Quality control requirements for the correct annotation of lipidomics data.

10.1038/s41467-021-24984-yNature Communications121477

OpenMS – An open-source software framework for mass spectrometry

<p>Abstract</p> <p>Background</p> <p>Mass spectrometry is an essential analytical technique for high-throughput analysis in proteomics and metabolomics. The development of new separation techniques, precise mass analyzers and experimental protocols is a very active field of research. This leads to more complex experimental setups yielding ever increasing amounts of data. Consequently, analysis of the data is currently often the bottleneck for experimental studies. Although software tools for many data analysis tasks are available today, they are often hard to combine with each other or not flexible enough to allow for rapid prototyping of a new analysis workflow.</p> <p>Results</p> <p>We present OpenMS, a software framework for rapid application development in mass spectrometry. OpenMS has been designed to be portable, easy-to-use and robust while offering a rich functionality ranging from basic data structures to sophisticated algorithms for data analysis. This has already been demonstrated in several studies.</p> <p>Conclusion</p> <p>OpenMS is available under the Lesser GNU Public License (LGPL) from the project website at <url>http://www.openms.de</url>.</p

Deciphering lipid structures based on platform-independent decision rule sets

We developed decision rule sets for Lipid Data Analyzer (LDA; http://genome.tugraz.at/lda2), enabling automated and reliable annotation of lipid species and their molecular structures in high-throughput data from chromatography-coupled tandem mass spectrometry. Platform independence was proven in various mass spectrometric experiments, comprising low- and high-resolution instruments and several collision energies. We propose that this independence and the capability to identify novel lipid molecular species render current state-of-the-art lipid libraries now obsolete

De novo fatty acid synthesis by Schwann cells is essential for peripheral nervous system myelination

Myelination calls for a remarkable surge in cell metabolism to facilitate lipid and membrane production. Endogenous fatty acid (FA) synthesis represents a potentially critical process in myelinating glia. Using genetically modified mice, we show that Schwann cell (SC) intrinsic activity of the enzyme essential for de novo FA synthesis, fatty acid synthase (FASN), is crucial for precise lipid composition of peripheral nerves and fundamental for the correct onset of myelination and proper myelin growth. Upon FASN depletion in SCs, epineurial adipocytes undergo lipolysis, suggestive of a compensatory role. Mechanistically, we found that a lack of FASN in SCs leads to an impairment of the peroxisome proliferator-activated receptor (PPAR) γ–regulated transcriptional program. In agreement, defects in myelination of FASN-deficient SCs could be ameliorated by treatment with the PPARγ agonist rosiglitazone ex vivo and in vivo. Our results reveal that FASN-driven de novo FA synthesis in SCs is mandatory for myelination and identify lipogenic activation of the PPARγ transcriptional network as a putative downstream functional mediator

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

ATHENIS_3D: Automotive tested high-voltage and embedded non-volatile integrated SoC platform with 3D technology

The ATHENIS_3D FP7 EU project aims at providing new enabling technologies (analog, digital and power components) for high-voltage and high-temperature applications, tested for power systems of new hybrid/electrical vehicles. Innovation is exploited at process/device level (3D chip stacking, wafer level packaging, trench capacitors and TSV-inductors integrated in the interposer, high-reliable non-volatile Magnetic RAM), circuit-level (inductorless high-voltage DC-DC converter, high-temperature 28nm System-on-Chip platform) and system-level (compact 3D embedded power mechatronic system). Enabling high integration levels of complex systems, operating in harsh environments, in a single packaged 3D device, ATHENIS_3D allows for one order of magnitude area reduction vs. today PCB-based power and control systems. Integration costs will be consequently reduced in key industrial sectors for Europe where high-voltage/temperature operations are mandatory (vehicles, avionics, space/defense, industrial automation, energy)

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