Mass spectrometry-based metabolomics, analysis of metabolite-protein interactions, and imaging

Multiomics analysis of platelet-rich plasma promoting biological performance of mesenchymal stem cells

Mesenchymal Stem Cells are ideal seed cells for tissue repair and cell therapy and have promising applications in regenerative medicine and tissue engineering. Using Platelet-Rich Plasma as an adjuvant to create and improve the microenvironment for Mesenchymal Stem Cells growth can enhance the biological properties of Mesenchymal Stem Cells and improve the efficacy of cell therapy. However, the mechanism by which Platelet-Rich Plasma improves the biological performance of Mesenchymal Stem Cells is still unknown. In this study, by examining the effects of Platelet-Rich Plasma on the biological performance of Mesenchymal Stem Cells, combined with multiomics analysis (Transcriptomics, Proteomics and Metabolomics) and related tests, we analyzed the specific pathways, related mechanisms and metabolic pathways of Platelet-Rich Plasma to improve the biological performance of Mesenchymal Stem Cells. In an in vitro cell culture system, the biological performance of Mesenchymal Stem Cells was significantly improved after replacing Foetal Bovine Serum with Platelet-Rich Plasma, and the genes (ESM1, PDGFB, CLEC7A, CCR1 and ITGA6 et al.) related to cell proliferation, adhesion, growth, migration and signal transduction were significantly upregulated. Platelet-Rich Plasma can enhance the secretion function of MSC exosomes, significantly upregulate many proteins related to tissue repair, immune regulation and anti-infection, and enhance the repair effect of exosomes on skin injury. After replacing Foetal Bovine Serum with Platelet-Rich Plasma, Mesenchymal Stem Cells underwent metabolic reprogramming, the metabolism of amino acids and fatty acids and various signaling pathways were changed, the anabolic pathways of various proteins were enhanced. These results provide a theoretical and technical reference for optimizing the Mesenchymal Stem Cells culture system, improving the biological characteristics and clinical application effects of Mesenchymal Stem Cells.

Mass spectrometry imaging of Arabidopsis thaliana with in vivo D2O labeling

The commonly used analytical tools for metabolomics cannot directly probe metabolic activities or distinguish metabolite differences between cells and suborgans in multicellular organisms. These issues can be addressed by in-vivo isotope labeling and mass spectrometry imaging (MSI), respectively, but the combination of the two, a newly emerging technology we call MSIi, has been rarely applied to plant systems. In this study, we explored MSIi of Arabidopsis thaliana with D2O labeling to study and visualize D-labeling in three classes of lipids: arabidopsides, chloroplast lipids, and epicuticular wax. Similar to other stress responses, D2O-induced stress increased arabidopsides in an hour, but it was relatively minor for matured plants and reverted to the normal level in a few hours. The D-labeling isotopologue patterns of arabidopsides matched with those of galactolipid precursors, supporting the currently accepted biosynthesis mechanism. Matrix-assisted laser desorption/ionization (MALDI)-MSI was used to visualize the spatiotemporal distribution of deuterated chloroplast lipids, pheophytin a, MGDGs, and DGDGs, after growing day-after-sowing (DAS) 28 plants in D2O condition for 3-12 days. There was a gradual change of deuteration amount along the leaf tissues and with a longer labeling time, which was attributed to slow respiration leading to low D2O concentration in the tissues. Finally, deuterium incorporation in epicuticular wax was visualized on the surfaces of the stem and flower. The conversion efficiency of newly synthesized C30 aldehyde to C29 ketone was very low in the lower stem but very high at the top of the stem near the flower or on the flower carpel. This study successfully demonstrated that MSIi can unveil spatiotemporal metabolic activities in various tissues of A. thaliana.

Applications of chromatographic methods in metabolomics: A review

Chromatography is a robust and reliable separation method that can use various stationary phases to separate complex mixtures commonly seen in metabolomics. This review examines the types of chromatography and stationary phases that have been used in targeted or untargeted metabolomics with methods such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. General considerations for sample pretreatment and separations in metabolomics are considered, along with the various supports and separation formats for chromatography that have been used in such work. The types of liquid chromatography (LC) that have been most extensively used in metabolomics will be examined, such as reversed-phase liquid chromatography and hydrophilic liquid interaction chromatography. In addition, other forms of LC that have been used in more limited applications for metabolomics (e.g., ion-exchange, size-exclusion, and affinity methods) will be discussed to illustrate how these techniques may be utilized for new and future research in this field. Multidimensional LC methods are also discussed, as well as the use of gas chromatography and supercritical fluid chromatography in metabolomics. In addition, the roles of chromatography in NMR- vs. MS-based metabolomics are considered. Applications are given within the field of metabolomics for each type of chromatography, along with potential advantages or limitations of these separation methods.

Regional Variations in Peucedanum japonicum Antioxidants and Phytochemicals

Peucedanum japonicum has long been a staple in East Asian cuisine. In the context of traditional medicine, various members of the Peucedanum genus have been investigated for potential medicinal properties. In laboratory settings, some compounds derived from this plant have shown antioxidant and anti-inflammatory properties-characteristics often associated with potential medicinal applications. This study aimed to determine which part of the P. japonicum plants cultivated on two Korean islands contains the most antioxidant compounds. This determination was made through assessments of total polyphenol content and total flavonoid content, coupled with evaluation of antioxidant activity via DPPH and ABTS assays. The results showed that the aerial parts contain a richer array of bioactive compounds and demonstrate superior antioxidant activity compared to their root counterparts in the plants from both islands. To characterize the phytochemicals underpinning this bioactivity, LC-MS/MS and HPLC analyses were carried out. These methods detected varying amounts of chlorogenic acid, peucedanol 7-O-glucoside, rutin, and peucedanol, with good separation and retention times. This study addresses the lack of research on the antioxidant activity of different parts of P. japonicum. The findings hold significance for traditional medicine, dietary supplements, and the development of functional foods. Understanding antioxidant distribution aids in the development of medicinal and nutritional applications, influences agricultural practices, and contributes to regional biodiversity-conservation efforts. The study's geographical scope provides insights into how location impacts the concentration of bioactive compounds in plants. Overall, the results contribute valuable data for future research in plant biology, biochemistry, and related fields.

Metabolomics Analysis and Diagnosis of Lung Cancer: Insights from Diverse Sample Types

Lung cancer is a highly fatal disease that poses a significant global health burden. The absence of characteristic clinical symptoms frequently results in the diagnosis of most patients at advanced stages of lung cancer. Although low-dose computed tomography (LDCT) screening has become increasingly prevalent in clinical practice, its high rate of false positives continues to present a significant challenge. In addition to LDCT screening, tumor biomarker detection represents a critical approach for early diagnosis of lung cancer; unfortunately, no tumor marker with optimal sensitivity and specificity is currently available. Metabolomics has recently emerged as a promising field for developing novel tumor biomarkers. In this paper, we introduce metabolic pathways, instrument platforms, and a wide variety of sample types for lung cancer metabolomics. Specifically, we explore the strengths, limitations, and distinguishing features of various sample types employed in lung cancer metabolomics research. Additionally, we present the latest advances in lung cancer metabolomics research that utilize diverse sample types. We summarize and enumerate research studies that have investigated lung cancer metabolomics using different metabolomic sample types. Finally, we provide a perspective on the future of metabolomics research in lung cancer. Our discussion of the potential of metabolomics in developing new tumor biomarkers may inspire further study and innovation in this dynamic field.

Modern venomics-Current insights, novel methods, and future perspectives in biological and applied animal venom research

Venoms have evolved >100 times in all major animal groups, and their components, known as toxins, have been fine-tuned over millions of years into highly effective biochemical weapons. There are many outstanding questions on the evolution of toxin arsenals, such as how venom genes originate, how venom contributes to the fitness of venomous species, and which modifications at the genomic, transcriptomic, and protein level drive their evolution. These questions have received particularly little attention outside of snakes, cone snails, spiders, and scorpions. Venom compounds have further become a source of inspiration for translational research using their diverse bioactivities for various applications. We highlight here recent advances and new strategies in modern venomics and discuss how recent technological innovations and multi-omic methods dramatically improve research on venomous animals. The study of genomes and their modifications through CRISPR and knockdown technologies will increase our understanding of how toxins evolve and which functions they have in the different ontogenetic stages during the development of venomous animals. Mass spectrometry imaging combined with spatial transcriptomics, in situ hybridization techniques, and modern computer tomography gives us further insights into the spatial distribution of toxins in the venom system and the function of the venom apparatus. All these evolutionary and biological insights contribute to more efficiently identify venom compounds, which can then be synthesized or produced in adapted expression systems to test their bioactivity. Finally, we critically discuss recent agrochemical, pharmaceutical, therapeutic, and diagnostic (so-called translational) aspects of venoms from which humans benefit.

Green Chemistry Preservation and Extraction of Biospecimens for Multi-omic Analyses

The Environmental Protection Agency's definition of "Green Chemistry" is "the design of chemical products and processes that reduces or eliminates the use or generation of hazardous substances. Green chemistry applies across the life cycle of a chemical product, including its design, manufacture, use, and ultimate disposal." Conventional omic tissue extraction procedures use solvents that are toxic and carcinogenic, such as chloroform and methyl-tert-butyl ether for lipidomics, or caustic chaotropic solutions for genomics and transcriptomics, such as guanidine or urea. A common preservation solution for pathology is formaldehyde, which is a carcinogen. Use of acetonitrile as a universal biospecimen preservation and extraction solvent will reduce these hazardous wastes, because it is less toxic and more environmentally friendly than the conventional solvents used in biorepository and biospecimen research. A new extraction method never applied to multi-omic, system biology research, called cold-induced phase separation (CIPS), uses freezing point temperatures to induce a phase separation of acetonitrile-water mixtures. Also, the CO₂ exposure during CIPS will acidify the water precipitating DNA out of aqueous phase. The resulting phase separation brings hydrophobic lipids to the top acetonitrile fraction that is easily decanted from the bottom aqueous fraction, especially when the water is frozen. This CIPS acetonitrile extract contains the lipidome (lipids), the bottom aqueous fraction is sampled to obtain the transcriptome (RNA) fraction, and the remaining water and pellet is extracted with 60% acetonitrile to isolate the metabolome (<1 kD polar molecules). Finally, steps 4 and 5 use a TRIzol™ liquid-liquid extraction SOP of the pellet to isolate the genome (DNA) and proteome (proteins). This chapter details the multi-omic sequential extraction SOP and potential problems associated with each of the 5 steps, with steps 2, 4, and 5 still requiring validation. The metabolomic and lipidomic extraction efficiencies using the CIPS SOP is compared to conventional solvent extraction SOPs and is analyzed by nuclear magnetic resonance (NMR) spectroscopy and liquid chromatography-mass spectrometry (LC-MS), respectively. Acetonitrile biospecimen preservation combined with the CIPS multi-omic extraction SOP is green chemistry technology that will eliminate the generation of the hazardous substances associated with biospecimen processing and permits separation and safe disposal of acetonitrile avoiding environmental contamination.

Mass Spectrometry-Based Technology and Workflows for Studying the Chemistry of Fungal Endophyte Derived Bioactive Compounds

Bioactive compounds have gained substantial attention in research and have conferred great advancements in the industrial and pharmacological fields. Highly diverse fungi and their metabolome serve as a big platform to be explored for their diverse bioactive compounds. Omics tools coupled with bioinformatics, statistical, and well-developed algorithm tools have elucidated immense knowledge about fungal endophyte derived bioactive compounds. Further, these compounds are subjected to chromatography-gas chromatography and liquid chromatography (LC), spectroscopy-nuclear magnetic resonance (NMR), and "soft ionization" technique-matrix-assisted laser desorption/ionization-time-of-flight (MALDI-TOF) based analytical techniques for structural characterization. The mass spectrometry (MS)-based approach, being highly sensitive, reproducible, and reliable, produces quick and high-profile identification. Coupling these techniques with MS has resulted in a descriptive account of the identification and quantification of fungal endophyte derived bioactive compounds. This paper emphasizes the workflows of the above-mentioned techniques, their advancement, and future directions to study the unraveled area of chemistry of fungal endophyte-derived bioactive compounds.

Innovation in drug toxicology: Application of mass spectrometry imaging technology

Mass spectrometry imaging (MSI) is a powerful molecular imaging technology that can obtain qualitative, quantitative, and location information by simultaneously detecting and mapping endogenous or exogenous molecules in biological tissue slices without specific chemical labeling or complex sample pretreatment. This article reviews the progress made in MSI and its application in drug toxicology research, including the tissue distribution of toxic drugs and their metabolites, the target organs (liver, kidney, lung, eye, and central nervous system) of toxic drugs, the discovery of toxicity-associated biomarkers, and explanations of the mechanisms of drug toxicity when MSI is combined with the cutting-edge omics methodologies. The unique advantages and broad prospects of this technology have been fully demonstrated to further promote its wider use in the field of pharmaceutical toxicology.

Recent progress in developing fluorescent probes for imaging cell metabolites

Cellular metabolites play a crucial role in promoting and regulating cellular activities, but it has been difficult to monitor these cellular metabolites in living cells and in real time. Over the past decades, iterative development and improvements of fluorescent probes have been made, resulting in the effective monitoring of metabolites. In this review, we highlight recent progress in the use of fluorescent probes for tracking some key metabolites, such as adenosine triphosphate, cyclic adenosine monophosphate, cyclic guanosine 5'-monophosphate, Nicotinamide adenine dinucleotide (NADH), reactive oxygen species, sugar, carbon monoxide, and nitric oxide for both whole cell and subcellular imaging.

Recent advances in high-throughput mass spectrometry that accelerates enzyme engineering for biofuel research

Enzymes play indispensable roles in producing biofuels, a sustainable and renewable source of transportation fuels. Lacking rational design rules, the development of industrially relevant enzyme catalysts relies heavily on high-throughput screening. However, few universal methods exist to rapidly characterize large-scale enzyme libraries. Therefore, assay development is necessary on an ad hoc basis to link enzyme properties to spectrophotometric signals and often requires the use of surrogate, optically active substrates. On the other hand, mass spectrometry (MS) performs label-free enzyme assays that utilize native substrates and is therefore generally applicable. But the analytical speed of MS is considered rate limiting, mainly due to the use of time-consuming chromatographic separation in traditional MS analysis. Thanks to new instrumentation and sample preparation methods, direct analyte introduction into a mass spectrometer without a prior chromatographic step can be achieved by laser, microfluidics, and acoustics, so that each sample can be analyzed within seconds. Here we review recent advances in MS platforms that improve the throughput of enzyme library screening and discuss how these advances can potentially facilitate biofuel research by providing high sensitivity, selectivity and quantitation that are difficult to obtain using traditional assays. We also highlight the limitations of current MS assays in studying biofuel-related enzymes and propose possible solutions.

Multimodal Imaging of Amyloid Plaques: Fusion of the Single-Probe Mass Spectrometry Image and Fluorescence Microscopy Image

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases. The formation of amyloid plaques by aggregated amyloid beta (Aβ) peptides is a primary event in AD pathology. Understanding the metabolomic features and related pathways is critical for studying plaque-related pathological events (e.g., cell death and neuron dysfunction). Mass spectrometry imaging (MSI), due to its high sensitivity and ability to obtain the spatial distribution of metabolites, has been applied to AD studies. However, limited studies of metabolites in amyloid plaques have been performed due to the drawbacks of the commonly used techniques such as matrix-assisted laser desorption/ionization MSI. In the current study, we obtained high spatial resolution (∼17 μm) MS images of the AD mouse brain using the Single-probe, a microscale sampling and ionization device, coupled to a mass spectrometer under ambient conditions. The adjacent slices were used to obtain fluorescence microscopy images to locate amyloid plaques. The MS image and the fluorescence microscopy image were fused to spatially correlate histological protein hallmarks with metabolomic features. The fused images produced significantly improved spatial resolution (∼5 μm), allowing for the determination of fine structures in MS images and metabolomic biomarkers representing amyloid plaques.

Spatio-Temporal Metabolite and Elemental Profiling of Salt Stressed Barley Seeds During Initial Stages of Germination by MALDI-MSI and µ-XRF Spectrometry

Seed germination is the essential first step in crop establishment, and can be severely affected by salinity stress which can inhibit essential metabolic processes during the germination process. Salt stress during seed germination can trigger lipid-dependent signalling cascades that activate plant adaptation processes, lead to changes in membrane fluidity to help resist the stress, and cause secondary metabolite responses due to increased oxidative stress. In germinating barley (Hordeum vulgare), knowledge of the changes in spatial distribution of lipids and other small molecules at a cellular level in response to salt stress is limited. In this study, mass spectrometry imaging (MSI), liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QToF-MS), inductively coupled plasma mass spectrometry (ICP-MS), and X-ray fluorescence (XRF) were used to determine the spatial distribution of metabolites, lipids and a range of elements, such as K+ and Na+, in seeds of two barley genotypes with contrasting germination phenology (Australian barley varieties Mundah and Keel). We detected and tentatively identified more than 200 lipid species belonging to seven major lipid classes (fatty acyls, glycerolipids, glycerophospholipids, sphingolipids, prenol lipids, sterol lipids, and polyketides) that differed in their spatial distribution based on genotype (Mundah or Keel), time post-imbibition (0 to 72 h), or treatment (control or salt). We found a tentative flavonoid was discriminant in post-imbibed Mundah embryos under saline conditions, and a delayed flavonoid response in Keel relative to Mundah. We further employed MSI-MS/MS and LC-QToF-MS/MS to explore the identity of the discriminant flavonoid and study the temporal pattern in five additional barley genotypes. ICP-MS was used to quantify the elemental composition of both Mundah and Keel seeds, showing a significant increase in Na+ in salt treated samples. Spatial mapping of elements using µ-XRF localized the elements within the seeds. This study integrates data obtained from three mass spectrometry platforms together with µ-XRF to yield information on the localization of lipids, metabolites and elements improving our understanding of the germination process under salt stress at a molecular level.

Anticancer Drug Affects Metabolomic Profiles in Multicellular Spheroids: Studies Using Mass Spectrometry Imaging Combined with Machine Learning

Multicellular spheroids (hereinafter referred to as spheroids) are 3D biological models. The metabolomic profiles inside spheroids provide crucial information reflecting the molecular phenotypes and microenvironment of cells. To study the influence of an anticancer drug on the spatially resolved metabolites, spheroids were cultured using HCT-116 colorectal cancer cells, treated with the anticancer drug Irinotecan under a series of time- and concentration-dependent conditions. The Single-probe mass spectrometry imaging (MSI) technique was utilized to conduct the experiments. The MSI data were analyzed using advanced data analysis methods to efficiently extract metabolomic information. Multivariate curve resolution alternating least square (MCR-ALS) was used to decompose each MS image into different components with grouped species. To improve the efficiency of data analysis, both supervised (Random Forest) and unsupervised (cluster large applications (CLARA)) machine learning (ML) methods were employed to cluster MS images according to their metabolomic features. Our results indicate that anticancer drug significantly affected the abundances of a variety of metabolites in different regions of spheroids. This integrated experiment and data analysis approach can facilitate the studies of metabolites in different types of 3D tumor models and tissues and potentially benefit the drug discovery, therapeutic resistance, and other biological research fields.

SECIMTools: a suite of metabolomics data analysis tools

Background

Metabolomics has the promise to transform the area of personalized medicine with the rapid development of high throughput technology for untargeted analysis of metabolites. Open access, easy to use, analytic tools that are broadly accessible to the biological community need to be developed. While technology used in metabolomics varies, most metabolomics studies have a set of features identified. Galaxy is an open access platform that enables scientists at all levels to interact with big data. Galaxy promotes reproducibility by saving histories and enabling the sharing workflows among scientists.

Results

SECIMTools (SouthEast Center for Integrated Metabolomics) is a set of Python applications that are available both as standalone tools and wrapped for use in Galaxy. The suite includes a comprehensive set of quality control metrics (retention time window evaluation and various peak evaluation tools), visualization techniques (hierarchical cluster heatmap, principal component analysis, modular modularity clustering), basic statistical analysis methods (partial least squares - discriminant analysis, analysis of variance, t-test, Kruskal-Wallis non-parametric test), advanced classification methods (random forest, support vector machines), and advanced variable selection tools (least absolute shrinkage and selection operator LASSO and Elastic Net).

Conclusions

SECIMTools leverages the Galaxy platform and enables integrated workflows for metabolomics data analysis made from building blocks designed for easy use and interpretability. Standard data formats and a set of utilities allow arbitrary linkages between tools to encourage novel workflow designs. The Galaxy framework enables future data integration for metabolomics studies with other omics data.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2134-1) contains supplementary material, which is available to authorized users.

Spectroscopic methods to analyze drug metabolites

Drug metabolites have been monitored with various types of newly developed techniques and/or combination of common analytical methods, which could provide a great deal of information on metabolite profiling. Because it is not easy to analyze whole drug metabolites qualitatively and quantitatively, a single solution of analytical techniques is combined in a multilateral manner to cover the widest range of drug metabolites. Mass-based spectroscopic analysis of drug metabolites has been expanded with the help of other parameter-based methods. The current development of metabolism studies through contemporary pharmaceutical research are reviewed with an overview on conventionally used spectroscopic methods. Several technical approaches for conducting drug metabolic profiling through spectroscopic methods are discussed in depth.

Serum and plasma amino acids as markers of prediabetes, insulin resistance, and incident diabetes

Presently, routine screening misses many cases of prediabetes and early type 2 diabetes (T2D). Therefore, better biomarkers are needed for a simple and early detection of abnormalities of glucose metabolism and prediction of future T2D. Possible candidates for this include plasma or serum amino acids because glucose and amino acid metabolism are closely connected. This review presents the available evidence of this connectivity and discusses its clinical implications. First, we examine the underlying physiological, pre-analytical, and analytical issues. Then, we summarize results of human studies that evaluate amino acid levels as markers for insulin resistance, prediabetes, and future incident T2D. Finally, we illustrate the interconnection of amino acid levels and metabolic syndrome with our own data from a deeply phenotyped human cohort. We also discuss how amino acids may contribute to the pathophysiology of T2D. We conclude that elevated branched-chain amino acids and reduced glycine are currently the most robust and consistent amino acid markers for prediabetes, insulin resistance, and future T2D. Yet, we are cautious regarding the clinical potential even of these parameters because their discriminatory power is insufficient and their levels depend not only on glycemia, but also on other components of the metabolic syndrome. The identification of more precise intermediates of amino acid metabolism or combinations with other biomarkers will, therefore, be necessary to obtain in order to develop laboratory tests that can improve T2D screening.

Imaging mass spectrometry for metabolites: technical progress, multimodal imaging, and biological interactions

Imaging mass spectrometry (IMS) allows the study of the spatial distribution of small molecules in biological samples. IMS is able to identify and quantify chemicals in situ from whole tissue sections to single cells. Both vacuum mass spectrometry (MS) and ambient MS systems have advanced considerably over the last decade; however, some limitations are still hard to surmount. Sample pretreatment, matrix or solvent choices, and instrument improvement are the key factors that determine the successful application of IMS to different samples and analytes. IMS with innovative MS analyzers, powerful MS spectrum databases, and analysis tools can efficiently dereplicate, identify, and quantify natural products. Moreover, multimodal imaging systems and multiple MS-based systems provide additional structural, chemical, and morphological information and are applied as complementary tools to explore new fields. IMS has been applied to reveal interactions between living organisms at molecular level. Recently, IMS has helped solve many previously unidentifiable relations between bacteria, fungi, plants, animals, and insects. Other significant interactions on the chemical level can also be resolved using expanding IMS techniques. WIREs Syst Biol Med 2017, 9:e1387. doi: 10.1002/wsbm.1387 For further resources related to this article, please visit the WIREs website.

Analysis of drugs in plasma samples from schizophrenic patients by column-switching liquid chromatography-tandem mass spectrometry with organic-inorganic hybrid cyanopropyl monolithic column

This study reports on the development of a rapid, selective, and sensitive column-switching liquid chromatography-tandem mass spectrometry (LC-MS/MS) method to analyze sixteen drugs (antidepressants, anticonvulsants, anxiolytics, and antipsychotics) in plasma samples from schizophrenic patients. The developed organic-inorganic hybrid monolithic column with cyanopropyl groups was used for the first dimension of the column-switching arrangement. This arrangement enabled online pre-concentration of the drugs (monolithic column) and their subsequent analytical separation on an XSelect SCH C18 column. The drugs were detected on a triple quadrupole tandem mass spectrometer (multiple reactions monitoring mode) with an electrospray ionization source in the positive ion mode. The developed method afforded adequate linearity for the sixteen target drugs; the coefficients of determination (R(2)) lay above 0.9932, the interassay precision had coefficients of variation lower than 6.5%, and the relative standard error values of the accuracy ranged from -14.0 to 11.8%. The lower limits of quantification in plasma samples ranged from 63 to 1250pgmL(-1). The developed method successfully analyzed the target drugs in plasma samples from schizophrenic patients for therapeutic drug monitoring (TDM).

Metabolic engineering to enhance the value of plants as green factories

The promise of plants to serve as the green factories of the future is ever increasing. Plants have been used traditionally for construction, energy, food and feed. Bioactive compounds primarily derived from specialized plant metabolism continue to serve as important scaffold molecules for pharmaceutical drug production. Yet, the past few years have witnessed a growing interest on plants as the ultimate harvesters of carbon and energy from the sun, providing carbohydrate and lipid biofuels that would contribute to balancing atmospheric carbon. How can the metabolic output from plants be increased even further, and what are the bottlenecks? Here, we present what we perceive to be the main opportunities and challenges associated with increasing the efficiency of plants as chemical factories. We offer some perspectives on when it makes sense to use plants as production systems because the amount of biomass needed makes any other system unfeasible. However, there are other instances in which plants serve as great sources of biological catalysts, yet are not necessarily the best-suited systems for production. We also present emerging opportunities for manipulating plant genomes to make plant synthetic biology a reality.

Studies of metabolite-protein interactions: a review

The study of metabolomics can provide valuable information about biochemical pathways and processes at the molecular level. There have been many reports that have examined the structure, identity and concentrations of metabolites in biological systems. However, the binding of metabolites with proteins is also of growing interest. This review examines past reports that have looked at the binding of various types of metabolites with proteins. An overview of the techniques that have been used to characterize and study metabolite-protein binding is first provided. This is followed by examples of studies that have investigated the binding of hormones, fatty acids, drugs or other xenobiotics, and their metabolites with transport proteins and receptors. These examples include reports that have considered the structure of the resulting solute-protein complexes, the nature of the binding sites, the strength of these interactions, the variations in these interactions with solute structure, and the kinetics of these reactions. The possible effects of metabolic diseases on these processes, including the impact of alterations in the structure and function of proteins, are also considered.

Characterization of an anthraquinone fluor from the bioluminescent, pelagic polychaete Tomopteris

Tomopteris is a cosmopolitan genus of polychaetes. Many species produce yellow luminescence in the parapodia when stimulated. Yellow bioluminescence is rare in the ocean, and the components of this luminescent reaction have not been identified. Only a brief description, half a century ago, noted fluorescence in the parapodia with a remarkably similar spectrum to the bioluminescence, which suggested that it may be the luciferin or terminal light-emitter. Here, we report the isolation of the fluorescent yellow–orange pigment found in the luminous exudate and in the body of the animals. Liquid chromatography-mass spectrometry revealed the mass to be 270 m/z with a molecular formula of C₁₅H₁₀O₅, which ultimately was shown to be aloe-emodin, an anthraquinone previously found in plants. We speculate that aloe-emodin could be a factor for resonant-energy transfer or the oxyluciferin for Tomopteris bioluminescence.

Application of metabolomics to diagnosis of insulin resistance

Metabolomics, the global interrogation of the biochemical components in a biological sample, has become an important complement to genomics and proteomics to aid in the understanding of pathophysiology. Major advantages of metabolomics are the size of the metabolome relative to the genome or proteome and the fact that it provides a view of the existing biochemical phenotype. As such, metabolomics is fast becoming an important discovery tool for new diagnostic and prognostic biomarkers. Although many methods exist for performing metabolomics, relatively few have led to successful development of new diagnostic tests. This review will aid the reader in understanding various metabolomic methods and their applications, as well as some of their inherent advantages and disadvantages. In addition, we present one example of the application of metabolomics to the identification of new fasting blood biomarkers for the diagnosis and monitoring of insulin resistance.

Application of metabolomics approaches to the study of respiratory diseases

Metabolomics is the global unbiased analysis of all the small-molecule metabolites within a biological system, under a given set of conditions. These methods offer the potential for a holistic approach to clinical medicine, as well as improving disease diagnosis and understanding of pathological mechanisms. Respiratory diseases including asthma and chronic obstructive pulmonary disorder are increasing globally, with the latter predicted to become the third leading cause of global mortality by 2020. The root causes for disease onset remain poorly understood and no cures are available. This review presents an overview of metabolomics followed by in-depth discussion of its application to the study of respiratory diseases, including the design of metabolomics experiments, choice of clinical material collected and potentially confounding experimental factors. Particular challenges in the field are presented and placed within the context of the future of the applications of metabolomics approaches to the study of respiratory diseases.

Microscope mode secondary ion mass spectrometry imaging with a Timepix detector

In-vacuum active pixel detectors enable high sensitivity, highly parallel time- and space-resolved detection of ions from complex surfaces. For the first time, a Timepix detector assembly was combined with a secondary ion mass spectrometer for microscope mode secondary ion mass spectrometry (SIMS) imaging. Time resolved images from various benchmark samples demonstrate the imaging capabilities of the detector system. The main advantages of the active pixel detector are the higher signal-to-noise ratio and parallel acquisition of arrival time and position. Microscope mode SIMS imaging of biomolecules is demonstrated from tissue sections with the Timepix detector.

Towards understanding region-specificity of triplet repeat diseases: coupled immunohistology and mass spectrometry imaging

Many trinucleotide repeat disorders exhibit region-specific toxicity within tissues, the basis of which cannot be explained by traditional methods. For example, in Huntington's Disease (HD), the toxic disease-causing protein is ubiquitously expressed. However, only the medium spiny neurons in the striatum are initially targeted for death. Many changes are likely to initiate in these cells at an intracellular and microstructural level long before there is a measureable phenotype, but why some regions of the brain are more susceptible to death is unknown. This chapter describes a method to detect functional changes among brain regions and cell types, and link them directly with region-specific physiology. Due to the neurodegeneration that accompanies many triplet repeat disorders, we focus on the brain, although the methods described in this chapter can be translated to other tissue types. We integrate immunohistology and traditional mass spectrometry with a novel mass spectrometry imaging technique, called nanostructure initiated mass spectrometry (NIMS). When used together, these tools offer unique insights into region-specific physiology of the brain, and a basis for understanding the region-specific toxicity associated with triplet repeat disorders.

Investigating metabolite-protein interactions: an overview of available techniques

Metabolites comprise the molar majority of chemical substances in living cells, and metabolite-protein interactions are expected to be quite common. Many interactions have already been identified and have been shown to be involved in the regulation of different types of cellular processes including signaling events, enzyme activities, protein localizations and interactions. Recent technological advances have greatly facilitated the detection of metabolite-protein interactions at high sensitivity and some of these have been applied on a large scale. In this manuscript, we review the available in vitro, in silico and in vivo technologies for mapping small-molecule-protein interactions. Although some of these were developed for drug-protein interactions they can be applied for mapping metabolite-protein interactions. Information gained from the use of these approaches can be applied to the manipulation of cellular processes and therapeutic applications.

Resolving brain regions using nanostructure initiator mass spectrometry imaging of phospholipids

In a variety of neurological diseases, pathological progression is cell type and region specific. Previous reports suggest that mass spectrometry imaging has the potential to differentiate between brain regions enriched in specific cell types. Here, we utilized a matrix-free surface mass spectrometry approach, nanostructure initiator mass spectrometry (NIMS), to show that spatial distributions of multiple lipids can be used as a 'fingerprint' to discriminate between neuronal- and glial- enriched brain regions. In addition, glial cells from different brain regions can be distinguished based on unique lipid profiles. NIMS images were generated from sagittal brain sections and were matched with immunostained serial sections to define glial cell enriched areas. Tandem mass spectrometry (LC-MS/MS QTOF) on whole brain extracts was used to identify 18 phospholipids. Multivariate statistical analysis (Nonnegative Matrix Factorization) enhanced differentiation of brain regions and cell populations compared to single ion imaging methods. This analysis resolved brain regions that are difficult to distinguish using conventional stains but are known to have distinct physiological functions. This method accurately distinguished the frontal (or somatomotor) and dorsal (or retrosplenial) regions of the cortex from each other and from the pons region.

Emerging technologies in mass spectrometry imaging

Mass spectrometry imaging (MSI) as an analytical tool for bio-molecular and bio-medical research targets accurate compound localization and identification. In terms of dedicated instrumentation, this translates into the demand for more detail in the image dimension (spatial resolution) and in the spectral dimension (mass resolution and accuracy), preferably combined in one instrument. At the same time, large area biological tissue samples require fast acquisition schemes, instrument automation and a robust data infrastructure. This review discusses the analytical capabilities of an "ideal" MSI instrument for bio-molecular and bio-medical molecular imaging. The analytical attributes of such an ideal system are contrasted with technological and methodological challenges in MSI. In particular, innovative instrumentation for high spatial resolution imaging in combination with high sample throughput is discussed. Detector technology that targets various shortcomings of conventional imaging detector systems is highlighted. The benefits of accurate mass analysis, high mass resolving power, additional separation strategies and multimodal three-dimensional data reconstruction algorithms are discussed to provide the reader with an insight in the current technological advances and the potential of MSI for bio-medical research.

Oocyte quality reflected by follicular fluid analysis in poly cystic ovary syndrome (PCOS): a hypothesis based on intermediates of energy metabolism

There is no clear acceptance of specific follicular fluid biomarker and its correlation with oocyte quality or related embryo variable till now. Most of the studies analyze correlation between certain biomolecules and the oocyte quality using single variable, instead of multivariate analysis algorithms. Our hypothesis is not based on single biomarker discovery, but attempts to explain oocyte quality in terms of energy metabolic pathways by considering its various intermediates. Reduced availability of glucose in the oocytes and follicular cells caused by defective transportation of glucose is expected in polycystic ovary syndrome (PCOS). This initiates alternative pathways to utilize fatty acid, amino acids etc. for energy as a compensatory mechanism to deal with the energy requirement. These compensations can be reflected by altered levels of various biomolecules in follicular fluid (e.g. ketone bodies, lipids, amino acids, lactate, pyruvate etc.). The amount of compensation, in order to meet the energy requirement can be directly correlated to quality of oocytes and better outcome after in vitro fertilization (IVF) in PCOS cases. This can be predicted with fair accuracy by doing a multivariate analysis of altered levels of various biomolecules in follicular fluid. Various supervised and unsupervised classification techniques based on spectroscopic data, obtained from follicular fluid samples may certainly prove to be an important tool to predict oocytes quality and IVF outcome with better accuracy in women with PCOS.

Tissue analysis by imaging MS

Imaging MS (IMS) is generating tremendous interest in scientific communities because of its unparalleled capabilities to provide chemical analysis of intact tissue. Advances in analytical chemistry and MS are providing new insights into chemical and biological processes. This review will discuss various IMS platforms and their applications in biomedical and pharmaceutical research.

Targeted mass spectrometry-based metabolomic profiling through multiple reaction monitoring of liver and other biological matrices

In a systemic viewpoint, relevant biological information on living systems can be grasped from the study of small, albeit pivotal molecules which constitute the fundamental bricks of metabolic pathways. This holds true for liver which plays, among its unique functions, a key role in metabolism. The nonbiased analysis of all this small-molecule complement in its entirety is known as metabolomics. However, no practical approach currently exists to investigate all metabolic species simultaneously without including a technical bias towards acidic or basic compounds, especially when performing mass spectrometry-based investigations. Technical aspects of rapid resolution reversed phase HPLC online with mass spectrometry are hereby described. Such an approach allows to discriminate and quantify a wide array of metabolites with extreme specificity and sensitivity, thus enabling to perform complex investigations even on extremely low quantities of biological material. The advantages also include the possibility to perform targeted investigations on a single (or a handful of) metabolite(s) simoultaneously through single (multiple) reaction monitoring, which further improves the dynamic range of concentrations to be monitored.Such an approach has already proven to represent a valid tool in the direct (on the liver) or indirect (on human red blood cell metabolism which is hereby presented as a representative model, but also on blood plasma or other biological fluids) assessment of metabolic poise modulation and pharmacokinetics for drug development.