DOI QR코드

DOI QR Code

A Comprehensive Review of Lipidomics and Its Application to Assess Food Obtained from Farm Animals

  • Song, Yinghua (College of Agronomy, Liaocheng Research Institute of Donkey High-Efficiency Breeding and Ecological Feeding, Liaocheng University) ;
  • Cai, Changyun (College of Agronomy, Liaocheng Research Institute of Donkey High-Efficiency Breeding and Ecological Feeding, Liaocheng University) ;
  • Song, Yingzi (College of Agronomy, Liaocheng Research Institute of Donkey High-Efficiency Breeding and Ecological Feeding, Liaocheng University) ;
  • Sun, Xue (College of Agronomy, Liaocheng Research Institute of Donkey High-Efficiency Breeding and Ecological Feeding, Liaocheng University) ;
  • Liu, Baoxiu (College of Agronomy, Liaocheng Research Institute of Donkey High-Efficiency Breeding and Ecological Feeding, Liaocheng University) ;
  • Xue, Peng (College of Agronomy, Liaocheng Research Institute of Donkey High-Efficiency Breeding and Ecological Feeding, Liaocheng University) ;
  • Zhu, Mingxia (College of Agronomy, Liaocheng Research Institute of Donkey High-Efficiency Breeding and Ecological Feeding, Liaocheng University) ;
  • Chai, Wenqiong (College of Agronomy, Liaocheng Research Institute of Donkey High-Efficiency Breeding and Ecological Feeding, Liaocheng University) ;
  • Wang, Yonghui (College of Agronomy, Liaocheng Research Institute of Donkey High-Efficiency Breeding and Ecological Feeding, Liaocheng University) ;
  • Wang, Changfa (College of Agronomy, Liaocheng Research Institute of Donkey High-Efficiency Breeding and Ecological Feeding, Liaocheng University) ;
  • Li, Mengmeng (College of Agronomy, Liaocheng Research Institute of Donkey High-Efficiency Breeding and Ecological Feeding, Liaocheng University)
  • Received : 2021.08.27
  • Accepted : 2021.10.13
  • Published : 2022.01.01

Abstract

Lipids are one of the major macronutrients essential for adequate growth and maintenance of human health. Their structure is not only complex but also diverse, which makes systematic and holistic analyses challenging; consequently, little is known regarding the relationship between phenotype and mechanism of action. In recent years, rapid advancements have been made in the fields of lipidomics and bioinformatics. In comparison with traditional approaches, mass spectrometry-based lipidomics can rapidly identify as well as quantify >1,000 lipid species at the same time, facilitating comprehensive, robust analyses of lipids in tissues, cells, and body fluids. Accordingly, lipidomics is now being widely applied in various fields, particularly food and nutrition science. In this review, we discuss lipid classification, extraction techniques, and detection and analysis using lipidomics. We also cover how lipidomics is being used to assess food obtained from livestock and poultry. The information included herein should serve as a reference to determine how to characterize lipids in animal food samples, enhancing our understanding of the application of lipidomics in the field in animal husbandry.

Keywords

Introduction

Lipids are closely related to the physiological health of organisms. They are involved in biofilm formation, provide energy for life activities, and participate in key biological processes, such as material transportation, energy conversion, and information recognition and transmission (Han, 2016; Kouba and Mourot, 2011). Considering their complex structure, diverse physical and chemical properties, and underdevelopment of robust detection methods, systematic and holistic analysis of lipids is challenging; consequently, little is known regarding the relationship between phenotype and mechanism of action. Lipidomics, which was first presented in 2003, is a branch of metabonomics that mainly focuses on revealing lipid species, abundance, and spatial distribution; further, it facilitates analyses of the biological role of lipids in metabolic pathways and networks, serving as a powerful tool to comprehensively analyze lipids in vivo (Han and Gross, 2003). In the past decade, lipidomics has attracted extensive attention of biologists and analytical scientists and has witnessed rapid advancements due to the development of various new analytical instruments and bioinformatics. Lipids can be classified into 8 categories according to their structure, covering >45, 000 lipid species (Fahy et al., 2005), with each category showing differences in polar head group, carbon chain length, and unsaturation. Mass spectrometry (MS)-based lipidomics has become an indispensable method because of its high flux, sensitivity, and accuracy, helping overcome the challenges associated with lipid complexity and diversity (Navas- Iglesias et al., 2009). Lipidomics has been successfully applied to assess food obtained from animals; for example, it has been used for the characterization and discrimination of domestic pig (Mi et al., 2019a) and Taihe black-boned silky fowl muscles (Mi et al., 2018). Further, Li et al. (2020b) used quantitative lipidomics to reveal alterations in donkey milk lipids during lactation, and Xie et al. (2020) used it for the efficient profiling of egg yolk lipids.

Herein we comprehensively review lipid classification and extraction from diverse samples, as well as lipid detection and analyses using lipidomics. The information reported in this review should serve as a reference for characterizing lipids found in food obtained from farm animals, shedding light on the application of lipidomics in the field in animal husbandry and helping us better recognize the quality of food.

Lipids

Lipids and categories

Lipids can be defined as hydrophobic or amphipathic small molecules. They are difficult to dissolve in water but are easily dissolvable in organic solvents, including chloroform, ether, and alcohol. Lipids are important components of various metabolites and have unique biological functions, particularly in maintaining physiological dynamic balance (Han, 2016). In humans, the abnormal metabolism of lipids is closely related to the occurrence and development of several diseases, such as diabetes, cancer, and Alzheimer’s disease (Wang et al., 2005; Yi et al., 2020). Moreover, lipids are associated with an improvement in food sensory parameters and absorption of fat-soluble vitamins (Cui et al., 2019).

Table 1. Lipid categories of the comprehensive classification system

The number in the LIPID MAPS database (Lipid structure, annotation, classification and pathways, analytical methods. http://www.lipidmaps.org. CC-BY).

Based on the classification system proposed by the Lipid Metabolites and Pathways Strategy (LIPID MAPS) project, lipids can be divided into 8 classes (Table 1). Glycerolipids (GL) are generally composed of a glycerol skeleton and fatty acids, and they are mainly classified into monoglycerides, diacylglycerides (DG), and triacylglycerides (TG). TG, with three fatty acids at different positions, such as sn-1, -2, and -3, are the most important and abundant. Glycerophospholipids (GP) widely exist in animal and plant cells, serving as essential structural and functional component of biofilms. They chiefly include phosphatidylcholine (PC), phosphatidylethanolamine (PE), phosphatidylglycerol (PG), phosphatidylinositol (PI), phosphatidylserine (PS), and sphingomyelin (SM). In GP, the number of carbons in side-chain fatty acids is between 14 and 24, with each chain having 0–6 double bonds (Li et al., 2018). Sphingolipids (SP), a type of amphoteric lipid with a sphingosine skeleton, are the predominant lipids in biofilms. They represent bioactive lipids that play a critical role in cell signal transduction (Alewijnse and Peters, 2008). SP principally include SM, ceramides (Cer), glucosylceramides (GluCer), and gangliosides (GLS); they have attracted much attention considering their anti-inflammatory properties and role in colon cancer prevention (Canela et al., 2016; Sugawara et al., 2019).

Physiological function of lipids

Lipids have complex and diverse structures, and they have many biological and nutritional functions in various tissues and cells. For example, lipids supply energy for life activities; phospholipids (PL), an important component of cell membrane structure, are involved in maintaining cell integrity. Moreover, lipids are a source of essential fatty acids and promote the absorption of fat-soluble substances; they also participate in key biological processes, such as material transportation as well as information recognition and transmission. PG evidently has potential therapeutic effects; for example, it has been reported to suppress influenza A virus infection and reduce the development of inflammatory cell infiltrates (Numata et al., 2012). Norris et al. (2017) reported that egg SM improved weight gain, hyperglycemia, and hypercholesterolemia in high-fat-diet- induced obese mice; furthermore, egg SM and a lesser extent milk SM decreased inflammation. SM and its metabolites are the basic components of the myelin sheath in the central nervous system, and dietary supplementation with SM has been reported to promote neural development in infants (Nilsson, 2016; Tanaka et al., 2013). Such data emphasize the importance of studying lipid profiles of food and understanding nutritional value.

Lipidomics

As shown in Fig. 1, a strategy of MS-based lipidomics, of which were divided into three main procedures: pretreatment (lipid extraction), data acquisition [Gas chromatography (GC)/ Liquid chromatography (LC)-MS], and data processing (data analysis).

Fig. 1. A strategy of MS-based lipidomics. (a) Pretreatment (lipid extraction) from food samples is commonly used by liquid-liquid extraction (LLE) and solid phase extraction (SPE) methods, LLE mainly includes Folch, Bligh-Dyer, and Matyash methods. (b) Liquid chromatography (LC) or gas chromatography (GC) is usually employed for separating different types of lipids and then combined with mass spectrometry (MS) identification, LC/GC-MS are the main detection methods used in lipidomics. (c) Data analysis requires the application of chemometric tools, specific software and databases, including univariate (multiple analysis, t-test, and analysis of variance), multivariate analyses (principal component analysis and hierarchical cluster analysis). Differential lipid molecules and biomarkers can be comprehensively screened and identified, of which were used for constructing lipid metabolic networks. GC-MS, gas chromatography-MS.

Lipid extraction

Lipid extraction is pivotal step in lipidomics and needs to be carefully performed to reduce, as much as possible, the influence of impurities on analyses. To improve the efficiency of extraction, suitable methods need to be chosen depending on sample types and target lipids. Lipids from biological or food samples are extracted by liquid-liquid extraction (LLE) and solid phase extraction (SPE) (Tang et al., 2016). LLE is the most popular approach in lipidomics as its separation efficiency is high. Briefly, in this method, target components are separated based on their solubility in immiscible solvents (Jia et al.,2020). While many solvent extraction systems have been applied to date, the three most commonly used methods are Folch, Bligh-Dyer, and Matyash. These methods involve a binary mixture of methanol with a non-polar organic solvent, which promote the extraction of diverse lipid classes (Züllig et al., 2020).

The Folch and Bligh-Dyer methods use the same reagents but in different proportions: the former uses chloroform: methanol:water (8:4:3, v/v/v) and the latter uses chloroform:methanol:water (2:2:1.8, v/v/v). The Bligh-Dyer method uses lesser quantity of solvents compared with Folch method, but the extraction speed is higher and lipid content recovery is much better, too. In these methods, the organic layer is deposited at on the bottom, making its collection difficult; in addition, non lipid contaminants and salts can be transferred and lipid content can get lost. Considering these demerits and that chloroform is toxic and harmful, but still widely used to assess food samples (Li et al., 2017; Muhamadali et al., 2016), the Matyash method was introduced. This method involves the use of methyl tert-butyryl ether:methanol:water (10:3:2.5, v/v/v), avoiding the use of chloroform and providing comparable results to the Folch and Bligh-Dyer methods (Matyash et al., 2008).

SPE is based on differences in the interaction between various substances in the solid and liquid phases. Substances are first adsorbed on the stationary phase, and then the mobile phase with a different elution ability is used for step-by-step elution, resulting in sample separation (Song et al., 2020). SPE has a high selectivity for lipids and is an important method for targeted lipid extraction, and it is often performed after LLE to eliminate the specific enrichment of certain lipids and remove interference substances from the extract. Solid phase microextraction, which was developed from SPE, is also widely used. This method involves using a coated fiber to extract analytes, and based on electrostatic, ion exchange, and hydrophobic interactions as well as other forces to achieve lipid separation and purification in biological samples, polyacrylonitrile, polydimethylsiloxane, and polyaniline are used as the main coatings (Bojko et al., 2014). Although solid phase microextraction is rapid and easy to perform, it shows low extraction ability and is thus used to analyze a small number of samples (Jia et al., 2020).

In addition to LLE and SPE, other methods have been used for sample pretreatment, such as microwave-assisted extraction (MAE), ultrasonic-assisted extraction (UAE), and supercritical fluid extraction (SFE). MAE uses microwave energy to increase temperature and pressure levels; it is associated with accelerated extraction speed, improved extraction efficiency, and reduced use of organic solvents in the extraction process (Teo et al., 2013). However, the increase in temperature during the extraction process may lead to decomposition of thermally unstable substances. UAE uses, for example, mechanical vibration and diffusion to accelerate material transfer between phases (Tsochatzidis et al., 2001). In comparison with MAE, UAE does not require higher temperatures for extraction, making it suitable for analyzing thermally unstable substances. SFE uses the fluid state of carbon dioxide whose temperature and pressure are above its critical points (general temperature is 31℃ and critical pressure is 7.4 MPa); the high diffusion and very low surface tension of the supercritical fluid leads to effective sample extraction or separation. The polarity of supercritical CO2 is similar to that of pentane, which is suitable for the extraction of hydrophobic substances. When methanol, dichloromethane, or water are used as additives, they can even be used to extract polar substances (Pourmortazavi and Hajimirsadeghi, 2007). SFE, as a new extraction and separation method, shows high extraction efficiency, little or no contamination, and is convenient to perform, making its application highly favorable for analyzing biological and food samples (Belayneh et al., 2017; Pourmortazavi et al., 2018). Such developments in lipid extraction and separation have considerably promoted the analysis of lipid structures.

Methods for lipid analyses

MS is the most popular method for lipid detection considering that it is high throughput and highly sensitive. In recent years, high-resolution MS has become the first choice for lipidomics, with several methods being widely used, including quadrupole- time-of-flight (Q-TOF) MS, ultrahigh performance liquid chromatography (UPLC)-triple quadrupole-ion trap MS, triple quadrupole MS, and Fourier transform ion cyclotron resonance MS (Triebl et al., 2017). MS involves two most common ionization techniques: electrospray ionization (ESI) and atmospheric pressure chemical ionization (Hebra et al., 2020). Shotgun lipidomics was developed on the basis of ESI and triple quadrupole tandem MS (MS/MS) (Han and Gross, 2003). Triple quadrupole MS/MS involves three quadrupole mass spectrometers: the first (Q1) and third (Q3) are commonly applied for mass separation, while the second (Q2) is commonly applied for collision-activated dissociation. By adapting the functions of Q1, Q2, and Q3, triple quadrupole MS/MS can be applied for product ion scan, precursor ion scan, and neutral loss scan measurements, making selective reaction and multi-reaction monitoring feasible (Johnson et al., 1990). Multidimensional MS- based shotgun lipidomics was developed in 2009 (Yang et al., 2009). In practical applications, the combination of MS and chromatography can effectively reduce matrix interference and facilitate the separation of lipid compounds.

GC-MS and LC-MS are the main detection methods used in lipidomics. GC is usually employed for separating volatile and thermally stable compounds. It is mostly combined with chemical ionization and electron ionization MS, which increases its separation efficiency, enhances reproducibility, and makes it relatively economical. For analyzing nonvolatile lipids, it is necessary to derivatize samples, which is time consuming and also requires a high number of samples. Such factors can easily cause changes in samples and/or result in the introduction of new interfering compounds (Miao et al., 2019). Considering these limitations, GC-MS is largely used for targeted lipidomics to analyze simple fatty acids and less polar lipids (Ghidotti et al., 2018). Targeted lipidomics is typically based on low resolution MS; specific lipid molecules are analyzed using triple quadrupole MS with multi-reaction monitoring and neutral loss and precursor ion scanning modes, which provides high specificity, high sensitivity, and quantitative accuracy. However, the number of targets is limited and the establishment method depends on the standard (Xu et al., 2020).

LC includes a normal and reverse phase and is based on hydrophilic separation, which makes it suitable for separating different types of lipids. Normal phase chromatography is used to separate lipids according to their polarity; PLs with different polar head groups can be separated, and when combined with MS, its separation efficiency is further enhanced. However, normal phase chromatography requires a long equilibrium time, which makes repeatability poor, and as the mobile phase used in this method is volatile, changes in polarity and retention time drift can easily occur (Samhan-Arias et al., 2012). Hydrophilic interaction chromatography has all the advantages of normal phase chromatography, and it also involves the use of a less volatile mobile phase. Considering its higher separation efficiency and better compatibility with MS, hydrophilic interaction chromatography has been successfully applied for analysis of serum cyclic phosphatidic acid and its related lipids in rat (Fukasawa et al., 2018). Indeed, liquid extraction is evidently the best method for nontargeted lipidomics. Nontargeted lipidomics profiling analysis has been performed using high-resolution LC-MS. This method can not only analyze most lipid types without any bias to reveal the characteristic lipid structure with significant changes but also highlight reasons for such changes (Alcoriza-Balaguer et al., 2019). Its advantages include fast scanning speed, high resolution, high analytical flux, and rich data output; however, there are some disadvantages too, such as low stability, poor repeatability, and small quantitative range (Cajka and Fiehn, 2014; Cajka and Fiehn, 2016).

Li et al. (2021e) recently reported that the single cell lipidomics based on shotgun MS was established. This method involved MS/MS to reveal the class and fatty acyl chains of lipids, as well as the C=C locations and sn-positions of lipids. Furthermore, electro-migration combined with droplet-assisted ESI made MS analyses not only simple but also efficient. Using the combination of chemical derivatization and MS/MS appears to be feasible for the highly complex structural analysis of lipid molecules in a single cell, and this method effectively overcomes problems associated with the limited sample amounts available (Li et al., 2021e). Thus, chemical derivatization coupled with MS/MS could become the important method for single-cell biology and precision medicine.

Data analysis

Lipidomics is a comprehensive method for lipid characterization and can produce a large amount of multidimensional data. The processing and analyses of lipidomics data are similar to those of metabonomics data, to reduce the dimension of multidimensional data (Saccenti et al., 2014). The methods used for the processing and analysis of lipidomics data have been previously reviewed (Checa et al., 2015). Briefly, data analysis requires the application of chemometric tools, including univariate and multivariate analyses. Univariate analysis often involves statistical hypothesis testing methods, including t-test, multiple analysis, and analysis of variance, to analyze whether significant differences exist between groups of samples under a certain variable condition (Gorrochategui et al., 2016). Multivariate analysis is predominantly used for analyzing lipidomics data and can be classified into two types: unsupervised pattern recognition (UPR) and supervised pattern recognition (SPR) (Song et al., 2009). UPR is applied to analyze the overall structure of data without grouping samples, and it includes principal component analysis (PCA) and hierarchical cluster analysis (HCA). PCA is a multivariate statistical analysis in which multidimensional data is transformed into several important variables, namely principal components, and projects datas into 2- or 3-D space; a principal component score graph facilitates the exhibition of data distribution and analysis of data aggregation and dispersion (He et al., 2021). HCA aggregates similar data into one cluster, whereas different data into different clusters. UPR gives too much attention to the details and is incapable of ignoring random errors within a group. On the other hand, SPR is generally used to reduce random errors within a group; it purpose is to found a classification model according to data, and them assign unknown samples to defined sample classes according to its pattern of measured features (Berrueta et al., 2007). SPR methods include linear discriminant analysis, partial least squares discrimination analysis, orthogonal partial least squares discriminant analysis, random forest, and support vector machine (Yi et al., 2016). In modeling analysis, overfitting is easy; fitting rate (R2) and prediction rate (Q2), as well as permutation validation and cross validation, are usually employed to evaluate the fitting and prediction ability of models (Eriksson et al., 2003). In practical applications, when orthogonal partial least squares discriminant analysis is used, permutation validation is often used to determine model overfitting (Boccard and Rutledge, 2013). Qualitative analyses should fully consider co-elution of heterogeneous lipids, overlap of isotope peaks, potential interference from additive ions, and fragmentation within the source, which can be analyzed using specific software and databases for lipidomics (Table 2). Using such methods, differential lipid molecules and biomarkers can be comprehensively screened and identified. Bioinformatics shows the potential to be extensively used for constructing lipid metabolic networks, particularly those related to specific proteins and genes. This should facilitate the monitoring of changes in lipid metabolic pathways and highlight the function of lipids in biological pathways.

Table 2. Common software and databases used in lipidomics

MS, mass spectrometry; LC-MS, liquid chromatography-MS; LIPID MAPS, Lipid Metabolites and Pathways Strategy

Application of lipidomics to assess food from farm animals

In recent years, many studies have been carried out in food from farm animals by using lipidomics (Table 3), which provides new idea for better understanding the nutritional value and quality of food.

Table 3. Application of lipidomics to assess food from farm animals

UPLC, ultrahigh performance liquid chromatography; ESI, electrospray ionization; MS, mass spectrometry; IMF, intramuscular fat; LPCs, lysophosphatidylcholines; Q-TOF, quadrupole-time-of-flight; LC-MS, liquid chromatography-MS; LPEs, lysophosphatidylethanolamines; FAC, FA carnitines.

Lipidomics for meat quality evaluation

Lipid is a key dietary component that affects meat quality and nutritional value. Using UPLC-ESI-MS/MS, Mi et al. (2019a) identified 1, 180 lipid species in pork samples collected from black pigs in different places. Tibetan pork contained 28 higher lipid species (14 GL, 8 GP, 2 SL, 2 FA, and 2 PR) and Jilin pork contained 18 higher lipid species (4 GL, 4 GP, 3 ST, 3 PK, 2 FA, and 2 PR). Further, polyunsaturated fatty acid (PUFA) content of Sanmenxia pork was significantly higher compared with Tibetan and Jilin pork. Such data indicate that lipid species can be used for the differentiation of the meat quality of different pork samples, facilitating the determination of the geographical origin of meat. Zhang et al. (2021b) performed lipidomics (UPLC-ESI-MS/MS) and transcriptomics analysis of the muscle of different breeds of pigs to reveal differences in intramuscular fat (IMF), and they found that Luchuan pigs showed significantly higher IMF content than Duroc pigs. Further, a total of 605 lipids were detected in 23 categories, including 211 TG, 95 PCs, 66 PEs, 46 DG, and 37 Cer. In comparison with Duroc pigs, 194 lipids (including 61 TG and 36 PCs) were upregulated and 45 (including 18 PCs) were downregulated in Luchuan pigs. Mi et al. (2018) applied UPLC-ESI-Q-TOF/MS-based lipidomics to study the lipid profile of chicken, and they found 1, 127 lipid species across 8 categories in Taihe black-boned silky fowls. Of them, 88, 11, and 1 lipid species were identified between different age, gender, and carcass parts; moreover, PUFA levels in birds with long feeding time (older months) were significantly higher than those in birds with short feeding time. LC-ESI-Q-TOF/MS lipidomics has also been used for the identification of beef from different countries. Forty potential markers were identified in the study by Wang et al. (2021), which mainly included GL, FA, PIs, PEs, lysophosphatidylethanolamines (LPEs), lysophosphatidylcholines (LPCs), Cer, SMs, and N-acylethanolamines. Beef samples from Canada, Argentina, and New Zealand showed higher similarity, while those from Brazil, Australia, and Uruguay were more similar. In another recent study (Jia et al., 2021), UHPLC-Q-Orbitrap MS/MS lipidomics was applied to analysis lipid variations in sheep meat during storage. Forty-eight significantly changed lipids were identified, including 8 FA carnitines (FAC), 23 PCs, 3 LPCs, 13 PEs, and 1 LPE. In the first 12 days, the levels of PCs, PEs, and FAC increased, and after 12 days, their levels decreased; the levels of LPEs and LPCs increased in whole cold chain storage. The transient accumulation of PCs, PEs, and FAC induced a large number of oxidation reactions after 12 days, indicating that the optimal cold chain time was 12 days. Collectively, such data suggest that MS lipidomics has made rapid progress, enhancing the analysis of lipid profiles, quality determination, and origin traceability of livestock and poultry meat. However, only few studies have explored the effects of feed nutrition on the lipid profile of livestock and poultry meat, and thus, further research is warranted. Based MS lipidomics has made rapid progress in the analysis of lipid prifiles, quality identification and origin traceability of livestock and poultry meat. However, there are few reports on the regulation of feed nutrition on meat lipid prifiles of livestock and poultry, which is a new interdisciplinary subject of nutrition and food chemistry needs further study.

Lipidomics for egg quality evaluation

Eggs are a cheap and high-quality source of nutrition, and provide abundant high-quality proteins, and lipids. Lipids account for >60% of the dry weight of egg yolk (Wang et al., 2017). At present, egg quality evaluation with lipidomics is mainly divided into three aspects including component analysis, breed and sex distinction, and feed effect. Lipidomics has been used to characterize lipid profiles of eggs. For example, Mi et al. (2019b) performed UPLC-Q-TOF MS/MS to differentiate eggs, identifying 1, 633 lipid species in total. Similarly, Ali et al. (2017) used UPLC-Q-TOF MS to identify PL molecules and classes in duck, hen, and quail egg yolks. In total, 57 molecules of PLs were identified and quantified. Wood et al. (2021) performed high-resolution mass spectrometric detection of lipid compositions in chicken egg yolks, reporting that yolks are a rich nutritional source of complex structural lipids required for lipid homeostasis, such as glycerophosphoethanolamines and glycerophosphocholines. In another study, the complex lipid family SL, including Cer, hexosylceramide, ceramide phosphoethanolamine, and hydroxysphingomyelin, was detected in egg yolks using lipidomics. Ceramide phosphoethanolamine, a naturally occurring lipid, is a key membrane component (Masood et al., 2010), whereas hydroxysphingomyelin plays an essential role in reducing the risk of prostate cancer and endometrial cancer (Li et al., 2017; Schmidt et al., 2020). Xie et al. (2020) used ultrasound-assisted one-phase solvent extraction method coupled with LC-ESI- Q-TOF MS for profiling of egg yolk lipids; 618 lipid species were identified in the yolks of green shell eggs, Tibetan eggs, native eggs, and docosahexaenoic acid eggs. Moreover, PCA can clearly distinguish the four types of eggs: Tibetan and docosahexaenoic acid egg yolks showed higher PL content and lower TG content. In another study, 900 lipid species were identified in the egg yolks of male and female chickens; 76 different lipid species were identified, of which 73 were downregulated and 3 were upregulated in female vs. male chickens. Acylcarnitine, TG (12:0/22:1/22:3), hemolytic serine (16:0/0:0), Cer (d18:0/18:0), cholesterol (16:1), and PC (18:3/20:4) were found to markedly contribute to sex discrimination (Xiang, 2019). Campos et al. (2016) applied HILIC-LC-MS/MS lipidomics to determine TG and PL profiles of egg yolks. They found that PC and PE content of eggs from free-range hens that were fed with vegetable origin food was higher, while TG content of eggs from hens fed with animal origin food was higher. UPLC-Q-TOF MS was applied by Wu et al. (2020) to determine egg yolk lipid profiles of hens fed different diets for 2 weeks. They found that eggs from laying hens were rich in n-3 PUFA; further, eggs from laying hens fed diets supplemented with flaxseed showed a significant increase in TG level, but the effect of flaxseed supplementation on highly unsaturated fatty acids (HUFA) enrichment was not obvious. In contract, HUFA content in eggs from laying hens fed diets supplemented with Schizochytrium sp. was significantly higher, and many classes of PC and PE were present in them. Most previous studies have used lipidomics approaches to assess lipid profiles of chicken and duck eggs; such studies need to be conducted using goose eggs in future. In addition, lipidomics-based investigations need to be conducted to determine changes in lipid molecules during the processing (cooking or frying) and storage of eggs.

Lipidomics for milk quality evaluation

Dairy products are rich in various bioactive lipids, such as GL, GP, SP, isopentenes, SL, PK, and FA (Fontecha et al., 2005). The lipid profiles (structure and class) in milk not only affect its physical and chemical properties and the quality and yield of dairy products but also the absorption efficiency and health of humans (Smiddy et al., 2012; Vyssotski et al., 2015; Zou et al., 2013). A study found that saturated and monounsaturated fatty acid levels in the milk of non-ruminants (horses and donkeys) were significantly lower than those in the milk of ruminants (buffaloes, cows, goats, and sheep), whereas the opposite was true for the content of PUFA, in particular 18:2n-6 and 18:3n-3 (Gantner et al., 2015). The variations in lipid classes and contents in milk lead to different types of dairy products with different nutritional values. Besides, due to the complexity of milk lipid composition and difficulties associated with lipid structure determination, only >500 types of lipids have been identified in milk lipid, with a large number of them still to be identified and characterized (Liu et al., 2018). Therefore, lipid profile analysis of milk samples using lipidomics plays a pivotal role in the quality evaluation and characterization of dairy products.

Using UHPLC-Q-TOF MS, 335 lipids across 13 categories were identified in donkey milk (Li et al., 2020b) and cow milk (Li et al., 2020a) at different lactation stages, including CL, PA, PC, PE, PG, PI, PS, Cer, SMs, species of simple Glc series (HexCer), Hex2Cers, DG, and TG. A total of 756 lipids across 14 categories were identified in the milk of goats from different geographical origins and at different lactation stages using UPLC-Q-Exactive Orbitrap MS, including 5 CL, 45 Cer, 17 LPCs, 4 LPEs, 36 PCs, 80 PEs, 9 PGs, 22 PIs, 2 acyl carnitine species, 55 HexCer, 56 SMs, 17 FA, 15 DG, and 416 TG (Liu et al., 2020). In another study, 13 lipid classes were identified in human, bovine, and caprine milk using UHPLC-Q-TOF MS, including TG, DG, SM, PC, Cer, HexCer, Hex2Cer, PE, PG, PS, PI, PA, and CL (Wang et al., 2020). Further, using UHPLC-Q-Exactive Orbitrap MS, 475 lipids across 11 categories were identified in donkey, human, and cow milk, including 380 TG, 13 DG, 67 GP, 14 SP, and 1 wax exter (Zhang et al., 2021a). Interest driven makes the adulteration of dairy products serious. Lipids are a key component affecting the quality and safety of dairy products. Garcia et al. (2012) found that the 20 TG classes with high speed and reliability were identified in bovine milk powder supplemented with oils or fats using matrix assisted laser desorption/ionization-Q-TOF MS. Subsequently, Calvano et al. (2013) proposed the use of intact PLs as markers of illegal bovine milk adulteration. They found the abundance ratio of the ions at m/z 706.6 and 703.6 to be species specific, suggesting that this approach could serve as an efficient, time-saving, and inexpensive way of detecting milk adulteration. Another study detected the lipid compositions of goat, soy, and bovine milk using UPLC-Q-Exactive Orbitrap MS. Goat milk was found to be rich in medium-chain TG, USFA, and n-6 and n-3 PUFA (particularly 20:5n-3 and 22:6n-3); soy milk was rich in PC, PE, PS, and PG, while bovine milk was rich in TG, DG, and Cer. Fourteen lipids were identified in total in the three types of milk, providing a new perspective for dairy product authentication and adulteration differentiation (Li et al., 2017). LC-MS has also been applied to study the effect of heat stress on the lipid profile of cow milk. Heat stress decreased the contents of short- and medium-chain fatty acids and increased that of long-chain fatty acids; moreover, it significantly reduced the abundance of five polar lipids, including PE, PS, PC, LPC, and glucoceramide. LPC could potentially serve as a biomarker of heat stress as its content in the milk of heat tolerant cows was higher than that in the milk of heat susceptible cows (Liu et al., 2017). Thus, the application of lipidomics is becoming increasingly prevalent to assess milk samples for various purposes. The results of such studies should serve as a reference for processing and development of dairy products, identification of adulteration, and breeding of dairy animals.

Conclusion

In this review, we present lipid classification and functions, and lipid extraction and analyses techniques. We also summarize how lipidomics is being used for the quality assessment of meat, eggs, and milk. Rapid advancements in the field of lipidomics has boosted its adoption for various purposes, such as food composition analysis, authentication and adulteration detection, and geographical tracing. However, some limitations exist that are hindering the widespread application of lipidomics. For example, the structure of lipids is complex and diverse in food, and it is difficult to separate and detect all lipid species using existing methodologies. Further, lipid concentration in a complex sample, potentially containing thousands of types of lipids, can considerably vary, making accurate quantitative analyses challenging. Besides, many lipids are difficult to synthesize, and lipidomics methodologies lack standards, seriously impacting lipid identification and quantification. Finally, information pertaining to lipid profiles collected using different instruments can show variations, and lipid classes and structure need to be accurately defined using a standardized naming system. Accordingly, both scientific and technological advancements are much expected in the field of lipidomics so as to improve lipid separation and quantification, MS-based detection and characterization, and data processing. Simultaneously, combining lipidomics with genomics, proteomics, and/or metabolomics should enhance genetic breeding, improve food quality and safety assessments, and benefit precision nutrition and diet intervention in animal husbandry.

Conflicts of Interest

The authors declare no potential conflicts of interest.

Acknowledgements

This work was supported by the Well-Bred Program of Shandong Province (2017LZGC020), the Open Project of Shandong Collaborative Innovation Center for Donkey Industry Technology (3193308), Innovation and Entrepreneurship Training Program for College Students (CXCY2021002 and 202110447017), the Scientific Research Fund of Liaocheng University (318052019), Taishan Leading Industry Talents-Agricultural Science of Shandong Province (LJNY201713), and Shandong Province Modern Agricultural Technology System Donkey Industrial Innovation Team (SDAIT-27).

Author Contributions

Conceptualization: Wang C, Li M. Writing - original draft: Song Y, Cai C, Song Y, Sun X, Liu B, Xue P. Writing - review & editing: Song Y, Cai C, Song Y, Sun X, Liu B, Xue P, Zhu M, Chai W, Wang Y, Wang C, Li M.

Ethics Approval

This article does not require IRB/IACUC approval because there are no human and animal participants.

References

  1. Alcoriza-Balaguer MI, Garcia-Canaveras JC, Lopez A, Conde I, Juan O, Carretero J, Lahoz A. 2019. LipidMS: An R package for lipid annotation in untargeted liquid chromatography-data independent acquisition-mass spectrometry lipidomics. Anal Chem 91:836-845. https://doi.org/10.1021/acs.analchem.8b03409
  2. Alewijnse AE, Peters SLM. 2008. Sphingolipid signalling in the cardiovascular system: Good, bad or both? Eur J Pharmacol 585:292-302. https://doi.org/10.1016/j.ejphar.2008.02.089
  3. Ali AH, Zou X, Lu J, Abed SM, Yao Y, Tao G, Jin Q, Wang X. 2017. Identification of phospholipids classes and molecular species in different types of egg yolk by using UPLC-Q-TOF-MS. Food Chem 221:58-66. https://doi.org/10.1016/j.foodchem.2016.10.043
  4. Belayneh HD, Wehling RL, Reddy AK, Cahoon EB, Ciftci ON. 2017. Ethanol-modified supercritical carbon dioxide extraction of the bioactive lipid components of Camelina sativa seed. J Am Oil Chem Soc 94:855-865. https://doi.org/10.1007/s11746-017-2993-z
  5. Berrueta LA, Alonso-Salces RM, Heberger K. 2007. Supervised pattern recognition in food analysis. J Chromatogr A 1158:196-214. https://doi.org/10.1016/j.chroma.2007.05.024
  6. Boccard J, Rutledge DN. 2013. A consensus orthogonal partial least squares discriminant analysis (OPLS-DA) strategy for multiblock Omics data fusion. Anal Chim Acta 769:30-39. https://doi.org/10.1016/j.aca.2013.01.022
  7. Bojko B, Reyes-Garces N, Bessonneau V, Gorynski K, Mousavi F, Souza Silva EA, Pawliszyn J. 2014. Solid-phase microextraction in metabolomics. TrAC Trends Analyt Chem 61:168-180. https://doi.org/10.1016/j.trac.2014.07.005
  8. Cajka T, Fiehn O. 2014. Comprehensive analysis of lipids in biological systems by liquid chromatography-mass spectrometry. TrAC Trends Analyt Chem 61:192-206. https://doi.org/10.1016/j.trac.2014.04.017
  9. Cajka T, Fiehn O. 2016. Toward merging untargeted and targeted methods in mass spectrometry-based metabolomics and lipidomics. Anal Chem 88:524-545. https://doi.org/10.1021/acs.analchem.5b04491
  10. Calvano CD, De Ceglie C, Aresta A, Facchini LA, Zambonin CG. 2013. MALDI-TOF mass spectrometric determination of intact phospholipids as markers of illegal bovine milk adulteration of high-quality milk. Anal Bioanal Chem 405:1641-1649. https://doi.org/10.1007/s00216-012-6597-z
  11. Campos AM, Ricardo F, Alves E, Reis A, Couto D, Domingues P, Domingues MRM. 2016. Lipidomic investigation of eggs' yolk: Changes in lipid profile of eggs from different conditions. Food Res Int 89:177-185. https://doi.org/10.1016/j.foodres.2016.07.006
  12. Canela N, Herrero P, Marine S, Nadal P, Ras MR, Rodriguez MA, Arola L. 2016. Analytical methods in sphingolipidomics: Quantitative and profiling approaches in food analysis. J Chromatogr A 1428:16-38. https://doi.org/10.1016/j.chroma.2015.07.110
  13. Checa A, Bedia C, Jaumot J. 2015. Lipidomic data analysis: Tutorial, practical guidelines and applications. Anal Chim Acta 885:1-16. https://doi.org/10.1016/j.aca.2015.02.068
  14. Cui Y, Dai Z, Shen Q, Wang L. 2019. Recent advances and prospect of lipidomics in food science. J Chin Inst Food Sci Technol 19:262-270.
  15. Eriksson L, Jaworska J, Worth AP, Cronin MTD, McDowell RM, Gramatica P. 2003. Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs. Environ Health Perspect 111:1361-1375. https://doi.org/10.1289/ehp.5758
  16. Fahy E, Subramaniam S, Brown HA, Glass CK, Merrill AH, Murphy RC, Raetz CRH, Russell DW, Seyama Y, Shaw W, Shimizu T, Spener F, van Meer G, VanNieuwenhze MS, White SH, Witztum JL, Dennis EA. 2005. A comprehensive classification system for lipids. J Lipid Res 46:839-861. https://doi.org/10.1194/jlr.E400004-JLR200
  17. Fontecha J, Goudjil H, Rios JJ, Fraga MJ, Juarez M. 2005. Identity of the major triacylglycerols in ovine milk fat. Int Dairy J 15:1217-1224. https://doi.org/10.1016/j.idairyj.2004.11.013
  18. Fukasawa K, Nakajima S, Gotoh M, Tanaka S, Murofushi H, Murakami-Murofushi K. 2018. Qualitative and quantitative comparison of cyclic phosphatidic acid and its related lipid species in rat serum using hydrophilic interaction liquid chromatography with tandem-mass spectrometry. J Chromatogr A 1567:177-184. https://doi.org/10.1016/j.chroma.2018.07.010
  19. Gantner V, Mijic P, Baban M, Skrtic Z, Turalija A. 2015. The overall and fat composition of milk of various species. Mljekarstvo J Dairy Prod Process Improv 65:223-231.
  20. Garcia JS, Sanvido GB, Saraiva SA, Zacca JJ, Cosso RG, Eberlin MN. 2012. Bovine milk powder adulteration with vegetable oils or fats revealed by MALDI-QTOF MS. Food Chem 131:722-726. https://doi.org/10.1016/j.foodchem.2011.09.062
  21. Ghidotti M, Fabbri D, Torri C, Piccinini S. 2018. Determination of volatile fatty acids in digestate by solvent extraction with dimethyl carbonate and gas chromatography-mass spectrometry. Anal Chim Acta 1034:92-101. https://doi.org/10.1016/j.aca.2018.06.082
  22. Gorrochategui E, Jaumot J, Lacorte S, Tauler R. 2016. Data analysis strategies for targeted and untargeted LC-MS metabolomic studies: Overview and workflow. TrAC Trends Analyt Chem 82:425-442. https://doi.org/10.1016/j.trac.2016.07.004
  23. Han X. 2016. Lipidomics for studying metabolism. Nat Rev Endocrinol 12:668-679. https://doi.org/10.1038/nrendo.2016.98
  24. Han X, Gross RW. 2003. Global analyses of cellular lipidomes directly from crude extracts of biological samples by ESI mass spectrometry: A bridge to lipidomics. J Lipid Res 44:1071-1079. https://doi.org/10.1194/jlr.R300004-JLR200
  25. He J, Wu X, Zhou Y, Chen J. 2021. Effects of different preheat treatments on volatile compounds of camellia (Camellia oleifera Abel.) seed oil and formation mechanism of key aroma compounds. J Food Biochem 45:e13649.
  26. Hebra T, Eparvier V, Touboul D. 2020. Atmospheric pressure photoionization versus electrospray for the dereplication of highly conjugated natural products using molecular networks. J Chromatogr A 1630:461533. https://doi.org/10.1016/j.chroma.2020.461533
  27. Jia B, Wu J, Zhang Y. 2020. Recent advances and prospect in the development of the biological samples preparation techniques in lipidomics. AnHui Chem Ind 46:1-5.
  28. Jia W, Li R, Liu S, Shi L, Wu X, Chu X. 2021. Lipidomics study of tan-sheep meat in cold chain storage based on ultra high performance liquid chromatography-quadrupole-orbitrap mass spectrometry. J Instrum Anal 40:295-302.
  29. Johnson JV, Yost RA, Kelley PE, Bradford DC. 1990. Tandem-in-space and tandem-in-time mass spectrometry: Triple quadrupoles and quadrupole ion traps. Anal Chem 62:2162-2172. https://doi.org/10.1021/ac00219a003
  30. Kouba M, Mourot J. 2011. A review of nutritional effects on fat composition of animal products with special emphasis on n-3 polyunsaturated fatty acids. Biochimie 93:13-17. https://doi.org/10.1016/j.biochi.2010.02.027
  31. Li J, Yang Y, Zhan T, Zhao Q, Zhang J, Ao X, He J, Zhou J, Tang C. 2021a. Effect of slaughter weight on carcass characteristics, meat quality, and lipidomics profiling in longissimus thoracis of finishing pigs. LWT-Food Sci Technol 140:110705. https://doi.org/10.1016/j.lwt.2020.110705
  32. Li J, Zhang J, Yang Y, Zhu J, He W, Zhao Q, Tang C, Qin Y, Zhang J. 2021b. Comparative characterization of lipids and volatile compounds of Beijing Heiliu and Laiwu Chinese black pork as markers. Food Res Int 146:110433. https://doi.org/10.1016/j.foodres.2021.110433
  33. Li M, Li Q, Kang S, Cao X, Zheng Y, Wu J, Wu R, Shao J, Yang M, Yue X. 2020a. Characterization and comparison of lipids in bovine colostrum and mature milk based on UHPLC-QTOF-MS lipidomics. Food Res Int 136:109490. https://doi.org/10.1016/j.foodres.2020.109490
  34. Li M, Li W, Wu J, Zheng Y, Shao J, Li Q, Kang S, Zhang Z, Yue X, Yang M. 2020b. Quantitative lipidomics reveals alterations in donkey milk lipids according to lactation. Food Chem 310:125866. https://doi.org/10.1016/j.foodchem.2019.125866
  35. Li M, Zhu M, Chai W, Wang Y, Fan D, Lv M, Jiang X, Liu Y, Wei Q, Wang C. 2021c. Determination of lipid profiles of Dezhou donkey meat using an LC-MS-based lipidomics method. J Food Sci 86:4511-4521. https://doi.org/10.1111/1750-3841.15917
  36. Li M, Zhu M, Chai W, Wang Y, Song Y, Liu B, Cai C, Song Y, Sun X, Xue P, Wang C. 2021d. Determination of the heterogeneity of intramuscular fat and visceral adipose tissue from Dezhou donkey by lipidomics and transcriptomics profiling. Front Nutr 8:746684. https://doi.org/10.3389/fnut.2021.746684
  37. Li Q, Zhao Y, Zhu D, Pang X, Liu Y, Frew R, Chen G. 2017. Lipidomics profiling of goat milk, soymilk and bovine milk by UPLC-Q-Exactive Orbitrap Mass Spectrometry. Food Chem 224:302-309. https://doi.org/10.1016/j.foodchem.2016.12.083
  38. Li X, He Q, Hou H, Zhang S, Zhang X, Zhang Y, Wang X, Han L, Liu K. 2018. Targeted lipidomics profiling of marine phospholipids from different resources by UPLC-Q-Exactive Orbitrap/MS approach. J Chromatogr B Analyt Technol Biomed Life Sci 1096:107-112. https://doi.org/10.1016/j.jchromb.2018.08.018
  39. Li Z, Cheng S, Lin Q, Cao W, Yang J, Zhang M, Shen A, Zhang W, Xia Y, Ma X, Ouyang Z. 2021e. Single-cell lipidomics with high structural specificity by mass spectrometry. Nat Commun 12:2869. https://doi.org/10.1038/s41467-021-23161-5
  40. Liu H, Guo X, Zhao Q, Qin Y, Zhang J. 2020. Lipidomics analysis for identifying the geographical origin and lactation stage of goat milk. Food Chem 309:125765. https://doi.org/10.1016/j.foodchem.2019.125765
  41. Liu Z, Ezernieks V, Wang J, Arachchillage NW, Garner JB, Wales WJ, Cocks BG, Rochfort S. 2017. Heat stress in dairy cattle alters lipid composition of milk. Sci Rep 7:961. https://doi.org/10.1038/s41598-017-01120-9
  42. Liu Z, Rochfort S, Cocks B. 2018. Milk lipidomics: What we know and what we don't. Prog Lipid Res 71:70-85. https://doi.org/10.1016/j.plipres.2018.06.002
  43. Masood MA, Yuan C, Acharya JK, Veenstra TD, Blonder J. 2010. Quantitation of ceramide phosphorylethanolamines containing saturated and unsaturated sphingoid base cores. Anal Biochem 400:259-269. https://doi.org/10.1016/j.ab.2010.01.033
  44. Matyash V, Liebisch G, Kurzchalia TV, Shevchenko A, Schwudke D. 2008. Lipid extraction by methyl-tert-butyl ether for high-throughput lipidomics. J Lipid Res 49:1137-1146. https://doi.org/10.1194/jlr.D700041-JLR200
  45. Mi S, Shang K, Jia W, Zhang CH, Li X, Fan YQ, Wang H. 2018. Characterization and discrimination of Taihe black-boned silky fowl (Gallus gallus domesticus Brisson) muscles using LC/MS-based lipidomics. Food Res Int 109:187-195. https://doi.org/10.1016/j.foodres.2018.04.038
  46. Mi S, Shang K, Li X, Zhang CH, Liu JQ, Huang DQ. 2019a. Characterization and discrimination of selected China's domestic pork using an LC-MS-based lipidomics approach. Food Control 100:305-314. https://doi.org/10.1016/j.foodcont.2019.02.001
  47. Mi S, Shang K, Zhang CH, Fan YQ. 2019b. Characterization and discrimination of selected chicken eggs in China's retail market based on multi-element and lipidomics analysis. Food Res Int 126:108668. https://doi.org/10.1016/j.foodres.2019.108668
  48. Miao QY, Gao W, Li J, Chen J. 2019. Progress on lipidomics analytical methods and their applications in studies of traditional Chinese medicines. Zhongguo Zhong Yao Za Zhi 44:1760-1766.
  49. Muhamadali H, Weaver D, Subaihi A, AlMasoud N, Trivedi DK, Ellis DI, Linton D, Goodacre R. 2016. Chicken, beams, and Campylobacter: Rapid differentiation of foodborne bacteria via vibrational spectroscopy and MALDI-mass spectrometry. Analyst 141:111-122. https://doi.org/10.1039/C5AN01945A
  50. Navas-Iglesias N, Carrasco-Pancorbo A, Cuadros-Rodriguez L. 2009. From lipids analysis towards lipidomics, a new challenge for the analytical chemistry of the 21st century. Part II: Analytical lipidomics. TrAC Trends Analyt Chem 28:393-403. https://doi.org/10.1016/j.trac.2008.12.004
  51. Nilsson A. 2016. Role of sphingolipids in infant gut health and immunity. J Pediatr 173:S53-S59. https://doi.org/10.1016/j.jpeds.2016.02.076
  52. Norris GH, Porter CM, Jiang C, Millar CL, Blesso CN. 2017. Dietary sphingomyelin attenuates hepatic steatosis and adipose tissue inflammation in high-fat-diet-induced obese mice. J Nutr Biochem 40:36-43. https://doi.org/10.1016/j.jnutbio.2016.09.017
  53. Numata M, Kandasamy P, Nagashima Y, Posey J, Hartshorn K, Woodland D, Voelker DR. 2012. Phosphatidylglycerol suppresses influenza a virus infection. Am J Respir Cell Mol Biol 46:479-487. https://doi.org/10.1165/rcmb.2011-0194OC
  54. Pourmortazavi SM, Hajimirsadeghi SS. 2007. Supercritical fluid extraction in plant essential and volatile oil analysis. J Chromatogr A 1163:2-24. https://doi.org/10.1016/j.chroma.2007.06.021
  55. Pourmortazavi SM, Saghafi Z, Ehsani A, Yousefi M. 2018. Application of supercritical fluids in cholesterol extraction from foodstuffs: A review. J Food Sci Technol 55:2813-2823. https://doi.org/10.1007/s13197-018-3205-z
  56. Saccenti E, Hoefsloot HCJ, Smilde AK, Westerhuis JA, Hendriks MMWB. 2014. Reflections on univariate and multivariate analysis of metabolomics data. Metabolomics 10:361-374. https://doi.org/10.1007/s11306-013-0598-6
  57. Samhan-Arias AK, Ji J, Demidova OM, Sparvero LJ, Feng W, Tyurin V, Tyurina YY, Epperly MW, Shvedova AA, Greenberger JS, Bayir H, Kagan VE, Amoscato AA. 2012. Oxidized phospholipids as biomarkers of tissue and cell damage with a focus on cardiolipin. Biochim Biophys Acta Biomembr BBA 1818:2413-2423. https://doi.org/10.1016/j.bbamem.2012.03.014
  58. Schmidt JA, Fensom GK, Rinaldi S, Scalbert A, Appleby PN, Achaintre D, Gicquiau A, Gunter MJ, Ferrari P, Kaaks R, Kuhn T, Boeing H, Trichopoulou A, Karakatsani A, Peppa E, Palli D, Sieri S, Tumino R, Bueno-de-Mesquita B, Agudo A, Sanchez MJ, Chirlaque MD, Ardanaz E, Larranaga N, Perez-Cornago A, Assi N, Riboli E, Tsilidis KK, Key TJ, Travis RC. 2020. Patterns in metabolite profile are associated with risk of more aggressive prostate cancer: A prospective study of 3,057 matched case-control sets from EPIC. Int J Cancer 146:720-730. https://doi.org/10.1002/ijc.32314
  59. Smiddy MA, Huppertz T, van Ruth SM. 2012. Triacylglycerol and melting profiles of milk fat from several species. Int Dairy J 24:64-69. https://doi.org/10.1016/j.idairyj.2011.07.001
  60. Song H, Ladenson J, Turk J. 2009. Algorithms for automatic processing of data from mass spectrometric analyses of lipids. J Chromatogr B Analyt Technol Biomed Life Sci 877:2847-2854. https://doi.org/10.1016/j.jchromb.2008.12.043
  61. Song S, Bai Y, Liu H. 2020. Advances in the development of the sample preparation techniques in lipidomics. China J Chromatogr 38:66-73.
  62. Sugawara T, Aida K, Duan J, Tomonaga N, Manabe Y, Hirata T. 2019. Analysis of chemical structures of glucosylceramides from rice and other foodstuffs. J Nutr Sci Vitaminol 65:S228-S230. https://doi.org/10.3177/jnsv.65.S228
  63. Tanaka K, Hosozawa M, Kudo N, Yoshikawa N, Hisata K, Shoji H, Shinohara K, Shimizu T. 2013. The pilot study: Sphingomyelin-fortified milk has a positive association with the neurobehavioural development of very low birth weight infants during infancy, randomized control trial. Brain Dev 35:45-52. https://doi.org/10.1016/j.braindev.2012.03.004
  64. Tang S, Zhang H, Lee HK. 2016. Advances in sample extraction. Anal Chem 88:228-249. https://doi.org/10.1021/acs.analchem.5b04040
  65. Teo CC, Chong WPK, Ho YS. 2013. Development and application of microwave-assisted extraction technique in biological sample preparation for small molecule analysis. Metabolomics 9:1109-1128. https://doi.org/10.1007/s11306-013-0528-7
  66. Triebl A, Hartler J, Trotzmuller M, Kofeler HC. 2017. Lipidomics: Prospects from a technological perspective. Biochim Biophys Acta Mol Cell Biol Lipids 1862:740-746.
  67. Tsochatzidis NA, Guiraud P, Wilhelm AM, Delmas H. 2001. Determination of velocity, size and concentration of ultrasonic cavitation bubbles by the phase-Doppler technique. Chem Eng Sci 56:1831-1840. https://doi.org/10.1016/S0009-2509(00)00460-7
  68. Vyssotski M, Bloor SJ, Lagutin K, Wong H, Williams DBG. 2015. Efficient separation and analysis of triacylglycerols: Quantitation of β-palmitate (OPO) in oils and infant formulas. J Agric Food Chem 63:5985-5992. https://doi.org/10.1021/acs.jafc.5b01835
  69. Wang C, Kong H, Guan Y, Yang J, Gu J, Yang S, Xu G. 2005. Plasma phospholipid metabolic profiling and biomarkers of type 2 diabetes mellitus based on high-performance liquid chromatography/electrospray mass spectrometry and multivariate statistical analysis. Anal Chem 77:4108-4116. https://doi.org/10.1021/ac0481001
  70. Wang K, Xu L, Wang X, Chen A, Xu Z. 2021. Discrimination of beef from different origins based on lipidomics: A comparison study of DART-QTOF and LC-ESI-QTOF. LWT-Food Sci Technol 149:111838. https://doi.org/10.1016/j.lwt.2021.111838
  71. Wang L, Li X, Liu L, da Zhang H, Zhang Y, Hao Chang Y, Zhu QP. 2020. Comparative lipidomics analysis of human, bovine and caprine milk by UHPLC-Q-TOF-MS. Food Chem 310:125865. https://doi.org/10.1016/j.foodchem.2019.125865
  72. Wang QL, Jin YG, Lu SL, Ji H, Ma MH. 2017. Lipidomic analysis of egg yolk triglycerides in different poultry eggs. Mod Food Sci Technol 33:210-216.
  73. Wood PL, Muir W, Christmann U, Gibbons P, Hancock CL, Poole CM, Emery AL, Poovey JR, Hagg C, Scarborough JH, Christopher JS, Dixon AT, Craney DJ. 2021. Lipidomics of the chicken egg yolk: High-resolution mass spectrometric characterization of nutritional lipid families. Poult Sci 100:887-899. https://doi.org/10.1016/j.psj.2020.11.020
  74. Wu B, Xie Y, Xu S, Lv X, Yin H, Xiang J, Chen H, Wei F. 2020. Comprehensive lipidomics analysis reveals the effects of different omega-3 polyunsaturated fatty acid-rich diets on egg yolk lipids. J Agric Food Chem 68:15048-15060. https://doi.org/10.1021/acs.jafc.0c05215
  75. Xiang X, 2019. Study on odor, multiomics matrix difference and non-destructive identification of male and female chicken eggs. Huazhong Agr Uni in China.
  76. Xie Y, Wu B, Wu Z, Tu X, Xu S, Lv X, Yin H, Xiang J, Chen H, Wei F. 2020. Ultrasound-assisted one-phase solvent extraction coupled with liquid chromatography-quadrupole time-of-flight mass spectrometry for efficient profiling of egg yolk lipids. Food Chem 319:126547. https://doi.org/10.1016/j.foodchem.2020.126547
  77. Xu T, Hu C, Xuan Q, Xu G. 2020. Recent advances in analytical strategies for mass spectrometry-based lipidomics. Anal Chim Acta 1137:156-169. https://doi.org/10.1016/j.aca.2020.09.060
  78. Yang K, Cheng H, Gross RW, Han X. 2009. Automated lipid identification and quantification by multidimensional mass spectrometry-based shotgun lipidomics. Anal Chem 81:4356-4368. https://doi.org/10.1021/ac900241u
  79. Yi L, Dong N, Yun Y, Deng B, Ren D, Liu S, Liang Y. 2016. Chemometric methods in data processing of mass spectrometrybased metabolomics: A review. Anal Chim Acta 914:17-34. https://doi.org/10.1016/j.aca.2016.02.001
  80. Yi M, Zhang C, Zhang Z, Yi P, Xu P, Huang J, Peng W. 2020. Integrated metabolomic and lipidomic analysis reveals the neuroprotective mechanisms of bushen tiansui formula in an aβ1-42-induced rat model of Alzheimer's disease. Oxid Med Cell Longev 2020:5243453.
  81. Zhang X, Li H, Yang L, Jiang G, Ji C, Zhang Q, Zhao F. 2021a. Comparative lipidomics profiling of donkey milk with cow and human milk by UHPLC-Q-Exactive Orbitrap mass spectrometry. J Food Compos Anal 101:103988. https://doi.org/10.1016/j.jfca.2021.103988
  82. Zhang Z, Liao Q, Sun Y, Pan T, Liu S, Miao W, Li Y, Zhou L, Xu G. 2021b. Lipidomic and transcriptomic analysis of the longissimus muscle of luchuan and duroc pigs. Front Nutr 8:667622. https://doi.org/10.3389/fnut.2021.667622
  83. Zou X, Huang J, Jin Q, Guo Z, Liu Y, Cheong L, Xu X, Wang X. 2013. Lipid composition analysis of milk fats from different mammalian species: Potential for use as human milk fat substitutes. J Agric Food Chem 61:7070-7080. https://doi.org/10.1021/jf401452y
  84. Zullig T, Troetzmueller M, Kofeler HC. 2020. Lipidomics from sample preparation to data analysis: A primer. Anal Bioanal Chem 412:2191-2209. https://doi.org/10.1007/s00216-019-02241-y