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Automated, parallel mass spectrometry imaging and structural identification of lipids

Nature Methods volume 15, pages 515–518 (2018)Cite this article

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Abstract

We report a method that enables automated data-dependent acquisition of lipid tandem mass spectrometry data in parallel with a high-resolution mass spectrometry imaging experiment. The method does not increase the total image acquisition time and is combined with automatic structural assignments. This lipidome-per-pixel approach automatically identified and validated 104 unique molecular lipids and their spatial locations from rat cerebellar tissue.

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Fig. 1: Experimental workflow for DDA-imaging and automatic structural identification of detected lipids using ALEX123.
Fig. 2: Automatic structural annotation of DDA-imaging data.

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Acknowledgements

This work was supported by the Link program of the Dutch province of Limburg (R.M.A.H.), ITEA and RVO (ITEA151003/ITEA 14001 to R.M.A.H.), the Danish Council for Independent Research | Natural Sciences (DFF – 6108-00493 to C.S.E.), the Lundbeckfonden (R54-A5858 to C.S.E.), the VILLUM Foundation (VKR023439 to C.S.E.), the VILLUM Center for Bioanalytical Sciences (VKR023179 to C.S.E.), Interreg V EMR, and the Netherlands Ministry of Economic Affairs within the “EURLIPIDS” project (S.R.E. and R.M.A.H.). We thank M. Belov (Spectroglyph) and C. Hemedinger (SAS Online Communities) for technical support, and L. Huizing (Maastricht University, Maastricht, the Netherlands) and R. Vreeken (Maastricht University, Maastricht, the Netherlends, and Janssen Pharmaceutica, Beerse, Belgium) for providing intestinal tissue samples from mini pigs.

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Author notes

  1. Josch K. Pauling

    Present address: Theoretical Biophysics, Institute of Biology, Humboldt-Universität zu Berlin, Berlin, Germany

Authors and Affiliations

  1. The Maastricht Multimodal Molecular Imaging Institute (M4I), Division of Imaging Mass Spectrometry, Maastricht University, Maastricht, the Netherlands

    Shane R. Ellis, Martin R. L. Paine, Gert B. Eijkel & Ron M. A. Heeren

  2. Department of Biochemistry and Molecular Biology, Villum Center for Bioanalytical Sciences, University of Southern Denmark, Odense, Denmark

    Josch K. Pauling, Mark W. Jervelund, Martin Hermansson & Christer S. Ejsing

  3. Department of Physics, Chemistry and Pharmacy, University of Southern Denmark, Odense, Denmark

    Peter Husen

  4. Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany

    Christer S. Ejsing

Authors
  1. Shane R. Ellis
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  2. Martin R. L. Paine
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Contributions

S.R.E. and R.M.A.H. conceived the study. S.R.E. and M.R.L.P. performed MSI and MS/MS experiments. M.R.L.P. prepared the tissue samples. S.R.E., M.R.L.P., and C.S.E. analyzed the data. G.B.E. developed MSI software. J.K.P., P.H., M.W.J., M.H., and C.S.E. developed the ALEX123 software and database. S.R.E., C.S.E., and R.M.A.H. wrote the manuscript with input from all other coauthors.

Corresponding authors

Correspondence to Christer S. Ejsing or Ron M. A. Heeren.

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The authors declare no competing interests.

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Supplementary information

Supplementary Text and Figures

Supplementary Tables 2 and 3 and Supplementary Figures 1–9

Reporting Summary

Supplementary Table 1

Complete list of ALEX123-identified lipids

Supplementary Software

MSI reconstruction and visualization software

Supplementary Data

Unfiltered ALEX123 identifications and MS/MS fragment assignments

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Ellis, S.R., Paine, M.R.L., Eijkel, G.B. et al. Automated, parallel mass spectrometry imaging and structural identification of lipids. Nat Methods 15, 515–518 (2018). https://doi.org/10.1038/s41592-018-0010-6

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