The scientific field of lipidomics has shown a constantly growing publication number in recent years, which is accompanied by an increasing need for quality standards. While the official shorthand nomenclature of lipids is a first and important step toward a reporting quality tool, an additional point score would reflect the quality of reported data at an even more detailed granularity. Thus, we propose a lipidomics scoring scheme that considers all the different layers of analytical information to be obtained by mass spectrometry, chromatography, and ion mobility spectrometry and awards scoring points for each of them. Furthermore, the scoring scheme is integrated with the annotation levels as proposed by the official shorthand nomenclature, with a point score, which roughly correlates with the annotated compound details. The merit of such a scoring system is the fact that it abstracts evidence for structural information into a number, which gives even the nonlipidomics expert an idea about the reporting, and by extension, data quality at first glance. Additionally, it could serve as an aid for internal quality control and for data quality assessment in the peer review process.

Barshop Institute for Longevity and Aging Studies Department of Medicine University of Texas Health Science Center at San Antonio San Antonio TX

College of Health and Life Sciences Hamad Bin Khalifa University Doha Qatar; Department of Biochemistry Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore

Core Facility Mass Spectrometry ZMF Medical University of Graz Graz Austria

Department of Chemistry Tsinghua University Beijing China

Department of Chemistry University of North Carolina at Chapel Hill Chapel Hill NC

Forschungszentrum Jülich GmbH Institute of Bio and Geosciences Jülich Germany; Institute of Analytical Chemistry University of Vienna Vienna Austria

Institute of Analytical Chemistry University of Vienna Vienna Austria

Institute of Clinical Chemistry and Laboratory Medicine University of Regensburg Regensburg Germany

Lipidomics Consulting Ltd Esbo Finland

University of Pardubice Faculty of Chemical Technology Department of Analytical Chemistry Pardubice Czech Republic

Zobrazit více v PubMed

van Meer G., Voelker D.R., Feigenson G.W. Membrane lipids: where they are and how they behave. Nat. Rev. Mol. Cell Bio. 2008;9:112–124. PubMed PMC

Quehenberger O., Armando A.M., Brown A.H., Milne S.B., Myers D.S., Merrill A.H., et al. Lipidomics reveals a remarkable diversity of lipids in human plasma. J. Lipid Res. 2010;51:3299–3305. PubMed PMC

Brugger B., Erben G., Sandhoff R., Wieland F.T., Lehmann W.D. Quantitative analysis of biological membrane lipids at the low picomole level by nano-electrospray ionization tandem mass spectrometry. P. Natl. Acad. Sci. U. S. A. 1997;94:2339–2344. PubMed PMC

Wenk M.R. The emerging field of lipidomics. Nat. Rev. Drug Discov. 2005;4:594–610. PubMed

Han X.L., Gross R.W. Shotgun lipidomics: electrospray ionization mass spectrometric analysis and quantitation of cellular lipidomes directly from crude extracts of biological samples. Mass Spectrom. Rev. 2005;24:367–412. PubMed

Ekroos K., Chernushevich I.V., Simons K., Shevchenko A. Quantitative profiling of phospholipids by multiple precursor ion scanning on a hybrid quadrupole time-of-flight mass spectrometer. Anal. Chem. 2002;74:941–949. PubMed

Fahy E., Subramaniam S., Brown H.A., Glass C.K., Merrill A.H., Murphy R.C., et al. A comprehensive classification system for lipids. J. Lipid Res. 2005;46:839–861. PubMed

Liebisch G., Vizcaino J.A., Kofeler H., Trotzmuller M., Griffiths W.J., Schmitz G., et al. Shorthand notation for lipid structures derived from mass spectrometry. J. Lipid Res. 2013;54:1523–1530. PubMed PMC

Liebisch G., Fahy E., Aoki J., Dennis E.A., Durand T., Ejsing C.S., et al. Update on LIPID MAPS classification, nomenclature, and shorthand notation for MS-derived lipid structures. J. Lipid Res. 2020;61:1539–1555. PubMed PMC

Liebisch G., Ejsing C.S., Ekroos K. Identification and annotation of lipid species in metabolomics studies need improvement. Clin. Chem. 2015;61:1542–1544. PubMed

Liebisch G., Ahrends R., Arita M., Arita M., Bowden J.A., Ejsing C.S., et al. Lipidomics needs more standardization. Nat. Metab. 2019;1:745–747. PubMed

Kofeler H.C., Eichmann T.O., Ahrends R., Bowden J.A., Danne-Rasche N., Dennis E.A., et al. Quality control requirements for the correct annotation of lipidomics data. Nat. Commun. 2021;12:4771. PubMed PMC

Pauling J.K., Hermansson M., Hartler J., Christiansen K., Gallego S.F., Peng B., et al. Proposal for a common nomenclature for fragment ions in mass spectra of lipids. PLoS One. 2017;12 PubMed PMC

Hsu F.F., Turk J. Electrospray ionization with low-energy collisionally activated dissociation tandem mass spectrometry of glycerophospholipids: mechanisms of fragmentation and structural characterization. J. Chromatogr. B, Anal. Tech. Biomed. Life Sci. 2009;877:2673–2695. PubMed PMC

Hong S., Lu Y., Yang R., Gotlinger K.H., Petasis N.A., Serhan C.N. Resolvin D1, protectin D1, and related docosahexaenoic acid-derived products: analysis via electrospray/low energy tandem mass spectrometry based on spectra and fragmentation mechanisms. J. Am. Soc. Mass Spectrom. 2007;18:128–144. PubMed PMC

Michaud A.L., Yurawecz M.P., Delmonte P., Corl B.A., Bauman D.E., Brenna J.T. Identification and characterization of conjugated fatty acid methyl esters of mixed double bond geometry by acetonitrile chemical ionization tandem mass spectrometry. Anal. Chem. 2003;75:4925–4930. PubMed

Ohba M., Saeki K., Koga T., Okuno T., Kobayashi Y., Yokomizo T. Profiling of bioactive lipids in different dendritic cell subsets using an improved multiplex quantitative LC-MS/MS method. Biochem. Biophys. Res. Commun. 2018;504:562–568. PubMed

Bui H.H., Sanders P.E., Bodenmiller D., Kuo M.S., Donoho G.P., Fischl A.S. Direct analysis of PI(3,4,5)P3 using liquid chromatography electrospray ionization tandem mass spectrometry. Anal. Biochem. 2018;547:66–76. PubMed

Adas F., Picart D., Berthou F., Simon B., Amet Y. Liquid chromatography-mass spectrometry and gas chromatography-mass spectrometry of omega- and (omega-1)-hydroxylated metabolites of elaidic and oleic acids in human and rat liver microsomes. J. Chromatogr. B Biomed. Sci. Appl. 1998;714:133–144. PubMed

Lisa M., Holcapek M. Characterization of triacylglycerol enantiomers using chiral HPLC/APCI-MS and synthesis of enantiomeric triacylglycerols. Anal. Chem. 2013;85:1852–1859. PubMed

Dodds J.N., Baker E.S. Ion mobility spectrometry: fundamental concepts, instrumentation, applications, and the road ahead. J. Am. Soc. Mass Spectrom. 2019;30:2185–2195. PubMed PMC

Groessl M., Graf S., Knochenmuss R. High resolution ion mobility-mass spectrometry for separation and identification of isomeric lipids. Analyst. 2015;140:6904–6911. PubMed

Paglia G., Kliman M., Claude E., Geromanos S., Astarita G. Applications of ion-mobility mass spectrometry for lipid analysis. Anal. Bioanal. Chem. 2015;407:4995–5007. PubMed

Hartler J., Triebl A., Ziegl A., Trotzmuller M., Rechberger G.N., Zeleznik O.A., et al. Deciphering lipid structures based on platform-independent decision rules. Nat. Methods. 2017;14:1171–1174. PubMed PMC

Pittenauer E., Allmaier G. The renaissance of high-energy CID for structural elucidation of complex lipids: MALDI-TOF/RTOF-MS of alkali cationized triacylglycerols. J. Am. Soc. Mass Spectrom. 2009;20:1037–1047. PubMed

Williams P.E., Klein D.R., Greer S.M., Brodbelt J.S. Pinpointing double bond and sn-positions in glycerophospholipids via hybrid 193 nm ultraviolet photodissociation (UVPD) mass spectrometry. J. Am. Chem. Soc. 2017;139:15681–15690. PubMed PMC

Ryan E., Nguyen C.Q.N., Shiea C., Reid G.E. Detailed structural characterization of sphingolipids via 193 nm ultraviolet photodissociation and ultra high resolution tandem mass spectrometry. J. Am. Soc. Mass Spectrom. 2017;28:1406–1419. PubMed

Brown S.H.J., Mitchell T.W., Blanksby S.J. Analysis of unsaturated lipids by ozone-induced dissociation. BBA-Mol. Cell Biol. L. 2011;1811:807–817. PubMed

Zhang W., Zhang D., Chen Q., Wu J., Ouyang Z., Xia Y. Online photochemical derivatization enables comprehensive mass spectrometric analysis of unsaturated phospholipid isomers. Nat. Commun. 2019;10:79. PubMed PMC

Baba T., Campbell J.L., Le Blanc J.C.Y., Baker P.R.S., Ikeda K. Quantitative structural multiclass lipidomics using differential mobility: electron impact excitation of ions from organics (EIEIO) mass spectrometry. J. Lipid Res. 2018;59:910–919. PubMed PMC

Prost K., Birk J.J., Lehndorff E., Gerlach R., Amelung W. Steroid biomarkers revisited - improved source identification of faecal remains in archaeological soil material. PLoS One. 2017;12 PubMed PMC

Wojcik R., Webb I.K., Deng L., Garimella S.V., Prost S.A., Ibrahim Y.M., et al. Lipid and glycolipid isomer analyses using ultra-high resolution ion mobility spectrometry separations. Int. J. Mol. Sci. 2017;18:183. PubMed PMC

Sud M., Fahy E., Cotter D., Brown A., Dennis E.A., Glass C.K., et al. LMSD: LIPID MAPS structure database. Nucleic Acids Res. 2007;35:D527–D532. PubMed PMC

Team R.C. R Foundation for Statistical Computing; Vienna, Austria: 2023. R: A Language and Environment for Statistical Computing.

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. PubMed PMC

Kopczynski D., Ejsing C.S., McDonald J.G., Bamba T., Baker E.S., Bertrand-Michel J., et al. The lipidomics reporting checklist a framework for transparency of lipidomic experiments and repurposing resource data. J. Lipid Res. 2024;65 PubMed PMC