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• W2805686311 abstract "BioanalysisVol. 10, No. 11 CommentaryUnderstanding neurodegenerative disorders by MS-based lipidomicsCosima D Calvano, Francesco Palmisano & Tommaso RI CataldiCosima D Calvano Dipartimento di Chimica, Università degli Studi di Bari Aldo Moro, via Orabona 4, 70126 Bari, ItalySearch for more papers by this author, Francesco Palmisano Dipartimento di Chimica, Università degli Studi di Bari Aldo Moro, via Orabona 4, 70126 Bari, ItalySearch for more papers by this author & Tommaso RI Cataldi*Author for correspondence: E-mail Address: tommaso.cataldi@uniba.it Dipartimento di Chimica, Università degli Studi di Bari Aldo Moro, via Orabona 4, 70126 Bari, ItalySearch for more papers by this authorPublished Online:4 Jun 2018https://doi.org/10.4155/bio-2018-0023AboutSectionsView ArticleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinkedInReddit View articleKeywords: biomarkerslipidomicsMSneurodegenerative diseasesneurolipidomicsReferences1 Wenk MR. The emerging field of lipidomics. Nat. Rev. Drug Discov. 4(7), 594–610 (2005).Crossref, Medline, CAS, Google Scholar2 Fahy E, Cotter D, Sud M, Subramaniam S. Lipid classification, structures and tools. Biochim. Biophys. Acta 1811(11), 637–647 (2011).Crossref, Medline, CAS, Google Scholar3 The Global Voice on Dementia. www.alz.co.uk/research/statistics.Google Scholar4 Kumar A, Singh A, Ekavali. A review on Alzheimer's disease pathophysiology and its management: an update. Pharmacol. Rep. 67(2), 195–203 (2015).Crossref, Medline, CAS, Google Scholar5 Han X. Neurolipidomics: challenges and developments. Front. Biosci. 12(1), 2601–2615 (2007).Crossref, Medline, CAS, Google Scholar6 MacEyka M, Spiegel S. Sphingolipid metabolites in inflammatory disease. Nature 510(7503), 58–67 (2014).Crossref, Medline, CAS, Google Scholar7 Cheng H, Guan S, Han X. Abundance of triacylglycerols in ganglia and their depletion in diabetic mice: implications for the role of altered triacylglycerols in diabetic neuropathy. J. Neurochem. 97(5), 1288–1300 (2006).Crossref, Medline, CAS, Google Scholar8 Han X. Multi-dimensional mass spectrometry-based shotgun lipidomics and the altered lipids at the mild cognitive impairment stage of Alzheimer's disease. Biochim. Biophys. Acta 1801(8), 774–783 (2010).Crossref, Medline, CAS, Google Scholar9 Han X, Rozen S, Boyle SH et al. Metabolomics in early Alzheimer's disease: identification of altered plasma sphingolipidome using shotgun lipidomics. PLoS ONE 6(7), e21643 (2011).Crossref, Medline, CAS, Google Scholar10 Proitsi P, Kim M, Whiley L et al. Plasma lipidomics analysis finds long chain cholesteryl esters to be associated with Alzheimer's disease. Transl. Psychiatry 5(1), e494 (2015).Crossref, Medline, CAS, Google Scholar11 Mapstone M, Cheema AK, Fiandaca MS et al. Plasma phospholipids identify antecedent memory impairment in older adults. Nat. Med. 20(4), 415–418 (2014).Crossref, Medline, CAS, Google Scholar12 Anand S, Barnes JM, Young SA et al. Discovery and confirmation of diagnostic serum lipid biomarkers for Alzheimer's disease using direct infusion mass spectrometry. J. Alzheimer's Dis. 59(1), 277–290 (2017).Medline, CAS, Google Scholar13 Wong MW, Braidy N, Poljak A, Pickford R, Thambisetty M, Sachdev PS. Dysregulation of lipids in Alzheimer's disease and their role as potential biomarkers. Alzheimer's Dement. 13(7), 810–827 (2017).Crossref, Medline, Google Scholar14 Chen-Plotkin AS. Unbiased approaches to biomarker discovery in neurodegenerative diseases. Neuron 84(3), 594–607 (2014).Crossref, Medline, CAS, Google Scholar15 Schubert KO, Weiland F, Baune BT, Hoffmann P. The use of MALDI-MSI in the investigation of psychiatric and neurodegenerative disorders: a review. Proteomics 16(11–12), 1747–1758 (2016).Crossref, Medline, CAS, Google Scholar16 Hong JH, Kang JW, Kim DK et al. Global changes of phospholipids identified by MALDI imaging mass spectrometry in a mouse model of Alzheimer's disease. J. Lipid Res. 57(1), 36–45 (2016).Crossref, Medline, CAS, Google Scholar17 Kaya I, Brinet D, Michno W et al. Delineating amyloid plaque associated neuronal sphingolipids in transgenic Alzheimer's disease mice (tgArcSwe) using MALDI imaging mass spectrometry. ACS Chem. Neurosci. 8(2), 347–355 (2017).Crossref, Medline, CAS, Google Scholar18 Yuki D, Sugiura Y, Zaima N et al. Hydroxylated and non-hydroxylated sulfatide are distinctly distributed in the human cerebral cortex. Neuroscience 193, 44–53 (2011).Crossref, Medline, CAS, Google Scholar19 Gónzalez de San Román E, Manuel I, Giralt MT, Ferrer I, Rodríguez-Puertas R. Imaging mass spectrometry (IMS) of cortical lipids from preclinical to severe stages of Alzheimer's disease. Biochim. Biophys. Acta 1859(9), 1604–1614 (2017).Crossref, Medline, CAS, Google Scholar20 Yuki D, Sugiura Y, Zaima N et al. DHA-PC and PSD-95 decrease after loss of synaptophysin and before neuronal loss in patients with Alzheimer's disease. Sci. Rep. 4(1), 7130 (2014).Crossref, Medline, CAS, Google Scholar21 Gö J, Streffer J, David D et al. Transgenic animal models of Alzheimer's disease and related disorders: histopathology, behavior and therapy. Mol. Psychiatry 9(7), 664–683 (2004).Crossref, Medline, Google Scholar22 Jucker M. The benefits and limitations of animal models for translational research in neurodegenerative diseases. Nat. Med. 16(11), 1210–1214 (2010).Crossref, Medline, CAS, Google Scholar23 Auburger G, Klinkenberg M, Drost J et al. Primary skin fibroblasts as a model of Parkinson's disease. Mol. Neurobiol. 46(1), 20–27 (2012).Crossref, Medline, CAS, Google Scholar24 Scherer M, Leuthäuser-Jaschinski K, Ecker J, Schmitz G, Liebisch G. A rapid and quantitative LC-MS/MS method to profile sphingolipids. J. Lipid Res. 51(7), 2001–2011 (2010).Crossref, Medline, CAS, Google Scholar25 Herzog K, Pras-Raves ML, Ferdinandusse S et al. Functional characterisation of peroxisomal β-oxidation disorders in fibroblasts using lipidomics. J. Inherit. Metab. Dis. 41(3), 479–487 (2017).Crossref, Medline, Google Scholar26 Herzog K, Pras-Raves ML, Vervaart MAT et al. Lipidomic analysis of fibroblasts from Zellweger spectrum disorder patients identifies disease-specific phospholipid ratios. J. Lipid Res. 57(8), 1447–1454 (2016).Crossref, Medline, CAS, Google Scholar27 Lobasso S, Tanzarella P, Vergara D, Maffia M, Cocco T, Corcelli A. Lipid profiling of parkin-mutant human skin fibroblasts. J. Cell. Physiol. 232(12), 3540–3551 (2017).Crossref, Medline, CAS, Google Scholar28 Calvano CD, Glaciale M, Granafei S et al. Advanced mass spectrometric techniques for the untargeted lipidome characterization of fibroblasts in early on-set Parkinson's disease patients. In: XXVI Meeting of the Italian Chemical Society – Paestum (SA), 10–14 September 2017. Italian Chemical Society, ISBN 9788886208802, Rome, Italy, 144 (2017).Google ScholarFiguresReferencesRelatedDetailsCited ByNontargeted Serum Lipid Profiling of Nonalcoholic Steatohepatitis by Multisegment Injection–Nonaqueous Capillary Electrophoresis–Mass Spectrometry: A Multiplexed Separation Platform for Resolving Ionic Lipids22 October 2021 | Journal of Proteome Research, Vol. 21, No. 3Application of Sebum Lipidomics to Biomarkers Discovery in Neurodegenerative Diseases29 November 2021 | Metabolites, Vol. 11, No. 12Lipidomics characterization of the mechanism of Cynomorium songaricum polysaccharide on treating type 2 diabetesJournal of Chromatography B, Vol. 1176Editorial to the Special Issue “Lipidomics and Neurodegenerative Diseases”28 January 2021 | International Journal of Molecular Sciences, Vol. 22, No. 3Phospholipidomics of peripheral blood mononuclear cells (PBMCs): the tricky case of children with autism spectrum disorder (ASD) and their healthy siblings1 August 2020 | Analytical and Bioanalytical Chemistry, Vol. 412, No. 25Effect of Heavy Ion 12C6+ Radiation on Lipid Constitution in the Rat Brain18 August 2020 | Molecules, Vol. 25, No. 16Mass spectrometry imaging of free-floating brain sections detects pathological lipid distribution in a mouse model of Alzheimer's-like pathology1 January 2020 | The Analyst, Vol. 145, No. 13Sphingolipids as prognostic biomarkers of neurodegeneration, neuroinflammation, and psychiatric diseases and their emerging role in lipidomic investigation methodsAdvanced Drug Delivery Reviews, Vol. 159Identification of neutral and acidic glycosphingolipids in the human dermal fibroblastsAnalytical Biochemistry, Vol. 581Imaging Mass Spectrometry: A New Tool to Assess Molecular Underpinnings of Neurodegeneration10 July 2019 | Metabolites, Vol. 9, No. 7An NMR-based lipidomic approach to identify Parkinson's disease-stage specific lipoprotein–lipid signatures in plasma1 January 2019 | The Analyst, Vol. 144, No. 4 Vol. 10, No. 11 Follow us on social media for the latest updates Metrics Downloaded 252 times History Received 25 January 2018 Accepted 21 March 2018 Published online 4 June 2018 Published in print June 2018 Information© 2018 Newlands PressKeywordsbiomarkerslipidomicsMSneurodegenerative diseasesneurolipidomicsAcknowledgementsThe authors apologize to all their colleagues whose important work could not be directly cited. We wish to acknowledge Fondazione Puglia for financial support of the project “Sviluppo ed uso di tecniche avanzate di spettrometria di massa per la caratterizzazione del profilo lipidomico cellulare e mitocondriale in fibroblasti controllo e di pazienti affetti da morbo di Parkinson”.Financial & competing interests disclosureThe authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.No writing assistance was utilized in the production of this manuscript.PDF download" @default.
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