Elucidating the diversity of lipid structures by computational mass spectrometry

We establish a state-of-the-art untargeted lipidomics platform, packaged in MS-DIAL 4, with a lipidome atlas of retention time and collision cross section (CCS), and tandem mass spectrum of 581,047 lipid structures of 117 lipid subclass categories.
Elucidating the diversity of lipid structures by computational mass spectrometry

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By Hiroshi Tsugawa & Makoto Arita

Lipids are extremely diverse molecules, and this is evidenced by the curation of >40,000 structures in LIPID MAPS [1]. This diversity of lipids creates complex biological systems with lipids acting as key components of cellular membranes, signaling molecules, and energy-storage molecules and substrates. In general, the dysregulation of lipid metabolism is associated with several human diseases such as obesity, atherosclerosis, stroke, hypertension, and diabetes. Thus, the precise determination of different molecular species of lipid—a process we term “LipoQuality”, is imperative to understanding their function in physiology and disease and for discovering novel bioactive lipids that modulate biological phenotypes. With LipoQuality, a word coined from the words “lipid” and “quality,” we aim to emphasize the notion that not only the quantity of lipids, but also the quality or the molecular diversity of lipids is important for human health and disease. For more information, see the website.     

            Liquid chromatography tandem mass spectrometry (LC-MS/MS) and ion mobility tandem mass spectrometry (IM-MS/MS) are the gold-standard techniques in lipidomics and metabolomics. They both provide thousands of molecule ions per biospecimen. Importantly, the full potential of lipidomics is only realized in conjunction with computational mass spectrometry (CompMS). CompMS aims to elucidate complex biological systems using a comprehensive profile of the metabolome (and proteome) by the computational conversion of raw MS data into molecule structures [2]. Using CompMS to reveal the structure of previously unknown lipid metabolites will uncover novel lipid pathways along with the integration of other omics data and imaging MS. Thus, advances in CompMS for lipidomics have the potential to substantially impact biology in advancing biomarker discovery, drug development, and clinical applications.

            In our latest study [3], we establish a state-of-the-art untargeted lipidomics platform, packaged in MS-DIAL 4 (Mass Spectrometry-Data Independent AnaLysis software version 4; http://prime.psc.riken.jp/). MS-DIAL 4 untangles lipid mass spectral fragmentations to make an accurate (~FDR rate of 1 to 2%) atlas of lipids. We analyzed 1,056 biological samples from a range of sources including human blood, mouse tissues, other mammalian cells, algae, and plants. From this we derived a catalog of 8,051 different lipid molecular species in 117 lipid subclass categories. We also created a comprehensive database of retention time and collision cross section (CCS) based on machine learning of Retip [4], and MS/MS of 581,047 lipid structures to correctly characterize lipids. Our product has been published as a “lipidome atlas”.

Notably, MS-DIAL 4 provides appropriate structure representations of 117 lipid subclasses through fragment evidence for species-, molecular species-, and sn-position level annotations to unequivocally translate lipidomics data into biology. The lipid classifications follow the Lipid MAPS [1] definition, structures are represented by an international shorthand notation system [5], and the lipidomics result can be exported by a common data format mzTab-M [6]. Overall, our autonomous lipidomics platform enhances the standardized lipidomics procedure compliant with the lipidomics standards initiative’s (LSI) [7] classification, nomenclature, and semi-quantitative definitions thereby contributing to harmonizing lipidomics data across laboratories to accelerate lipid research.


  1. Fahy, E. et al. Update of the LIPID MAPS comprehensive classification system for lipids. Lipid Res. 50, S9–S14 (2009).
  2. Tsugawa, H. Advances in computational metabolomics and databases deepen the understanding of metabolisms. Opin. Biotechnol. 54, 10–17 (2018).
  3. Tsugawa, H. A lipidome atlas in MS-DIAL 4. Biotech. https://doi.org/10.1038/s41587-020-0531-2 (2020)
  4. Bonini, P. et al. Retip: Retention Time Prediction for Compound Annotation in Untargeted Metabolomics. Anal. Chem. 92, 7515–7522, (2020). 
  5. Liebisch, G. et al. Shorthand notation for lipid structures derived from mass spectrometry. Lipid Res. 54, 1523–1530 (2013).
  6. Hoffmann, N. et al. MzTab-M: a data standard for sharing quantitative results in mass spectrometry metabolomics. Chem. 2019, 91, 3302–3310 (2019).
  7. Liebisch, G. et al. Lipidomics needs more standardization. Metab. 1, 745–747 (2019).

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