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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.

Mass spectrometry (MS)-based metabolomics approaches have been used for characterizing the metabolite content and composition of biological samples in drug discovery and development, as well as in metabolic engineering, and food and plant sciences applications. Here, we describe a protocol routinely used in our laboratory to conduct a metabolic profiling of small polar metabolites from biological samples. Metabolites can be extracted from each sample using a methanol-based single-phase extraction procedure. The combination of LC-based hydrophilic interaction liquid chromatography (HILIC) and a hybrid quadrupole–time of flight (Q-ToF) mass spectrometer allows the comprehensive analysis of small polar metabolites including sugars, phosphorylated compounds, purines and pyrimidines, nucleotides, nucleosides, acylcarnitines, carboxylic acids, hydrophilic vitamins and amino acids. Retention times and accurate masses of metabolites involved in key metabolic pathways are annotated for routine high-throughput screening in both untargeted and targeted metabolomics analyses.

Ion mobility (IM) spectrometry can separate gas-phase ions according to their charge, molecular shape, and size. In recent years, several IM technologies have been integrated with mass spectrometry (MS) and launched as commercially available instrumentation for metabolomics analysis. The addition of IM to MS-based metabolomics workflows provides an additional degree of separation to chromatography and MS resolving power, improving peak capacity and signal-to-noise ratio. Moreover, it makes possible to experimentally derive collision cross section (CCS), which can be used as an additional coordinate for metabolite identification, together with accurate mass and fragmentation information. The addition of CCS to current metabolome database would allow to filter and score molecules based on their CCS values, adding more confidence in the identification process during metabolomics experiments. In this chapter, we present procedures for the integration of travelling-wave (TW)-IM into traditional MS-based metabolomics workflows.

Mass spectrometry (MS)-based lipidomics and metabolomics approaches have been used to discover new diagnostic and therapeutic targets of neurodegenerative disorders. Here, we describe a protocol to conduct an integrated metabolomics and lipidomics profiling of postmortem brains of frozen tissue samples from clinically characterized patients and age-matched controls. Metabolites and lipids can be extracted from each brain tissue sample, using a biphasic liquid/liquid extraction method. An unbiased liquid chromatography MS-based lipidomics and metabolomics workflows allows to screen for the content and composition of lipids and polar metabolites for each brain tissue. Data processing and statistical analysis are then used to compare the molecular content of all the samples, grouping them into cluster based on molecular similarities. The final results highlight classes of metabolites and biochemical pathways that are altered in brain samples from diseased brains compared to those from healthy subjects, helping to generate novel hypotheses on their mechanistic and functional significance.

Ion mobility (IM) is a post ionization tool that can be used to separate lipid ions in the gas phase according to their charge, shape and size. IM can be combined with mass spectrometry (MS), adding three main benefits to traditional approaches for lipid analysis. First, IM allows determination of the collision cross section of a lipid molecule, a physicochemical property related to the conformational structure, which facilitates lipid identification. Second, IM improves the peak capacity and the signal-to-noise ratio, allowing the separation of interfering isobaric lipid species. Third, IM allows a new set of hybrid fragmentation experiments that combine collision-induced dissociation with gas phase separation, improving the specificity of the tandem MS-based approaches. Here, we review the current applications of IM-MS for lipid analysis.

Twenty-eight sponge specimens were collected at a shallow water hydrothermal vent site north of Iceland. Extracts were prepared and tested in vitro for cytotoxic activity, and eight of them were shown to be cytotoxic. A mass spectrometry (MS)-based metabolomics approach was used to determine the chemical composition of the extracts. This analysis highlighted clear differences in the metabolomes of three sponge specimens, and all of them were identified as Haliclona (Rhizoniera) rosea (Bowerbank, 1866). Therefore, these specimens were selected for further investigation. Haliclona rosea metabolomes contained a class of potential key compounds, the 3-alkyl pyridine alkaloids (3-APA) responsible for the cytotoxic activity of the fractions. Several 3-APA compounds were tentatively identified including haliclamines, cyclostellettamines, viscosalines and viscosamines. Among these compounds, cyclostellettamine P was tentatively identified for the first time by using ion mobility MS in time-aligned parallel (TAP) fragmentation mode. In this work, we show the potential of applying metabolomics strategies and in particular the utility of coupling ion mobility with MS for the molecular characterization of sponge specimens.

Among lipidomics’ major challenges, there is the molecular complexity of the lipidome. State-of-the-art technology, such as ion mobility (IM) spectrometry, is a promising new tool for supporting lipidomics research. IM is a gas-phase electrophoretic technique that enables the separation of ions in the gas phase according to their charge, shape, and size. IM separation, which occurs on a time scale of milliseconds, is compatible with modern mass spectrometers with microsecond scan speeds. Thus, IM-mass spectrometry (MS) can be integrated into conventional lipidomics MS workflows to improve separation and enhance peak capacity, spectral clarity, and fragmentation specificity. Furthermore, IM allows for the determination of the collision cross section (CCS), an orthogonal physicochemical measure that can be used, together with accurate mass and fragmentation information, to increase the confidence of lipid identification. In recent years, the expanding sophistication of hardware and software products has enabled IM-MS to perform an increasingly important role in traditional lipidomic approaches. In this chapter, we present IM-MS procedures for lipidomics research.

Human exposure to elements is a process difficult to control and monitor. Studies on this topic usually rely on single spot urine sample to assess exposure, with the risk of ignoring variability over a longer period. In this work, we measured the urinary excretion of toxic and essential elements during 1 year with the overall goal of exploring the variability caused by seasonality on their concentration. Seven participants were recruited, and first morning urines were collected three times for each season, during November, January, April and July. Participants followed the same balanced diet during the week of collection. We then monitored nineteen essential and toxic elements in urines by inductively coupled plasma mass spectrometry. Unsupervised multivariate statistical analysis separated samples collected during summer from the ones collected during other seasons. Twelve elements had a significant seasonal variation (ANOVA test, p<0.05) and their levels resulted increased during summer. These elements were both contaminants, such as Ni, Hg, Cd and Tl, and essential elements such as Se and Cu. However, none of these elements was detected at toxic concentration. In this study, we point out for the first time the variability of urine element concentration due to seasonality and we propose that the season of collection should be considered when providing urinary reference values of elements.

Alzheimer's disease (AD) is the most common cause of adult dementia. Yet the complete set of molecular changes accompanying this inexorable, neurodegenerative disease remains elusive. Here we adopted an unbiased lipidomics and metabolomics approach to surveying frozen frontal cortex samples from clinically characterized AD patients (n = 21) and age-matched controls (n = 19), revealing marked molecular differences between them. Then, by means of metabolomic pathway analysis, we incorporated the novel molecular information into the known biochemical pathways and compared it with the results of a metabolomics meta-analysis of previously published AD research. We found six metabolic pathways of the central metabolism as well as glycerophospholipid metabolism predominantly altered in AD brains. Using targeted metabolomics approaches and MS imaging, we confirmed a marked dysregulation of mitochondrial aspartate metabolism. The altered metabolic pathways were further integrated with clinical data, showing various degrees of correlation with parameters of dementia and AD pathology. Our study highlights specific, altered biochemical pathways in the brains of individuals with AD compared with those of control subjects, emphasizing dysregulation of mitochondrial aspartate metabolism and supporting future venues of investigation.

Despite recent advances in analytical and computational chemistry, lipid identification remains a significant challenge in lipidomics. Ion-mobility spectrometry provides an accurate measure of the molecules' rotationally averaged collision cross-section (CCS) in the gas phase and is thus related to ionic shape. Here, we investigate the use of CCS as a highly specific molecular descriptor for identifying lipids in biological samples. Using traveling wave ion mobility mass spectrometry (MS), we measured the CCS values of over 200 lipids within multiple chemical classes. CCS values derived from ion mobility were not affected by instrument settings or chromatographic conditions, and they were highly reproducible on instruments located in independent laboratories (interlaboratory RSD < 3% for 98% of molecules). CCS values were used as additional molecular descriptors to identify brain lipids using a variety of traditional lipidomic approaches. The addition of CCS improved the reproducibility of analysis in a liquid chromatography-MS workflow and maximized the separation of isobaric species and the signal-to-noise ratio in direct-MS analyses (e.g., "shotgun" lipidomics and MS imaging). These results indicate that adding CCS to databases and lipidomics workflows increases the specificity and selectivity of analysis, thus improving the confidence in lipid identification compared to traditional analytical approaches. The CCS/accurate-mass database described here is made publicly available.

Here, we propose a novel strategy that combines a typical ultra high performance liquid chromatography (UHPLC), data-independent mass spectrometry (MSE) workflow with traveling wave ion mobility (TWIM) and UV detection, to improve the characterization of carotenoids and chlorophylls in complex biological matrices. UV detection selectively highlighted pigments absorbing at specific wavelengths, while TWIM coupled to MS was used to maximize the peak capacity. We applied this approach for the analysis of pigments in different microalgae samples, including Chlorella vulgaris, Dunaliella salina, and Phaeodactylum tricornutum. Using UHPLC-UV-MSE information (retention time, absorbance at 450 nm, and accurate masses of precursors and product ions), we tentatively identified 26 different pigments (carotenes, chlorophylls, and xanthophylls). By adding TWIM information (collision cross sections), we further resolved 5 isobaric pigments, not resolved by UHPLC-UV-MSE alone. The characterization of the molecular phenotypes allowed us to differentiate the microalgae species. Our results demonstrate that a combination of TWIM and UV detection with traditional analytical approaches increases the selectivity and specificity of analysis, providing a new tool to characterize pigments in biological samples. We anticipate that such an analytical approach will be extended to other lipidomics and metabolomics applications.

Ion mobility spectrometry is a gas-phase electrophoretic technique that separates ions according to their charge, shape and size [1]. The separation occurs within milliseconds, making it compatible with LC and MS. Ion mobility spectrometry can be integrated into current workflows for metabolomics, improving peak capacity and separation of the metabolome, when compared with conventional analyses without IMS. Ion mobility-derived information provides an orthogonal physicochemical parameter, the collision cross-section, which is related to the shape of the metabolite. When used in addition to retention time and accurate mass, collision cross-section values can enhance the specificity of metabolite identification. In this chapter, we review the use of ion mobility MS for metabolomics applications.

The high chemical complexity of the lipidome is one of the major challenges in lipidomics research. Ion-mobility spectrometry (IMS), a gas-phase electrophoretic technique, makes possible the separation of ions in the gas phase according to their charge, shape, and size. IMS can be combined with mass spectrometry (MS), adding three major benefits to traditional lipidomic approaches. First, IMS-MS allows the determination of the collision cross section (CCS), a physicochemical measure related to the conformational structure of lipid ions. The CCS is used to improve the confidence of lipid identification. Second, IMS-MS provides a new set of hybrid fragmentation experiments. These experiments, which combine collision-induced dissociation with ion-mobility separation, improve the specificity of MS/MS-based approaches. Third, IMS-MS improves the peak capacity and signal-to-noise ratio of traditional analytical approaches. In doing so, it allows the separation of complex lipid extracts from interfering isobaric species. Developing in parallel with advances in instrumentation, informatics solutions enable analysts to process and exploit IMS-MS data for qualitative and quantitative applications. Here we review the current approaches for lipidomics research based on IMS-MS, including liquid chromatography-MS and direct-MS analyses of "shotgun" lipidomics and MS imaging.

Metabolomics is a rapidly evolving analytical approach in life and health sciences. The structural elucidation of the metabolites of interest remains a major analytical challenge in the metabolomics workflow. Here, we investigate the use of ion mobility as a tool to aid metabolite identification. Ion mobility allows for the measurement of the rotationally averaged collision cross-section (CCS), which gives information about the ionic shape of a molecule in the gas phase. We measured the CCSs of 125 common metabolites using traveling-wave ion mobility-mass spectrometry (TW-IM-MS). CCS measurements were highly reproducible on instruments located in three independent laboratories (RSD < 5% for 99%). We also determined the reproducibility of CCS measurements in various biological matrixes including urine, plasma, platelets, and red blood cells using ultra performance liquid chromatography (UPLC) coupled with TW-IM-MS. The mean RSD was < 2% for 97% of the CCS values, compared to 80% of retention times. Finally, as proof of concept, we used UPLC-TW-IM-MS to compare the cellular metabolome of epithelial and mesenchymal cells, an in vitro model used to study cancer development. Experimentally determined and computationally derived CCS values were used as orthogonal analytical parameters in combination with retention time and accurate mass information to confirm the identity of key metabolites potentially involved in cancer. Thus, our results indicate that adding CCS data to searchable databases and to routine metabolomics workflows will increase the identification confidence compared to traditional analytical approaches. © 2014 American Chemical Society

Item does not contain fulltext, BACKGROUND: Apolipoprotein E (ApoE) is known for its role in lipid trafficking and the varepsilon4 allele is a risk factor for late onset Alzheimer's disease (AD). Recently, aberrant ceramide and fatty acid (FA) levels have been implicated in AD. OBJECTIVE: To determine the specific effects of human ApoE4 (hE4) on cerebral ceramide and FA content during chow or a high fat/high cholesterol (HFHC) diet. METHODS: Cerebral ceramide and FA profiles were determined by LC-MSMS in 15-month-old female wild-type (WT), ApoE-knockout (E0), and hE4-knockin mice fed chow or a HFHC diet for 3 months. mRNA levels of genes involved in ceramide and FA metabolism were determined by qPCR. RESULTS: Similar to E0, hE4 mice displayed lower cerebral total ceramide, Cer16 : 0, and Cer24 : 1 levels than WT mice on both diets. Akin to WT mice, hE4 mice had lower total and saturated FA levels on chow than E0 mice. The HFHC diet significantly increased total and saturated FA levels in hE4 mice. Chow-fed hE4 mice showed lower mRNA levels of ceramide synthase (CerS) 6, acid sphingomyelinase, and of most ceramide and FA transporters than WT and E0 mice. The HFHC diet downregulated the expression of CerSs in hE4 and WT mice, and of ceramide and FA transporters in WT mice, but not in E0 mice. CONCLUSION: hE4 reduced cerebral ceramide levels to levels observed in E0 mice independent of diet. The HFHC diet increased cerebral FA levels in hE4 mice. This was associated with alterations in the expression of ceramide and FA transporters specifically in hE4 mice.

Metabolomics and lipidomics aim to profile the wide range of metabolites and lipids that are present in biological samples. recently, ion mobility spectrometry (IMs) has been used to support metabolomics and lipidomics applications to facilitate the separation and the identification of complex mixtures of analytes. IMs is a gas-phase electrophoretic technique that enables the separation of ions in the gas phase according to their charge, shape and size. occurring within milliseconds, IMs separation is compatible with modern mass spectrometry (Ms) operating with microsecond scan speeds. thus, the time required for acquiring IMs data does not affect the overall run time of traditional liquid chromatography (lc)-Ms-based metabolomics and lipidomics experiments. the addition of IMs to conventional lc-Ms-based metabolomics and lipidomics workflows has been shown to enhance peak capacity, spectral clarity and fragmentation specificity. Moreover, by enabling determination of a collision cross-section (ccs) value-a parameter related to the shape of ions-IMs can improve the accuracy of metabolite identification. In this protocol, we describe how to integrate traveling-wave ion mobility spectrometry (tWIMs) into traditional lc-Ms-based metabolomic and lipidomic workflows. In particular, we describe procedures for the following: tuning and calibrating a sYnapt High-Definition Ms (HDMs) system (Waters) specifically for metabolomics and lipidomics applications; extracting polar metabolites and lipids from brain samples; setting up appropriate chromatographic conditions; acquiring simultaneously m/z, retention time and ccs values for each analyte; processing and analyzing data using dedicated software solutions, such as progenesis QI (nonlinear Dynamics); and, finally, performing metabolite and lipid identification using ccs databases and tWIMs-derived fragmentation information.