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Principles and practice of lipidomics

  • COMPLEX LIPIDS
  • Published:
Journal of Inherited Metabolic Disease

Abstract

The technical advances in mass spectrometry, particularly the development of (ultra)-high-resolution/mass accuracy measurement capabilities in combination with refinement of soft ionization techniques, have increased the application and success of lipidomics to answer biological questions in relation to lipid metabolism. Together with other omics technologies, lipidomics has become an important tool to practice systems biology as lipids comprise a very significant part of the metabolome and play pleiotropic roles in cellular functions. As an increasing number of disorders are linked to lipid metabolism, lipidomics is used to search for biomarkers, understand disease mechanism and follow the efficacy of therapeutic options. This review provides a first introduction to the major methodological strategies currently used for mass spectrometry-based lipidomics and associated data pre-processing and analysis.

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Abbreviations

ANOVA:

Analysis of variance

APCI:

Atmospheric pressure chemical ionization

(D)FBA (Dynamic):

flux-balance analysis

ESI:

Electrospray ionization

FDR:

False discovery rate

FWHM:

Full width at half maximum

GSEA:

Gene set enrichment analysis

LC:

Liquid chromatography

MALDI:

Matrix-assisted laser desorption/ionization

MDMS:

Multi-dimensional MS

MSEA:

Metabolite set enrichment analysis

MRM:

Multiple reaction monitoring

PCA:

Principal component analysis

PLS-DA:

Partial-least-squares discriminant analysis

(U)HPLC:

(Ultra)-high performance chromatography

Q:

Quadrupole

QC:

Quality control

QqQ:

Triple quadrupole instrument

TOF:

Time-of-flight

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Acknowledgments

We thank Willem Kulik for critical reading of the manuscript and Henk van Lenthe for technical assistance.

Compliance with ethics guidelines

This article does not contain any studies with human or animal subjects performed by any of the authors.

Conflict of interest

None.

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Authors and Affiliations

  1. Laboratory Genetic Metabolic Disease (F0-224), Departments of Clinical Chemistry and Pediatrics, University of Amsterdam, Academic Medical Center (AMC), Amsterdam, 1105 AZ, The Netherlands

    Frédéric M. Vaz, Mia Pras-Raves & Albert H. Bootsma

  2. Bioinformatics Laboratory, Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Center, University of Amsterdam, Amsterdam, 1005 AZ, The Netherlands

    Mia Pras-Raves & Antoine H. C. van Kampen

  3. Biosystems Data Analysis, Swammerdam Institute for Life Sciences, University of Amsterdam, Science Park 904, 1098 XH, Amsterdam, The Netherlands

    Antoine H. C. van Kampen

Authors
  1. Frédéric M. Vaz
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  2. Mia Pras-Raves
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  3. Albert H. Bootsma
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  4. Antoine H. C. van Kampen
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Corresponding author

Correspondence to Frédéric M. Vaz.

Additional information

Communicated by: Matthias Baumgartner

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Vaz, F.M., Pras-Raves, M., Bootsma, A.H. et al. Principles and practice of lipidomics. J Inherit Metab Dis 38, 41–52 (2015). https://doi.org/10.1007/s10545-014-9792-6

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  • DOI: https://doi.org/10.1007/s10545-014-9792-6

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