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Ren, W.; Sun, M.; Shi, X.; Wang, T.; Wang, Y.; Wang, C.; Li, M. Mass Spectrometry-Based Lipidomic Technologies. Encyclopedia. Available online: (accessed on 11 December 2023).
Ren W, Sun M, Shi X, Wang T, Wang Y, Wang C, et al. Mass Spectrometry-Based Lipidomic Technologies. Encyclopedia. Available at: Accessed December 11, 2023.
Ren, Wei, Mengqi Sun, Xiaoyuan Shi, Tianqi Wang, Yonghui Wang, Changfa Wang, Mengmeng Li. "Mass Spectrometry-Based Lipidomic Technologies" Encyclopedia, (accessed December 11, 2023).
Ren, W., Sun, M., Shi, X., Wang, T., Wang, Y., Wang, C., & Li, M.(2023, June 07). Mass Spectrometry-Based Lipidomic Technologies. In Encyclopedia.
Ren, Wei, et al. "Mass Spectrometry-Based Lipidomic Technologies." Encyclopedia. Web. 07 June, 2023.
Mass Spectrometry-Based Lipidomic Technologies

Lipids play important biological roles, such as providing essential fatty acids and signaling. The wide variety and structural diversity of lipids, and the limited technical means to study them, have seriously hampered the resolution of the mechanisms of action of lipids. With advances in mass spectrometry (MS) and bioinformatic technologies, large amounts of lipids have been detected and analyzed quickly using MS-based lipidomic techniques. Milk lipids, as complex structural metabolites, play a crucial role in human health. 

lipidomics mass spectrometry dairy products lipids

1. Introduction

Lipids are divided into eight classes based on their chemical structure: fatty acids (FAs (e.g., linoleic acid and linolenic acid), glycerolipids (GLs, e.g., diacylglycerol and TG), glycerophospholipids (GPs, e.g., phosphatidylcholine and phosphatidylethanolamine) sphingolipids (SPs, e.g., ceramide and sphingomyelin), sterol lipids (STs, e.g., cholesterol), saccharolipid (SLs, e.g., digalactosyldiacylglycerol), prenol lipids (PRs, e.g., carotenoid) and polyketides (PKs). With their complex structure, variety, and a large number, lipids are considered to be the most complex substances in nature in terms of their composition. Analytical methods fail to comprehensively characterize lipid molecules, making it challenging to examine their metabolic pathways and functional energy regulation in depth. As a subset of metabolomics, lipidomics comprises the study of the molecular properties of lipids in organisms, tissues, or cells. Lipidomics is advantageous for comprehensive and systematic research into lipid structure, metabolic pathways, and functions, including lipid-related protein expression and gene regulation [1][2]. Mass spectrometry (MS) is a method to define the relative molecular weight and structure of samples by determining the mass number of molecular ions and fragments in the sample. MS has the advantages of high throughput, sensitivity, and accuracy. High-performance liquid chromatography-atmospheric pressure chemical ionization-mass spectrometry/mass spectrometry (HPLC-APCI-MS/MS) was found to provide a comprehensive characterization of TG molecules and individual regional isomers in milk lipids [3]. Matrix-assisted laser desorption ionization-time of flight MS (MALDI-TOF MS) can be used to identify non-cow milk from cow milk and to characterize milk lipids in cheese and other dairy products [4][5]. Gas chromatography-MS (GC-MS) has been widely used in the separation and identification of milk FAs [6]

2. Lipid Extraction

The process of lipid extraction from biological samples is inseparable from lipid analysis and commonly includes Folch extraction [7], Bligh extraction [8], methyl-tert-butyl ether (MTBE) extraction [9], butanol-methanol (BUME) extraction [10], solid phase extraction (SPE) [11], and supercritical fluid extraction (SFE) [12]. Folch extraction was the earliest proposed chloroform-methanol system, which can efficiently extract both free and bound lipids but is highly toxic and unsuitable for food lipid extraction [7]. Bligh extraction uses less solvent, extracts faster, and recovers more lipids [13]. Lipids are dissolved in the MTBE organic phase to protect unstable lipids from degradation and avoid the use of chloroform [9]. For BUME, the butanol content is not readily evaporated [13]. Convenient operation and reduced consumption of reagents are hallmarks of SPE, which is used to extract liquid-phase lipids and reduce the specific enrichment of lipids [14]. SFE is based on the difference in solubility of different compounds in SF and has recyclable extractants [12].

3. Isolation and Identification

Currently, targeting is the accurate analysis of one or several classes of target lipids, whereas the analysis of lipid profiles in biological samples uses non-targeted methods [15]. Separation methods include GC, liquid chromatography (LC), supercritical fluid chromatography (SFC), hydrophilic interaction chromatography (HILIC), and capillary electrophoresis (CE) [16][17][18]. GC is often used to analyze the composition of milk FAs; however, it requires derivatization or primary separation by thin-layer chromatography (TLC), followed by further analysis by GC, which is time-consuming [19]. However, when using GC analysis alone, 200 m SLB-IL111 columns are the first choice for a more complete analysis of all FAs in cow and sheep milk [20]. Furthermore, LC is applied to biomolecules that have a high relative molecular weight, are difficult to vaporize, are non-volatile, and are heat-sensitive. Examples of LC include reversed-phase high-performance liquid chromatography (RP-HPLC), normal-phase high-performance liquid chromatography (NP-HPLC), silver-ion liquid chromatography, and chiral high-performance liquid chromatography. TLC can separate lipids intuitively and quickly but has limited throughput, and weak sensitivity and resolution, which can disrupt lipid structure [16][17]. SFC provides a high-resolution, rapid, and comprehensive analysis of mixed lipids and improves the elution capacity of the mobile phase by varying its density [21]. HILIC is applied to the separation of polar lipids [22]. Nuclear magnetic resonance sensitivity is inadequate to detect low-abundance lipids [23]. Furthermore, MS, which consists of an injection system, an ion source, a mass analyzer, a detector, and an amplifier recorder, is also the most common approach for lipid identification. The injection system is separated into direct injection (e.g., diffusion injection) and indirect injection (e.g., probe injection and chromatographic injection) [24]. Electron bombardment ionization sources (EI), electrospray ionization (ESI), APCI, atmospheric pressure photoionization (APPI), and MALDI are the most common ion sources [14]. The main mass analyzers are quadrupole (Q), time of flight (TOF), ion trap, and electrostatic field orbital trap (Orbitrap) [25][26][27].
Tandem MS technology has received considerable emphasis and development to realize the in-depth analysis of lipids. GC-MS can be used to determine whether cow milk contains vegetable milk (β-sitosterol) [28]. The quantitative study of lipids, FAs, and region-specific sites is possible using the LC-MS approach, whereas the HPLC-ESI-MS quantification approach requires no derivatization and offers precise relative molecular mass and structure information, although it takes longer to analyze [29]. HPLC-APCI-MS/MS is used for the complete identification of TG molecules in milk lipids and the identification of individual regional isomers [3]. HPLC-evaporative light-scattering detection (ELSD) is suitable for the analysis of the main lipid classes in dairy products [30]. Research found that HPLC-ELSD was suitable for the quantitative analysis of PLs in human milk and infant formula. Qualitative analysis of PL categories in human milk can be achieved using HILIC-TOF-MS, while further quantitative analysis of the identified PL structures can be undertaken using HILIC-MS/MS [31]. MALDI-TOF-MS is commonly used for the rapid screening of polar and non-polar lipids, and can be used to identify milk and non-milk samples by comparing the mass spectra generated with those in the corresponding reference databases; however, it lacks the quantitative analysis of the full range of lipids [4][32].

4. Data Analysis

Data analyses include pre-processing (filtering, alignment, peak identification, lipid annotation, and normalization), lipid classification, multivariate statistical analysis, such as the P value, variable importance in projection (VIP), principal component analysis (PCA), partial least squares regression (PLS), and orthogonal partial least squares (OPLS), differential lipid screening, differential lipid statistical analysis, cluster analysis, and functional enrichment. Lipid molecule data are routinely analyzed using software such as Lipid View, LipidXplorer, MZmine, and XCMS, as well as databases such as CyberLipids, the Human Metabolome Database, the Kyoto Encyclopedia of Genes and Genomes, LIPIDAT, the Human Metabolome Database, Lipid Bank, Lipid Maps, Lipid Library, and LipidBlast [1].


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