Mass Spectrometry Based Lipidomics: An Overview of Technological Platforms <s> Data Analysis <s> We report a method for the identification and quantification of glycerophospholipid molecular species that is based on the simultaneous automated acquisition and processing of 41 precursor ion spectra, specific for acyl anions of common fatty acids moieties and several lipid class-specific fragment ions. Absolute quantification of identified species was linear within a concentration range of 10 nM-100 microM and was achieved by spiking into total lipid extracts a set of synthetic lipid standards with diheptadecanoyl (17:0/17:0) fatty acid moieties, representing six common classes of glycerophospholipids. The automated analysis of total lipid extracts was powered by a robotic nanoflow ion source and produced currently the most detailed description of the glycerophospholipidome. <s> BIB001 </s> Mass Spectrometry Based Lipidomics: An Overview of Technological Platforms <s> Data Analysis <s> Data-dependent acquisition of MS/MS spectra from lipid precursors enables to emulate the simultaneous acquisition of an unlimited number of precursor and neutral loss scans in a single analysis. This approach takes full advantage of rich fragment patterns in tandem mass spectra of lipids and enables their profiling by complex (Boolean) scans, in which masses of several fragment ions are considered within a single logical framework. No separation of lipids is required, and the accuracy of identification and quantification is not compromised, compared to conventional precursor and neutral loss scanning. <s> BIB002 </s> Mass Spectrometry Based Lipidomics: An Overview of Technological Platforms <s> Data Analysis <s> Both genetic and environmental factors are involved in the etiology of obesity and the associated lipid disturbances. We determined whether acquired obesity is associated with changes in global serum lipid profiles independent of genetic factors in young adult monozygotic (MZ) twins. 14 healthy MZ pairs discordant for obesity (10 to 25 kg weight difference) and ten weight concordant control pairs aged 24–27 years were identified from a large population-based study. Insulin sensitivity was assessed by the euglycemic clamp technique, and body composition by DEXA (% body fat) and by MRI (subcutaneous and intra-abdominal fat). Global characterization of lipid molecular species in serum was performed by a lipidomics strategy using liquid chromatography coupled to mass spectrometry. Obesity, independent of genetic influences, was primarily related to increases in lysophosphatidylcholines, lipids found in proinflammatory and proatherogenic conditions and to decreases in ether phospholipids, which are known to have antioxidant properties. These lipid changes were associated with insulin resistance, a pathogonomic characteristic of acquired obesity in these young adult twins. Our results show that obesity, already in its early stages and independent of genetic influences, is associated with deleterious alterations in the lipid metabolism known to facilitate atherogenesis, inflammation and insulin resistance. <s> BIB003 </s> Mass Spectrometry Based Lipidomics: An Overview of Technological Platforms <s> Data Analysis <s> Shotgun lipidome profiling relies on direct mass spectrometric analysis of total lipid extracts from cells, tissues or organisms and is a powerful tool to elucidate the molecular composition of lipidomes. We present a novel informatics concept of the molecular fragmentation query language implemented within the LipidXplorer open source software kit that supports accurate quantification of individual species of any ionizable lipid class in shotgun spectra acquired on any mass spectrometry platform. <s> BIB004 </s> Mass Spectrometry Based Lipidomics: An Overview of Technological Platforms <s> Data Analysis <s> BackgroundMass spectrometry (MS) coupled with online separation methods is commonly applied for differential and quantitative profiling of biological samples in metabolomic as well as proteomic research. Such approaches are used for systems biology, functional genomics, and biomarker discovery, among others. An ongoing challenge of these molecular profiling approaches, however, is the development of better data processing methods. Here we introduce a new generation of a popular open-source data processing toolbox, MZmine 2.ResultsA key concept of the MZmine 2 software design is the strict separation of core functionality and data processing modules, with emphasis on easy usability and support for high-resolution spectra processing. Data processing modules take advantage of embedded visualization tools, allowing for immediate previews of parameter settings. Newly introduced functionality includes the identification of peaks using online databases, MSn data support, improved isotope pattern support, scatter plot visualization, and a new method for peak list alignment based on the random sample consensus (RANSAC) algorithm. The performance of the RANSAC alignment was evaluated using synthetic datasets as well as actual experimental data, and the results were compared to those obtained using other alignment algorithms.ConclusionsMZmine 2 is freely available under a GNU GPL license and can be obtained from the project website at: http://mzmine.sourceforge.net/. The current version of MZmine 2 is suitable for processing large batches of data and has been applied to both targeted and non-targeted metabolomic analyses. <s> BIB005 </s> Mass Spectrometry Based Lipidomics: An Overview of Technological Platforms <s> Data Analysis <s> Motivation: The accurate measurement of the lipidome permits insights into physiological and pathological processes. Of the present high-throughput technologies, LC-MS especially bears potential of monitoring quantitative changes in hundreds of lipids simultaneously. In order to extract valuable information from huge amount of mass spectrometry data, the aid of automated, reliable, highly sensitive and specific analysis algorithms is indispensable. ::: ::: Results: We present here a novel approach for the quantitation of lipids in LC-MS data. The new algorithm obtains its analytical power by two major innovations: (i) a 3D algorithm that confines the peak borders in m/z and time direction and (ii) the use of the theoretical isotopic distribution of an analyte as selection/exclusion criterion. The algorithm is integrated in the Lipid Data Analyzer (LDA) application which additionally provides standardization, a statistics module for results analysis, a batch mode for unattended analysis of several runs and a 3D viewer for the manual verification. The statistics module offers sample grouping, tests between sample groups and export functionalities, where the results are visualized by heat maps and bar charts. The presented algorithm has been applied to data from a controlled experiment and to biological data, containing analytes distributed over an intensity range of 106. Our approach shows improved sensitivity and an extremely high positive predictive value compared with existing methods. Consequently, the novel algorithm, integrated in a user-friendly application, is a valuable improvement in the high-throughput analysis of the lipidome. ::: ::: Implementation and availability: The Java application is freely available for non-commercial users at http://genome.tugraz.at/lda. Raw data associated with this manuscript may be downloaded from ProteomeCommons.org Tranche using the following hash: ZBh3nS5bXk6I/Vn32tB5Vh0qnMpVIW71HByFFQqM0RmdF4/4Hcn H3Wggh9kU2teYVOtM1JWwHIeMHqSS/bc2yYNFmyUAAAAAAACl DQ == ::: ::: Contact: Gerhard.Thallinger@tugraz.at ::: ::: Supplementary information:Supplementary data are available from Bioinformatics online. <s> BIB006
Within the last decade a constantly increasing amount of information is accessible by lipid mass spectrometry. The bottleneck in translation of acquired mass raw data into useful quantitative biological information is data processing. Data processing encompasses primarily identification and quantitation of lipid species. Inevitably, some tradeoffs like semi-quantitation instead of accurate quantitation are necessary when processing up to hundreds of lipids per analytical run. Unfortunately, no platform independent commercial lipidomic data processing software is available so far. Nevertheless, some open source software packages and one commercial vendor dependent software offer solutions for data processing. A summary about the most important features of the discussed software packages is given in Table 1 . Developed by AB Sciex, Lipid View is the only commercially available lipidomic processing software so far. The concept of this software is based on the earlier developments of Lipid Profiler BIB001 and Lipid Inspector BIB002 . Basically all three software packages rely on shotgun data acquired by MPIS either on low resolution or high resolution instrumentation. The software processes information about precursor and fragment masses obtained from full scan and MS/MS data and matches it against an internal database containing possible fragments for molecular lipid species. On the downside, Lipid View is only able to process data acquired on AB Sciex triple quadrupole or quadrupole-TOF instruments, which severely limits the software's availability to the lipid mass spectrometry community. Recently, the group of Shevchenko has launched LipidXplorer, an informatics concept based on molecular fragmentation query language (MFQL) BIB004 . LipidXplorer is designed for shotgun lipidomics and takes MS full scan data and product ion scans into account. Although it is also possible to process low resolution data, this program is primarily developed for high resolution spectra and was shown to work best with LTQ-Orbitrap or quadrupole-TOF instrumentation. In contrast to other shotgun lipidomics software packages, LipidXplorer does not rely on a database for MS/MS spectra but rather depends on the concept of fragmentation queries, which reflects the variability of MS/MS spectra due to different experimental settings much better. The software allows a lot of freedom for the user, like, for example, customized adjustment to various experimental parameters, but this requires some dedication. Processing of LC-MS generated data is usually more challenging because retention time adds another dimension of information. Originally developed for metabolomic data acquired by mass spectrometry  BIB005 , m/zMine and its sequel m/zMine2 were also successfully applied on lipidomic data BIB003 . Following peak detection, identification of lipids is performed by searches in public libraries or customized internal databases containing exact mass and approximate retention times. Furthermore also isotopic distributions and adducts can be taken into account for lipid identification. Although originally applied on quadrupole-TOF LC-MS data, m/zMine2 has become a versatile and highly flexible software, which can be used for data generated with various experimental platforms. In contrast to other software solutions, Lipid Data Analyzer (LDA) is based on a 3D algorithm (m/z, retention time, intensity) for peak detection BIB006 . This offers the advantage to detect peaks with overlapping retention times and m/z values which might have remained undetected in a 2D plot. Identification of lipids is performed by exact mass, retention time and isotopic distribution of a compound, resulting in very high identification certainty ( Figure 5 ). Originally designed for an FT-ICR-MS instrument, the software is highly dependent on exact mass and works best at a resolution of 100,000 or more. Nevertheless, it was also shown to perform well with quadrupole TOF data. A desirable expansion of the program would be automatic processing of MS/MS data acquired in data-dependent fashion on the most abundant m/z values of each high resolution full scan spectrum. Quantitation of lipids is performed with sets of internal standards covering the whole elution range of the respective lipid class. Subsequently the software performs calculations of either the mean or the median intensity of all internal standards. This procedure allows for compensation of internal standard intensity fluctuations arising from variable ion suppression effects in each elution profile.