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.

Advances in mass spectrometry imaging for spatial cancer metabolomics

Mass spectrometry (MS) has become a central technique in cancer research. The ability to analyze various types of biomolecules in complex biological matrices makes it well suited for understanding biochemical alterations associated with disease progression. Different biological samples, including serum, urine, saliva, and tissues have been successfully analyzed using mass spectrometry. In particular, spatial metabolomics using MS imaging (MSI) allows the direct visualization of metabolite distributions in tissues, thus enabling in-depth understanding of cancer-associated biochemical changes within specific structures. In recent years, MSI studies have been increasingly used to uncover metabolic reprogramming associated with cancer development, enabling the discovery of key biomarkers with potential for cancer diagnostics. In this review, we aim to cover the basic principles of MSI experiments for the nonspecialists, including fundamentals, the sample preparation process, the evolution of the mass spectrometry techniques used, and data analysis strategies. We also review MSI advances associated with cancer research in the last 5 years, including spatial lipidomics and glycomics, the adoption of three-dimensional and multimodal imaging MSI approaches, and the implementation of artificial intelligence/machine learning in MSI-based cancer studies. The adoption of MSI in clinical research and for single-cell metabolomics is also discussed. Spatially resolved studies on other small molecule metabolites such as amino acids, polyamines, and nucleotides/nucleosides will not be discussed in the context.

Spatial Transcriptomic and Metabolomic Landscapes of Oral Submucous Fibrosis-Derived Oral Squamous Cell Carcinoma and its Tumor Microenvironment

In South and Southeast Asia, the habit of chewing betel nuts is prevalent, which leads to oral submucous fibrosis (OSF). OSF is a well-established precancerous lesion, and a portion of OSF cases eventually progress to oral squamous cell carcinoma (OSCC). However, the specific molecular mechanisms underlying the malignant transformation of OSCC from OSF are poorly understood. In this study, the leading-edge techniques of Spatial Transcriptomics (ST) and Spatial Metabolomics (SM) are integrated to obtain spatial location information of cancer cells, fibroblasts, and immune cells, as well as the transcriptomic and metabolomic landscapes in OSF-derived OSCC tissues. This work reveals for the first time that some OSF-derived OSCC cells undergo partial epithelial-mesenchymal transition (pEMT) within the in situ carcinoma (ISC) region, eventually acquiring fibroblast-like phenotypes and participating in collagen deposition. Complex interactions among epithelial cells, fibroblasts, and immune cells in the tumor microenvironment are demonstrated. Most importantly, significant metabolic reprogramming in OSF-derived OSCC, including abnormal polyamine metabolism, potentially playing a pivotal role in promoting tumorigenesis and immune evasion is discovered. The ST and SM data in this study shed new light on deciphering the mechanisms of OSF-derived OSCC. The work also offers invaluable clues for the prevention and treatment of OSCC.

Spatial amine metabolomics and histopathology reveal localized brain alterations in subacute traumatic brain injury and the underlying mechanism of herbal treatment

INTRODUCTION

Spatial changes of amine metabolites and histopathology of the whole brain help to reveal the mechanism of traumatic brain injury (TBI) and treatment.

METHODS

A newly developed liquid microjunction surface sampling-tandem mass tag-ultra performance liquid chromatography-mass spectrometry technique is applied to profile brain amine metabolites in five brain regions after impact-induced TBI at the subacute stage. H&E, Nissl, and immunofluorescence staining are performed to spatially correlate microscopical changes to metabolic alterations. Then, bioinformatics, molecular docking, ELISA, western blot, and immunofluorescence are integrated to uncover the mechanism of Xuefu Zhuyu decoction (XFZYD) against TBI.

RESULTS

Besides the hippocampus and cortex, the thalamus, caudate-putamen, and fiber tracts also show differentiated metabolic changes between the Sham and TBI groups. Fourteen amine metabolites (including isomers such as L-leucine and L-isoleucine) are significantly altered in specific regions. The metabolic changes are well matched with the degree of neuronal damage, glia activation, and neurorestoration. XFZYD reverses the dysregulation of several amine metabolites, such as hippocampal Lys-Phe/Phe-Lys and dopamine. Also, XFZYD enhances post-TBI angiogenesis in the hippocampus and the thalamus.

CONCLUSION

This study reveals the local amine-metabolite and histological changes in the subacute stage of TBI. XFZYD may promote TBI recovery by normalizing amine metabolites and spatially promoting dopamine production and angiogenesis.

Spatiotemporal metabolic responses to water deficit stress in distinct leaf cell-types of poplar

The impact of water-deficit (WD) stress on plant metabolism has been predominantly studied at the whole tissue level. However, plant tissues are made of several distinct cell types with unique and differentiated functions, which limits whole tissue 'omics'-based studies to determine only an averaged molecular signature arising from multiple cell types. Advancements in spatial omics technologies provide an opportunity to understand the molecular mechanisms underlying plant responses to WD stress at distinct cell-type levels. Here, we studied the spatiotemporal metabolic responses of two poplar (Populus tremula× P. alba) leaf cell types -palisade and vascular cells- to WD stress using matrix-assisted laser desorption/ionization-mass spectrometry imaging (MALDI-MSI). We identified unique WD stress-mediated metabolic shifts in each leaf cell type when exposed to early and prolonged WD stresses and recovery from stress. During water-limited conditions, flavonoids and phenolic metabolites were exclusively accumulated in leaf palisade cells. However, vascular cells mainly accumulated sugars and fatty acids during stress and recovery conditions, respectively, highlighting the functional divergence of leaf cell types in response to WD stress. By comparing our MALDI-MSI metabolic data with whole leaf tissue gas chromatography-mass spectrometry (GC-MS)-based metabolic profile, we identified only a few metabolites including monosaccharides, hexose phosphates, and palmitic acid that showed a similar accumulation trend at both cell-type and whole leaf tissue levels. Overall, this work highlights the potential of the MSI approach to complement the whole tissue-based metabolomics techniques and provides a novel spatiotemporal understanding of plant metabolic responses to WD stress. This will help engineer specific metabolic pathways at a cellular level in strategic perennial trees like poplars to help withstand future aberrations in environmental conditions and to increase bioenergy sustainability.

[Application advances of mass spectrometry imaging technology in environmental pollutants analysis and their toxicity research]

Environmental exposures have significant impacts on human health and can contribute to the occurrence and development of diseases. Pollutants can enter the body through ingestion, inhalation, dermal absorption, or mother-to-child transmission, and can metabolize and/or accumulate in different tissues and organs. These pollutants can recognize and interact with various biomolecules, including DNA, RNA, proteins, and metabolites, disrupting biological processes and leading to adverse effects in living organisms. Thus, it is crucial to analysis the exogenous pollutants in the body, identify potential biomarkers and investigate their toxic effects. Numerous studies have shown that the metabolism rate of environmental pollutants greatly differs in various tissues and organs, their accumulation is also heterogeneous and dynamically changing. Moreover, the synthesis and accumulation of endogenous metabolites exhibit precise spatial distributions in tissues and cells. Mapping the spatial distributions of both pollutants and endogenous metabolites can discover relevant exposure biomarkers and provide a better understanding of their toxic effects and molecular mechanisms. Mass spectrometry is currently the preferred method for the qualitative and quantitative analysis of various compounds, and has been extensively utilized in pollutant and metabolomics analyses. Mass spectrometry imaging (MSI) is an emerging technology for molecular imaging that combines the information obtained by mass spectrometry with the visualization of the two- and three-dimensional spatial distributions of various molecular species in thin sample sections. Unlike other molecular imaging techniques, MSI can perform the label-free and untargeted analysis of thousands of molecules, such as elements, metabolites, lipids, peptides, proteins, pollutants, and drugs, in a single experiment with high sensitivity and throughput. Different MSI technologies, such as matrix-assisted laser desorption ionization mass spectrometry imaging, secondary ion mass spectrometry imaging, desorption electrospray ionization mass spectrometry imaging, and laser ablation inductively coupled plasma mass spectrometry imaging, have been introduced for the mapping of compounds and elements in biological, medical, and clinical research. MSI technologies have recently been utilized to characterize the spatial distribution of pollutants in the whole body and specific tissues of organisms, assess the toxic effects of pollutants at the molecular level, and identify exposure biomarkers. Such developments have brought new perspectives to investigate the toxicity of environmental pollutants. In this review, we provide an overview of the principles, characteristics, mass analyzers, and workflows of different MSI techniques and introduce their latest application advances in the analysis of environmental pollutants and their toxic effects.

Breaks for Precision Medicine in Cancer: Development and Prospects of Spatiotemporal Transcriptomics

With the development of the social economy and the deepening understanding of cancer, cancer has become a significant cause of death, threatening human health. Although researchers have made rapid progress in cancer treatment strategies in recent years, the overall survival of cancer patients is still not optimistic. Therefore, it is essential to reveal the spatial pattern of gene expression, spatial heterogeneity of cell populations, microenvironment interactions, and other aspects of cancer. Spatiotemporal transcriptomics can help analyze the mechanism of cancer occurrence and development, greatly help precise cancer treatment, and improve clinical prognosis. Here, we review the integration strategies of single-cell RNA sequencing and spatial transcriptomics data, summarize the recent advances in spatiotemporal transcriptomics in cancer studies, and discuss the combined application of spatial multiomics, which provides new directions and strategies for the precise treatment and clinical prognosis of cancer.

Active Compounds of Panax ginseng in the Improvement of Alzheimer's Disease and Application of Spatial Metabolomics

Panax ginseng C.A. Meyer (P. ginseng) is one of the more common traditional Chinese medicines (TCMs). It contains numerous chemical components and exhibits a range of pharmacological effects. An enormous burden is placed on people's health and life by Alzheimer's disease (AD), a neurodegenerative condition. Recent research has shown that P. ginseng's chemical constituents, particularly ginsenosides, have a significant beneficial impact on the prevention and management of neurological disorders. To understand the current status of research on P. ginseng to improve AD, this paper discusses the composition of P. ginseng, its mechanism of action, and its clinical application. The pathogenesis of AD includes amyloid beta protein (Aβ) generation and aggregation, tau protein hyperphosphorylation, oxidant stress, neuroinflammation, mitochondrial damage, and neurotransmitter and gut microbiota disorders. This review presents the key molecular mechanisms and signaling pathways of the active ingredients in P. ginseng involved in improving AD from the perspective of AD pathogenesis. A P. ginseng-related signaling pathway network was constructed to provide effective targets for the treatment of AD. In addition, the application of spatial metabolomics techniques in studying P. ginseng and AD is discussed. In summary, this paper discusses research perspectives for the study of P. ginseng in the treatment of AD, including a systematic and in-depth review of the mechanisms of action of the active substances in P. ginseng, and evaluates the feasibility of applying spatial metabolomics in the study of AD pathogenesis and pharmacological treatment.

Spatially Resolved Co-Imaging of Polyhalogenated Xenobiotics and Endogenous Metabolites Reveals Xenobiotic-Induced Metabolic Alterations

A large group of polyhalogenated compounds has been added to the list of persistent organic pollutants in a global convention endorsed by over 100 nations. Once entering the biotas, these pollutants are transported to focal sites of toxicological action and affected endogenous metabolites, which exhibited distinct tissue or organ distribution patterns. However, no study is available to achieve simultaneous mapping of the spatial distributions of xenobiotics and endogenous metabolites for clarifying the molecular mechanism of toxicities. Herein, we present a sensitive mass spectrometry imaging method─tetraphenyl phosphonium chloride-enhanced ionization coupled with air flow-assisted ionization-Orbitrap mass spectrometry─which simultaneously determined the spatial distributions of polyhalogenated xenobiotics and endogenous metabolites. The spatially resolved toxicokinetics and toxicodynamics of typical polyhalogenated compounds (chlorinated paraffins (CPs) and hexabromocyclododecane (HBCD)) were assessed in zebrafish. Co-imaging of polyhalogenated compounds and metabolites visualized the major accumulation organs and maternal transfer of HBCD and CPs, and it clarified the reproductive toxicity of HBCD. CPs were accumulated in the liver, heart, and brain and decreased the concentrations of polyamine/inosine-related metabolites and lipid molecules in these organs. HBCD accumulated in the ovary and was effectively transferred to eggs, and it also disrupted normal follicular development and impaired the production of mature eggs from the ovary by inhibiting expressions of the luteinizing hormone/choriogonadotropin receptor gene. The toxic effects of metabolic disruptions were validated by organ-specific histopathological examinations. These results highlight the necessity to assess the distributions and bioeffects of pollutants in a spatial perspective.

Heat-evolved algal symbionts enhance bleaching tolerance of adult corals without trade-off against growth

Ocean warming has caused coral mass bleaching and mortality worldwide and the persistence of symbiotic reef-building corals requires rapid acclimation or adaptation. Experimental evolution of the coral's microalgal symbionts followed by their introduction into coral is one potential method to enhance coral thermotolerance. Heat-evolved microalgal symbionts of the generalist species, Cladocopium proliferum (strain SS8), were exposed to elevated temperature (31°C) for ~10 years, and were introduced into four genotypes of chemically bleached adult fragments of the scleractinian coral, Galaxea fascicularis. Two of the four coral genotypes acquired SS8. The new symbionts persisted for the 5 months of the experiment and enhanced adult coral thermotolerance, compared with corals that were inoculated with the wild-type C. proliferum strain. Thermotolerance of SS8-corals was similar to that of coral fragments from the same colony hosting the homologous symbiont, Durusdinium sp., which is naturally heat tolerant. However, SS8-coral fragments exhibited faster growth and recovered cell density and photochemical efficiency more quickly following chemical bleaching and inoculation under ambient temperature relative to Durusdinium-corals. Mass spectrometry imaging suggests that algal pigments involved in photobiology and oxidative stress were the greatest contributors to the thermotolerance differences between coral hosting heat-evolved versus wild-type C. proliferum. These pigments may have increased photoprotection in the heat-evolved symbionts. This is the first laboratory study to show that thermotolerance of adult corals (G. fascicularis) can be enhanced via the uptake of exogenously supplied, heat-evolved symbionts, without a trade-off against growth under ambient temperature. Importantly, heat-evolved C. proliferum remained in the corals in moderate abundance 2 years after first inoculation, suggesting long-term stability of this novel symbiosis and potential long-term benefits to coral thermotolerance.

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.

Strigolactones repress nodule development and senescence in pea

Strigolactones are a class of phytohormones that are involved in many different plant developmental processes, including the rhizobium-legume nodule symbiosis. Although both positive and negative effects of strigolactones on the number of nodules have been reported, the influence of strigolactones on nodule development is still unknown. Here, by means of the ramosus (rms) mutants of Pisum sativum (pea) cv Terese, we investigated the impact of strigolactone biosynthesis (rms1 and rms5) and signaling (rms3 and rms4) mutants on nodule growth. The rms mutants had more red, that is, functional, and larger nodules than the wild-type plants. Additionally, the increased nitrogen fixation and senescence zones with consequently reduced meristematic and infection zones indicated that the rms nodules developed faster than the wild-type nodules. An enhanced expression of the nodule zone-specific molecular markers for meristem activity and senescence supported the enlarged, fast maturing nodules. Interestingly, the master nodulation regulator, NODULE INCEPTION, NIN, was strongly induced in nodules of all rms mutants but not prior to inoculation. Determination of sugar levels with both bulk and spatial metabolomics in roots and nodules, respectively, hints at slightly increased malic acid levels early during nodule primordia formation and reduced sugar levels at later stages, possibly the consequence of an increased carbon usage of the enlarged nodules, contributing to the enhanced senescence. Taken together, these results suggest that strigolactones regulate the development of nodules, which is probably mediated through NIN, and available plant sugars.

Integrated mass spectrometry imaging and metabolomics reveals sublethal effects of indoxacarb on the red fire ant Solenopsis invicta

BACKGROUND

Indoxacarb, representing an efficient insecticide, is normally made into a bait to spread the poison among red fire ants so that it can be widely applied in the prevention and control of Solenopsis invicta. However, the potential toxicity mechanism of S. invicta in response to indoxacarb remains to be explored. In this study, we integrated mass spectrometry imaging (MSI) and untargeted metabolomics methods to reveal disturbed metabolic expression levels and spatial distribution within the whole-body tissue of S. invicta treated with indoxacarb.

RESULTS

Metabolomics results showed a significantly altered level of metabolites after indoxacarb treatment, such as carbohydrates, amino acids and pyrimidine and derivatives. Additionally, the spatial distribution and regulation of several crucial metabolites resulting from the metabolic pathway and lipids can be visualized using label-free MSI methods. Specifically, xylitol, aspartate, and uracil were distributed throughout the whole body of S. invicta, while sucrose-6'-phosphate and glycerol were mainly distributed in the abdomen of S. invicta, and thymine was distributed in the head and chest of S. invicta. Taken together, the integrated MSI and metabolomics results indicated that the toxicity mechanism of indoxacarb in S. invicta is closely associated with the disturbance in several key metabolic pathways, such as pyrimidine metabolism, aspartate metabolism, pentose and glucuronate interconversions, and inhibited energy synthesis.

CONCLUSION

Collectively, these findings provide a new perspective for the understanding of toxicity assessment between targeted organisms S. invicta and pesticides. © 2023 Society of Chemical Industry.

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.

Primary Aldosteronism: Spatial Multiomics Mapping of Genotype-Dependent Heterogeneity and Tumor Expansion of Aldosterone-Producing Adenomas

BACKGROUND

Primary aldosteronism is frequently caused by an adrenocortical aldosterone-producing adenoma (APA) carrying a somatic mutation that drives aldosterone overproduction. APAs with a mutation in KCNJ5 (APA-KCNJ5MUT) are characterized by heterogeneous CYP11B2 (aldosterone synthase) expression, a particular cellular composition and larger tumor diameter than those with wild-type KCNJ5 (APA-KCNJ5WT). We exploited these differences to decipher the roles of transcriptome and metabolome reprogramming in tumor pathogenesis.

METHODS

Consecutive adrenal cryosections (7 APAs and 7 paired adjacent adrenal cortex) were analyzed by spatial transcriptomics (10x Genomics platform) and metabolomics (in situ matrix-assisted laser desorption/ionization mass spectrometry imaging) co-integrated with CYP11B2 immunohistochemistry.

RESULTS

We identified intratumoral transcriptional heterogeneity that delineated functionally distinct biological pathways. Common transcriptomic signatures were established across all APA specimens which encompassed 2 distinct transcriptional profiles in CYP11B2-immunopositive regions (CYP11B2-type 1 or 2). The CYP11B2-type 1 signature was characterized by zona glomerulosa gene markers and was detected in both APA-KCNJ5MUT and APA-KCNJ5WT. The CYP11B2-type 2 signature displayed markers of the zona fasciculata or reticularis and predominated in APA-KCNJ5MUT. Metabolites that promote oxidative stress and cell death accumulated in APA-KCNJ5WT. In contrast, antioxidant metabolites were abundant in APA-KCNJ5MUT. Finally, APA-like cell subpopulations-negative for CYP11B2 gene expression-were identified in adrenocortical tissue adjacent to APAs suggesting the existence of tumor precursor states.

CONCLUSIONS

Our findings provide insight into intra- and intertumoral transcriptional heterogeneity and support a role for prooxidant versus antioxidant systems in APA pathogenesis highlighting genotype-dependent capacities for tumor expansion.

Mapping metabolite change in the mouse brain after esketamine injection by ambient mass spectrometry imaging and metabolomics

Ketamine is a new, fast, and effective antidepression treatment method; however, the possible dissociation effects, sensory changes, abuse risk, and the inability to accurately identify whether patients have a significant response to ketamine limit its clinical use. Further exploration of the antidepressant mechanisms of ketamine will contribute to its safe and practical application. Metabolites, the products of upstream gene expression and protein regulatory networks, play an essential role in various physiological and pathophysiological processes. In traditional metabonomics it is difficult to achieve the spatial localization of metabolites, which limits the further analysis of brain metabonomics by researchers. Here, we used a metabolic network mapping method called ambient air flow-assisted desorption electrospray ionization (AFADESI)-mass spectrometry imaging (MSI). We found the main changes in glycerophospholipid metabolism around the brain and sphingolipid metabolism changed mainly in the globus pallidus, which showed the most significant metabolite change after esketamine injection. The spatial distribution of metabolic changes was evaluated in the whole brain, and the potential mechanism of esketamine's antidepressant effect was explored in this research.

Single-cell and spatial multi-omics in the plant sciences: Technical advances, applications, and perspectives

Plants contain a large number of cell types and exhibit complex regulatory mechanisms. Studies at the single-cell level have gradually become more common in plant science. Single-cell transcriptomics, spatial transcriptomics, and spatial metabolomics techniques have been combined to analyze plant development. These techniques have been used to study the transcriptomes and metabolomes of plant tissues at the single-cell level, enabling the systematic investigation of gene expression and metabolism in specific tissues and cell types during defined developmental stages. In this review, we present an overview of significant breakthroughs in spatial multi-omics in plants, and we discuss how these approaches may soon play essential roles in plant research.

SmartGate is a spatial metabolomics tool for resolving tissue structures

Imaging mass spectrometry (IMS) is one of the powerful tools in spatial metabolomics for obtaining metabolite data and probing the internal microenvironment of organisms. It has dramatically advanced the understanding of the structure of biological tissues and the drug treatment of diseases. However, the complexity of IMS data hinders the further acquisition of biomarkers and the study of certain specific activities of organisms. To this end, we introduce an artificial intelligence tool, SmartGate, to enable automatic peak selection and spatial structure identification in an iterative manner. SmartGate selects discriminative m/z features from the previous iteration by differential analysis and employs a graph attention autoencoder model to perform spatial clustering for tissue segmentation using the selected features. We applied SmartGate to diverse IMS data at multicellular or subcellular spatial resolutions and compared it with four competing methods to demonstrate its effectiveness. SmartGate can significantly improve the accuracy of spatial segmentation and identify biomarker metabolites based on tissue structure-guided differential analysis. For multiple consecutive IMS data, SmartGate can effectively identify structures with spatial heterogeneity by introducing three-dimensional spatial neighbor information.

When cancer drug resistance meets metabolomics (bulk, single-cell and/or spatial): Progress, potential, and perspective

Resistance to drug treatment is a critical barrier in cancer therapy. There is an unmet need to explore cancer hallmarks that can be targeted to overcome this resistance for therapeutic gain. Over time, metabolic reprogramming has been recognised as one hallmark that can be used to prevent therapeutic resistance. With the advent of metabolomics, targeting metabolic alterations in cancer cells and host patients represents an emerging therapeutic strategy for overcoming cancer drug resistance. Driven by technological and methodological advances in mass spectrometry imaging, spatial metabolomics involves the profiling of all the metabolites (metabolomics) so that the spatial information is captured bona fide within the sample. Spatial metabolomics offers an opportunity to demonstrate the drug-resistant tumor profile with metabolic heterogeneity, and also poses a data-mining challenge to reveal meaningful insights from high-dimensional spatial information. In this review, we discuss the latest progress, with the focus on currently available bulk, single-cell and spatial metabolomics technologies and their successful applications in pre-clinical and translational studies on cancer drug resistance. We provide a summary of metabolic mechanisms underlying cancer drug resistance from different aspects; these include the Warburg effect, altered amino acid/lipid/drug metabolism, generation of drug-resistant cancer stem cells, and immunosuppressive metabolism. Furthermore, we propose solutions describing how to overcome cancer drug resistance; these include early detection during cancer initiation, monitoring of clinical drug response, novel anticancer drug and target metabolism, immunotherapy, and the emergence of spatial metabolomics. We conclude by describing the perspectives on how spatial omics approaches (integrating spatial metabolomics) could be further developed to improve the management of drug resistance in cancer patients.

Ion mobility mass spectrometry in the omics era: Challenges and opportunities for metabolomics and lipidomics

Researchers worldwide are taking advantage of novel, commercially available, technologies, such as ion mobility mass spectrometry (IM-MS), for metabolomics and lipidomics applications in a variety of fields including life, biomedical, and food sciences. IM-MS provides three main technical advantages over traditional LC-MS workflows. Firstly, in addition to mass, IM-MS allows collision cross-section values to be measured for metabolites and lipids, a physicochemical identifier related to the chemical shape of an analyte that increases the confidence of identification. Second, IM-MS increases peak capacity and the signal-to-noise, improving fingerprinting as well as quantification, and better defining the spatial localization of metabolites and lipids in biological and food samples. Third, IM-MS can be coupled with various fragmentation modes, adding new tools to improve structural characterization and molecular annotation. Here, we review the state-of-the-art in IM-MS technologies and approaches utilized to support metabolomics and lipidomics applications and we assess the challenges and opportunities in this growing field.

Spatial Metabolomics Reveals Localized Impact of Influenza Virus Infection on the Lung Tissue Metabolome

The influenza virus (IAV) is a major cause of respiratory disease, with significant infection increases in pandemic years. Vaccines are a mainstay of IAV prevention but are complicated by IAV’s vast strain diversity and manufacturing and vaccine uptake limitations. While antivirals may be used for treatment of IAV, they are most effective in early stages of the infection, and several virus strains have become drug resistant. Therefore, there is a need for advances in IAV treatment, especially host-directed therapeutics. Given the spatial dynamics of IAV infection and the relationship between viral spatial distribution and disease severity, a spatial approach is necessary to expand our understanding of IAV pathogenesis. We used spatial metabolomics to address this issue. Spatial metabolomics combines liquid chromatography-tandem mass spectrometry of metabolites extracted from systematic organ sections, 3D models, and computational techniques to develop spatial models of metabolite location and their role in organ function and disease pathogenesis. In this project, we analyzed serum and systematically sectioned lung tissue samples from uninfected or infected mice. Spatial mapping of sites of metabolic perturbations revealed significantly lower metabolic perturbation in the trachea compared to other lung tissue sites. Using random forest machine learning, we identified metabolites that responded differently in each lung position based on infection, including specific amino acids, lipids and lipid-like molecules, and nucleosides. These results support the implementation of spatial metabolomics to understand metabolic changes upon respiratory virus infection.

IMPORTANCE The influenza virus is a major health concern. Over 1 billion people become infected annually despite the wide distribution of vaccines, and antiviral agents are insufficient to address current clinical needs. In this study, we used spatial metabolomics to understand changes in the lung and serum metabolome of mice infected with influenza A virus compared to uninfected controls. We determined metabolites altered by infection in specific lung tissue sites and distinguished metabolites perturbed by infection between lung tissue and serum samples. Our findings highlight the utility of a spatial approach to understanding the intersection between the lung metabolome, viral infection, and disease severity. Ultimately, this approach will expand our understanding of respiratory disease pathogenesis.

Spatial metabolomics for evaluating response to neoadjuvant therapy in non-small cell lung cancer patients

Background

The response to neoadjuvant chemotherapy (NAC) differs substantially among individual patients with non‐small cell lung cancer (NSCLC). Major pathological response (MPR) is a histomorphological read‐out used to assess treatment response and prognosis in patients NSCLC after NAC. Although spatial metabolomics is a promising tool for evaluating metabolic phenotypes, it has not yet been utilized to assess therapy responses in patients with NSCLC. We evaluated the potential application of spatial metabolomics in cancer tissues to assess the response to NAC, using a metabolic classifier that utilizes mass spectrometry imaging combined with machine learning.

Methods

Resected NSCLC tissue specimens obtained after NAC (n = 88) were subjected to high‐resolution mass spectrometry, and these data were used to develop an approach for assessing the response to NAC in patients with NSCLC. The specificities of the generated tumor cell and stroma classifiers were validated by applying this approach to a cohort of biologically matched chemotherapy‐naïve patients with NSCLC (n = 85).

Results

The developed tumor cell metabolic classifier stratified patients into different prognostic groups with 81.6% accuracy, whereas the stroma metabolic classifier displayed 78.4% accuracy. By contrast, the accuracies of MPR and TNM staging for stratification were 62.5% and 54.1%, respectively. The combination of metabolic and MPR classifiers showed slightly lower accuracy than either individual metabolic classifier. In multivariate analysis, metabolic classifiers were the only independent prognostic factors identified (tumor: P = 0.001, hazards ratio [HR] = 3.823, 95% confidence interval [CI] = 1.716–8.514; stroma: P = 0.049, HR = 2.180, 95% CI = 1.004–4.737), whereas MPR (P = 0.804; HR = 0.913; 95% CI = 0.445–1.874) and TNM staging (P = 0.078; HR = 1.223; 95% CI = 0.977–1.550) were not independent prognostic factors. Using Kaplan‐Meier survival analyses, both tumor and stroma metabolic classifiers were able to further stratify patients as NAC responders (P < 0.001) and non‐responders (P < 0.001).

Conclusions

Our findings indicate that the metabolic constitutions of both tumor cells and the stroma are valuable additions to the classical histomorphology‐based assessment of tumor response.

Direct Imaging of Plant Metabolites in the Rhizosphere Using Laser Desorption Ionization Ultra-High Resolution Mass Spectrometry

The interplay of rhizosphere components such as root exudates, microbes, and minerals results in small-scale gradients of organic molecules in the soil around roots. The current methods for the direct chemical imaging of plant metabolites in the rhizosphere often lack molecular information or require labeling with fluorescent tags or isotopes. Here, we present a novel workflow using laser desorption ionization (LDI) combined with mass spectrometric imaging (MSI) to directly analyze plant metabolites in a complex soil matrix. Undisturbed samples of the roots and the surrounding soil of Zea mays L. plants from either field- or laboratory-scale experiments were embedded and cryosectioned to 100 μm thin sections. The target metabolites were detected with a spatial resolution of 25 μm in the root and the surrounding soil based on accurate masses using ultra-high mass resolution laser desorption ionization Fourier-transform ion cyclotron resonance mass spectrometry (LDI-FT-ICR-MS). Using this workflow, we could determine the rhizosphere gradients of a dihexose (e.g., sucrose) and other plant metabolites (e.g., coumaric acid, vanillic acid). The molecular gradients for the dihexose showed a high abundance of this metabolite in the root and a strong depletion of the signal intensity within 150 μm from the root surface. Analyzing several sections from the same undisturbed soil sample allowed us to follow molecular gradients along the root axis. Benefiting from the ultra-high mass resolution, isotopologues of the dihexose could be readily resolved to enable the detection of stable isotope labels on the compound level. Overall, the direct molecular imaging via LDI-FT-ICR-MS allows for the first time a non-targeted or targeted analysis of plant metabolites in undisturbed soil samples, paving the way to study the turnover of root-derived organic carbon in the rhizosphere with high chemical and spatial resolution.

Metabolic tumor constitution is superior to tumor regression grading for evaluating response to neoadjuvant therapy of esophageal adenocarcinoma patients

The response to neoadjuvant therapy can vary widely between individual patients. Histopathological tumor regression grading (TRG) is a strong factor for treatment response and survival prognosis of esophageal adenocarcinoma (EAC) patients following neoadjuvant treatment and surgery. However, TRG systems are usually based on the estimation of residual tumor but do not consider stromal or metabolic changes after treatment. Spatial metabolomics analysis is a powerful tool for molecular tissue phenotyping but has not been used so far in the context of neoadjuvant treatment of esophageal cancer. We used imaging mass spectrometry to assess the potential of spatial metabolomics on tumor and stroma tissue for evaluating therapy response of neoadjuvant-treated EAC patients. With an accuracy of 89.7%, the binary classifier trained on spatial tumor metabolite data proved to be superior for stratifying patients when compared to histopathological response assessment which had an accuracy of 70.5%. Sensitivities and specificities for the poor and favorable survival patient groups ranged from 84.9 to 93.3% using the metabolic classifier and from 62.2 to 78.1% using TRG. The tumor classifier was the only significant prognostic factor (HR 3.38, 95% CI = 1.40-8.12, P = 0.007) when adjusted for clinicopathological parameters such as TRG (HR 1.01, 95% CI = 0.67-1.53, P = 0.968) or stromal classifier (HR 1.856, 95% CI = 0.81-4.25, P = 0.143). The classifier even allowed to further stratify patients within the TRG1-3 categories. The underlying mechanisms of response to treatment has been figured out through network analysis. In summary, metabolic response evaluation outperformed histopathological response evaluation in our study with regard to prognostic stratification. This finding indicates that the metabolic constitution of tumor may have a greater impact on patient survival than the quantity of residual tumor cells or the stroma. This article is protected by copyright. All rights reserved.

Metabolomics Reveals Heterogeneity in the Chemical Composition of Green and White Spears of Asparagus (A. officinalis)

Green and white asparagus are quite different crops but can be harvested from the same plant. They have distinct morphological differences due to their mode of cultivation and they are characterised by having contrasting appearance and flavour. Significant chemical differences are therefore expected. Spears from three varieties of both green and white forms, harvested in two consecutive seasons were analysed using headspace GC-MS and LC-MS with an untargeted metabolomic workflow. Mainly C5 and C8 alcohols and aldehydes, and phenolic compounds were more abundant in green spears, whereas benzenoids, monoterpenes, unsaturated aldehydes and steroidal saponins were more abundant in white ones. Previously reported key asparagus volatiles and non-volatiles were detected at similar or not significantly different levels in the two asparagus types. Spatial metabolomics revealed also that many volatiles with known positive aroma attributes were significantly more abundant in the upper parts of the spears and showed a decreasing trend towards the base. These findings provide valuable insights into the metabolome of raw asparagus, the contrasts between green and white spears as well as the different chemical distributions along the stem.

Spatial metabolomics using imaging mass spectrometry to identify the localization of asparaptine A in Asparagus officinalis

Spatial metabolomics uses imaging mass spectrometry (IMS) to localize metabolites within tissue section. Here, we performed matrix-assisted laser desorption/ionization-Fourier transform ion cyclotron resonance-IMS (MALDI-FTICR-IMS) to identify the localization of asparaptine A, a naturally occurring inhibitor of angiotensin-converting enzyme, in green spears of asparagus (Asparagus officinalis). Spatial metabolome data were acquired in an untargeted manner. Segmentation analysis using the data characterized tissue-type-dependent and independent distribution patterns in cross-sections of asparagus spears. Moreover, asparaptine A accumulated at high levels in developing lateral shoot tissues. Quantification of asparaptine A in lateral shoots using liquid chromatography-tandem mass spectrometry (LC-MS/MS) validated the IMS analysis. These results provide valuable information for understanding the function of asparaptine A in asparagus, and identify the lateral shoot as a potential region of interest for multiomics studies to examine gene-to-metabolite associations in the asparaptine A biosynthesis.

Ambient Single-Cell Analysis and Native Tissue Imaging Using Laser-Ablation Electrospray Ionization Mass Spectrometry with Increased Spatial Resolution

Laser-ablation electrospray ionization mass spectrometry (LAESI-MS) is an emerging method that has the potential to transform the field of metabolomics. This is in part due to LAESI-MS being an ambient ionization method that requires minimal sample preparation and uses (endogenous) water for in situ analysis. This application note details the employment of the "LAESI microscope" source to perform spatially resolved MS analysis of cells and MS imaging (MSI) of tissues at high spatial resolution. This source configuration utilizes a long-working-distance reflective objective that permits both visualization of the sample and a smaller LAESI laser beam profile than conventional LAESI setups. Here, we analyzed 200 single cells of Allium cepa (red onion) and imaged Fittonia argyroneura (nerve plant) in high spatial resolution using this source coupled to a Fourier transform mass spectrometer for high-mass-resolution and high-mass-accuracy metabolomics.

Facilitating Imaging Mass Spectrometry of Microbial Specialized Metabolites with METASPACE

Metabolite annotation from imaging mass spectrometry (imaging MS) data is a difficult undertaking that is extremely resource intensive. Here, we adapted METASPACE, cloud software for imaging MS metabolite annotation and data interpretation, to quickly annotate microbial specialized metabolites from high-resolution and high-mass accuracy imaging MS data. Compared with manual ion image and MS1 annotation, METASPACE is faster and, with the appropriate database, more accurate. We applied it to data from microbial colonies grown on agar containing 10 diverse bacterial species and showed that METASPACE was able to annotate 53 ions corresponding to 32 different microbial metabolites. This demonstrates METASPACE to be a useful tool to annotate the chemistry and metabolic exchange factors found in microbial interactions, thereby elucidating the functions of these molecules.

Unique and highly specific cyanogenic glycoside localization in stigmatic cells and pollen in the genus Lomatia (Proteaceae)

BACKGROUND AND AIMS

Floral chemical defence strategies remain understudied despite the significance of flowers to plant fitness, and the fact that many flowers contain secondary metabolites that confer resistance to herbivores. Optimal defence and apparency theories predict that the most apparent plant parts and/or those most important to fitness should be most defended. To test whether within-flower distributions of chemical defence are consistent with these theories we used cyanogenic glycosides (CNglycs), which are constitutive defence metabolites that deter herbivores by releasing hydrogen cyanide upon hydrolysis.

METHODS

We used cyanogenic florets of the genus Lomatia to investigate at what scale there may be strategic allocation of CNglycs in flowers, what their localization reveals about function, and whether levels of floral CNglycs differ between eight congeneric species across a climatic gradient. Within-flower distributions of CNglycs during development were quantified, CNglycs were identified and their localization was visualized in cryosectioned florets using matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI).

KEY RESULTS

Florets of all congeneric species studied were cyanogenic, and concentrations differed between species. Within florets there was substantial variation in CNglyc concentrations, with extremely high concentrations (up to 14.6 mg CN g-1 d. wt) in pollen and loose, specialized surface cells on the pollen presenter, among the highest concentrations reported in plant tissues. Two tyrosine-derived CNglycs, the monoglycoside dhurrin and diglycoside proteacin, were identified. MALDI-MSI revealed their varying ratios in different floral tissues; proteacin was primarily localized to anthers and ovules, and dhurrin to specialized cells on the pollen presenter. The mix of transient specialized cells and pollen of L. fraxinifolia was ~11 % dhurrin and ~1.1 % proteacin by mass.

CONCLUSIONS

Tissue-specific distributions of two CNglycs and substantial variation in their concentrations within florets suggests their allocation is under strong selection. Localized, high CNglyc concentrations in transient cells challenge the predictions of defence theories, and highlight the importance of fine-scale metabolite visualization, and the need for further investigation into the ecological and metabolic roles of CNglycs in floral tissues.

Special Issue on "Fruit Metabolism and Metabolomics"

Over the past 10 years, knowledge about several aspects of fruit metabolism has been greatly improved. Notably, high-throughput metabolomic technologies have allowed quantifying metabolite levels across various biological processes, and identifying the genes that underly fruit development and ripening. This Special Issue is designed to exemplify the current use of metabolomics studies of temperate and tropical fruit for basic research as well as practical applications. It includes articles about different aspects of fruit biochemical phenotyping, fruit metabolism before and after harvest, including primary and specialized metabolisms, and bioactive compounds involved in growth and environmental responses. The effect of genotype, stages of development or fruit tissue on metabolomic profiles and corresponding metabolism regulations are addressed, as well as the combination of other omics with metabolomics for fruit metabolism studies.

Spatial Metabolomics and Imaging Mass Spectrometry in the Age of Artificial Intelligence

Spatial metabolomics is an emerging field of omics research that has enabled localizing metabolites, lipids, and drugs in tissue sections, a feat considered impossible just two decades ago. Spatial metabolomics and its enabling technology-imaging mass spectrometry-generate big hyper-spectral imaging data that have motivated the development of tailored computational methods at the intersection of computational metabolomics and image analysis. Experimental and computational developments have recently opened doors to applications of spatial metabolomics in life sciences and biomedicine. At the same time, these advances have coincided with a rapid evolution in machine learning, deep learning, and artificial intelligence, which are transforming our everyday life and promise to revolutionize biology and healthcare. Here, we introduce spatial metabolomics through the eyes of a computational scientist, review the outstanding challenges, provide a look into the future, and discuss opportunities granted by the ongoing convergence of human and artificial intelligence.

Mass spectrometry imaging: An emerging technology for the analysis of metabolites in insects

Mass spectrometry imaging (MSI) can visualize the composition, abundance, and spatial distribution of molecules in tissues or cells, which has been widely used in the research of life science. Insects, especially the agricultural pests, have received a great deal of interests from the scientists in biodiversity and food security. This review introduces the major characteristics of MSI, summarizes its application to the investigation of insect endogenous metabolites, exogenous metabolites, and the spatiotemporal changes of metabolites between insects and plants, and discusses its shortfalls and perspectives. The significance of these concerns is beneficial for future insect research such as physiology and metabolism.

Comparative spatial lipidomics analysis reveals cellular lipid remodelling in different developmental zones of barley roots in response to salinity

Salinity-induced metabolic, ionic, and transcript modifications in plants have routinely been studied using whole plant tissues, which do not provide information on spatial tissue responses. The aim of this study was to assess the changes in the lipid profiles in a spatial manner and to quantify the changes in the elemental composition in roots of seedlings of four barley cultivars before and after a short-term salt stress. We used a combination of liquid chromatography-tandem mass spectrometry, inductively coupled plasma mass spectrometry, matrix-assisted laser desorption/ionization mass spectrometry imaging, and reverse transcription - quantitative real time polymerase chain reaction platforms to examine the molecular signatures of lipids, ions, and transcripts in three anatomically different seminal root tissues before and after salt stress. We found significant changes to the levels of major lipid classes including a decrease in the levels of lysoglycerophospholipids, ceramides, and hexosylceramides and an increase in the levels of glycerophospholipids, hydroxylated ceramides, and hexosylceramides. Our results revealed that modifications to lipid and transcript profiles in plant roots in response to a short-term salt stress may involve recycling of major lipid species, such as phosphatidylcholine, via resynthesis from glycerophosphocholine.