Challenges and perspectives for naming lipids in the context of lipidomics.

INTRODUCTION: Lipids are key compounds in the study of metabolism and are increasingly studied in biology projects. It is a very broad family that encompasses many compounds, and the name of the same compound may vary depending on the community where they are studied. OBJECTIVES: In addition, their structures are varied and complex, which complicates their analysis. Indeed, the structural resolution does not always allow a complete level of annotation so the actual compound analysed will vary from study to study and should be clearly stated. For all these reasons the identification and naming of lipids is complicated and very variable from one study to another, it needs to be harmonized. METHODS & RESULTS: In this position paper we will present and discuss the different way to name lipids (with chemoinformatic and semantic identifiers) and their importance to share lipidomic results. CONCLUSION: Homogenising this identification and adopting the same rules is essential to be able to share data within the community and to map data on functional networks.

Critical shifts in lipid metabolism promote megakaryocyte differentiation and proplatelet formation.

During megakaryopoiesis, megakaryocytes (MK) undergo cellular morphological changes with strong modification of membrane composition and lipid signaling. Here we adopt a lipid-centric multiomics approach to create a quantitative map of the MK lipidome during maturation and proplatelet formation. Data reveal that MK differentiation is driven by an increased fatty acyl import and de novo lipid synthesis, resulting in an anionic membrane phenotype. Pharmacological perturbation of fatty acid import and phospholipid synthesis blocked membrane remodeling and directly reduced MK polyploidization and proplatelet formation resulting in thrombocytopenia. The anionic lipid shift during megakaryopoiesis was paralleled by lipid-dependent relocalization of the scaffold protein CKIP-1 and recruitment of the kinase CK2α to the plasma membrane, which seems to be essential for sufficient platelet biogenesis. Overall, this study provides a framework to understand how the MK lipidome is altered during maturation and the impact of MK membrane lipid remodeling on MK kinase signaling involved in thrombopoiesis.

LipidSpace: Simple Exploration, Reanalysis, and Quality Control of Large-Scale Lipidomics Studies.

Lipid analysis gained significant importance due to the enormous range of lipid functions, e.g., energy storage, signaling, or structural components. Whole lipidomes can be quantitatively studied in-depth thanks to recent analytical advancements. However, the systematic comparison of thousands of distinct lipidomes remains challenging. We introduce LipidSpace, a standalone tool for analyzing lipidomes by assessing their structural and quantitative differences. A graph-based comparison of lipid structures is the basis for calculating structural space models and subsequently computing lipidome similarities. When adding study variables such as body weight or health condition, LipidSpace can determine lipid subsets across all lipidomes that describe these study variables well by utilizing machine-learning approaches. The user-friendly GUI offers four built-in tutorials and interactive visual interfaces with pdf export. Many supported data formats allow an efficient (re)analysis of data sets from different sources. An integrated interactive workflow guides the user through the quality control steps. We used this suite to reanalyze and combine already published data sets (e.g., one with about 2500 samples and 576 lipids in one run) and made additional discoveries to the published conclusions with the potential to fill gaps in the current lipid biology understanding. LipidSpace is available for Windows or Linux (https://lifs-tools.org).

LORA, Lipid Over-Representation Analysis Based on Structural Information.

With the increasing number of lipidomic studies, there is a need for an efficient and automated analysis of lipidomic data. One of the challenges faced by most existing approaches to lipidomic data analysis is lipid nomenclature. The systematic nomenclature of lipids contains all available information about the molecule, including its hierarchical representation, which can be used for statistical evaluation. The Lipid Over-Representation Analysis (LORA) web application (https://lora.metabolomics.fgu.cas.cz) analyzes this information using the Java-based Goslin framework, which translates lipid names into a standardized nomenclature. Goslin provides the level of lipid hierarchy, including information on headgroups, acyl chains, and their modifications, up to the "complete structure" level. LORA allows the user to upload the experimental query and reference data sets, select a grammar for lipid name normalization, and then process the data. The user can then interactively explore the results and perform lipid over-representation analysis based on selected criteria. The results are graphically visualized according to the lipidome hierarchy. The lipids present in the most over-represented terms (lipids with the highest number of enriched shared structural features) are defined as Very Important Lipids (VILs). For example, the main result of a demo data set is the information that the query is significantly enriched with "glycerophospholipids" containing "acyl 20:4" at the "sn-2 position". These terms define a set of VILs (e.g., PC 18:2/20:4;O and PE 16:0/20:4(5,8,10,14);OH). All results, graphs, and visualizations are summarized in a report. LORA is a tool focused on the smart mining of epilipidomics data sets to facilitate their interpretation at the molecular level.

Proteomics Standards Initiative at Twenty Years: Current Activities and Future Work.

The Human Proteome Organization (HUPO) Proteomics Standards Initiative (PSI) has been successfully developing guidelines, data formats, and controlled vocabularies (CVs) for the proteomics community and other fields supported by mass spectrometry since its inception 20 years ago. Here we describe the general operation of the PSI, including its leadership, working groups, yearly workshops, and the document process by which proposals are thoroughly and publicly reviewed in order to be ratified as PSI standards. We briefly describe the current state of the many existing PSI standards, some of which remain the same as when originally developed, some of which have undergone subsequent revisions, and some of which have become obsolete. Then the set of proposals currently being developed are described, with an open call to the community for participation in the forging of the next generation of standards. Finally, we describe some synergies and collaborations with other organizations and look to the future in how the PSI will continue to promote the open sharing of data and thus accelerate the progress of the field of proteomics.

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.

A Current Encyclopedia of Bioinformatics Tools, Data Formats and Resources for Mass Spectrometry Lipidomics.

Mass spectrometry is a widely used technology to identify and quantify biomolecules such as lipids, metabolites and proteins necessary for biomedical research. In this study, we catalogued freely available software tools, libraries, databases, repositories and resources that support lipidomics data analysis and determined the scope of currently used analytical technologies. Because of the tremendous importance of data interoperability, we assessed the support of standardized data formats in mass spectrometric (MS)-based lipidomics workflows. We included tools in our comparison that support targeted as well as untargeted analysis using direct infusion/shotgun (DI-MS), liquid chromatography-mass spectrometry, ion mobility or MS imaging approaches on MS1 and potentially higher MS levels. As a result, we determined that the Human Proteome Organization-Proteomics Standards Initiative standard data formats, mzML and mzTab-M, are already supported by a substantial number of recent software tools. We further discuss how mzTab-M can serve as a bridge between data acquisition and lipid bioinformatics tools for interpretation, capturing their output and transmitting rich annotated data for downstream processing. However, we identified several challenges of currently available tools and standards. Potential areas for improvement were: adaptation of common nomenclature and standardized reporting to enable high throughput lipidomics and improve its data handling. Finally, we suggest specific areas where tools and repositories need to improve to become FAIRer.

Goslin 2.0 Implements the Recent Lipid Shorthand Nomenclature for MS-Derived Lipid Structures.

Goslin is the first grammar-based computational library for the recognition/parsing and normalization of lipid names following the hierarchical lipid shorthand nomenclature. The new version Goslin 2.0 implements the latest nomenclature and adds an additional grammar to recognize systematic IUPAC-IUB fatty acyl names as stored, e.g., in the LIPID MAPS database and is perfectly suited to update lipid names in LIPID MAPS or HMDB databases to the latest nomenclature. Goslin 2.0 is available as a standalone web application with a REST API as well as C++, C#, Java, Python 3, and R libraries. Importantly, it can be easily included in lipidomics tools and scripts providing direct access to translation functions. All implementations are open source.

Has the COVID-19 outbreak changed the way we are treating prostate cancer? An EAU - YAU Prostate Cancer Working Group multi-institutional study.

INTRODUCTION: The COVID-19 outbreak has become the dominant issue throughout the world whilst the governments, nations and health services are trying to deal with its impact. The aim of our study is to assess the impact of COVID-19 on patients treated with radical prostatectomy (RP) for prostate cancer (PCa) at European referral centers in terms of surgical volume (SV), waiting list meant as time from biopsy to surgery (WL) and risk of adverse pathologic findings at RP due to the selection of men with more adverse disease characteristics at final pathology. MATERIAL AND METHODS: Consecutive patients with a diagnosis of histologically proven PCa treated with RP between March 2020 (WHO declaration of pandemic) and December 2020 were identified. Patients with metastatic disease not eligible to local treatment and recurrent prostate cancer after RP or RT were excluded. Patients treated at the same institutions between March 2019 and December 2019 were considered as the control group. Multivariable logistic regression analysis tested the impact of the COVID-19 outbreak on the risk of adverse pathologic findings at RP after adjusting for confounders. The percentage change of SV and WL was assessed comparing the months of pandemic with the equivalent timespan of the previous year. RESULTS: A total of 2,574 patients treated with RP (927 cases and 1647 controls) were identified in 8 European tertiary referral centers. At multivariable analysis patients who were treated during the pandemic had higher risk of extra prostatic disease (OR:1.35, p = 0.038) and lymph node invasion (LNI) (OR:1.72, p = 0.048). An average 23% reduction of the SV with the equivalent timespan of the previous year allowed an illusory reduction of the WL after the peak gained during the first wave of COVID-19. CONCLUSIONS: Our results showed that the COVID-19 outbreak resulted in a delay in the administration of curative-intent therapies in patients with localized PCa. This, in turn, resulted in a stage migration phenomenon with a potential impact on oncologic control.

Multiomics of synaptic junctions reveals altered lipid metabolism and signaling following environmental enrichment.

Membrane lipids and their metabolism have key functions in neurotransmission. Here we provide a quantitative lipid inventory of mouse and rat synaptic junctions. To this end, we developed a multiomics extraction and analysis workflow to probe the interplay of proteins and lipids in synaptic signal transduction from the same sample. Based on this workflow, we generate hypotheses about novel mechanisms underlying complex changes in synaptic connectivity elicited by environmental stimuli. As a proof of principle, this approach reveals that in mice exposed to an enriched environment, reduced endocannabinoid synthesis and signaling is linked to increased surface expression of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR) in a subset of Cannabinoid-receptor 1 positive synapses. This mechanism regulates synaptic strength in an input-specific manner. Thus, we establish a compartment-specific multiomics workflow that is suitable to extract information from complex lipid and protein networks involved in synaptic function and plasticity.

Recommendations for good practice in MS-based lipidomics.

In the last 2 decades, lipidomics has become one of the fastest expanding scientific disciplines in biomedical research. With an increasing number of new research groups to the field, it is even more important to design guidelines for assuring high standards of data quality. The Lipidomics Standards Initiative is a community-based endeavor for the coordination of development of these best practice guidelines in lipidomics and is embedded within the International Lipidomics Society. It is the intention of this review to highlight the most quality-relevant aspects of the lipidomics workflow, including preanalytics, sample preparation, MS, and lipid species identification and quantitation. Furthermore, this review just does not only highlights examples of best practice but also sheds light on strengths, drawbacks, and pitfalls in the lipidomic analysis workflow. While this review is neither designed to be a step-by-step protocol by itself nor dedicated to a specific application of lipidomics, it should nevertheless provide the interested reader with links and original publications to obtain a comprehensive overview concerning the state-of-the-art practices in the field.

Goslin: A Grammar of Succinct Lipid Nomenclature.

We introduce Goslin, a polyglot grammar for common lipid shorthand nomenclatures based on the LIPID MAPS nomenclature and the shorthand nomenclature established by Liebisch and coauthors and used by LipidHome and SwissLipids. Goslin was designed to address the following pressing issues in the lipidomics field: (1) to simplify the implementation of lipid name handling for developers of mass spectrometry-based lipidomics tools, (2) to offer a tool that unifies and normalizes the main existing lipid name dialects enabling a lipidomics analysis in a high-throughput fashion, and (3) to provide a consistent mapping from lipid shorthand names to lipid building blocks and structural properties. We provide implementations of Goslin in four major programming languages, namely, C++, Java, Python 3, and R to kick-start adoption and integration. Further, we set up a web service for users to work with Goslin directly. All implementations are available free of charge under a permissive open source license.

LipidCreator workbench to probe the lipidomic landscape.

Mass spectrometry (MS)-based targeted lipidomics enables the robust quantification of selected lipids under various biological conditions but comprehensive software tools to support such analyses are lacking. Here we present LipidCreator, a software that fully supports targeted lipidomics assay development. LipidCreator offers a comprehensive framework to compute MS/MS fragment masses for over 60 lipid classes. LipidCreator provides all functionalities needed to define fragments, manage stable isotope labeling, optimize collision energy and generate in silico spectral libraries. We validate LipidCreator assays computationally and analytically and prove that it is capable to generate large targeted experiments to analyze blood and to dissect lipid-signaling pathways such as in human platelets.

The metaRbolomics Toolbox in Bioconductor and beyond.

Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub.

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.

The de.NBI / ELIXIR-DE training platform - Bioinformatics training in Germany and across Europe within ELIXIR.

The German Network for Bioinformatics Infrastructure (de.NBI) is a national and academic infrastructure funded by the German Federal Ministry of Education and Research (BMBF). The de.NBI provides (i) service, (ii) training, and (iii) cloud computing to users in life sciences research and biomedicine in Germany and Europe and (iv) fosters the cooperation of the German bioinformatics community with international network structures. The de.NBI members also run the German node (ELIXIR-DE) within the European ELIXIR infrastructure. The de.NBI / ELIXIR-DE training platform, also known as special interest group 3 (SIG 3) 'Training & Education', coordinates the bioinformatics training of de.NBI and the German ELIXIR node. The network provides a high-quality, coherent, timely, and impactful training program across its eight service centers. Life scientists learn how to handle and analyze biological big data more effectively by applying tools, standards and compute services provided by de.NBI. Since 2015, more than 300 training courses were carried out with about 6,000 participants and these courses received recommendation rates of almost 90% (status as of July 2020). In addition to face-to-face training courses, online training was introduced on the de.NBI website in 2016 and guidelines for the preparation of e-learning material were established in 2018. In 2016, ELIXIR-DE joined the ELIXIR training platform. Here, the de.NBI / ELIXIR-DE training platform collaborates with ELIXIR in training activities, advertising training courses via TeSS and discussions on the exchange of data for training events essential for quality assessment on both the technical and administrative levels. The de.NBI training program trained thousands of scientists from Germany and beyond in many different areas of bioinformatics.

Identification of key lipids critical for platelet activation by comprehensive analysis of the platelet lipidome.

Platelet integrity and function critically depend on lipid composition. However, the lipid inventory in platelets was hitherto not quantified. Here, we examined the lipidome of murine platelets using lipid-category tailored protocols on a quantitative lipidomics platform. We could show that the platelet lipidome comprises almost 400 lipid species and covers a concentration range of 7 orders of magnitude. A systematic comparison of the lipidomics network in resting and activated murine platelets, validated in human platelets, revealed that <20% of the platelet lipidome is changed upon activation, involving mainly lipids containing arachidonic acid. Sphingomyelin phosphodiesterase-1 (Smpd1) deficiency resulted in a very specific modulation of the platelet lipidome with an order of magnitude upregulation of lysosphingomyelin (SPC), and subsequent modification of platelet activation and thrombus formation. In conclusion, this first comprehensive quantitative lipidomic analysis of platelets sheds light on novel mechanisms important for platelet function, and has therefore the potential to open novel diagnostic and therapeutic opportunities.