Gerhard Liebisch, Eoin Fahy, Junken Aoki, Edward A. Dennis, Thierry Durand, Christer S. Ejsing, Maria Fedorova, Ivo Feussner, William J. Griffiths, Harald Köfeler, Alfred H. Merrill Jr., Robert C. Murphy, Valerie B. O’Donnell, Olga Oskolkova, Shankar Subramaniam, Michael J. O. Wakelam, Friedrich Spener

There is a need for a standardized, practical annotation for structures of lipid species derived from mass spectrometric approaches; i.e., for high-throughput data obtained from instruments operating in either high- or low-resolution modes. This proposal is based on common, officially accepted terms and builds upon the LIPID MAPS terminology. It aims to add defined levels of information below the LIPID MAPS nomenclature, as detailed chemical structures, including stereochemistry, are usually not automatically provided by mass spectrometric analysis. To this end, rules for lipid species annotation were developed that reflect the structural information derived from the analysis. For example, commonly used head group-specific analysis of glycerophospholipids (GP) by low-resolution instruments is neither capable of differentiating the fatty acids linked to the glycerol backbone nor able to define their bond type (ester, alkyl-, or alk-1-enyl-ether). This and other missing structural information is covered by the proposed shorthand notation presented here. Beyond GPs, we provide shorthand notation for fatty acids/acyls (FA), glycerolipids (GL), sphingolipids (SP), and sterols (ST). In summary, this defined shorthand nomenclature provides a standard methodology for reporting lipid species from mass spectrometric analysis and for constructing databases.

John A. Bowden, Alan Heckert, Candice Z. Ulmer, Christina M. Jones, Jeremy P. Koelmel, Laila Abdullah, Linda Ahonen, Yazen Alnouti, Aaron M. Armando, John M. Asara, Takeshi Bamba, John R. Barr, Jonas Bergquist, Christoph H. Borchers, Joost Brandsma, Susanne B. Breitkopf, Tomas Cajka, Amaury Cazenave-Gassiot, Antonio Checa, Michelle A. Cinel, Romain A. Colas, Serge Cremers, Edward A. Dennis, James E. Evans, Alexander Fauland, Oliver Fiehn, Michael S. Gardner, Timothy J. Garrett, Katherine H. Gotlinger, Jun Han, Yingying Huang, Aveline Huipeng Neo, Tuulia Hyötyläinen, Yoshihiro Izumi, Hongfeng Jiang, Houli Jiang, Jiang Jiang, Maureen Kachman, Reiko Kiyonami, Kristaps Klavins, Christian Klose, Harald C. Köfeler, Johan Kolmert, Therese Koal, Grielof Koster, Zsuzsanna Kuklenyik, Irwin J. Kurland, Michael Leadley, Karen Lin, Krishna Rao Maddipati, Danielle McDougall, Peter J. Meikle, Natalie A. Mellett, Cian Monnin, M. Arthur Moseley, Renu Nandakumar, Matej Oresic, Rainey Patterson, David Peake, Jason S. Pierce, Martin Post, Anthony D. Postle, Rebecca Pugh, Yunping Qiu, Oswald Quehenberger, Parsram Ramrup, Jon Rees, Barbara Rembiesa, Denis Reynaud, Mary R. Roth, Susanne Sales, Kai Schuhmann, Michal Laniado Schwartzman, Charles N. Serhan, Andrej Shevchenko, Stephen E. Somerville, Lisa St. John-Williams, Michal A. Surma, Hiroaki Takeda, Rhishikesh Thakare, J. Will Thompson, Federico Torta, Alexander Triebl, Martin Trötzmüller, S. J. Kumari Ubhayasekera, Dajana Vuckovic, Jacquelyn M. Weir, Ruth Welti, Markus R. Wenk, Craig E. Wheelock, Libin Yao, Min Yuan, Xueqing Heather Zhao, Senlin Zhou

Lipids are amongst the most important organic compounds in living organisms, where they serve as building blocks for cellular membranes as well as energy storage and signaling molecules. Lipidomics is the science of the large-scale determination of individual lipid species, and the underlying analytical technology that is used to identify and quantify the lipidome is generally mass spectrometry (MS). This review article provides an overview of the crucial steps in MS-based lipidomics workflows, including sample preparation, either liquid–liquid or solid-phase extraction, derivatization, chromatography, ion-mobility spectrometry, MS, and data processing by various software packages. The associated concepts are discussed from a technical perspective as well as in terms of their application. Furthermore, this article sheds light on recent advances in the technology used in this field and its current limitations. Particular emphasis is placed on data quality assurance and adequate data reporting; some of the most common pitfalls in lipidomics are discussed, along with how to circumvent them.

In the yeast Saccharomyces cerevisiae as in other eukaryotes non-polar lipids are a reservoir of energy and building blocks for membrane lipid synthesis. The yeast non-polar lipids, triacylglycerols (TG) and steryl esters (SE) are stored in so-called lipid particles/droplets (LP) as biologically inert form of fatty acids and sterols. To understand LP structure and function in more detail we investigated the molecular equipment of this compartment making use of mass spectrometric analysis of lipids (TG, SE, phospholipids) and proteins. We addressed the question whether or not lipid and protein composition of LP influence each other and performed analyses of LP from cells grown on two different carbon sources, glucose and oleate. Growth of cells on oleate caused dramatic cellular changes including accumulation of TG at the expense of SE, enhanced the amount of glycerophospholipids and strongly increased the degree of unsaturation in all lipid classes. Most interestingly, oleate as a carbon source led to adaptation of the LP proteome resulting in the appearance of several novel LP proteins. Localization of these new LP proteins was confirmed by cell fractionation. Proteomes of LP variants from cells grown on glucose or oleate, respectively, were compared and are discussed with emphasis on the different groups of proteins detected through this analysis. In summary, we demonstrate flexibility of the yeast LP lipidome and proteome and the ability of LP to adapt to environmental changes.

The mammalian target of rapamycin (mTOR) pathway integrates multiple signals and regulates crucial cell functions via the molecular complexes mTORC1 and mTORC2. These complexes are functionally dependent on their raptor (mTORC1) or rictor (mTORC2) subunits. mTOR has been associated with oligodendrocyte differentiation and myelination downstream of the PI3K/Akt pathway, but the functional contributions of individual complexes are largely unknown. We show, by oligodendrocyte-specific genetic deletion of Rptor and/or Rictor in the mouse, that CNS myelination is mainly dependent on mTORC1 function, with minor mTORC2 contributions. Myelin-associated lipogenesis and protein gene regulation are strongly reliant on mTORC1. We found that also oligodendrocyte-specific overactivation of mTORC1, via ablation of tuberous sclerosis complex 1 (TSC1), causes hypomyelination characterized by downregulation of Akt signaling and lipogenic pathways. Our data demonstrate that a delicately balanced regulation of mTORC1 activation and action in oligodendrocytes is essential for CNS myelination, which has practical overtones for understanding CNS myelin disorders.