Comprehensive analysis of lipids in biological systems by liquid chromatography-mass spectrometry

https://doi.org/10.1016/j.trac.2014.04.017Get rights and content

Highlights

  • State of the art in LC-MS-based lipidomics.

  • Sample extraction, separation, ionization and detection in LC-MS-based lipidomics.

  • Data processing, lipid identification/quantification, quality control in lipidomics.

  • Highlights of recent lipidomics studies.

Abstract

Liquid chromatography-mass spectrometry (LC-MS)-based lipidomics has undergone dramatic developments over the past decade. This review focuses on state of the art in LC-MS-based lipidomics, covering all the steps of global lipidomic profiling.

By reviewing 185 original papers and application notes, we can conclude that current advanced LC-MS-based lipidomics methods involve:

  • (1)

    lipid extraction schemes using chloroform/MeOH or methyl tert-butyl ether (MTBE)/MeOH, both with addition of internal standards covering each lipid class;

  • (2)

    LC separation of lipids using short microbore C18 or C8 columns with sub-2-µm or 2.6–2.8-µm (fused-core) particle size with analysis time <30 min;

  • (3)

    electrospray ionization in positive- and negative-ion modes with full spectra acquisition using high-resolution MS with capability to MS/MS.

Phospholipids (phosphatidylcholines, phosphatidylethanolamines, phosphatidylinositols, phosphatidylserines, phosphatidylglycerols) followed by sphingomyelins, di- and tri-acylglycerols, and ceramides were the most frequently targeted lipid species.

Introduction

Since its introduction in 2003 [1], lipidomics has emerged as one of the most promising research fields as a result of advances in mass spectrometry (MS). Direct infusion (shotgun) techniques were prevalent in the beginning of lipidomics research due to their relative simplicity of operation, fast analysis, and possibility to detect various lipid classes within a single run. In most cases, these methods used tandem MS in a class-specific or targeted way, so detection and subsequent identification of unknowns were impossible. This was followed by rapid progress in liquid-chromatography (LC) separation and computational methods [2], [3], [4]. The popularity of LC-MS-based methods can be explained by several advantages over direct infusion techniques, such as more reliable identification of individual lipid species, even at trace levels, separation of isomers and isobars, or reduced ion-suppression effects. In addition, current LC instruments permit more effective separation, and reduce analysis time and solvent consumption [5], [6]. Currently, direct infusion-MS(/MS) and LC-MS(/MS) methods are reported in the scientific literature in almost equal ratio, while complementary techniques and their combinations, such as gas chromatography (GC), thin-layer chromatography (TLC) and nuclear magnetic resonance (NMR), are less frequently used in lipidomics (Fig. 1).

LC-MS-based lipidomic analyses (Fig. 2) typically start with extraction of the lipids from the biological sample followed by LC separation, which can be performed based on lipid species [e.g., reversed-phase LC (RPLC)] or classes [e.g., normal-phase LC (NPLC)]. Once chromatographically separated, the molecules enter the ion source where they undergo ionization followed by detection of particular ions using a mass analyzer. This can be conducted in an untargeted (full spectra acquisition), class-specific (product-ion scanning, precursor-ion scanning or neutral-loss scanning) or targeted (multiple-reaction monitoring) way [7]. The data handling represents a post-acquisition phase, which is focused on identification and (semi)-quantification of detected lipids, followed by statistical analysis if the primary focus of the study is to distinguish groups of samples.

For this article, we reviewed 185 original LC-MS-based lipidomics papers and application notes published over the past decade (see references in Supplementary material, Tables S1 and S2). All the aspects, such as sample extraction, LC separation and MS detection are discussed in subsequent sections of this review. Since our primary focus was on the analysis of complex lipid mixtures in various biological systems, we omitted those papers dedicated to only a single lipid class (e.g., triacylglycerols or fatty acids).

Section snippets

Sample extraction

In general, lipidomics applications require sample-preparation methods that are fast, reproducible, and able to extract a wide range of analytes with different polarities, and that, at the same time, are compatible with the instrumental technique. Analytical strategies, which allow for increased coverage of metabolites determined in one sample, are therefore desirable [8], [9]. In addition, samples may be available in only limited amounts, posing practical requirements to develop efficient,

Liquid-chromatography separation

Coupling LC to MS substantially reduces some of the limitations linked to direct infusion MS, such as detection of isobars and isomers or ion-suppression effects caused by molecules competing for ionization [6], [20]. Moreover, use of LC gives the possibility to separate or to concentrate different classes of compounds according to their physicochemical properties.

Several LC configurations were described for the analysis of complex lipid mixtures (Fig. 2). The three most important ones include

Ionization techniques

The choice of ionization mode used in LC-MS analyses plays a major role in the lipidome profile that will be obtained. One methodology cannot cover all types of molecules, since some lipids are better ionized in one ionization mode while other lipids are ionized more efficiently in another mode (Table 1) [23], [37], [38], [39], [40]. Ionization efficiency can be enhanced by the additives dissolved in the mobile phases leading to formation of different type of adducts. Electrospray ionization

Data processing

Raw data processing is the first important step following data acquisition. In LC-MS analysis, three dimensions represent retention time, m/z value, and signal intensity. The data-processing pipeline usually proceeds through multiple stages, including:

  • (1)

    filtering;

  • (2)

    feature detection;

  • (3)

    alignment; and,

  • (4)

    normalization.

Filtering methods process the raw signal with the aim of removing the noise or baseline. Feature detection is conducted to identify all signals caused by true ions and to avoid detection of

Lipid identification and automated annotation

Compound identification is still the bottleneck in LC-MS-based metabolomics. By utilizing MS/MS, the lipid class, carbon-chain length, and degree of unsaturation of fatty-acid components of lipid can be annotated. Although library matches for some of those spectra may be found in MS/MS databases of pure chemical standards, the identification rates are usually low because libraries, such as Metlin, MassBank, and the US National Institutes of Standards and Technology (NIST) database, cover fewer

Lipid quantification

Subtle variations during sample extraction and data acquisition cause analytical errors. To compensate for these errors, non-naturally occurring standards can be spiked into all the samples at different stages of the sample processing [6]. In most cases, internal standards are added prior to the extraction by adding a small volume of concentrated stock solution of these standards, or they can be present already as part of the extraction solvents. Alternatively, internal standards can be added

Quality control in large-scale lipidomics studies

Keeping up the high quality of acquired data within a large-scale lipidomics study of more than 1000 samples represents a real challenge for laboratory practice. When performing a lipidomic profiling analysis, changes in instrument sensitivity caused by degradation of the extracts, contamination of ion source by non-volatiles or retention-time shifts may be observed over time. In order to avoid false expectations raised by stating a comprehensive metabolomics approach, it is important that

Highlights of recent lipidomics studies

Here, we briefly highlight some recent applications of lipidomics conducted with the technologies described above. The lipidomic profiles from body fluids, animal and plant tissues, or cells provide a global snapshot of lipid concentrations in a particular biological sample at specific physiological state, time or intervention response.

Orešič et al. [76] focused on UHPLC-MS analysis of lipids in blood samples of 679 well-characterized individuals in whom liver-fat content was measured using

Conclusions

Recent advances in LC-MS have revolutionized lipidomics analysis by simplifying the analytical protocol and by increasing the chromatographic separation power and sensitivity of detection. RPLC with positive electrospray and HRMS can now be seen as the “gold standard” for many lipidomics studies. The regular use of internal standards for quantifications means that lipidomics has a real chance of achieving a level of stability enabling inter-laboratory comparison in ring trials and direct

Disclaimer

Mention of brand or firm names in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the University of California, Davis, USA.

Authors' contributions

Both authors contributed equally to the work.

Acknowledgements

This study was supported by the US National Institutes of Health (NIH) (Grants P20 HL113452 and U24 DK097154).

References (80)

  • H. Gallart-Ayala et al.

    Versatile lipid profiling by liquid chromatography-high resolution mass spectrometry using all ion fragmentation and polarity switching. Preliminary application for serum samples phenotyping related to canine mammary cancer

    Anal. Chim. Acta

    (2013)
  • H. Ogiso et al.

    Development of a reverse-phase liquid chromatography electrospray ionization mass spectrometry method for lipidomics, improving detection of phosphatidic acid and phosphatidylserine

    Anal. Biochem

    (2008)
  • M.R. Gama et al.

    Hydrophilic interaction chromatography

    TrAC Trends Anal. Chem

    (2012)
  • L. Novakova et al.

    A review of current trends and advances in modern bio-analytical methods: chromatography and sample preparation

    Anal. Chim. Acta

    (2009)
  • Q. Zhou et al.

    Chemical profiling of triacylglycerols and diacylglycerols in cow milk fat by ultra-performance convergence chromatography combined with a quadrupole time-of-flight mass spectrometry

    Food Chem

    (2014)
  • T. Yamada et al.

    Supercritical fluid chromatography/Orbitrap mass spectrometry based lipidomics platform coupled with automated lipid identification software for accurate lipid profiling

    J. Chromatogr. A

    (2013)
  • T. Bamba et al.

    High throughput and exhaustive analysis of diverse lipids by using supercritical fluid chromatography-mass spectrometry for metabolomics

    J. Biosci. Bioeng

    (2008)
  • J.W. Lee et al.

    Application of supercritical fluid chromatography/mass spectrometry to lipid profiling of soybean

    J. Biosci. Bioeng

    (2012)
  • T. Bamba et al.

    Metabolic profiling of lipids by supercritical fluid chromatography/mass spectrometry

    J. Chromatogr. A

    (2012)
  • M. Lisa et al.

    Lipidomic profiling of biological tissues using off-line two-dimensional high-performance liquid chromatography mass spectrometry

    J. Chromatogr. A

    (2011)
  • D.S. Myers et al.

    Quantitative analysis of glycerophospholipids by LC-MS: acquisition, data handling, and interpretation

    Biochim. Biophys. Acta

    (2011)
  • M. Kliman et al.

    Lipid analysis and lipidomics by structurally selective ion mobility-mass spectrometry

    Biochim. Biophys. Acta

    (2011)
  • M. Holcapek et al.

    Recent developments in liquid chromatography-mass spectrometry and related techniques

    J. Chromatogr. A

    (2012)
  • S.N. Jackson et al.

    A study of phospholipids by ion mobility TOFMS

    J. Am. Soc. Mass Spectrom

    (2008)
  • T. Yamada et al.

    Development of a lipid profiling system using reverse-phase liquid chromatography coupled to high-resolution mass spectrometry with rapid polarity switching and an automated lipid identification software

    J. Chromatogr. A

    (2013)
  • M. Katajamaa et al.

    Data processing for mass spectrometry-based metabolomics

    J. Chromatogr. A

    (2007)
  • G.A. Theodoridis et al.

    Liquid chromatography-mass spectrometry based global metabolite profiling: a review

    Anal. Chim. Acta

    (2012)
  • H. Song et al.

    Algorithm for processing raw mass spectrometric data to identify and quantitate complex lipid molecular species in mixtures by data-dependent scanning and fragment ion database searching

    J. Am. Soc. Mass Spectrom

    (2007)
  • H.G. Nie et al.

    Lipid profiling of rat peritoneal surface layers by online normal- and reversed-phase 2D LC QToF-MS

    J. Lipid Res

    (2010)
  • S. Wang et al.

    A novel stop-flow two-dimensional liquid chromatography-mass spectrometry method for lipid analysis

    J. Chromatogr. A

    (2013)
  • T.W. Mitchell et al.

    Identification of double bond position in lipids: from GC to OzID

    J. Chromatogr. B

    (2009)
  • B.L.J. Poad et al.

    Ozone-induced dissociation on a modified tandem linear ion-trap: observations of different reactivity for isomeric lipids

    J. Am. Soc. Mass Spectrom

    (2010)
  • M. Lisa et al.

    Characterization of fatty acid and triacylglycerol composition in animal fats using silver-ion and non-aqueous reversed-phase high-performance liquid chromatography/mass spectrometry and gas chromatography/flame ionization detection

    J. Chromatogr. A

    (2011)
  • M. Koivusalo et al.

    Quantitative determination of phospholipid compositions by ESI-MS: effects of acyl chain length, unsaturation, and lipid concentration on instrument response

    J. Lipid Res

    (2001)
  • L. Denoroy et al.

    Ultra high performance liquid chromatography as a tool for the discovery and the analysis of biomarkers of diseases: a review

    J. Chromatogr. B

    (2013)
  • J.M. Weir et al.

    Plasma lipid profiling in a large population-based cohort

    J. Lipid Res

    (2013)
  • C. Zhu et al.

    An efficient hydrophilic interaction liquid chromatography separation of 7 phospholipid classes based on a diol column

    J. Chromatogr. A

    (2012)
  • W. Hou et al.

    Technological developments in lipidomics

    Brief. Funct. Genomic Proteomic

    (2008)
  • U. Loizides-Mangold

    On the future of mass-spectrometry-based lipidomics

    FEBS J.

    (2013)
  • M.R. Wenk

    The emerging field of lipidomics

    Nat. Rev. Drug Discov

    (2005)
  • Cited by (461)

    • Analytical techniques for the characterization of nanoparticles for mRNA delivery

      2024, European Journal of Pharmaceutics and Biopharmaceutics
    View all citing articles on Scopus
    View full text