Guiding the choice of informatics software and tools for lipidomics research applications.

Progress in mass spectrometry lipidomics has led to a rapid proliferation of studies across biology and biomedicine. These generate extremely large raw datasets requiring sophisticated solutions to support automated data processing. To address this, numerous software tools have been developed and tailored for specific tasks. However, for researchers, deciding which approach best suits their application relies on ad hoc testing, which is inefficient and time consuming. Here we first review the data processing pipeline, summarizing the scope of available tools. Next, to support researchers, LIPID MAPS provides an interactive online portal listing open-access tools with a graphical user interface. This guides users towards appropriate solutions within major areas in data processing, including (1) lipid-oriented databases, (2) mass spectrometry data repositories, (3) analysis of targeted lipidomics datasets, (4) lipid identification and (5) quantification from untargeted lipidomics datasets, (6) statistical analysis and visualization, and (7) data integration solutions. Detailed descriptions of functions and requirements are provided to guide customized data analysis workflows.

Decoding Distinct Ganglioside Patterns of Native and Differentiated Mesenchymal Stem Cells by a Novel Glycolipidomics Profiling Strategy.

Gangliosides are an indispensable glycolipid class concentrated on cell surfaces with a critical role in stem cell differentiation. Nonetheless, owing to the lack of suitable methods for scalable analysis covering the full scope of ganglioside molecular diversity, their mechanistic properties in signaling and differentiation remain undiscovered to a large extent. This work introduces a sensitive and comprehensive ganglioside assay based on liquid chromatography, high-resolution mass spectrometry, and multistage fragmentation. Complemented by an open-source data evaluation workflow, we provide automated in-depth lipid species-level and molecular species-level annotation based on decision rule sets for all major ganglioside classes. Compared to conventional state-of-the-art methods, the presented ganglioside assay offers (1) increased sensitivity, (2) superior structural elucidation, and (3) the possibility to detect novel ganglioside species. A major reason for the highly improved sensitivity is the optimized spectral readout based on the unique capability of two parallelizable mass analyzers for multistage fragmentation. We demonstrated the high-throughput universal capability of our novel analytical strategy by identifying 254 ganglioside species. As a proof of concept, 137 unique gangliosides were annotated in native and differentiated human mesenchymal stem cells including 78 potential cell-state-specific markers and 38 previously unreported gangliosides. A general increase of the ganglioside numbers upon differentiation was observed as well as cell-state-specific clustering based on the ganglioside species patterns. The combination of the developed glycolipidomics assay with the extended automated annotation tool enables comprehensive in-depth ganglioside characterization as shown on biological samples of interest. Our results suggest ganglioside patterns as a promising quality control tool for stem cells and their differentiation products. Additionally, we believe that our analytical workflow paves the way for probing glycolipid-based biochemical processes shedding light on the enigmatic processes of gangliosides and glycolipids in general.

Characterization of Constituents with Potential Anti-Inflammatory Activity in Chinese Lonicera Species by UHPLC-HRMS Based Metabolite Profiling.

This study centered on detecting potentially anti-inflammatory active constituents in ethanolic extracts of Chinese Lonicera species by taking an UHPLC-HRMS-based metabolite profiling approach. Extracts from eight different Lonicera species were subjected to both UHPLC-HRMS analysis and to pharmacological testing in three different cellular inflammation-related assays. Compounds exhibiting high correlations in orthogonal projections to latent structures discriminant analysis (OPLS-DA) of pharmacological and MS data served as potentially activity-related candidates. Of these candidates, 65 were tentatively or unambiguously annotated. 7-Hydroxy-5,3',4',5'-tetramethoxyflavone and three bioflavonoids, as well as three C32- and one C34-acetylated polyhydroxy fatty acid, were isolated from Lonicera hypoglauca leaves for the first time, and their structures were fully or partially elucidated. Of the potentially active candidate compounds, 15 were subsequently subjected to pharmacological testing. Their activities could be experimentally verified in part, emphasizing the relevance of Lonicera species as a source of anti-inflammatory active constituents. However, some compounds also impaired the cell viability. Overall, the approach was found useful to narrow down the number of potentially bioactive constituents in the complex extracts investigated. In the future, the application of more refined concepts, such as extract prefractionation combined with bio-chemometrics, may help to further enhance the reliability of candidate selection.

α-Linolenic acid and product octadecanoids in Styrian pumpkin seeds and oils: How processing impacts lipidomes of fatty acid, triacylglycerol and oxylipin molecular structures.

Styrian pumpkin seed oil is a conditioned green-colored oil renowned for nutty smell and taste. Due to α-linolenic acid (ALA) contents below 1% of total fatty acids and the prospect of nutritional health claims based on its potential oxidation products, we investigated the fate of ALA and product oxylipins in the course of down-stream processing of seeds and in oils. Lipidomic analyses with Lipid Data Analyzer 2.8.1 revealed: Processing did not change (1) main fatty acid composition in the oils, (2) amounts of triacylglycerol species, (3) structures of triacylglycerol molecular species containing ALA. (4) Minor precursor ALA in fresh Styrian and normal pumpkins produced 6 product phytoprostanes in either cultivar, quantitatively more in the latter. (5) In oil samples 7 phytoprostanes and 2 phytofurans were detected. The latter two are specific for their presence in pumpkin seed oils, of note, quantitatively more in conditioned oils than in cold-pressed native oils.

Nonalcoholic fatty liver disease stratification by liver lipidomics.

Nonalcoholic fatty liver disease (NAFLD) is a common metabolic dysfunction leading to hepatic steatosis. However, NAFLD's global impact on the liver lipidome is poorly understood. Using high-resolution shotgun mass spectrometry, we quantified the molar abundance of 316 species from 22 major lipid classes in liver biopsies of 365 patients, including nonsteatotic patients with normal or excessive weight, patients diagnosed with NAFL (nonalcoholic fatty liver) or NASH (nonalcoholic steatohepatitis), and patients bearing common mutations of NAFLD-related protein factors. We confirmed the progressive accumulation of di- and triacylglycerols and cholesteryl esters in the liver of NAFL and NASH patients, while the bulk composition of glycerophospho- and sphingolipids remained unchanged. Further stratification by biclustering analysis identified sphingomyelin species comprising n24:2 fatty acid moieties as membrane lipid markers of NAFLD. Normalized relative abundance of sphingomyelins SM 43:3;2 and SM 43:1;2 containing n24:2 and n24:0 fatty acid moieties, respectively, showed opposite trends during NAFLD progression and distinguished NAFL and NASH lipidomes from the lipidome of nonsteatotic livers. Together with several glycerophospholipids containing a C22:6 fatty acid moiety, these lipids serve as markers of early and advanced stages of NAFL.

Automated Annotation of Sphingolipids Including Accurate Identification of Hydroxylation Sites Using MSn Data.

Sphingolipids constitute a heterogeneous lipid category that is involved in many key cellular functions. For high-throughput analyses of sphingolipids, tandem mass spectrometry (MS/MS) is the method of choice, offering sufficient sensitivity, structural information, and quantitative precision for detecting hundreds to thousands of species simultaneously. While glycerolipids and phospholipids are predominantly non-hydroxylated, sphingolipids are typically dihydroxylated. However, species containing one or three hydroxylation sites can be detected frequently. This variability in the number of hydroxylation sites on the sphingolipid long-chain base and the fatty acyl moiety produces many more isobaric species and fragments than for other lipid categories. Due to this complexity, the automated annotation of sphingolipid species is challenging, and incorrect annotations are common. In this study, we present an extension of the Lipid Data Analyzer (LDA) "decision rule set" concept that considers the structural characteristics that are specific for this lipid category. To address the challenges inherent to automated annotation of sphingolipid structures from MS/MS data, we first developed decision rule sets using spectra from authentic standards and then tested the applicability on biological samples including murine brain and human plasma. A benchmark test based on the murine brain samples revealed a highly improved annotation quality as measured by sensitivity and reliability. The results of this benchmark test combined with the easy extensibility of the software to other (sphingo)lipid classes and the capability to detect and correctly annotate novel sphingolipid species make LDA broadly applicable to automated sphingolipid analysis, especially in high-throughput settings.

Chasing the Major Sphingolipids on Earth: Automated Annotation of Plant Glycosyl Inositol Phospho Ceramides by Glycolipidomics.

Glycosyl inositol phospho ceramides (GIPCs) are the major sphingolipids on earth, as they account for a considerable fraction of the total lipids in plants and fungi, which in turn represent a large portion of the biomass on earth. Despite their obvious importance, GIPC analysis remains challenging due to the lack of commercial standards and automated annotation software. In this work, we introduce a novel GIPC glycolipidomics workflow based on reversed-phase ultra-high pressure liquid chromatography coupled to high-resolution mass spectrometry. For the first time, automated GIPC assignment was performed using the open-source software Lipid Data Analyzer (LDA), based on platform-independent decision rules. Four different plant samples (salad, spinach, raspberry, and strawberry) were analyzed and the results revealed 64 GIPCs based on accurate mass, characteristic MS2 fragments and matching retention times. Relative quantification using lactosyl ceramide for internal standardization revealed GIPC t18:1/h24:0 as the most abundant species in all plants. Depending on the plant sample, GIPCs contained mainly amine, N-acetylamine or hydroxyl residues. Most GIPCs revealed a Hex-HexA-IPC core and contained a ceramide part with a trihydroxylated t18:0 or a t18:1 long chain base and hydroxylated fatty acid chains ranging from 16 to 26 carbon atoms in length (h16:0-h26:0). Interestingly, four GIPCs containing t18:2 were observed in the raspberry sample, which was not reported so far. The presented workflow supports the characterization of different plant samples by automatic GIPC assignment, potentially leading to the identification of new GIPCs. For the first time, automated high-throughput profiling of these complex glycolipids is possible by liquid chromatography-high-resolution tandem mass spectrometry and subsequent automated glycolipid annotation based on decision rules.

A Metabolomics Workflow for Analyzing Complex Biological Samples Using a Combined Method of Untargeted and Target-List Based Approaches.

In the highly dynamic field of metabolomics, we have developed a method for the analysis of hydrophilic metabolites in various biological samples. Therefore, we used hydrophilic interaction chromatography (HILIC) for separation, combined with a high-resolution mass spectrometer (MS) with the aim of separating and analyzing a wide range of compounds. We used 41 reference standards with different chemical properties to develop an optimal chromatographic separation. MS analysis was performed with a set of pooled biological samples human cerebrospinal fluid (CSF), and human plasma. The raw data was processed in a first step with Compound Discoverer 3.1 (CD), a software tool for untargeted metabolomics with the aim to create a list of unknown compounds. In a second step, we combined the results obtained with our internally analyzed reference standard list to process the data along with the Lipid Data Analyzer 2.6 (LDA), a software tool for a targeted approach. In order to demonstrate the advantages of this combined target-list based and untargeted approach, we not only compared the relative standard deviation (%RSD) of the technical replicas of pooled plasma samples (n = 5) and pooled CSF samples (n = 3) with the results from CD, but also with XCMS Online, a well-known software tool for untargeted metabolomics studies. As a result of this study we could demonstrate with our HILIC-MS method that all standards could be either separated by chromatography, including isobaric leucine and isoleucine or with MS by different mass. We also showed that this combined approach benefits from improved precision compared to well-known metabolomics software tools such as CD and XCMS online. Within the pooled plasma samples processed by LDA 68% of the detected compounds had a %RSD of less than 25%, compared to CD and XCMS online (57% and 55%). The improvements of precision in the pooled CSF samples were even more pronounced, 83% had a %RSD of less than 25% compared to CD and XCMS online (28% and 8% compounds detected). Particularly for low concentration samples, this method showed a more precise peak area integration with its 3D algorithm and with the benefits of the LDAs graphical user interface for fast and easy manual curation of peak integration. The here-described method has the advantage that manual curation for larger batch measurements remains minimal due to the target list containing the information obtained by an untargeted approach.

Identification and Quantification of Oxidized Lipids in LC-MS Lipidomics Data.

Changes in lipid homeostasis can lead to a plethora of diseases, raising the importance of reliable identification and measurement of lipids enabled by bioinformatics tools. However, due to the enormous diversity of lipids, most contemporary tools cover only a marginal range of lipid classes. To reduce such a shortcoming, this work extends the lipid species covered by Lipid Data Analyzer (LDA) to galactolipids and oxidized lipids. Appropriate mass lists were generated for MS1 identifications and the proprietary decision rule sets were extended for MS2 identifications of the novel lipid classes. Furthermore, LDA was extended to enable identification of oxidatively modified fatty acyl chains. With these extensions, LDA can reliably identify the most important galactolipids as well as oxidatively modified versions of the 22 previously implemented lipid classes. Comparison with other up to date lipidomics tools show that LDA has a better coverage of the newly implemented lipid species. The extended version of LDA provides researchers with a powerful platform to elucidate diseases caused by perturbations in the oxidized lipidome. LDA is freely available from https://genome.tugraz.at/lda.

jmzTab-M: A Reference Parser, Writer, and Validator for the Proteomics Standards Initiative mzTab 2.0 Metabolomics Standard.

mzTab 2.0 for metabolomics (mzTab-M) is the most recent standard format developed in collaboration by the Proteomics and Metabolomics Standards Initiatives including contributions by the recently founded Lipidomics Standards Initiative. mzTab-M is a redesign of the original mzTab format which was geared toward reporting of proteomics results and, as such, provided only limited support for metabolites. As a tab-delimited, spreadsheet-like format, mzTab-M captures experimental metadata, summary information on small molecules across assays, MS features as a basis for quantitation, and evidence to support the reporting of individual or feature group identifications. Here, we present the Java reference implementation for reading, writing, and validating mzTab-M files. Furthermore, we provide a web application for conveniently validating mzTab-M files by a graphical user interface, and a command line validator that accompanies the library. The jmzTab-M library, version 1.0.4 ( https://doi.org/10.5281/zenodo.3362151 ), is available at https://github.com/lifs-tools/jmzTab-m and from Maven Central at https://search.maven.org/search?q=jmztabm under the terms of the open source Apache License 2.0. The web application as well as the Python and R implementations are available at https://github.com/lifs-tools . The respective Web sites link to additional API documentation, as well as to usage examples.

mzTab-M: A Data Standard for Sharing Quantitative Results in Mass Spectrometry Metabolomics.

Mass spectrometry (MS) is one of the primary techniques used for large-scale analysis of small molecules in metabolomics studies. To date, there has been little data format standardization in this field, as different software packages export results in different formats represented in XML or plain text, making data sharing, database deposition, and reanalysis highly challenging. Working within the consortia of the Metabolomics Standards Initiative, Proteomics Standards Initiative, and the Metabolomics Society, we have created mzTab-M to act as a common output format from analytical approaches using MS on small molecules. The format has been developed over several years, with input from a wide range of stakeholders. mzTab-M is a simple tab-separated text format, but importantly, the structure is highly standardized through the design of a detailed specification document, tightly coupled to validation software, and a mandatory controlled vocabulary of terms to populate it. The format is able to represent final quantification values from analyses, as well as the evidence trail in terms of features measured directly from MS (e.g., LC-MS, GC-MS, DIMS, etc.) and different types of approaches used to identify molecules. mzTab-M allows for ambiguity in the identification of molecules to be communicated clearly to readers of the files (both people and software). There are several implementations of the format available, and we anticipate widespread adoption in the field.

Determination of the Isotopic Enrichment of 13C- and 2H-Labeled Tracers of Glucose Using High-Resolution Mass Spectrometry: Application to Dual- and Triple-Tracer Studies.

Multiple-tracer approaches for investigating glucose metabolism in humans usually involve the administration of stable and radioactive glucose tracers and the subsequent determination of tracer enrichments in sampled blood. When using conventional, low-resolution mass spectrometry (LRMS), the number of spectral interferences rises rapidly with the number of stable tracers employed. Thus, in LRMS, both computational effort and statistical uncertainties associated with the correction for spectral interferences limit the number of stable tracers that can be simultaneously employed (usually two). Here we show that these limitations can be overcome by applying high-resolution mass spectrometry (HRMS). The HRMS method presented is based on the use of an Orbitrap mass spectrometer operated at a mass resolution of 100 000 to allow electrospray-generated ions of the deprotonated glucose molecules to be monitored at their exact masses. The tracer enrichment determination in blood plasma is demonstrated for several triple combinations of 13C- and 2H-labeled glucose tracers (e.g., [1-2H1]-, [6,6-2H2]-, [1,6-13C2]glucose). For each combination it is shown that ions arising from 2H-labeled tracers are completely differentiated from those arising from 13C-labeled tracers, thereby allowing the enrichment of a tracer to be simply calculated from the observed ion intensities using a standard curve with curve parameters unaffected by the presence of other tracers. For each tracer, the HRMS method exhibits low limits of detection and good repeatability in the tested 0.1-15.0% enrichment range. Additionally, due to short sample preparation and analysis times, the method is well-suited for high-throughput determination of multiple glucose tracer enrichments in plasma samples.

Proposal for a common nomenclature for fragment ions in mass spectra of lipids.

Advances in mass spectrometry-based lipidomics have in recent years prompted efforts to standardize the annotation of the vast number of lipid molecules that can be detected in biological systems. These efforts have focused on cataloguing, naming and drawing chemical structures of intact lipid molecules, but have provided no guidelines for annotation of lipid fragment ions detected using tandem and multi-stage mass spectrometry, albeit these fragment ions are mandatory for structural elucidation and high confidence lipid identification, especially in high throughput lipidomics workflows. Here we propose a nomenclature for the annotation of lipid fragment ions, describe its implementation and present a freely available web application, termed ALEX123 lipid calculator, that can be used to query a comprehensive database featuring curated lipid fragmentation information for more than 430,000 potential lipid molecules from 47 lipid classes covering five lipid categories. We note that the nomenclature is generic, extendable to stable isotope-labeled lipid molecules and applicable to automated annotation of fragment ions detected by most contemporary lipidomics platforms, including LC-MS/MS-based routines.

Deciphering lipid structures based on platform-independent decision rules.

We achieve automated and reliable annotation of lipid species and their molecular structures in high-throughput data from chromatography-coupled tandem mass spectrometry using decision rule sets embedded in Lipid Data Analyzer (LDA; http://genome.tugraz.at/lda2). Using various low- and high-resolution mass spectrometry instruments with several collision energies, we proved the method's platform independence. We propose that the software's reliability, flexibility, and ability to identify novel lipid molecular species may now render current state-of-the-art lipid libraries obsolete.

Erratum: Enzyme discovery beyond homology: a unique hydroxynitrile lyase in the Bet v1 superfamily.

This corrects the article DOI: 10.1038/srep46738.

Enzyme discovery beyond homology: a unique hydroxynitrile lyase in the Bet v1 superfamily.

Homology and similarity based approaches are most widely used for the identification of new enzymes for biocatalysis. However, they are not suitable to find truly novel scaffolds with a desired function and this averts options and diversity. Hydroxynitrile lyases (HNLs) are an example of non-homologous isofunctional enzymes for the synthesis of chiral cyanohydrins. Due to their convergent evolution, finding new representatives is challenging. Here we show the discovery of unique HNL enzymes from the fern Davallia tyermannii by coalescence of transcriptomics, proteomics and enzymatic screening. It is the first protein with a Bet v1-like protein fold exhibiting HNL activity, and has a new catalytic center, as shown by protein crystallography. Biochemical properties of D. tyermannii HNLs open perspectives for the development of a complementary class of biocatalysts for the stereoselective synthesis of cyanohydrins. This work shows that systematic integration of -omics data facilitates discovery of enzymes with unpredictable sequences and helps to extend our knowledge about enzyme diversity.

Lipidomics by ultrahigh performance liquid chromatography-high resolution mass spectrometry and its application to complex biological samples.

An improved approach for selective and sensitive identification and quantitation of lipid molecular species using reversed phase chromatography coupled to high resolution mass spectrometry was developed. The method is applicable to a wide variety of biological matrices using a simple liquid-liquid extraction procedure. Together, this approach combines multiple selectivity criteria: Reversed phase chromatography separates lipids according to their acyl chain length and degree of unsaturation and is capable of resolving positional isomers of lysophospholipids, as well as structural isomers of diacyl phospholipids and glycerolipids. Orbitrap mass spectrometry delivers the elemental composition of both positive and negative ions with high mass accuracy. Finally, automatically generated tandem mass spectra provide structural insight into numerous glycerolipids, phospholipids, and sphingolipids within a single run. Calibration showed linearity ranges of more than four orders of magnitude, good values for accuracy and precision at biologically relevant concentration levels, and limits of quantitation of a few femtomoles on column. Hundreds of lipid molecular species were detected and quantified in three different biological matrices, which cover well the wide variety and complexity of various model organisms in lipidomic research. Together with a software package, this method is a prime choice for global lipidomic analysis of even the most complex biological samples.