High Speed Data Processing for Imaging MS-Based Molecular Histology Using Graphical Processing Units

Imaging MS enables the distributions of hundreds of biomolecular ions to be determined directly from tissue samples. The application of multivariate methods, to identify pixels possessing correlated MS profiles, is referred to as molecular histology as tissues can be annotated on the basis of the MS profiles. The application of imaging MS-based molecular histology to larger tissue series, for clinical applications, requires significantly increased computational capacity in order to efficiently analyze the very large, highly dimensional datasets. Such datasets are highly suited to processing using graphical processor units, a very cost-effective solution for high speed processing. Here we demonstrate up to 13× speed improvements for imaging MS-based molecular histology using off-the-shelf components, and demonstrate equivalence with CPU based calculations. It is then discussed how imaging MS investigations may be designed to fully exploit the high speed of graphical processor units.     

Targeted analysis of glycomics liquid chromatography/mass spectrometry data

Hydrophilic interaction chromatography (HILIC) liquid chromatography/mass spectrometry (LC/MS) is appropriate for all native and reductively aminated glycan classes. HILIC carries the advantage that retention times vary predictably according to oligosaccharide composition. Chromatographic conditions are compatible with sensitive and reproducible glycomics analysis of large numbers of samples. The data are extremely useful for quantitative profiling of glycans expressed in biological tissues. With these analytical developments, the rate-limiting factor for widespread use of HILIC LC/MS in glycomics is the analysis of the data. In order to eliminate this problem, a Java-based open source software tool, Manatee, was developed for targeted analysis of HILIC LC/MS glycan datasets. This tool uses user-defined lists of compositions that specify the glycan chemical space in a given biological context. The program accepts high-resolution LC/MS data using the public mzXML format and is capable of processing a large data file in a few minutes on a standard desktop computer. The program allows mining of HILIC LC/MS data with an output compatible with multivariate statistical analysis. It is envisaged that the Manatee tool will complement more computationally intensive LC/MS processing tools based on deconvolution and deisotoping of LC/MS data. The capabilities of the tool were demonstrated using a set of HILIC LC/MS data on organ-specific heparan sulfates.     

A concise review of mass spectrometry imaging

Mass spectrometric imaging allows the investigation of the spatial distribution of molecules at complex surfaces. The combination of molecular speciation with local analysis renders a chemical microscope that can be used for the direct biomolecular characterization of histological tissue surfaces. MS based imaging advantageously allows label-free detection and mapping of a wide-range of biological compounds whose presence or absence can be the direct result of disease pathology. Successful detection of the analytes of interest at the desired spatial resolution requires careful attention to several steps in the mass spectrometry imaging protocol. This review will describe and discuss a selected number of crucial developments in ionization, instrumentation, and application of this innovative technology. The focus of this review is on the latest developments in imaging MS. Selected biological applications are employed to illustrate some of the novel features discussed. Two commonly used MS imaging techniques, secondary ion mass spectrometric (SIMS) imaging and matrix-assisted laser desorption ionization (MALDI) mass spectrometric imaging, center this review. New instrumental developments are discussed that extend spatial resolution, mass resolving power, mass accuracy, tandem-MS capabilities, and offer new gas-phase separation capabilities for both imaging techniques. It will be shown how the success of MS imaging is crucially dependent on sample preparation protocols as they dictate the nature and mass range of detected biomolecules that can be imaged. Finally, developments in data analysis strategies for large imaging datasets will be briefly discussed.     

Extension of least squares spectral resolution algorithm to high-resolution lipidomics data

Lipidomics, which focuses on the global study of molecular lipids in biological systems, has been driven tremendously by technical advances in mass spectrometry (MS) instrumentation, particularly high-resolution MS. This requires powerful computational tools that handle the high-throughput lipidomics data analysis. To address this issue, a novel computational tool has been developed for the analysis of high-resolution MS data, including the data pretreatment, visualization, automated identification, deconvolution and quantification of lipid species. The algorithm features the customized generation of a lipid compound library and mass spectral library, which covers the major lipid classes such as glycerolipids, glycerophospholipids and sphingolipids. Next, the algorithm performs least squares resolution of spectra and chromatograms based on the theoretical isotope distribution of molecular ions, which enables automated identification and quantification of molecular lipid species. Currently, this methodology supports analysis of both high and low resolution MS as well as liquid chromatography-MS (LC-MS) lipidomics data. The flexibility of the methodology allows it to be expanded to support more lipid classes and more data interpretation functions, making it a promising tool in lipidomic data analysis.     

Analysis of colorectal adenocarcinoma tissue by desorption electrospray ionization mass spectrometric imaging

Negative ion desorption electrospray ionization (DESI) was used for the analysis of an ex vivo tissue sample set comprising primary colorectal adenocarcinoma samples and colorectal adenocarcinoma liver metastasis samples. Frozen sections (12 μm thick) were analyzed by means of DESI imaging mass spectrometry (IMS) with spatial resolution of 100 μm using a computer-controlled DESI imaging stage mounted on a high resolution Orbitrap mass spectrometer. DESI-IMS data were found to predominantly feature complex lipids, including phosphatidyl-inositols, phophatidyl-ethanolamines, phosphatidyl-serines, phosphatidyl-ethanolamine plasmalogens, phosphatidic acids, phosphatidyl-glycerols, ceramides, sphingolipids, and sulfatides among others. Molecular constituents were identified based on their exact mass and MS/MS fragmentation spectra. An identified set of molecules was found to be in good agreement with previously reported DESI imaging data. Different histological tissue types were found to yield characteristic mass spectrometric data in each individual section. Histological features were identified by comparison to hematoxylin-eosin stained neighboring sections. Ions specific to certain histological tissue types (connective tissue, smooth muscle, healthy mucosa, healthy liver parenchyma, and adenocarcinoma) were identified by semi-automated screening of data. While each section featured a number of tissue-specific species, no potential global biomarker was found in the full sample set for any of the tissue types. As an alternative approach, data were analyzed by principal component analysis (PCA) and linear discriminant analysis (LDA) which resulted in efficient separation of data points based on their histological types. A pixel-by-pixel tissue identification method was developed, featuring the PCA/LDA analysis of authentic data set, and localization of unknowns in the resulting 60D, histologically assigned LDA space. Novel approach was found to yield results which are in 95% agreement with the results of classical histology. KRAS mutation status was determined for each sample by standard molecular biology methods and a similar PCA/LDA approach was developed to assess the feasibility of the determination of this important parameter using solely DESI imaging data. Results showed that the mutant and wild-type samples fully separated. DESI-MS and molecular biology results were in agreement in 90% of the cases.     

Extraction, interpretation and validation of information for comparing samples in metabolic LC/MS data sets

LC/MS is an analytical technique that, due to its high sensitivity, has become increasingly popular for the generation of metabolic signatures in biological samples and for the building of metabolic data bases. However, to be able to create robust and interpretable (transparent) multivariate models for the comparison of many samples, the data must fulfil certain specific criteria: (i) that each sample is characterized by the same number of variables, (ii) that each of these variables is represented across all observations, and (iii) that a variable in one sample has the same biological meaning or represents the same metabolite in all other samples. In addition, the obtained models must have the ability to make predictions of, e.g. related and independent samples characterized accordingly to the model samples. This method involves the construction of a representative data set, including automatic peak detection, alignment, setting of retention time windows, summing in the chromatographic dimension and data compression by means of alternating regression, where the relevant metabolic variation is retained for further modelling using multivariate analysis. This approach has the advantage of allowing the comparison of large numbers of samples based on their LC/MS metabolic profiles, but also of creating a means for the interpretation of the investigated biological system. This includes finding relevant systematic patterns among samples, identifying influential variables, verifying the findings in the raw data, and finally using the models for predictions. The presented strategy was here applied to a population study using urine samples from two cohorts, Shanxi (People's Republic of China) and Honolulu (USA). The results showed that the evaluation of the extracted information data using partial least square discriminant analysis (PLS-DA) provided a robust, predictive and transparent model for the metabolic differences between the two populations. The presented findings suggest that this is a general approach for data handling, analysis, and evaluation of large metabolic LC/MS data sets.     

Fluorocarbon-based injectable gaseous microbubbles for diagnosis and therapy

Fluorocarbon gases have been key to the recent development of several commercial injectable microbubble products that serve as contrast agents for ultrasound imaging. Microbubble-specific imaging is obtained by using harmonic and pulse inversion techniques. Controlled bubble destruction and monitoring of their re-entry into tissues provide unique tools for blood flow and tissue perfusion studies. Contrast echosonography allows assessment of structural and functional cardiovascular abnormalities and solid organ lesions, including tumors. New microbubble agents that target specific tissues, allowing molecular imaging of thrombi, atherosclerotic plaques, inflammation area and angiogenesis related to tumor growth, are being investigated. Microbubbles also have potential as therapeutic tools, and as targeted and ultrasound-triggered drug and gene delivery systems.     

Mobility-Modulated Sequential Dissociation Analysis Enables Structural Lipidomics in Mass Spectrometry Imaging

Spatial lipidomics based on mass spectrometry imaging (MSI) is a powerful tool for fundamental biology studies and biomarker discovery. But the structure-resolving capability of MSI is limited because of the lack of multiplexed tandem mass spectrometry (MS/MS) method, primarily due to the small sample amount available from each pixel and the poor ion usage in MS/MS analysis. Here, we report a mobility-modulated sequential dissociation (MMSD) strategy for multiplex MS/MS imaging of distinct lipids from biological tissues. With ion mobility-enabled data-independent acquisition and automated spectrum deconvolution, MS/MS spectra of a large number of lipid species from each tissue pixel are acquired, at no expense of imaging speed. MMSD imaging is highlighted by MS/MS imaging of 24 structurally distinct lipids in the mouse brain and the revealing of the correlation of a structurally distinct phosphatidylethanolamine isomer (PE 18 : 1_18 : 1) from a human hepatocellular carcinoma (HCC) tissue. Mapping of structurally distinct lipid isomers is now enabled and spatial lipidomics becomes feasible for MSI.     

Natural products dereplication by diffusion ordered NMR spectroscopy (DOSY)

Diffusion-ordered NMR spectroscopy (DOSY) can be used to analyze mixtures of compounds since resonances deriving from different compounds are distinguished by their diffusion coefficients (D). Previously, DOSY has mostly been used for organometallic and polymer analysis, we have now applied DOSY to investigate diffusion coefficients of structurally diverse organic compounds such as natural products (NP). The experimental Ds derived from 55 diverse NPs has allowed us to establish a power law relationship between D and molecular weight (MW) and therefore predict MW from experimental D. We have shown that D is also affected by factors such as hydrogen bonding, molar density and molecular shape of the compound and we have generated new models that incorporate experimentally derived variables for these factors so that more accurate predictions of MW can be calculated from experimental D. The recognition that multiple physicochemical properties affect D has allowed us to generate a polynomial equation based on multiple linear regression analysis of eight calculated physicochemical properties from 63 compounds to accurately correlate predicted D with experimental D for any known organic compound. This equation has been used to calculate predicted D for 217 043 compounds present in a publicly available natural product database (DEREP-NP) and to dereplicate known NPs in a mixture based on matching of experimental D and structural features derived from NMR analysis with predicted D and calculated structural features in the database. These models have been validated by the dereplication of a mixture of two known sesquiterpenes obtained from Tasmannia xerophila and the identification of new alkaloids from the bryozoan Amathia lamourouxi. These new methodologies allow the MW of compounds in mixtures to be predicted without the need for MS analysis, the dereplication of known compounds and identification of new compounds based solely on parameters derived by DOSY NMR.

We report accurate DOSY NMR based molecular weight and diffusion coefficient prediction tools. These tools can be used to dereplicate known natural products from databases using structurally rich NMR data as a surrogate for mass spectrometric data.     

Is nontarget screening of emerging contaminants by LC-HRMS successful? A plea for compound libraries and computer tools

This review focuses on the possibilities and limits of nontarget screening of emerging contaminants, with emphasis on recent applications and developments in data evaluation and compound identification by liquid chromatography-high-resolution mass spectrometry (HRMS). The general workflow includes determination of the elemental composition from accurate mass, a further search for the molecular formula in compound libraries or general chemical databases, and a ranking of the proposed structures using further information, e.g., from mass spectrometry (MS) fragmentation and retention times. The success of nontarget screening is in some way limited to the preselection of relevant compounds from a large data set. Recently developed approaches show that statistical analysis in combination with suspect and nontarget screening are useful methods to preselect relevant compounds. Currently, the unequivocal identification of unknowns still requires information from an authentic standard which has to be measured or is already available in user-defined MS/MS reference databases or libraries containing HRMS spectral information and retention times. In this context, we discuss the advantages and future needs of publicly available MS and MS/MS reference databases and libraries which have mostly been created for the metabolomic field. A big step forward has been achieved with computer-based tools when no MS library or MS database entry is found for a compound. The numerous search results from a large chemical database can be condensed to only a few by in silico fragmentation. This has been demonstrated for selected compounds and metabolites in recent publications. Still, only very few compounds have been identified or tentatively identified in environmental samples by nontarget screening. The availability of comprehensive MS libraries with a focus on environmental contaminants would tremendously improve the situation.     

Metabonomics investigation of human urine after ingestion of green tea with gas chromatography/mass spectrometry, liquid chromatography/mass spectrometry and (1)H NMR spectroscopy

A method using gas chromatography/mass spectrometry (GC/MS), liquid chromatography/mass spectrometry (LC/MS) and (1)H NMR with pattern recognition tools such as principle components analysis (PCA) was used to study the human urinary metabolic profiles after the intake of green tea. From the normalized peak areas obtained from GC/MS and LC/MS and peak heights from (1)H NMR, statistical analyses were used in the identification of potential biomarkers. Metabolic profiling by GC/MS provided a different set of quantitative signatures of metabolites that can be used to characterize the molecular changes in human urine samples. A comparison of normalized metabonomics data for selected metabolites in human urine samples in the presence of potential overlapping peaks after tea ingestion from LC/MS and (1)H NMR showed the reliability of the current approach and method of normalization. The close agreements of LC/MS with (1)H NMR data showed that the effects of ion suppression in LC/MS for early eluting metabolites were not significant. Concurrently, the specificity of detecting the stated metabolites by (1)H NMR and LC/MS was demonstrated. Our data showed that a number of metabolites involved in glucose metabolism, citric acid cycle and amino acid metabolism were affected immediately after the intake of green tea. The proposed approach provided a more comprehensive picture of the metabolic changes after intake of green tea in human urine. The multiple analytical approach together with pattern recognition tools is a useful platform to study metabolic profiles after ingestion of botanicals and medicinal plants.     

Quantification of pharmaceutical compounds in tissue and plasma samples using selective ion accumulation with multiple mass isolation windows

Quantification of pharmaceutical compounds using matrix-assisted laser desorption/ionization (MALDI) mass spectrometry (MS) is an alternative to traditional liquid chromatography (LC)-MS techniques. Benefits of MALDI-based approaches include rapid analysis times for liquid samples and imaging mass spectrometry capabilities for tissue samples. As in most quantification experiments, the use of internal standards can compensate for spot-to-spot and shot-to-shot variability associated with MALDI sampling. However, the lack of chromatographic separation in traditional MALDI analyses results in diminished peak capacity due to the chemical noise background, which can be detrimental to the dynamic range and limit of detection of these approaches. These issues can be mitigated by using a hybrid mass spectrometer equipped with a quadrupole mass filter (QMF) that can be used to fractionate ions based on their mass-to-charge ratios. When the masses of the analytes and internal standards are sufficiently disparate in mass, it can be beneficial to effect multiple narrow mass isolation windows using the QMF, as opposed to a single wide mass isolation window, to minimize chemical noise while allowing for internal standard normalization. Herein, we demonstrate a MALDI MS quantification workflow incorporating multiple sequential mass isolation windows enabled on a QMF, which divides the total number of MALDI laser shots into multiple segments (i.e., one segment for each mass isolation window). This approach is illustrated through the quantitative analysis of the pharmaceutical compound enalapril in human plasma samples as well as the simultaneous quantification of three pharmaceutical compounds (enalapril, ramipril, and verapamil). Results show a decrease in the limit of detection, relative standard deviations below 10%, and accuracy above 85% for drug quantification using multiple mass isolation windows. This approach has also been applied to the quantification of enalapril in brain tissue from a rat dosed in vitro. The average concentration of enalapril determined by imaging mass spectrometry is in agreement with the concentration determined by LC–MS, giving an accuracy of 104%.     

正负离子切换扫描质谱成像分析方法及其整体动物体内代谢应用研究

基于质谱成像(MSI)技术的空间分辨代谢组学方法已经成为生物组织学、肿瘤分子病理诊断和新药药效毒理研究的有力工具。该研究采用敞开式空气动力辅助解吸电喷雾离子化(AFADESI)技术,开发了一种正负离子切换扫描的质谱成像方法。该方法在离子化过程中无需施加高电压,既提高了敞开式离子化质谱成像的操作安全性,还可将生物组织切片,尤其是较大面积的整体动物组织切片中的各类不同性质的内源性代谢物进行高效率解吸和离子化,同时获得多胺、氨基酸、磷脂(正离子模式易电离)和核苷、磷脂酰甘油(负离子模式易电离)等内源性代谢物的空间分布特征。通过正负离子化的同时扫描和质谱成像分析,节省了数据采集的时间和样本切片的数量,扩展了代谢物的覆盖范围,使得一次实验可以成像检出更多种类的代谢物。因此,该方法在整体动物各组织器官的空间分辨代谢组学研究中具有优势,有望从整体动物体内代谢水平深刻理解与生理、病理和药理相关的分子空间分布特征及分子机制。     

Analysis of erlotinib and its metabolites in rat tissue sections by MALDI quadrupole time-of-flight mass spectrometry

A qualitative and quantitative analysis of erlotinib (RO0508231) and its metabolites was carried out on rat tissue sections from liver, spleen and muscle. Following oral administration at a dose of 5 mg/kg, samples were analyzed by matrix-assisted laser desorption ionization (MALDI) with mass spectrometry (MS) using an orthogonal quadrupole time-of-flight instrument. The parent compound was detected in all tissues analyzed. The metabolites following drug O-dealkylation could also be detected in liver sections. Sinapinic acid (SA) matrix combined with the dried-droplet method resulted in better conditions for our analysis on tissues. Drug quantitation was investigated by the standard addition method and liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis on the tissue extracts. The presence of the parent compound and of its O-demethylated metabolites was confirmed in all tissue types and their absolute amounts calculated. In liver the intact drug was found to be 3.76 ng/mg tissue, while in spleen and muscle 6- and 30-fold lower values, respectively, were estimated. These results were compared with drug quantitation obtained by whole-body autoradiography, which was found to be similar. The potential for direct quantitation on tissue sections in the presence of an internal standard was also investigated using MALDI-MS. The use of alpha-cyano-4-hydroxycinnamic acid (CHCA) as the matrix resulted in better linearity for the calibration curves obtained with reference solutions of the drug when compared to SA, but on tissue samples no reliable quantitative analysis was possible owing to the large variability in the signal response. MS imaging experiments using MALDI in MS/MS mode allowed visualizing the distribution of the parent compound in liver and spleen tissues. By calculating the ratio between the total ion intensities of MS images for liver and spleen sections, a value of 6 : 1 was found, which is in good agreement with the quantitative data obtained by LC-MS/MS analysis.     

Intermolecular interaction energies in molecular crystals: comparison and agreement of localized Møller-Plesset 2, dispersion-corrected density functional, and classical empirical two-body calculations

A comparative analysis of the intermolecular energy for a data set including 60 molecular crystals with a large variety of functional groups has been carried out using three different computational approaches: (i) a method based on a physically meaningful empirical partition of the interaction energy (PIXEL), (ii) density functional methods with a posteriori empirical correction for the dispersion interactions (DFT-D), and (iii) a full periodic ab initio quantum mechanical method based on M?ller-Plesset perturbation theory for the electron correlation using localized crystal orbitals (LMP2). Due to the large computational cost, LMP2 calculations have been restricted to a subset of seven molecular crystal comprising benzene, formic acid, formamide, succinic anhydride, urea, oxalic acid, and nitroguanidine, and the results compared with PIXEL and DFT-D data as well as with the experimental data show excellent agreement among all adopted methods. This shows that both DFT-D and PIXEL approaches are robust predictive tools for studying molecular crystals. A detailed analysis shows a very similar dispersion contribution of the two methods across the 60 considered molecular crystals. The study also confirms that pure DFT shows serious deficiencies in properly handling molecular crystals in which the dispersive contribution is large. Due to the negligible requested computational resources, PIXEL is the method of choice in screening of a large number of molecular crystals, an essential step to predict crystal polymorphism or to study crystal growth processes. DFT-D can then be used to refine the ranking emerged from PIXEL calculations due to its general applicability and robustness in properly handling short-range interactions.     

Automated Precursor Ion Exclusion During LC-MS/MS Data Acquisition for Optimal Ion Identification

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is widely used for characterizing multiple samples of complex mixtures with similar compositions. This article addresses a data acquisition strategy for collecting a maximal number of unique, high-quality MS/MS during LC-MS/MS analysis of multiple samples. Based on the concept that a component only needs to be identified once when analyzing multiple samples with similar compositions, an automated intersample data-dependent acquisition strategy was developed. The strategy is based on precursor ion exclusion (PIE) and is implemented in MassAnalyzer in an automated fashion for Thermo Scientific (San Jose, CA, USA) mass spectrometers. In this method, MassAnalyzer submits one sample at a time to the sample queue. After data acquisition of each sample, MassAnalyzer automatically analyzes the data to generate a PIE list based on the MS/MS precursor ions, merges this list with the list generated from previous runs, adds the list to the MS method file, and submits the next sample to the queue. The PIE list contains both m/z value and time window for each precursor ion, and is generated intelligently so that if an MS/MS is insufficient for identifying the peak of interest, it will be collected again near the top of the peak in the next run. Therefore, the strategy maximizes both quality and the number of unique MS/MS. When automated PIE was used to acquire LC-MS/MS data of an antibody tryptic digest and a soy hydrolysate sample, the number of identified ions increased by 52% and 93%, respectively, compared with data acquired without using PIE.     

Interactive hyperspectral approach for exploring and interpreting DESI-MS images of cancerous and normal tissue sections

Desorption electrospray ionization (DESI) is an ambient mass spectrometry (MS) technique that can be operated in an imaging mode. It is known to provide valuable information on disease state and grade based on lipid profiles in tissue sections. Comprehensive exploration of the spatial and chemical information contained in 2D MS images requires further development of methods for data treatment and interpretation in conjunction with multivariate analysis. In this study, we employ an interactive approach based on principal component analysis (PCA) to interpret the chemical and spatial information obtained from MS imaging of human bladder, kidney, germ cell and prostate cancer and adjacent normal tissues. This multivariate strategy facilitated distinction between tumor and normal tissue by correlating the lipid information with pathological evaluation of the same samples. Some common lipid ions, such as those of m/z 885.5 and m/z 788.5, nominally PI(18 : 0/20 : 4) and PS(18 : 0/18 : 1), as well as ions of free fatty acids and their dimers, appeared to be highly characterizing for different types of human cancers, while other ions, such as those of m/z 465.5 (cholesterol sulfate) for prostate cancer tissue and m/z 795.5 (seminolipid 16 : 0/16 : 0) for germ tissue, appeared to be extremely selective for the type of tissue analyzed. These data confirm that lipid profiles can reflect not only the disease/health state of tissue but also are characteristic of tissue type. The manual interactive strategy presented here is particularly useful to visualize the information contained in hyperspectral MS images by automatically connecting regions of PCA score space to pixels of the 2D physical object. The procedures developed in this study consider all the spectral variables and their inter-correlations, and guide subsequent investigations of the mass spectra and single ion images to allow one to maximize characterization between different regions of any DESI-MS image.     

Analysis of high molecular mass proteins larger than 150 kDa using cyanogen bromide cleavage and conventional 2-DE

Proteomic approaches including high-resolution 2-DE are providing the tools needed to discover disease-associated biomarkers in complex biological samples. Although 2-DE is an extremely powerful approach to analyze the proteome, the separation of proteins with extreme molecular masses still remains an issue requiring improvement. Because high molecular mass (HMM) proteins larger than 150 kDa have already been observed to be differentially expressed in several pathologies such as cancer, we developed an original strategy to analyze this part of the proteome that is not easily separated by 2-DE in polyacrylamide gels. This strategy is based on the 2-DE separation of cyanogen bromide (CNBr) fragments of purified HMM protein fractions, and combines techniques including SEC fractionation, TCA precipitation, CNBr cleavage, 2-DE and MS analysis. The method was first tested on a model protein, the BSA. Preliminary results obtained using colonic tissues led to the identification of six HMM proteins with M(r) comprised between 163 and 533 kDa in their reduced state. These results demonstrated that our CNBr/2-DE approach should provide a powerful tool for identification of new biomarkers larger than 150 kDa.     

Metabolic profiling for the identification of Huntington biomarkers by on‐line solid‐phase extraction capillary electrophoresis mass spectrometry combined with advanced data analysis tools

In this work, an untargeted metabolomic approach based on sensitive analysis by on‐line solid‐phase extraction capillary electrophoresis mass spectrometry (SPE‐CE‐MS) in combination with multivariate data analysis is proposed as an efficient method for the identification of biomarkers of Huntington's disease (HD) progression in plasma. For this purpose, plasma samples from wild‐type (wt) and HD (R6/1) mice of different ages (8, 12, and 30 weeks), were analyzed by C₁₈‐SPE‐CE‐MS in order to obtain the characteristic electrophoretic profiles of low molecular mass compounds. Then, multivariate curve resolution alternating least squares (MCR‐ALS) was applied to the multiple full scan MS datasets. This strategy permitted the resolution of a large number of metabolites being characterized by their electrophoretic peaks and their corresponding mass spectra. A total number of 29 compounds were relevant to discriminate between wt and HD plasma samples, as well as to follow‐up the HD progression. The intracellular signaling was found to be the most affected metabolic pathway in HD mice after 12 weeks of birth, when mice already showed motor coordination deficiencies and cognitive decline. This fact agreed with the atrophy and dysfunction of specific neurons, loss of several types of receptors, and changed expression of neurotransmitters.     

Visualization of acetylcholine distribution in central nervous system tissue sections by tandem imaging mass spectrometry

Metabolite distribution imaging via imaging mass spectrometry (IMS) is an increasingly utilized tool in the field of neurochemistry. As most previous IMS studies analyzed the relative abundances of larger metabolite species, it is important to expand its application to smaller molecules, such as neurotransmitters. This study aimed to develop an IMS application to visualize neurotransmitter distribution in central nervous system tissue sections. Here, we raise two technical problems that must be resolved to achieve neurotransmitter imaging: (1) the lower concentrations of bioactive molecules, compared with those of membrane lipids, require higher sensitivity and/or signal-to-noise (S/N) ratios in signal detection, and (2) the molecular turnover of the neurotransmitters is rapid; thus, tissue preparation procedures should be performed carefully to minimize postmortem changes. We first evaluated intrinsic sensitivity and matrix interference using Matrix Assisted Laser Desorption/Ionization (MALDI) mass spectrometry (MS) to detect six neurotransmitters and chose acetylcholine (ACh) as a model for study. Next, we examined both single MS imaging and MS/MS imaging for ACh and found that via an ion transition from m/z 146 to m/z 87 in MS/MS imaging, ACh could be visualized with a high S/N ratio. Furthermore, we found that in situ freezing method of brain samples improved IMS data quality in terms of the number of effective pixels and the image contrast (i.e., the sensitivity and dynamic range). Therefore, by addressing the aforementioned problems, we demonstrated the tissue distribution of ACh, the most suitable molecular specimen for positive ion detection by IMS, to reveal its localization in central nervous system tissues.