Spatially resolved sampling for untargeted metabolomics: A new tool for salivomics.
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ABSTRACT:
SUBMITTER: Ciurli A
PROVIDER: S-EPMC8271151 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The pathological diagnosis of benign and malignant follicular thyroid tumors remains a major challenge using the current histopathological technique. To improve diagnosis accuracy, spatially resolved metabolomics analysis based on air flow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI) technique was used to establish a molecular diagnostic strategy for discriminating four pathological types of thyroid tumor. Without any specific labels, numerous metabolite features with their spatial distribution information can be acquired by AFADESI-MSI. The underlying metabolic heterogeneity can be visualized in line with the cellular heterogeneity in native tumor tissue. Through micro-regional feature extraction and in situ metabolomics analysis, three sets of metabolic biomarkers for the visual discrimination of benign follicular adenoma and differentiated thyroid carcinomas were discovered. Additionally, the automated prediction of tumor foci was supported by a diagnostic model based on the metabolic profile of 65 thyroid nodules. The model prediction accuracy was 83.3% when a test set of 12 independent samples was used. This diagnostic strategy presents a new way of performing in situ pathological examinations using small molecular biomarkers and provides a model diagnosis for clinically indeterminate thyroid tumor cases.
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Project description:Metabolomics is increasingly important for biomedical research, but large-scale metabolite identification in untargeted metabolomics is still challenging. Here, we present Jumbo Mass spectrometry-based Program of Metabolomics (JUMPm) software, a streamlined software tool for identifying potential metabolite formulas and structures in mass spectrometry. During database search, the false discovery rate is evaluated by a target-decoy strategy, where the decoys are produced by breaking the octet rule of chemistry. We illustrated the utility of JUMPm by detecting metabolite formulas and structures from liquid chromatography coupled tandem mass spectrometry (LC-MS/MS) analyses of unlabeled and stable-isotope labeled yeast samples. We also benchmarked the performance of JUMPm by analyzing a mixed sample from a commercially available metabolite library in both hydrophilic and hydrophobic LC-MS/MS. These analyses confirm that metabolite identification can be significantly improved by estimating the element composition in formulas using stable isotope labeling, or by introducing LC retention time during a spectral library search, which are incorporated into JUMPm functions. Finally, we compared the performance of JUMPm and two commonly used programs, Compound Discoverer 3.1 and MZmine 2, with respect to putative metabolite identifications. Our results indicate that JUMPm is an effective tool for metabolite identification of both unlabeled and labeled data in untargeted metabolomics.
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Project description:Highlights • Both post-ripening stages and banana varieties contribute to metabolite variation.• AuNP-assisted LDI-MSI was firstly used in mapping functional metabolites in pulps.• AAs and monoamines exclusively accumulated in the middle region near the seed zone.• Monosaccharides locate in whole pulps but enrich in the intermediate microregion.• Di/trisaccharides exhibit different accumulation patterns as monosaccharides. Banana is one of most popular fruits globally due to health-promoting and disease-preventing effects, yet little is known about in situ metabolic changes across banana varieties. Here, we integrated gold nanoparticle (AuNP)-assisted laser desorption/ionization mass spectrometry imaging (LDI-MSI) and metabolomics to investigate the spatiotemporal distribution and levels of metabolites within Brazil and Dongguan banana pulps during postharvest senescence. Metabolomics results indicated that both postripening stages and banana varieties contribute to metabolite levels. Benefiting from improved ionization efficiency of small-molecule metabolites and less peak interference, we visualized the spatiotemporal distribution of sugars, amino acids (AAs) and monoamines within pulps using AuNP-assisted LDI-MSI for the first time, revealing that AAs and monoamines exclusively accumulated in the middle region near the seed zone. Monosaccharides and di/trisaccharides were generally distributed across entire pulps but exhibited different accumulation patterns. These findings provide a guide for breeding new varieties and improving extraction efficiency of bioactive compounds.
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Project description:BackgroundAcute kidney injury (AKI) is one of the most common clinical emergencies characterized by rapid progression, difficulty in early diagnosis, and high mortality. Currently, there are no effective AKI early diagnostic methods and treatments. Therefore, identifying new mechanisms of AKI have become urgent for development new targets for early diagnosis and treatment of AKI in the current clinical setting.MethodsIn this study, systematic analysis and comparison of serum metabolic profiles of clinical AKI patients, chronic kidney disease (CKD) patients, and healthy subjects were performed using untargeted metabolomics. Moreover, the first spatial metabolomic analysis of kidney tissues in an AKI mouse model using MALDI-TOF MS technology was conducted. Differentially expressed metabolites were identified using a comprehensive, publicly available database. The metabolic data obtained were evaluated using principal component analysis, (orthogonal) partial least squares discriminant analysis, and metabolic pathway analysis to explore the unique serum metabolic profile of the patients, as well as to characterize the spatial distribution of differential metabolites in the kidneys of AKI mice.ResultsSignificant changes in the metabolite levels of amino acids, carnitine, and lipids were observed in the AKI and CKD groups versus the healthy population, suggesting that kidney injury may lead to abnormalities in various metabolic pathways, such as amino acids, fatty acids, and lipids. The significant difference between the AKI and CKD groups were found for the first time in these indexes including amino acid, carnitine, fatty acid, and lipid levels. Additionally, spatial metabolomics results revealed that amino acid, carnitine, organic acid, and fatty acid metabolites were more likely significantly altered in the renal cortex, while lipid metabolites were both differentially distributed in the cortex and medulla of the AKI group.ConclusionAbnormalities in the serum metabolism of amino acids, carnitine, and lipids in patients with kidney diseases, such as AKI and CKD, are closely associated with the physiological dysfunction of kidney injury. Metabolic differences between patients with AKI and CKD were compared for the first time, showing that fatty acid oxidative inhibition was more severe in patients with AKI. Furthermore, spatial metabolomics has revealed metabolic reprogramming with tissue heterogeneity in AKI mice model. Our study provides valuable information in the molecular pathological features of AKI in the kidney tissues.
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Project description:Within hydrothermal plumes, chemosynthetic processes and microbe-mineral interactions drive primary productivity in deep-ocean food webs and may influence transport of elements such as iron. However, the source of microorganisms in plumes and the factors governing how these communities assemble are poorly understood, in part due to lack of data from early stages of plume formation. In this study, we examined microbial community composition of rising hydrothermal plumes from five vent fields along the Eastern Lau Spreading Center. Seafloor and plume microbial communities were significantly dissimilar and shared few phylotypes. Plume communities were highly similar to each other with significant differences in community membership only between Kilo Moana and Mariner, two vents that are separated by extremes in depth, latitude and geochemistry. Systematic sampling of waters surrounding the vents revealed that species richness and phylogenetic diversity was typically highest near the vent orifice, implying mixing of microbial communities from the surrounding habitats. Above-plume background communities were primarily dominated by SAR11, SAR324 and MG-I Archaea, while SUP05, Sulfurovum, Sulfurimonas, SAR324 and Alteromonas were abundant in plume and near-bottom background communities. These results show that the ubiquitous water-column microorganisms populate plume communities, and that the composition of background seawater exerts primary influence on plume community composition, with secondary influence from geochemical and/or physical properties of vents. Many of these pervasive deep-ocean organisms are capable of lithotrophy, suggesting that they are poised to use inorganic electron donors encountered in hydrothermal plumes.
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Project description:The rapidly increasing number of engineered nanoparticles (NPs), and products containing NPs, raises concerns for human exposure and safety. With this increasing, and ever changing, catalogue of NPs it is becoming more difficult to adequately assess the toxic potential of new materials in a timely fashion. It is therefore important to develop methods which can provide high-throughput screening of biological responses. The use of omics technologies, including metabolomics, can play a vital role in this process by providing relatively fast, comprehensive, and cost-effective assessment of cellular responses. These techniques thus provide the opportunity to identify specific toxicity pathways and to generate hypotheses on how to reduce or abolish toxicity.We have used untargeted metabolome analysis to determine differentially expressed metabolites in human lung epithelial cells (A549) exposed to copper oxide nanoparticles (CuO NPs). Toxicity hypotheses were then generated based on the affected pathways, and critically tested using more conventional biochemical and cellular assays. CuO NPs induced regulation of metabolites involved in oxidative stress, hypertonic stress, and apoptosis. The involvement of oxidative stress was clarified more easily than apoptosis, which involved control experiments to confirm specific metabolites that could be used as standard markers for apoptosis; based on this we tentatively propose methylnicotinamide as a generic metabolic marker for apoptosis.Our findings are well aligned with the current literature on CuO NP toxicity. We thus believe that untargeted metabolomics profiling is a suitable tool for NP toxicity screening and hypothesis generation.
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Project description:New insights into tumor-associated metabolic reprogramming have provided novel vulnerabilities that can be targeted for cancer therapy. Here, we propose a mass spectrometry imaging (MSI)-based metabolomic strategy to visualize the spatially resolved reprogramming of carnitine metabolism in heterogeneous breast cancer. Methods: A wide carnitine coverage MSI method was developed to investigate the spatial alternations of carnitines in cancer tissues of xenograft mouse models and human samples. Spatial expression of key metabolic enzymes that are closely associated with the altered carnitines was examined in adjacent cancer tissue sections. Results: A total of 17 carnitines, including L-carnitine, 6 short-chain acylcarnitines, 3 middle-chain acylcarnitines, and 7 long-chain acylcarnitines were imaged. L-carnitine and short-chain acylcarnitines are significantly reprogrammed in breast cancer. A classification model based on the carnitine profiles of 170 cancer samples and 128 normal samples enables an accurate identification of breast cancer. CPT 1A, CPT 2, and CRAT, which are extensively involved in carnitine system-mediated fatty acid β-oxidation pathway were also found to be abnormally expressed in breast cancer. Remarkably, the expressions of CPT 2 and CRAT were found for the first time to be altered in breast cancer. Conclusion: These data not only expand our understanding of the complex tumor metabolic reprogramming, but also provide the first evidence that carnitine metabolism is reprogrammed at both the metabolite and enzyme levels in breast cancer.
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