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For:	Parsons H, Viant M. Variance stabilising transformations for NMR metabolomics data. BMC Syst Biol 2007. [DOI: 10.1186/1752-0509-1-s1-p22] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]

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Cited by Other Article(s)

Akyol S, Ugur Z, Yilmaz A, Ustun I, Gorti SKK, Oh K, McGuinness B, Passmore P, Kehoe PG, Maddens ME, Green BD, Graham SF. Lipid Profiling of Alzheimer's Disease Brain Highlights Enrichment in Glycerol(phospho)lipid, and Sphingolipid Metabolism. Cells 2021;10:2591. [PMID: 34685570 PMCID: PMC8534054 DOI: 10.3390/cells10102591] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 09/22/2021] [Accepted: 09/25/2021] [Indexed: 11/16/2022] Open

Abstract

Alzheimer's disease (AD) is reported to be closely linked with abnormal lipid metabolism. To gain a more comprehensive understanding of what causes AD and its subsequent development, we profiled the lipidome of postmortem (PM) human brains (neocortex) of people with a range of AD pathology (Braak 0-6). Using high-resolution mass spectrometry, we employed a semi-targeted, fully quantitative lipidomics profiling method (Lipidyzer) to compare the biochemical profiles of brain tissues from persons with mild AD (n = 15) and severe AD (AD; n = 16), and compared them with age-matched, cognitively normal controls (n = 16). Univariate analysis revealed that the concentrations of 420 lipid metabolites significantly (p < 0.05; q < 0.05) differed between AD and controls. A total of 49 lipid metabolites differed between mild AD and controls, and 439 differed between severe AD and mild AD. Interestingly, 13 different subclasses of lipids were significantly perturbed, including neutral lipids, glycerolipids, glycerophospholipids, and sphingolipids. Diacylglycerol (DAG) (14:0/14:0), triacylglycerol (TAG) (58:10/FA20:5), and TAG (48:4/FA18:3) were the most notably altered lipids when AD and control brains were compared (p < 0.05). When we compare mild AD and control brains, phosphatidylethanolamine (PE) (p-18:0/18:1), phosphatidylserine (PS) (18:1/18:2), and PS (14:0/22:6) differed the most (p < 0.05). PE (p-18:0/18:1), DAG (14:0/14:0), and PS (18:1/20:4) were identified as the most significantly perturbed lipids when AD and mild AD brains were compared (p < 0.05). Our analysis provides the most extensive lipid profiling yet undertaken in AD brain tissue and reveals the cumulative perturbation of several lipid pathways with progressive disease pathology. Lipidomics has considerable potential for studying AD etiology and identifying early diagnostic biomarkers.

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Affiliation(s)

Sumeyya Akyol Metabolomics Department, Beaumont Research Institute, Beaumont Health, Royal Oak, MI 48073, USA; (S.A.); (Z.U.); (A.Y.); (K.O.)
Zafer Ugur Metabolomics Department, Beaumont Research Institute, Beaumont Health, Royal Oak, MI 48073, USA; (S.A.); (Z.U.); (A.Y.); (K.O.)
Ali Yilmaz Metabolomics Department, Beaumont Research Institute, Beaumont Health, Royal Oak, MI 48073, USA; (S.A.); (Z.U.); (A.Y.); (K.O.) William Beaumont School of Medicine, Oakland University, Rochester, MI 48073, USA
Ilyas Ustun College of Computing and Digital Media, DePaul University, Chicago, IL 60604, USA; (I.U.); (M.E.M.)
Santosh Kapil Kumar Gorti SCIEX, 500 Old Connecticut, Framingham, MA 01701, USA;
Kyungjoon Oh Metabolomics Department, Beaumont Research Institute, Beaumont Health, Royal Oak, MI 48073, USA; (S.A.); (Z.U.); (A.Y.); (K.O.) Department of Obstetrics and Gynecology, Seoul National University Bundang Hospital, 82, Gumi-ro 173 Beon-gil, Bundang-gu, Seongnam-si 13620, Gyeonggi-do, Korea
Bernadette McGuinness Centre for Public Health, School of Medicine, Dentistry and Biomedical Sciences, Queen’s University Belfast, Belfast BT12 6BA, UK; (B.M.); (P.P.)
Peter Passmore Centre for Public Health, School of Medicine, Dentistry and Biomedical Sciences, Queen’s University Belfast, Belfast BT12 6BA, UK; (B.M.); (P.P.)
Patrick G. Kehoe Dementia Research Group, Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol BS10 5NB, UK;
Michael E. Maddens College of Computing and Digital Media, DePaul University, Chicago, IL 60604, USA; (I.U.); (M.E.M.)
Brian D. Green Institute for Global Food Security, School of Biological Sciences, Queen’s University Belfast, Belfast BT9 5DL, UK;
Stewart F. Graham Metabolomics Department, Beaumont Research Institute, Beaumont Health, Royal Oak, MI 48073, USA; (S.A.); (Z.U.); (A.Y.); (K.O.) College of Computing and Digital Media, DePaul University, Chicago, IL 60604, USA; (I.U.); (M.E.M.)

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Davidson RL, Weber RJM, Liu H, Sharma-Oates A, Viant MR. Galaxy-M: a Galaxy workflow for processing and analyzing direct infusion and liquid chromatography mass spectrometry-based metabolomics data. Gigascience 2016;5:10. [PMID: 26913198 PMCID: PMC4765054 DOI: 10.1186/s13742-016-0115-8] [Citation(s) in RCA: 71] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2015] [Accepted: 02/06/2016] [Indexed: 12/05/2022] Open

Abstract

BACKGROUND

Metabolomics is increasingly recognized as an invaluable tool in the biological, medical and environmental sciences yet lags behind the methodological maturity of other omics fields. To achieve its full potential, including the integration of multiple omics modalities, the accessibility, standardization and reproducibility of computational metabolomics tools must be improved significantly.

RESULTS

Here we present our end-to-end mass spectrometry metabolomics workflow in the widely used platform, Galaxy. Named Galaxy-M, our workflow has been developed for both direct infusion mass spectrometry (DIMS) and liquid chromatography mass spectrometry (LC-MS) metabolomics. The range of tools presented spans from processing of raw data, e.g. peak picking and alignment, through data cleansing, e.g. missing value imputation, to preparation for statistical analysis, e.g. normalization and scaling, and principal components analysis (PCA) with associated statistical evaluation. We demonstrate the ease of using these Galaxy workflows via the analysis of DIMS and LC-MS datasets, and provide PCA scores and associated statistics to help other users to ensure that they can accurately repeat the processing and analysis of these two datasets. Galaxy and data are all provided pre-installed in a virtual machine (VM) that can be downloaded from the GigaDB repository. Additionally, source code, executables and installation instructions are available from GitHub.

CONCLUSIONS

The Galaxy platform has enabled us to produce an easily accessible and reproducible computational metabolomics workflow. More tools could be added by the community to expand its functionality. We recommend that Galaxy-M workflow files are included within the supplementary information of publications, enabling metabolomics studies to achieve greater reproducibility.

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