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