1
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Liu CH, Shen PC, Lin WJ, Liu HC, Tsai MH, Huang TY, Chen IC, Lai YL, Wang YD, Hung MC, Cheng WC. LipidSig 2.0: integrating lipid characteristic insights into advanced lipidomics data analysis. Nucleic Acids Res 2024; 52:W390-W397. [PMID: 38709887 PMCID: PMC11223864 DOI: 10.1093/nar/gkae335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 03/29/2024] [Accepted: 04/16/2024] [Indexed: 05/08/2024] Open
Abstract
In the field of lipidomics, where the complexity of lipid structures and functions presents significant analytical challenges, LipidSig stands out as the first web-based platform providing integrated, comprehensive analysis for efficient data mining of lipidomic datasets. The upgraded LipidSig 2.0 (https://lipidsig.bioinfomics.org/) simplifies the process and empowers researchers to decipher the complex nature of lipids and link lipidomic data to specific characteristics and biological contexts. This tool markedly enhances the efficiency and depth of lipidomic research by autonomously identifying lipid species and assigning 29 comprehensive characteristics upon data entry. LipidSig 2.0 accommodates 24 data processing methods, streamlining diverse lipidomic datasets. The tool's expertise in automating intricate analytical processes, including data preprocessing, lipid ID annotation, differential expression, enrichment analysis, and network analysis, allows researchers to profoundly investigate lipid properties and their biological implications. Additional innovative features, such as the 'Network' function, offer a system biology perspective on lipid interactions, and the 'Multiple Group' analysis aids in examining complex experimental designs. With its comprehensive suite of features for analyzing and visualizing lipid properties, LipidSig 2.0 positions itself as an indispensable tool for advanced lipidomics research, paving the way for new insights into the role of lipids in cellular processes and disease development.
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Affiliation(s)
- Chia-Hsin Liu
- Cancer Biology and Precision Therapeutics Center, China Medical University, Taichung 404328, Taiwan
| | - Pei-Chun Shen
- Cancer Biology and Precision Therapeutics Center, China Medical University, Taichung 404328, Taiwan
| | - Wen-Jen Lin
- Cancer Biology and Precision Therapeutics Center, China Medical University, Taichung 404328, Taiwan
- School of Medicine, China Medical University, Taichung 404328, Taiwan
| | - Hsiu-Cheng Liu
- Cancer Biology and Precision Therapeutics Center, China Medical University, Taichung 404328, Taiwan
| | - Meng-Hsin Tsai
- Cancer Biology and Precision Therapeutics Center, China Medical University, Taichung 404328, Taiwan
| | - Tzu-Ya Huang
- Cancer Biology and Precision Therapeutics Center, China Medical University, Taichung 404328, Taiwan
| | - I-Chieh Chen
- Cancer Biology and Precision Therapeutics Center, China Medical University, Taichung 404328, Taiwan
| | - Yo-Liang Lai
- Department of Radiation Oncology, China Medical University, Taichung 404328, Taiwan
| | - Yu-De Wang
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung 404328, Taiwan
- Department of Urology, China Medical University, Taichung 404328, Taiwan
| | - Mien-Chie Hung
- Cancer Biology and Precision Therapeutics Center, China Medical University, Taichung 404328, Taiwan
- Institute of Biochemistry and Molecular Biology, China Medical University, Taichung 404328, Taiwan
- Molecular Medicine Center, China Medical University Hospital, China Medical University, Taichung 404328, Taiwan
- Department of Biotechnology, Asia University, Taichung 413305, Taiwan
| | - Wei-Chung Cheng
- Cancer Biology and Precision Therapeutics Center, China Medical University, Taichung 404328, Taiwan
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung 404328, Taiwan
- The Ph.D. program for Cancer Biology and Drug Discovery, China Medical University and Academia Sinica, Taichung 404328, Taiwan
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2
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Lattau SSJ, Borsch LM, Auf dem Brinke K, Klose C, Vinhoven L, Nietert M, Fitzner D. Plasma Lipidomic Profiling Using Mass Spectrometry for Multiple Sclerosis Diagnosis and Disease Activity Stratification (LipidMS). Int J Mol Sci 2024; 25:2483. [PMID: 38473733 DOI: 10.3390/ijms25052483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 02/02/2024] [Accepted: 02/13/2024] [Indexed: 03/14/2024] Open
Abstract
This investigation explores the potential of plasma lipidomic signatures for aiding in the diagnosis of Multiple Sclerosis (MS) and evaluating the clinical course and disease activity of diseased patients. Plasma samples from 60 patients with MS (PwMS) were clinically stratified to either a relapsing-remitting (RRMS) or a chronic progressive MS course and 60 age-matched controls were analyzed using state-of-the-art direct infusion quantitative shotgun lipidomics. To account for potential confounders, data were filtered for age and BMI correlations. The statistical analysis employed supervised and unsupervised multivariate data analysis techniques, including a principal component analysis (PCA), a partial least squares discriminant analysis (oPLS-DA) and a random forest (RF). To determine whether the significant absolute differences in the lipid subspecies have a relevant effect on the overall composition of the respective lipid classes, we introduce a class composition visualization (CCV). We identified 670 lipids across 16 classes. PwMS showed a significant increase in diacylglycerols (DAG), with DAG 16:0;0_18:1;0 being proven to be the lipid with the highest predictive ability for MS as determined by RF. The alterations in the phosphatidylethanolamines (PE) were mainly linked to RRMS while the alterations in the ether-bound PEs (PE O-) were found in chronic progressive MS. The amount of CE species was reduced in the CPMS cohort whereas TAG species were reduced in the RRMS patients, both lipid classes being relevant in lipid storage. Combining the above mentioned data analyses, distinct lipidomic signatures were isolated and shown to be correlated with clinical phenotypes. Our study suggests that specific plasma lipid profiles are not merely associated with the diagnosis of MS but instead point toward distinct clinical features in the individual patient paving the way for personalized therapy and an enhanced understanding of MS pathology.
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Affiliation(s)
| | - Lisa-Marie Borsch
- Department of Neurology, University Medical Center Göttingen, 37075 Göttingen, Germany
| | | | | | - Liza Vinhoven
- Department of Medical Bioinformatics, University Medical Center Göttingen, 37075 Göttingen, Germany
| | - Manuel Nietert
- Department of Medical Bioinformatics, University Medical Center Göttingen, 37075 Göttingen, Germany
| | - Dirk Fitzner
- Department of Neurology, University Medical Center Göttingen, 37075 Göttingen, Germany
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3
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Kopczynski D, Hoffmann N, Troppmair N, Coman C, Ekroos K, Kreutz MR, Liebisch G, Schwudke D, Ahrends R. LipidSpace: Simple Exploration, Reanalysis, and Quality Control of Large-Scale Lipidomics Studies. Anal Chem 2023; 95:15236-15244. [PMID: 37792961 PMCID: PMC10585661 DOI: 10.1021/acs.analchem.3c02449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 08/09/2023] [Indexed: 10/06/2023]
Abstract
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).
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Affiliation(s)
- Dominik Kopczynski
- Institute
of Analytical Chemistry, University of Vienna, Vienna 1070, Austria
| | - Nils Hoffmann
- Forschungszentrum
Jülich GmbH, Institute for Bio- and Geosciences (IBG-5), Jülich 52428, Germany
| | - Nina Troppmair
- Institute
of Analytical Chemistry, University of Vienna, Vienna 1070, Austria
| | - Cristina Coman
- Institute
of Analytical Chemistry, University of Vienna, Vienna 1070, Austria
| | - Kim Ekroos
- Lipidomics
Consulting Ltd., Esbo 02230, Finland
| | - Michael R. Kreutz
- Leibniz
Group “Dendritic Organelles and Synaptic Function” University
Medical Center Hamburg-Eppendorf, Center
for Molecular Neurobiology, ZMNH, Hamburg 20251, Germany
- RG
Neuroplasticity, Leibniz Institute for Neurobiology, Magdeburg 39118, Germany
| | - Gerhard Liebisch
- Institute
of Clinical Chemistry and Laboratory Medicine, University of Regensburg, Regensburg 93053, Germany
| | - Dominik Schwudke
- German
Center for Infection Research (DZIF), Site
Hamburg-Lübeck-Borstel-Riems, Hamburg 22297, Germany
- Airway
Research Center North (ARCN), German Center
for Lung Research (DZL), Grosshansdorf 22927, Germany
- Bioanalytical
Chemistry, Research Center Borstel, Borstel 23845, Germany
| | - Robert Ahrends
- Institute
of Analytical Chemistry, University of Vienna, Vienna 1070, Austria
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4
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Vondrackova M, Kopczynski D, Hoffmann N, Kuda O. LORA, Lipid Over-Representation Analysis Based on Structural Information. Anal Chem 2023; 95:12600-12604. [PMID: 37584663 PMCID: PMC10469370 DOI: 10.1021/acs.analchem.3c02039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 08/01/2023] [Indexed: 08/17/2023]
Abstract
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.
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Affiliation(s)
- Michaela Vondrackova
- Institute
of Physiology, Czech Academy of Sciences, Videnska 1083, 14220 Prague, Czechia
| | - Dominik Kopczynski
- Institute
of Analytical Chemistry, University of Vienna, 1090 Vienna, Austria
| | - Nils Hoffmann
- Forschungszentrum
Jülich, Institute of Bio- and Geosciences
(IBG-5), 52428 Jülich, Germany
| | - Ondrej Kuda
- Institute
of Physiology, Czech Academy of Sciences, Videnska 1083, 14220 Prague, Czechia
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5
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Ni Z, Wölk M, Jukes G, Mendivelso Espinosa K, Ahrends R, Aimo L, Alvarez-Jarreta J, Andrews S, Andrews R, Bridge A, Clair GC, Conroy MJ, Fahy E, Gaud C, Goracci L, Hartler J, Hoffmann N, Kopczyinki D, Korf A, Lopez-Clavijo AF, Malik A, Ackerman JM, Molenaar MR, O'Donovan C, Pluskal T, Shevchenko A, Slenter D, Siuzdak G, Kutmon M, Tsugawa H, Willighagen EL, Xia J, O'Donnell VB, Fedorova M. Guiding the choice of informatics software and tools for lipidomics research applications. Nat Methods 2023; 20:193-204. [PMID: 36543939 PMCID: PMC10263382 DOI: 10.1038/s41592-022-01710-0] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 11/02/2022] [Indexed: 12/24/2022]
Abstract
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.
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Affiliation(s)
- Zhixu Ni
- Center of Membrane Biochemistry and Lipid Research, Faculty of Medicine Carl Gustav Carus of TU Dresden, Dresden, Germany
| | - Michele Wölk
- Center of Membrane Biochemistry and Lipid Research, Faculty of Medicine Carl Gustav Carus of TU Dresden, Dresden, Germany
| | - Geoff Jukes
- Systems Immunity Research Institute, School of Medicine, Cardiff University, Cardiff, UK
| | | | - Robert Ahrends
- Department of Analytical Chemistry, University of Vienna, Vienna, Austria
| | - Lucila Aimo
- Swiss-Prot group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, Geneva, Switzerland
| | - Jorge Alvarez-Jarreta
- Systems Immunity Research Institute, School of Medicine, Cardiff University, Cardiff, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | - Simon Andrews
- Babraham Institute, Babraham Research Campus, Cambridge, UK
| | - Robert Andrews
- Systems Immunity Research Institute, School of Medicine, Cardiff University, Cardiff, UK
| | - Alan Bridge
- Swiss-Prot group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, Geneva, Switzerland
| | - Geremy C Clair
- Biological Science Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Matthew J Conroy
- Systems Immunity Research Institute, School of Medicine, Cardiff University, Cardiff, UK
| | - Eoin Fahy
- Department of Bioengineering, University of California, San Diego, CA, USA
| | - Caroline Gaud
- Babraham Institute, Babraham Research Campus, Cambridge, UK
| | - Laura Goracci
- Department of Chemistry, Biology and Biotechnology, University of Perugia, Perugia, Italy
| | - Jürgen Hartler
- Institute of Pharmaceutical Sciences, University of Graz, Graz, Austria
- Field of Excellence BioHealthe-University of Graz, Graz, Austria
| | - Nils Hoffmann
- Center for Biotechnology, University of Bielefeld, Bielefeld, Germany
| | - Dominik Kopczyinki
- Department of Analytical Chemistry, University of Vienna, Vienna, Austria
| | - Ansgar Korf
- Bruker Daltonics GmbH & Co. KG, Bremen, Germany
| | | | - Adnan Malik
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | | | - Martijn R Molenaar
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Claire O'Donovan
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | - Tomáš Pluskal
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic
| | - Andrej Shevchenko
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
| | - Denise Slenter
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, Maastricht, The Netherlands
| | - Gary Siuzdak
- Scripps Center for Metabolomics and Mass Spectrometry, The Scripps Research Institute, La Jolla, CA, USA
| | - Martina Kutmon
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, Maastricht, The Netherlands
- Maastricht Centre for Systems Biology, Maastricht University, Maastricht, The Netherlands
| | - Hiroshi Tsugawa
- Department of Biotechnology and Life Science, Tokyo University of Agriculture and Technology, Tokyo, Japan
- RIKEN Center for Sustainable Resource Science, Yokohama, Japan
- RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Graduate School of Medical Life Science, Yokohama City University, Yokohama, Japan
| | - Egon L Willighagen
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, Maastricht, The Netherlands
| | - Jianguo Xia
- Institute of Parasitology, McGill University, Montreal, Canada
| | - Valerie B O'Donnell
- Systems Immunity Research Institute, School of Medicine, Cardiff University, Cardiff, UK.
| | - Maria Fedorova
- Center of Membrane Biochemistry and Lipid Research, Faculty of Medicine Carl Gustav Carus of TU Dresden, Dresden, Germany.
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6
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Damiani T, Bonciarelli S, Thallinger GG, Koehler N, Krettler CA, Salihoğlu AK, Korf A, Pauling JK, Pluskal T, Ni Z, Goracci L. Software and Computational Tools for LC-MS-Based Epilipidomics: Challenges and Solutions. Anal Chem 2023; 95:287-303. [PMID: 36625108 PMCID: PMC9835057 DOI: 10.1021/acs.analchem.2c04406] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Affiliation(s)
- Tito Damiani
- Institute
of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Flemingovo nám. 2, 160 00 Praha 6, Czech Republic
| | - Stefano Bonciarelli
- Department
of Chemistry, Biology and Biotechnology, University of Perugia, Via Elce di Sotto 8, 06123 Perugia, Italy
| | - Gerhard G. Thallinger
- Institute
of Biomedical Informatics, Graz University
of Technology, 8010 Graz, Austria,
| | - Nikolai Koehler
- LipiTUM,
Chair of Experimental Bioinformatics, Technical
University of Munich, Maximus-von-Imhof Forum 3, 85354 Freising, Germany
| | | | - Arif K. Salihoğlu
- Department
of Physiology, Faculty of Medicine and Institute of Health Sciences, Karadeniz Technical University, 61080 Trabzon, Turkey
| | - Ansgar Korf
- Bruker Daltonics
GmbH & Co. KG, Fahrenheitstraße 4, 28359 Bremen, Germany
| | - Josch K. Pauling
- LipiTUM,
Chair of Experimental Bioinformatics, Technical
University of Munich, Maximus-von-Imhof Forum 3, 85354 Freising, Germany
| | - Tomáš Pluskal
- Institute
of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Flemingovo nám. 2, 160 00 Praha 6, Czech Republic
| | - Zhixu Ni
- Center of
Membrane Biochemistry and Lipid Research, University Hospital and Faculty of Perugia, Via Elce di Sotto 8, 06123 Perugia, Italy,
| | - Laura Goracci
- Department
of Chemistry, Biology and Biotechnology, University of Perugia, Via Elce di Sotto 8, 06123 Perugia, Italy,
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7
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Barrero-Rodríguez R, Rodriguez JM, Tarifa R, Vázquez J, Mastrangelo A, Ferrarini A. TurboPutative: A web server for data handling and metabolite classification in untargeted metabolomics. Front Mol Biosci 2022; 9:952149. [PMID: 36158581 PMCID: PMC9493301 DOI: 10.3389/fmolb.2022.952149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 08/01/2022] [Indexed: 11/13/2022] Open
Abstract
Untargeted metabolomics aims at measuring the entire set of metabolites in a wide range of biological samples. However, due to the high chemical diversity of metabolites that range from small to large and more complex molecules (i.e., amino acids/carbohydrates vs. phospholipids/gangliosides), the identification and characterization of the metabolome remain a major bottleneck. The first step of this process consists of searching the experimental monoisotopic mass against databases, thus resulting in a highly redundant/complex list of candidates. Despite the progress in this area, researchers are still forced to manually explore the resulting table in order to prioritize the most likely identifications for further biological interpretation or confirmation with standards. Here, we present TurboPutative (https://proteomics.cnic.es/TurboPutative/), a flexible and user-friendly web-based platform composed of four modules (Tagger, REname, RowMerger, and TPMetrics) that streamlines data handling, classification, and interpretability of untargeted LC-MS-based metabolomics data. Tagger classifies the different compounds and provides preliminary insights into the biological system studied. REname improves putative annotation handling and visualization, allowing the recognition of isomers and equivalent compounds and redundant data removal. RowMerger reduces the dataset size, facilitating the manual comparison among annotations. Finally, TPMetrics combines different datasets with feature intensity and relevant information for the researcher and calculates a score based on adduct probability and feature correlations, facilitating further identification, assessment, and interpretation of the results. The TurboPutative web application allows researchers in the metabolomics field that are dealing with massive datasets containing multiple putative annotations to reduce the number of these entries by 80%–90%, thus facilitating the extrapolation of biological knowledge and improving metabolite prioritization for subsequent pathway analysis. TurboPutative comprises a rapid, automated, and customizable workflow that can also be included in programmed bioinformatics pipelines through its RESTful API services. Users can explore the performance of each module through demo datasets supplied on the website. The platform will help the metabolomics community to speed up the arduous task of manual data curation that is required in the first steps of metabolite identification, improving the generation of biological knowledge.
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Affiliation(s)
- Rafael Barrero-Rodríguez
- Cardiovascular Proteomics Laboratory, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain
| | - Jose Manuel Rodriguez
- Cardiovascular Proteomics Laboratory, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain
| | - Rocío Tarifa
- Cardiovascular Proteomics Laboratory, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain
| | - Jesús Vázquez
- Cardiovascular Proteomics Laboratory, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain
| | - Annalaura Mastrangelo
- Immunobiology Laboratory, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain
- *Correspondence: Annalaura Mastrangelo, ; Alessia Ferrarini,
| | - Alessia Ferrarini
- Cardiovascular Proteomics Laboratory, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain
- *Correspondence: Annalaura Mastrangelo, ; Alessia Ferrarini,
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8
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Manke MC, Ahrends R, Borst O. Platelet lipid metabolism in vascular thrombo-inflammation. Pharmacol Ther 2022; 237:108258. [DOI: 10.1016/j.pharmthera.2022.108258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 07/12/2022] [Accepted: 07/25/2022] [Indexed: 11/17/2022]
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9
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Hoffmann N, Mayer G, Has C, Kopczynski D, Al Machot F, Schwudke D, Ahrends R, Marcus K, Eisenacher M, Turewicz M. A Current Encyclopedia of Bioinformatics Tools, Data Formats and Resources for Mass Spectrometry Lipidomics. Metabolites 2022; 12:metabo12070584. [PMID: 35888710 PMCID: PMC9319858 DOI: 10.3390/metabo12070584] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 06/17/2022] [Accepted: 06/19/2022] [Indexed: 12/13/2022] Open
Abstract
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.
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Affiliation(s)
- Nils Hoffmann
- Forschungszentrum Jülich GmbH, Institute for Bio- and Geosciences (IBG-5), 52425 Jülich, Germany
- Correspondence: (N.H.); (M.T.); Tel.: +49-(0)521-106-86780 (N.H.)
| | - Gerhard Mayer
- Institute of Medical Systems Biology, Ulm University, 89081 Ulm, Germany;
| | - Canan Has
- Biological Mass Spectrometry, Max Planck Institute of Molecular Cell Biology and Genetics, 01307 Dresden, Germany;
- University Hospital Carl Gustav Carus, 01307 Dresden, Germany
- CENTOGENE GmbH, 18055 Rostock, Germany
| | - Dominik Kopczynski
- Department of Analytical Chemistry, University of Vienna, 1090 Vienna, Austria; (D.K.); (R.A.)
| | - Fadi Al Machot
- Faculty of Science and Technology, Norwegian University for Life Science (NMBU), 1433 Ås, Norway;
| | - Dominik Schwudke
- Bioanalytical Chemistry, Forschungszentrum Borstel, Leibniz Lung Center, 23845 Borstel, Germany;
- Airway Research Center North, German Center for Lung Research (DZL), 23845 Borstel, Germany
- German Center for Infection Research (DZIF), TTU Tuberculosis, 23845 Borstel, Germany
| | - Robert Ahrends
- Department of Analytical Chemistry, University of Vienna, 1090 Vienna, Austria; (D.K.); (R.A.)
| | - Katrin Marcus
- Center for Protein Diagnostics (ProDi), Medical Proteome Analysis, Ruhr University Bochum, 44801 Bochum, Germany; (K.M.); (M.E.)
| | - Martin Eisenacher
- Center for Protein Diagnostics (ProDi), Medical Proteome Analysis, Ruhr University Bochum, 44801 Bochum, Germany; (K.M.); (M.E.)
- Faculty of Medicine, Medizinisches Proteom-Center, Ruhr University Bochum, 44801 Bochum, Germany
| | - Michael Turewicz
- Institute for Clinical Biochemistry and Pathobiochemistry, German Diabetes Center (DDZ), Leibniz Center for Diabetes Research at Heinrich-Heine-University Düsseldorf, 40225 Düsseldorf, Germany
- German Center for Diabetes Research (DZD), Partner Düsseldorf, 85764 Neuherberg, Germany
- Correspondence: (N.H.); (M.T.); Tel.: +49-(0)521-106-86780 (N.H.)
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Koch J, Watschinger K, Werner ER, Keller MA. Tricky Isomers—The Evolution of Analytical Strategies to Characterize Plasmalogens and Plasmanyl Ether Lipids. Front Cell Dev Biol 2022; 10:864716. [PMID: 35573699 PMCID: PMC9092451 DOI: 10.3389/fcell.2022.864716] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 03/23/2022] [Indexed: 01/27/2023] Open
Abstract
Typically, glycerophospholipids are represented with two esterified fatty acids. However, by up to 20%, a significant proportion of this lipid class carries an ether-linked fatty alcohol side chain at the sn-1 position, generally referred to as ether lipids, which shape their specific physicochemical properties. Among those, plasmalogens represent a distinct subgroup characterized by an sn-1 vinyl-ether double bond. The total loss of ether lipids in severe peroxisomal defects such as rhizomelic chondrodysplasia punctata indicates their crucial contribution to diverse cellular functions. An aberrant ether lipid metabolism has also been reported in multifactorial conditions including Alzheimer’s disease. Understanding the underlying pathological implications is hampered by the still unclear exact functional spectrum of ether lipids, especially in regard to the differentiation between the individual contributions of plasmalogens (plasmenyl lipids) and their non-vinyl-ether lipid (plasmanyl) counterparts. A primary reason for this is that exact identification and quantification of plasmalogens and other ether lipids poses a challenging and usually labor-intensive task. Diverse analytical methods for the detection of plasmalogens have been developed. Liquid chromatography–tandem mass spectrometry is increasingly used to resolve complex lipid mixtures, and with optimized parameters and specialized fragmentation strategies, discrimination between ethers and plasmalogens is feasible. In this review, we recapitulate historic and current methodologies for the recognition and quantification of these important lipids and will discuss developments in this field that can contribute to the characterization of plasmalogens in high structural detail.
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Affiliation(s)
- Jakob Koch
- Institute of Human Genetics, Medical University of Innsbruck, Innsbruck, Austria
| | - Katrin Watschinger
- Institute of Biological Chemistry, Biocenter, Medical University of Innsbruck, Innsbruck, Austria
| | - Ernst R. Werner
- Institute of Biological Chemistry, Biocenter, Medical University of Innsbruck, Innsbruck, Austria
| | - Markus A. Keller
- Institute of Human Genetics, Medical University of Innsbruck, Innsbruck, Austria
- *Correspondence: Markus A. Keller,
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Kopczynski D, Hoffmann N, Peng B, Liebisch G, Spener F, Ahrends R. Goslin 2.0 Implements the Recent Lipid Shorthand Nomenclature for MS-Derived Lipid Structures. Anal Chem 2022; 94:6097-6101. [PMID: 35404045 PMCID: PMC9047418 DOI: 10.1021/acs.analchem.1c05430] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 03/21/2022] [Indexed: 01/10/2023]
Abstract
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.
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Affiliation(s)
- Dominik Kopczynski
- Institute
of Analytical Chemistry, University of Vienna, 1090 Vienna, Austria
| | - Nils Hoffmann
- Center
for Biotechnology (CeBiTec), Bielefeld University, 33594 Bielefeld, Germany
| | - Bing Peng
- Division
of Rheumatology, Department of Medicine, Solna, Karolinska Institutet and Karolinska University Hospital, 17176 Stockholm, Sweden
| | - Gerhard Liebisch
- Institute
of Clinical Chemistry and Laboratory Medicine, Regensburg University Hospital, 93053 Regensburg, Germany
| | - Friedrich Spener
- Department
of Molecular Biosciences, University of
Graz, 8010 Graz, Austria
- Division
of Molecular Biology and Biochemistry, Gottfried Schatz Research Center, Medical University of Graz, 8036 Graz, Austria
| | - Robert Ahrends
- Institute
of Analytical Chemistry, University of Vienna, 1090 Vienna, Austria
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12
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Köfeler HC, Ahrends R, Baker ES, Ekroos K, Han X, Hoffmann N, Holčapek M, Wenk MR, Liebisch G. Recommendations for good practice in MS-based lipidomics. J Lipid Res 2021; 62:100138. [PMID: 34662536 PMCID: PMC8585648 DOI: 10.1016/j.jlr.2021.100138] [Citation(s) in RCA: 68] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 09/29/2021] [Accepted: 10/01/2021] [Indexed: 12/17/2022] Open
Abstract
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.
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Affiliation(s)
- Harald C Köfeler
- Core Facility Mass Spectrometry, Medical University of Graz, Graz, Austria.
| | - Robert Ahrends
- Department for Analytical Chemistry, University of Vienna, Vienna, Austria
| | - Erin S Baker
- Department of Chemistry, North Carolina State University, Raleigh, NC, USA
| | - Kim Ekroos
- Lipidomics Consulting Ltd., Esbo, Finland
| | - Xianlin Han
- Barshop Inst Longev & Aging Studies, Univ Texas Hlth Sci Ctr San Antonio, San Antonio, TX, USA
| | - Nils Hoffmann
- Center for Biotechnology, Universität Bielefeld, Bielefeld, Germany
| | - Michal Holčapek
- Department of Analytical Chemistry, Faculty of Chemical Technology, University of Pardubice, Pardubice, Czech Republic
| | - Markus R Wenk
- Singapore Lipidomics Incubator (SLING), Department of Biochemistry, YLL School of Medicine, National University of Singapore, Singapore, Singapore
| | - Gerhard Liebisch
- Institute of Clinical Chemistry and Laboratory Medicine, Regensburg University Hospital, Regensburg, Germany.
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Mohamed A, Hill MM. LipidSuite: interactive web server for lipidomics differential and enrichment analysis. Nucleic Acids Res 2021; 49:W346-W351. [PMID: 33950258 PMCID: PMC8262688 DOI: 10.1093/nar/gkab327] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 04/06/2021] [Accepted: 04/19/2021] [Indexed: 11/14/2022] Open
Abstract
Advances in mass spectrometry enabled high throughput profiling of lipids but differential analysis and biological interpretation of lipidomics datasets remains challenging. To overcome this barrier, we present LipidSuite, an end-to-end differential lipidomics data analysis server. LipidSuite offers a step-by-step workflow for preprocessing, exploration, differential analysis and enrichment analysis of untargeted and targeted lipidomics. Three lipidomics data formats are accepted for upload: mwTab file from Metabolomics Workbench, Skyline CSV Export, and a numerical matrix. Experimental variables to be used in analysis are uploaded in a separate file. Conventional lipid names are automatically parsed to enable lipid class and chain length analyses. Users can interactively explore data, choose subsets based on sample types or lipid classes or characteristics, and conduct univariate, multivariate and unsupervised analyses. For complex experimental designs and clinical cohorts, LipidSuite offers confounding variables adjustment. Finally, data tables and plots can be both interactively viewed or downloaded for publication or reports. Overall, we anticipate this free, user-friendly webserver to facilitate differential lipidomics data analysis and re-analysis, and fully harness biological interpretation from lipidomics datasets. LipidSuite is freely available at http://suite.lipidr.org.
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Affiliation(s)
- Ahmed Mohamed
- Precision & Systems Biomedicine Laboratory, QIMR Berghofer Medical Research Institute, Herston, QLD 4006, Australia
| | - Michelle M Hill
- Precision & Systems Biomedicine Laboratory, QIMR Berghofer Medical Research Institute, Herston, QLD 4006, Australia.,Centre for Clinical Research, Faculty of Medicine, The University of Queensland, Herston, QLD 4006, Australia
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High-coverage lipidomics for functional lipid and pathway analyses. Anal Chim Acta 2020; 1147:199-210. [PMID: 33485579 DOI: 10.1016/j.aca.2020.11.024] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Revised: 11/10/2020] [Accepted: 11/18/2020] [Indexed: 12/19/2022]
Abstract
Rapid advances in front-end separation approaches and analytical technologies have accelerated the development of lipidomics, particularly in terms of increasing analytical coverage to encompass an expanding repertoire of lipids within a single analytical approach. Developments in lipid pathway analysis, however, have somewhat lingered behind, primarily due to (1) the lack of coherent alignment between lipid identifiers in common databases versus that generated from experiments, owing to the differing structural resolution of lipids at molecular level that is specific to the analytical approaches adopted by various laboratories; (2) the immense complexity of lipid metabolic relationships that may entail head group changes, fatty acyls modifications of various forms (e.g. elongation, desaturation, oxidation), as well as active remodeling that demands a multidimensional, panoramic view to take into account all possibilities in lipid pathway analyses. Herein, we discuss current efforts undertaken to address these challenges, as well as alternative form of "pathway analyses" that may be particularly useful for uncovering functional lipid interactions under different biological contexts. Consolidating lipid pathway analyses will be indispensable in facilitating the transition of lipidomics from its prior role of phenotype validation to a hypothesis-generating tool that uncovers novel molecular targets to drive downstream mechanistic pursuits under biomedical settings.
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