circulating tumor cell-mediated multi-organ metastases

Circulating tumor cells (CTC) are considered metastatic precursors that are shed from the primary or metastatic deposits and navigate the bloodstream before undergoing extravasation to establish distant metastases. Metabolic reprogramming appears to be a hallmark of metastatic progression, yet current methods for evaluating metabolic heterogeneity within organ-specific metastases in vivo are limited. To overcome this challenge, we present Biofluorescence Imaging-Guided Spatial Metabolic Tracing (BIGSMT), a novel approach integrating in vivo biofluorescence imaging, stable isotope tracing, stain-free laser capture microdissection, and liquid chromatography-mass spectrometry. This innovative technology obviates the need for staining or intricate sample preparation, mitigating metabolite loss, and substantially enhances detection sensitivity and accuracy through chemical derivatization of polar metabolites in central carbon pathways. Application of BIGSMT to a preclinical CTC-mediated metastasis mouse model revealed significant heterogeneity in the in vivo carbon flux from glucose into glycolysis and the tricarboxylic acid (TCA) cycle across distinct metastatic sites. Our analysis indicates that carbon predominantly enters the TCA cycle through the enzymatic reaction catalyzed by pyruvate dehydrogenase. Thus, our spatially resolved BIGSMT technology provides fresh insights into the metabolic heterogeneity and evolution during melanoma CTC-mediated metastatic progression and points to novel therapeutic opportunities.

Dietary fructose enhances tumour growth indirectly via interorgan lipid transfer

Fructose consumption has increased considerably over the past five decades, largely due to the widespread use of high-fructose corn syrup as a sweetener1. It has been proposed that fructose promotes the growth of some tumours directly by serving as a fuel2,3. Here we show that fructose supplementation enhances tumour growth in animal models of melanoma, breast cancer and cervical cancer without causing weight gain or insulin resistance. The cancer cells themselves were unable to use fructose readily as a nutrient because they did not express ketohexokinase-C (KHK-C). Primary hepatocytes did express KHK-C, resulting in fructolysis and the excretion of a variety of lipid species, including lysophosphatidylcholines (LPCs). In co-culture experiments, hepatocyte-derived LPCs were consumed by cancer cells and used to generate phosphatidylcholines, the major phospholipid of cell membranes. In vivo, supplementation with high-fructose corn syrup increased several LPC species by more than sevenfold in the serum. Administration of LPCs to mice was sufficient to increase tumour growth. Pharmacological inhibition of ketohexokinase had no direct effect on cancer cells, but it decreased circulating LPC levels and prevented fructose-mediated tumour growth in vivo. These findings reveal that fructose supplementation increases circulating nutrients such as LPCs, which can enhance tumour growth through a cell non-autonomous mechanism.

Reciprocal links between methionine metabolism, DNA repair and therapy resistance in glioblastoma

Glioblastoma (GBM) is uniformly lethal due to profound treatment resistance. Altered cellular metabolism is a key mediator of GBM treatment resistance. Uptake of the essential sulfur-containing amino acid methionine is drastically elevated in GBMs compared to normal cells, however, it is not known how this methionine is utilized or whether it relates to GBM treatment resistance. Here, we find that radiation acutely increases the levels of methionine-related metabolites in a variety of treatment-resistant GBM models. Stable isotope tracing studies further revealed that radiation acutely activates methionine to S-adenosyl methionine (SAM) conversion through an active signaling event mediated by the kinases of the DNA damage response. In vivo tumor SAM synthesis increases after radiation, while normal brain SAM production remains unchanged, indicating a tumor- specific metabolic alteration to radiation. Pharmacological and dietary strategies to block methionine to SAM conversion slowed DNA damage response and increased cell death following radiation in vitro. Mechanistically, these effects are due to depletion of DNA repair proteins and are reversed by SAM supplementation. These effects are selective to GBMs lacking the methionine salvage enzyme methylthioadenosine phosphorylase. Pharmacological inhibition of SAM synthesis hindered tumor growth in flank and orthotopic in vivo GBM models when combined with radiation. By contrast, methionine depletion does not reduce tumor SAM levels and fails to radiosensitize intracranial models, indicating depleting SAM, as opposed to simply lowering methionine, is critical for hindering tumor growth in intracranial models of GBM. These results highlight a new signaling link between DNA damage and SAM synthesis and define the metabolic fates of methionine in GBM in vivo . Inhibiting radiation-induced SAM synthesis slows DNA repair and augments radiation efficacy in GBM. Using MAT2A inhibitors to deplete SAM may selectively overcome treatment resistance in GBMs with defective methionine salvage while sparing normal brain.

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.

The political stakes of cancer epistemics

We demonstrate a transnationally situated dialogue as a method to bring ethnographic and historical research in Brazil, East Africa (Kenya, Tanzania and Uganda), India, Russia and Spain into conversation to show three cancer epistemics sites (research, detection, and care access) where the politics of cancer epistemics are at play. First, in the field of research, we show how certain ways of knowing, and certain questions about and interests in cancer, are privileged over others. Using examples from Spain and East Africa, we highlight how a shift towards microbiological and high-technology research has outpriced many more locally grounded research agendas, ignoring questions of industrial and capital accountability in cancer aetiology. Second, we look at ways of making cancer visible, how knowledge is mobilised in cancer detection and screening, where and for whom. We discuss the increased individualisation of risk which is reframing cancer surveillance and therapeutic agendas. Using examples from India, Spain and Brazil, we demonstrate how the epistemics of cancer detection generate discourses of blame and responsibility at the individual level and accentuate existing inequities whilst simultaneously absorbing patients and their families into complex networks of surveillance. Lastly, we examine how the epistemics of cancer implicate the very possibilities of accessing cancer care, shaping care pathways and possibilities for patients. With ethnographic examples from India, Russia and Brazil, we demonstrate how an orientation towards the individual shifts attention away from the commercialisation of healthcare and dominance of logics of profit in therapeutics. Throughout the paper, we point towards what is holding these cancer discourses together and grapple with how the politics of cancer epistemics are at play across the globe, even if they appear to be taking many different forms. Our approach highlights how practices are mirrored in the framing, implementation, detection and care of cancer with far-reaching effects.

Spatial single-cell isotope tracing reveals heterogeneity of de novo fatty acid synthesis in cancer

While heterogeneity is a key feature of cancer, understanding metabolic heterogeneity at the single-cell level remains a challenge. Here we present 13C-SpaceM, a method for spatial single-cell isotope tracing that extends the previously published SpaceM method with detection of 13C6-glucose-derived carbons in esterified fatty acids. We validated 13C-SpaceM on spatially heterogeneous models using liver cancer cells subjected to either normoxia-hypoxia or ATP citrate lyase depletion. This revealed substantial single-cell heterogeneity in labelling of the lipogenic acetyl-CoA pool and in relative fatty acid uptake versus synthesis hidden in bulk analyses. Analysing tumour-bearing brain tissue from mice fed a 13C6-glucose-containing diet, we found higher glucose-dependent synthesis of saturated fatty acids and increased elongation of essential fatty acids in tumours compared with healthy brains. Furthermore, our analysis uncovered spatial heterogeneity in lipogenic acetyl-CoA pool labelling in tumours. Our method enhances spatial probing of metabolic activities in single cells and tissues, providing insights into fatty acid metabolism in homoeostasis and disease.

An isotopically-labelled temporal mass spectrometry imaging data analysis workflow to reveal glucose spatial metabolism patterns in bovine lens tissue

Application of stable isotopically labelled (SIL) molecules in Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging (MALDI-MSI) over a series of time points allows the temporal and spatial dynamics of biochemical reactions to be tracked in a biological system. However, these large kinetic MSI datasets and the inherent variability of biological replicates presents significant challenges to the rapid analysis of the data. In addition, manual annotation of downstream SIL metabolites involves human input to carefully analyse the data based on prior knowledge and personal expertise. To overcome these challenges to the analysis of spatiotemporal MALDI-MSI data and improve the efficiency of SIL metabolite identification, a bioinformatics pipeline has been developed and demonstrated by analysing normal bovine lens glucose metabolism as a model system. The pipeline consists of spatial alignment to mitigate the impact of sample variability and ensure spatial comparability of the temporal data, dimensionality reduction to rapidly map regional metabolic distinctions within the tissue, and metabolite annotation coupled with pathway enrichment modules to summarise and display the metabolic pathways induced by the treatment. This pipeline will be valuable for the spatial metabolomics community to analyse kinetic MALDI-MSI datasets, enabling rapid characterisation of spatio-temporal metabolic patterns from tissues of interest.

Spatial metabolomics in tissue injury and regeneration

Tissue homeostasis is intricately linked to cellular metabolism and metabolite exchange within the tissue microenvironment. The orchestration of adaptive cellular responses during injury and repair depends critically upon metabolic adaptation. This adaptation, in turn, shapes cell fate decisions required for the restoration of tissue homeostasis. Understanding the nuances of metabolic processes within the tissue context and comprehending the intricate communication between cells is therefore imperative for unraveling the complexity of tissue homeostasis and the processes of injury and repair. In this review, we focus on mass spectrometry imaging as an advanced platform with the potential to provide such comprehensive insights into the metabolic instruction governing tissue function. Recent advances in this technology allow to decipher the intricate metabolic networks that determine cellular behavior in the context of tissue resilience, injury, and repair. These insights not only advance our fundamental understanding of tissue biology but also hold implications for therapeutic interventions by targeting metabolic pathways critical for maintaining tissue homeostasis.

Challenges of Spatially Resolved Metabolism in Cancer Research

Stable isotope-resolved metabolomics comprises a critical set of technologies that can be applied to a wide variety of systems, from isolated cells to whole organisms, to define metabolic pathway usage and responses to perturbations such as drugs or mutations, as well as providing the basis for flux analysis. As the diversity of stable isotope-enriched compounds is very high, and with newer approaches to multiplexing, the coverage of metabolism is now very extensive. However, as the complexity of the model increases, including more kinds of interacting cell types and interorgan communication, the analytical complexity also increases. Further, as studies move further into spatially resolved biology, new technical problems have to be overcome owing to the small number of analytes present in the confines of a single cell or cell compartment. Here, we review the overall goals and solutions made possible by stable isotope tracing and their applications to models of increasing complexity. Finally, we discuss progress and outstanding difficulties in high-resolution spatially resolved tracer-based metabolic studies.

Intersection of Immunology and Metabolism in Myocardial Disease

Immunometabolism is an emerging field at the intersection of immunology and metabolism. Immune cell activation plays a critical role in the pathogenesis of cardiovascular diseases and is integral for regeneration during cardiac injury. We currently possess a limited understanding of the processes governing metabolic interactions between immune cells and cardiomyocytes. The impact of this intercellular crosstalk can manifest as alterations to the steady state flux of metabolites and impact cardiac contractile function. Although much of our knowledge is derived from acute inflammatory response, recent work emphasizes heterogeneity and flexibility in metabolism between cardiomyocytes and immune cells during pathological states, including ischemic, cardiometabolic, and cancer-associated disease. Metabolic adaptation is crucial because it influences immune cell activation, cytokine release, and potential therapeutic vulnerabilities. This review describes current concepts about immunometabolic regulation in the heart, focusing on intercellular crosstalk and intrinsic factors driving cellular regulation. We discuss experimental approaches to measure the cardio-immunologic crosstalk, which are necessary to uncover unknown mechanisms underlying the immune and cardiac interface. Deeper insight into these axes holds promise for therapeutic strategies that optimize cardioimmunology crosstalk for cardiac health.

Stable Isotope Tracing Analysis in Cancer Research: Advancements and Challenges in Identifying Dysregulated Cancer Metabolism and Treatment Strategies

Metabolic reprogramming is a hallmark of cancer, driving the development of therapies targeting cancer metabolism. Stable isotope tracing has emerged as a widely adopted tool for monitoring cancer metabolism both in vitro and in vivo. Advances in instrumentation and the development of new tracers, metabolite databases, and data analysis tools have expanded the scope of cancer metabolism studies across these scales. In this review, we explore the latest advancements in metabolic analysis, spanning from experimental design in stable isotope-labeling metabolomics to sophisticated data analysis techniques. We highlight successful applications in cancer research, particularly focusing on ongoing clinical trials utilizing stable isotope tracing to characterize disease progression, treatment responses, and potential mechanisms of resistance to anticancer therapies. Furthermore, we outline key challenges and discuss potential strategies to address them, aiming to enhance our understanding of the biochemical basis of cancer metabolism.

Immunological Aspects of Cancer Cell Metabolism

Cancer cells adeptly manipulate their metabolic processes to evade immune detection, a phenomenon intensifying the complexity of cancer progression and therapy. This review delves into the critical role of cancer cell metabolism in the immune-editing landscape, highlighting how metabolic reprogramming facilitates tumor cells to thrive despite immune surveillance pressures. We explore the dynamic interactions within the tumor microenvironment (TME), where cancer cells not only accelerate their glucose and amino acid metabolism but also induce an immunosuppressive state that hampers effective immune response. Recent findings underscore the metabolic competition between tumor and immune cells, particularly focusing on how this interaction influences the efficacy of emerging immunotherapies. By integrating cutting-edge research on the metabolic pathways of cancer cells, such as the Warburg effect and glutamine addiction, we shed light on potential therapeutic targets. The review proposes that disrupting these metabolic pathways could enhance the response to immunotherapy, offering a dual-pronged strategy to combat tumor growth and immune evasion.

Mass spectrometry imaging methods for visualizing tumor heterogeneity

Profiling spatial distributions of lipids, metabolites, and proteins in tumors can reveal unique cellular microenvironments and provide molecular evidence for cancer cell dysfunction and proliferation. Mass spectrometry imaging (MSI) is a label-free technique that can be used to map biomolecules in tumors in situ. Here, we discuss current progress in applying MSI to uncover molecular heterogeneity in tumors. First, the analytical strategies to profile small molecules and proteins are outlined, and current methods for multimodal imaging to maximize biological information are highlighted. Second, we present and summarize biological insights obtained by MSI of tumor tissue. Finally, we discuss important considerations for designing MSI experiments and several current analytical challenges.

Imaging plant metabolism in situ

Mass spectrometry imaging (MSI) has emerged as an invaluable analytical technique for investigating the spatial distribution of molecules within biological systems. In the realm of plant science, MSI is increasingly employed to explore metabolic processes across a wide array of plant tissues, including those in leaves, fruits, stems, roots, and seeds, spanning various plant systems such as model species, staple and energy crops, and medicinal plants. By generating spatial maps of metabolites, MSI has elucidated the distribution patterns of diverse metabolites and phytochemicals, encompassing lipids, carbohydrates, amino acids, organic acids, phenolics, terpenes, alkaloids, vitamins, pigments, and others, thereby providing insights into their metabolic pathways and functional roles. In this review, we present recent MSI studies that demonstrate the advances made in visualizing the plant spatial metabolome. Moreover, we emphasize the technical progress that enhances the identification and interpretation of spatial metabolite maps. Within a mere decade since the inception of plant MSI studies, this robust technology is poised to continue as a vital tool for tackling complex challenges in plant metabolism.

13C-SpaceM: Spatial single-cell isotope tracing reveals heterogeneity of de novo fatty acid synthesis in cancer

Metabolism has emerged as a key factor in homeostasis and disease including cancer. Yet, little is known about the heterogeneity of metabolic activity of cancer cells due to the lack of tools to directly probe it. Here, we present a novel method, 13C-SpaceM for spatial single-cell isotope tracing of glucose-dependent de novo lipogenesis. The method combines imaging mass spectrometry for spatially-resolved detection of 13C6-glucose-derived 13C label incorporated into esterified fatty acids with microscopy and computational methods for data integration and analysis. We validated 13C-SpaceM on a spatially-heterogeneous normoxia-hypoxia model of liver cancer cells. Investigating cultured cells, we revealed single-cell heterogeneity of lipogenic acetyl-CoA pool labelling degree upon ACLY knockdown that is hidden in the bulk analysis and its effect on synthesis of individual fatty acids. Next, we adapted 13C-SpaceM to analyze tissue sections of mice harboring isocitrate dehydrogenase (IDH)-mutant gliomas. We found a strong induction of de novo fatty acid synthesis in the tumor tissue compared to the surrounding brain. Comparison of fatty acid isotopologue patterns revealed elevated uptake of mono-unsaturated and essential fatty acids in the tumor. Furthermore, our analysis uncovered substantial spatial heterogeneity in the labelling of the lipogenic acetyl-CoA pool indicative of metabolic reprogramming during microenvironmental adaptation. Overall, 13C-SpaceM enables novel ways for spatial probing of metabolic activity at the single cell level. Additionally, this methodology provides unprecedented insight into fatty acid uptake, synthesis and modification in normal and cancerous tissues.

Spatial analysis of the osteoarthritis microenvironment: techniques, insights, and applications

Osteoarthritis (OA) is a debilitating degenerative disease affecting multiple joint tissues, including cartilage, bone, synovium, and adipose tissues. OA presents diverse clinical phenotypes and distinct molecular endotypes, including inflammatory, metabolic, mechanical, genetic, and synovial variants. Consequently, innovative technologies are needed to support the development of effective diagnostic and precision therapeutic approaches. Traditional analysis of bulk OA tissue extracts has limitations due to technical constraints, causing challenges in the differentiation between various physiological and pathological phenotypes in joint tissues. This issue has led to standardization difficulties and hindered the success of clinical trials. Gaining insights into the spatial variations of the cellular and molecular structures in OA tissues, encompassing DNA, RNA, metabolites, and proteins, as well as their chemical properties, elemental composition, and mechanical attributes, can contribute to a more comprehensive understanding of the disease subtypes. Spatially resolved biology enables biologists to investigate cells within the context of their tissue microenvironment, providing a more holistic view of cellular function. Recent advances in innovative spatial biology techniques now allow intact tissue sections to be examined using various -omics lenses, such as genomics, transcriptomics, proteomics, and metabolomics, with spatial data. This fusion of approaches provides researchers with critical insights into the molecular composition and functions of the cells and tissues at precise spatial coordinates. Furthermore, advanced imaging techniques, including high-resolution microscopy, hyperspectral imaging, and mass spectrometry imaging, enable the visualization and analysis of the spatial distribution of biomolecules, cells, and tissues. Linking these molecular imaging outputs to conventional tissue histology can facilitate a more comprehensive characterization of disease phenotypes. This review summarizes the recent advancements in the molecular imaging modalities and methodologies for in-depth spatial analysis. It explores their applications, challenges, and potential opportunities in the field of OA. Additionally, this review provides a perspective on the potential research directions for these contemporary approaches that can meet the requirements of clinical diagnoses and the establishment of therapeutic targets for OA.

Metabolic flux analysis in adipose tissue reprogramming

Obesity is a growing epidemic in the United States and worldwide and is associated with insulin resistance and cardiovascular disease, among other comorbidities. Understanding of the pathology that links overnutrition to these disease processes is ongoing. Adipose tissue is a heterogeneous organ comprised of multiple different cell types and it is likely that dysregulated metabolism within these cell populations disrupts both inter- and intracellular interactions and is a key driver of human disease. In recent years, metabolic flux analysis, which offers a precise quantification of metabolic pathway fluxes in biological systems, has emerged as a candidate strategy for uncovering the metabolic changes that stoke these disease processes. In this mini review, we discuss metabolic flux analysis as an experimental tool, with a specific emphasis on mass spectrometry with isotope tracing as this is the technique most frequently used for metabolic flux analysis in adipocytes. Furthermore, we examine existing literature that uses metabolic flux analysis to further our understanding of adipose tissue biology. Our group has a specific interest in understanding the role of white adipose tissue inflammation in the progression of cardiometabolic disease, as we know that in obesity the accumulation of pro-inflammatory adipose tissue macrophages is associated with significant morbidity, so we use this as a paradigm throughout our review for framing the application of these experimental techniques. However, there are many other biological applications to which they can be applied to further understanding of not only adipose tissue biology but also systemic homeostasis.

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.

Strategies for uncovering stable isotope tracing patterns between cell populations

Despite practical complexities, isotope tracing studies in humans are becoming increasingly feasible. However, several technological challenges need to be addressed in order to take full advantage of human tracing studies. First, absolute metabolic flux measurements in mice are not so easily applied to human models, given that tissue resection is restricted to a single surgical time point. Second, isotope tracing has yet to be employed to detect metabolic differences between cells types in vivo. Here, we discuss the current models and propose an alternative, liquid tumor environment, that could overcome these limitations. Furthermore, we highlight current strategies used to maintain isotopolog enrichment following cell isolation techniques to facilitate cell-type-specific analysis.

Spatially resolved metabolomics and isotope tracing reveal dynamic metabolic responses of dentate granule neurons with acute stimulation

Neuronal activity creates an intense energy demand that must be met by rapid metabolic responses. To investigate metabolic adaptations in the neuron-enriched dentate granule cell (DGC) layer within its native tissue environment, we employed murine acute hippocampal brain slices, coupled with fast metabolite preservation and followed by mass spectrometry (MS) imaging, to generate spatially resolved metabolomics and isotope-tracing data. Here we show that membrane depolarization induces broad metabolic changes, including increased glycolytic activity in DGCs. Increased glucose metabolism in response to stimulation is accompanied by mobilization of endogenous inosine into pentose phosphates via the action of purine nucleotide phosphorylase (PNP). The PNP reaction is an integral part of the neuronal response to stimulation, because inhibition of PNP leaves DGCs energetically impaired during recovery from strong activation. Performing MS imaging on brain slices bridges the gap between live-cell physiology and the deep chemical analysis enabled by MS.

Metabolomics and lipidomics strategies in modern drug discovery and development

Metabolomics and lipidomics have an increasingly pivotal role in drug discovery and development. In the context of drug discovery, monitoring changes in the levels or composition of metabolites and lipids relative to genetic variations yields functional insights, bolstering human genetics and (meta)genomic methodologies. This approach also sheds light on potential novel targets for therapeutic intervention. In the context of drug development, metabolite and lipid biomarkers contribute to enhanced success rates, promising a transformative impact on precision medicine. In this review, we deviate from analytical chemist-focused perspectives, offering an overview tailored to drug discovery. We provide introductory insight into state-of-the-art mass spectrometry (MS)-based metabolomics and lipidomics techniques utilized in drug discovery and development, drawing from the collective expertise of our research teams. We comprehensively outline the application of metabolomics and lipidomics in advancing drug discovery and development, spanning fundamental research, target identification, mechanisms of action, and the exploration of biomarkers.

Spatially resolved metabolomics and isotope tracing reveal dynamic metabolic responses of dentate granule neurons with acute stimulation

Neuronal activity creates an intense energy demand that must be met by rapid metabolic responses. To investigate metabolic adaptations in the neuron-enriched dentate granule cell (DGC) layer within its native tissue environment, we employed murine acute hippocampal brain slices coupled with fast metabolite preservation, followed by mass spectrometry imaging (MALDI-MSI) to generate spatially resolved metabolomics and isotope tracing data. Here we show that membrane depolarization induces broad metabolic changes, including increased glycolytic activity in DGCs. Increased glucose metabolism in response to stimulation is accompanied by mobilization of endogenous inosine into pentose phosphates, via the action of purine nucleotide phosphorylase (PNP). The PNP reaction is an integral part of the neuronal response to stimulation, as inhibiting PNP leaves DGCs energetically impaired during recovery from strong activation. Performing MSI on brain slices bridges the gap between live cell physiology and the deep chemical analysis enabled by mass spectrometry.