Analytical strategies in lipidomics and applications in disease biomarker discovery

A novel in-depth "static- dynamic" lipidomics workflow to reveal lipids reprogramming in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths globally, and with current treatments proving less effective, there is an urgent need for specific biomarkers and therapeutic targets. Lipid metabolism reprogramming is a crucial cancer hallmark, yet comprehensive studies on lipid metabolic fluxes remain limited. In this study, combined with non-targeted lipidomics, a comprehensive workflow for stable isotope tracing lipidomics was established to analyze changes in lipid levels of HepG2 cells and LO2 cells from both static and dynamic perspectives. Through the screening of differential metabolites and the enrichment analysis of lipid metabolic pathways, the most significant differential metabolic pathways were found. Finally, the TCGA and CPTAC databases were utilized to analyze the gene expression levels and protein expression levels of pivotal enzymes in the differential metabolic pathways, and these findings were verified by Western Blotting experiments. The results demonstrated that the lipid metabolism of HCC was disordered, and the metabolic pathways that caused lipid changes in HCC were mainly glycerophospholipid metabolism and sphingolipid signaling pathway. LPCAT1 and SMPD1 played a crucial role in the reprogramming of lipid metabolism in HCC. The established "static-dynamic" lipidomics workflow improves the coverage and accuracy of dynamic lipid monitoring, elucidating the roles of lipids in physiological and pathological processes, providing tools for studying lipid function, and offering new perspectives on the pathogenesis of HCC as well as the identification of drug targets.

Lipidomics-driven drug discovery and delivery strategies in glioblastoma

With few viable treatment options, glioblastoma (GBM) is still one of the most aggressive and deadly types of brain cancer. Recent developments in lipidomics have demonstrated the potential of lipid metabolism as a therapeutic target in GBM. The thorough examination of lipids in biological systems, or lipidomics, is essential to comprehending the changed lipid profiles found in GBM, which are linked to the tumor's ability to grow, survive, and resist treatment. The use of lipidomics in drug delivery and discovery is examined in this study, focusing on how it may be used to find new biomarkers, create multi-target directed ligands, and improve drug delivery systems. We also cover the use of FDA-approved medications, clinical trials that use lipid-targeted medicines, and the integration of lipidomics with other omics technologies. This study emphasizes lipidomics as a possible tool in developing more effective treatment methods for GBM by exploring various lipid-centric techniques.

Different effects of low- and high-density lipoproteins in egg yolk on lipid metabolism of mouse: Role of phospholipids-to-cholesterol intake ratio

Recent evidence increasingly indicates that moderate egg consumption (up to one egg per day) poses no significant risk of cardiovascular diseases in healthy individuals, leading researchers to speculate whether other lipids in eggs affect lipid metabolism. In this research, C57BL/6J mice were given chow diets containing egg yolk low-density lipoprotein (LDL) and high-density lipoprotein (HDL), two natural components with different proportions of phospholipids and cholesterol, to explore the effects of the phospholipids-to-cholesterol intake ratio on lipid metabolism. Increases in liver weight and liver index were observed following LDL intake, while body weight and epididymal fat decreased after HDL intake in a dose-dependent manner. The food efficiency of chow diets decreased with increasing phospholipids content. Lipid biomarkers indicated that LDL intake significantly increased plasma LDL cholesterol and liver cholesterol levels. At equivalent cholesterol doses, the cholesterol-raising effects of HDL were weaker than those of LDL. Higher protein expression of cytochrome P450 family 7 subfamily A member 1 in the HDL groups suggested increased bile acid excretion at higher phospholipids-to-cholesterol intake ratios. Fecal metabolite analysis revealed significant changes in lipid composition, primarily involving glycerophospholipids, sphingolipids, and sterol lipids, consistent with the lipid profiles of LDL and HDL. The upregulation of cholic acid and deoxycholic acid in feces further confirmed increased bile acid excretion. This study highlights the differences in lipid metabolism in mice under varying phospholipids-to-cholesterol intake ratios, which may be associated with changes in dietary energy supply and bile excretion following phospholipids involvement.

Ion Source Complementarity for Characterization of Complex Organic Mixtures Using Fourier Transform Mass Spectrometry: A Review

Complex organic mixtures are found in many areas of research, such as energy, environment, health, planetology, and cultural heritage, to name but a few. However, due to their complex chemical composition, which holds an extensive potential of information at the molecular level, their molecular characterization is challenging. In mass spectrometry, the ionization step is the key step, as it determines which species will be detected. This review presents an overview of the main ionization sources employed to characterize these kinds of samples in Fourier transform mass spectrometry (FT-MS), namely electrospray (ESI), atmospheric pressure photoionization (APPI), atmospheric pressure chemical ionization (APCI), atmospheric pressure laser ionization (APLI), and (matrix-assisted) laser desorption ionization ((MA)LDI), and their complementarity in the characterization of complex organic mixtures. First, the ionization techniques are examined in the common direct introduction (DI) usage. Second, these approaches are discussed in the context of coupling chromatographic techniques such as gas chromatography, liquid chromatography, and supercritical fluid chromatography.

Lipid Identification of Biomarkers in Esophageal Squamous Cell Carcinoma by Lipidomic Analysis

Lipids participate in many important biological functions through energy storage, membrane structure stabilization, signal transduction, and molecular recognition. Previous studies have shown that patients with esophageal squamous cell carcinoma (ESCC) have abnormal lipid metabolism. However, studies characterizing lipid metabolism in ESCC patients through lipidomics are limited. Plasma lipid profiles of 65 ESCC patients and 42 healthy controls (HC) were characterized by lipidomics-based ultraperformance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS). Single-factor and multi-factor statistical analysis were used to screen the differences in blood lipids between groups, and combined with component ratio analysis and receiver operating characteristic (ROC) curve diagnostic efficiency assessment, to reveal the potential mechanisms and biomarkers of ESCC. There were significant differences in lipid profiles between the ESCC and HC groups. Thirty-six differential lipids (11 up-regulated and 25 down-regulated) were selected based on the criteria of p < .05 and fold change > 1.3 or < 0.77. Glycerophospholipids were the major differential lipids, suggesting that these lipid metabolic pathways exhibit a significant imbalance that may contribute to the development of esophageal squamous cell carcinoma. Among them, the seven candidate biomarkers for esophageal squamous cell carcinoma with the highest diagnostic value are three phosphatidylserine (PS), three fatty acids (FA) and one phosphatidylcholine (PC).

Combining imaging mass spectrometry and immunohistochemistry to analyse the lipidome of spinal cord inflammation

Inflammation is a complex process that accompanies many pathologies. Actually, dysregulation of the inflammatory process is behind many autoimmune diseases. Thus, treatment of such pathologies may benefit from in-depth knowledge of the metabolic changes associated with inflammation. Here, we developed a strategy to characterize the lipid fingerprint of inflammation in a mouse model of spinal cord injury. Using lipid imaging mass spectrometry (LIMS), we scanned spinal cord sections from nine animals injected with lysophosphatidylcholine, a chemical model of demyelination. The lesions were demonstrated to be highly heterogeneous, and therefore, comparison with immunofluorescence experiments carried out in the same section scanned by LIMS was required to accurately identify the morphology of the lesion. Following this protocol, three main areas were defined: the lesion core, the peri-lesion, which is the front of the lesion and is rich in infiltrating cells, and the uninvolved tissue. Segmentation of the LIMS experiments allowed us to isolate the lipid fingerprint of each area in a precise way, as demonstrated by the analysis using classification models. A clear difference in lipid signature was observed between the lesion front and the epicentre, where the damage was maximized. This study is a first step to unravel the changes in the lipidome associated with inflammation in the context of diverse pathologies, such as multiple sclerosis.

Mass filtering combined with photochemical derivatization enables high throughput mass spectrometric analysis of unsaturated phosphatidylcholine isomers

Phosphatidylcholines (PCs) are closely related to coronary heart disease, such as myocardial infarction. The analysis of the deep structure of PCs is of great significance for exploring the effects of exercise rehabilitation and lipid metabolism. Here, we present a mass filtering combined with photochemical derivatization method for rapid screening and accurate identification of the CC position and sn-location isomer of PCs. This method is simple to execute and easily implementable for routine analysis. The accurate qualitative and quantitative analysis of PCs and isomers facilitates the discovery of biomarkers for exercise rehabilitation of patients with myocardial infarction.

Mass Spectrometry Based on Chemical Derivatization Has Brought Novel Discoveries to Lipidomics: A Comprehensive Review

Lipids, as one of the most important organic compounds in organisms, are important components of cells and participate in energy storage and signal transduction of living organisms. As a rapidly rising field, lipidomics research involves the identification and quantification of multiple classes of lipid molecules, as well as the structure, function, dynamics, and interactions of lipids in living organisms. Due to its inherent high selectivity and high sensitivity, mass spectrometry (MS) is the "gold standard" analysis technique for small molecules in biological samples. The combination chemical derivatization with MS detection is a unique strategy that could improve MS ionization efficiency, facilitate structure identification and quantitative analysis. Herein, this review discusses derivatization-based MS strategies for lipidomic analysis over the past decade and focuses on all the reported lipid categories, including fatty acids and modified fatty acids, glycerolipids, glycerophospholipids, sterols and saccharolipids. The functional groups of lipids mainly involved in chemical derivatization include the C=C group, carboxyl group, hydroxyl group, amino group, carbonyl group. Furthermore, representative applications of these derivatization-based lipid profiling methods were summarized. Finally, challenges and countermeasures of lipid derivatization are mentioned and highlighted to guide future studies of derivatization-based MS strategy in lipidomics.

Cadmium influence on lipid metabolism in Sprague-Dawley rats through linoleic acid and glycerophospholipid metabolism pathways

Cadmium (Cd) is widely distributed in the environment and easy adsorbed by living organisms with adverse effects. Exposure to Cd-contaminated food may disrupt lipid metabolism and increase human health risk. To study the perturbation effect of Cd on lipid metabolism in vivo, 24 male Sprague-Dawley (SD) rats were randomly assigned four groups and treated by Cd chloride solution (0, 1.375 mg/kg, 5.5 mg/kg, 22 mg/kg) for 14 days. The characteristic indexes of serum lipid metabolism were analyzed. Afterwards, untargeted metabolomics analysis was applied to explore the adverse effects of Cd on rats by liquid chromatography coupled with mass spectrometry (LC-MS). The results revealed that Cd exposure obviously decreased the average serum of triglycerides (TG) and low-density lipoprotein cholesterol (LDL-C) and caused an imbalance of endogenous compounds in the 22 mg/kg Cd-exposed group. Compared with the control group, 30 metabolites with significant differences were identified in the serum. Our results indicated that Cd caused lipid metabolic disorders in rats by disrupting linoleic acid and glycerophospholipid metabolism pathways. Furthermore, there were three kinds of remarkable differential metabolites-9Z,12Z-octadecadienoic acid, PC(20:4(8Z,11Z,14Z,17Z)/0:0), and PC(15:0/18:2(9Z,12Z)), which enriched the two significant metabolism pathways and could be the potential biomarkers.

Plasma lipidomics reveals potential lipid markers for the prediction of delayed neurocognitive recovery after cardiac surgery with cardiopulmonary bypass

BACKGROUND

Delayed neurocognitive recovery (DNR) is a cognitive change that affects patients after anesthesia and surgery, with a high incidence, severely impairing their quality of life. We identified relevant metabolic pathways to determine predictive lipid markers and understand the pathogenesis of DNR.

METHODS

A non-targeted lipidomic strategy using Waters Acquity I Class UPLC-Xevo G2-S QTOF UPLC-MS was employed to identify different lipids in the 24 h postoperative plasma of patients whose cognitive function was assessed 7-14 days after surgery. Principal component analysis, orthogonal partial least squares discriminant analysis, and receiver operating characteristic (ROC) curves were used to identify potential biomarkers for predicting DNR. Metabolic pathway analysis was performed using the online platform MetaboAnalyst 5.0.

RESULTS

Pre-operative hemoglobin was identified as an independent risk factor for DNR. The individual areas under the ROC curves (AUC) of the 5 representative lipids were all > 0.6, and the AUC of the combined predictor was 0.912. Glycerophospholipid metabolic pathway was dysregulated in patients with subsequent DNR.

CONCLUSION

This study identified a series of potential predictive biomarkers for DNR. These data suggested that glycerophospholipid metabolism may be the mechanism underlying the occurrence and progression of DNR.

DiffN Selection of Tandem Mass Spectrometry Precursors

Current data-dependent acquisition (DDA) approaches select precursor ions for tandem mass spectrometry (MS/MS) characterization based on their absolute intensity, known as a TopN approach. Low-abundance species may not be identified as biomarkers in a TopN approach. Herein, a new DDA approach is proposed, DiffN, which uses the relative differential intensity of ions between two samples to selectively target species undergoing the largest fold changes for MS/MS. Using a dual nano-electrospray (nESI) ionization source which allows samples contained in separate capillaries to be analyzed in parallel, the DiffN approach was developed and validated with well-defined lipid extracts. A dual nESI source and DiffN DDA approach was applied to quantify the differences in lipid abundance between two colorectal cancer cell lines. The SW480 and SW620 lines represent a matched pair from the same patient: the SW480 cells from a primary tumor and the SW620 cells from a metastatic lesion. A comparison of TopN and DiffN DDA approaches on these cancer cell samples highlights the ability of DiffN to increase the likelihood of biomarker discovery and the decreased probability of TopN to efficiently select lipid species that undergo large fold changes. The ability of the DiffN approach to efficiently select precursor ions of interest makes it a strong candidate for lipidomic analyses. This DiffN DDA approach may also apply to other molecule classes (e.g., other metabolites or proteins) that are amenable to shotgun analyses.

PDMS-TiO2 composite films combined with LC-MS/MS for determination of phospholipids of urine in non-small cell lung cancer patients with traditional Chinese medicine syndromes

Lung cancer is one of the most common malignant tumors in China. Most patients are already in the mid to advanced stages during the consultation and the survival rate is less than 23 % with a poor prognosis. Therefore, effective dialectical diagnosis of advanced cancer can guide individualized treatment to improve survival. Phospholipids are the building blocks of cell membranes and abnormal phospholipid metabolism is associated with plentiful diseases. Most studies of disease markers use blood as a sample. However, urine covers extensive metabolites that are produced during the body's metabolic processes. Therefore, the study of markers in urine can be used as a complement to improve the diagnosis rate of marker diseases. Moreover, urine is characterized by high water content, high polarity, and high inorganic salt, therefore the detection of phospholipids in urine is challenging. In this study, an original Polydimethylsiloxane (PDMS)-titanium dioxide (TiO₂) composite film for sample pre-treatment coupled with the LC-MS/MS method for the determination of phospholipids in the urine with high selectivity and low matrix effects was prepared and developed. The extraction process was scientifically optimized by the single-factor test. After systematic validation, the established method was successfully applied to the accurate determination of phospholipid substances in the urine of lung cancer patients and healthy subjects. In conclusion, the developed method has great potential for the development of lipid enrichment analysis in urine and can be used as a beneficial tool for cancer diagnosis and Chinese medicine syndrome typing.

Comprehensive mass spectrometry lipidomics of human biofluids and ocular tissues

Evaluating lipid profiles in human tissues and biofluids is critical in identifying lipid metabolites in dysregulated metabolic pathways. Due to various chemical characteristics, single-run lipid analysis has not yet been documented. Such approach is essential for analyzing pathology-related lipid metabolites. Age-related macular degeneration, the leading cause of vision loss in western countries, is emblematic of this limitation. Several studies have identified alterations in individual lipids but the majority are based on targeted approaches. In this study, we analyzed and identified approximately 500 lipid species in human biofluids (plasma and erythrocytes) and ocular tissues (retina and retinal pigment epithelium) using the complementarity of hydrophilic interaction liquid chromatography (HILIC) and reversed-phase chromatography (RPC), coupled to high-resolution mass spectrometry. For that, lipids were extracted from human eye globes and blood from 10 subjects and lipidomic analysis was carried out through analysis in HILIC and RPC, alternately. Furthermore, we illustrate the advantages and disadvantages of both techniques for lipid characterization. RPC showed greater sensitivity in hydrophobicity-based lipid separation, detecting diglycerides, triglycerides, cholesterol, and cholesteryl esters, whereas no signal of these molecules was obtained in HILIC. However, due to coelution, RPC was less effective in separating polar lipids like phospholipids, which were separated effectively in HILIC in both ionization modes. The complementary nature of these analytical approaches was essential for the detection and identification of lipid classes/subclasses, which can then provide distinct insights into lipid metabolism, a determinant of the pathophysiology of several diseases involving lipids, notably age-related macular degeneration.

Guiding the choice of informatics software and tools for lipidomics research applications

Progress in mass spectrometry lipidomics has led to a rapid proliferation of studies across biology and biomedicine. These generate extremely large raw datasets requiring sophisticated solutions to support automated data processing. To address this, numerous software tools have been developed and tailored for specific tasks. However, for researchers, deciding which approach best suits their application relies on ad hoc testing, which is inefficient and time consuming. Here we first review the data processing pipeline, summarizing the scope of available tools. Next, to support researchers, LIPID MAPS provides an interactive online portal listing open-access tools with a graphical user interface. This guides users towards appropriate solutions within major areas in data processing, including (1) lipid-oriented databases, (2) mass spectrometry data repositories, (3) analysis of targeted lipidomics datasets, (4) lipid identification and (5) quantification from untargeted lipidomics datasets, (6) statistical analysis and visualization, and (7) data integration solutions. Detailed descriptions of functions and requirements are provided to guide customized data analysis workflows.

Integration Profiling Between Plasma Lipidomics, Epstein–Barr Virus and Clinical Phenomes in Nasopharyngeal Carcinoma Patients

Plasma lipidomics has been commonly used for biomarker discovery. Studies in cancer have suggested a significant alteration of circulating metabolite profiles which is correlated with cancer characteristics and treatment outcome. However, the lipidomics characteristics of nasopharyngeal carcinoma (NPC) have rarely been studied. We previously described the phenomenon of lipid droplet accumulation in NPC cells and showed that such accumulation could be regulated by latent infection of Epstein–Barr virus (EBV). Here, we compared the plasma lipidome of NPC patients to that of healthy controls by liquid chromatography-tandem mass spectrometry (LC–MS/MS). We found 19 lipids (e.g., phosphatidylinositols 18:0/20:4 and 18:0/18:2 and free fatty acid 22:6) to be remarkably decreased, whereas 2 lipids (i.e., diacylglycerols 16:0/16:1 and 16:0/20:3) to be increased, in the plasma of NPC patients, compared with controls. Different lipid profiles were also observed between patients with different titers of EBV antibodies (e.g., EA-IgA and VCA-IgA) as well as between patients with and without lymph node or distant organ metastasis. In conclusion, plasma lipidomics might help to differentiate NPC cases from controls, whereas EBV infection might influence the risk and prognosis of NPC through modulating lipid metabolism in both tumor cells and peripheral blood.

Microbes: A Hidden Treasure of Polyunsaturated Fatty Acids

Microbes have gained a lot of attention for their potential in producing polyunsaturated fatty acids (PUFAs). PUFAs are gaining scientific interest due to their important health-promoting effects on higher organisms including humans. The current sources of PUFAs (animal and plant) have associated limitations that have led to increased interest in microbial PUFAs as most reliable alternative source. The focus is on increasing the product value of existing oleaginous microbes or discovering new microbes by implementing new biotechnological strategies in order to compete with other sources. The multidisciplinary approaches, including metabolic engineering, high-throughput screening, tapping new microbial sources, genome-mining as well as co-culturing and elicitation for the production of PUFAs, have been considered and discussed in this review. The usage of agro-industrial wastes as alternative low-cost substrates in fermentation for high-value single-cell oil production has also been discussed. Multidisciplinary approaches combined with new technologies may help to uncover new microbial PUFA sources that may have nutraceutical and biotechnological importance.

Single-Cell Multiomics Analysis for Drug Discovery

Given the heterogeneity seen in cell populations within biological systems, analysis of single cells is necessary for studying mechanisms that cannot be identified on a bulk population level. There are significant variations in the biological and physiological function of cell populations due to the functional differences within, as well as between, single species as a result of the specific proteome, transcriptome, and metabolome that are unique to each individual cell. Single-cell analysis proves crucial in providing a comprehensive understanding of the biological and physiological properties underlying human health and disease. Omics technologies can help to examine proteins (proteomics), RNA molecules (transcriptomics), and the chemical processes involving metabolites (metabolomics) in cells, in addition to genomes. In this review, we discuss the value of multiomics in drug discovery and the importance of single-cell multiomics measurements. We will provide examples of the benefits of applying single-cell omics technologies in drug discovery and development. Moreover, we intend to show how multiomics offers the opportunity to understand the detailed events which produce or prevent disease, and ways in which the separate omics disciplines complement each other to build a broader, deeper knowledge base.

The Fatty Acid-Based Erythrocyte Membrane Lipidome in Dogs with Chronic Enteropathy

Canine chronic enteropathies (CEs) are inflammatory processes resulting from complex interplay between the mucosal immune system, intestinal microbiome, and dietary components in susceptible dogs. Fatty acids (FAs) play important roles in the regulation of physiologic and metabolic pathways and their role in inflammation seems to be dual, as they exhibit pro-inflammatory and anti-inflammatory functions. Analysis of red blood cell (RBC) membrane fatty acid profile represents a tool for assessing the quantity and quality of structural and functional molecular components. This study was aimed at comparing the FA membrane profile, determined by Gas Chromatography and relevant lipid parameter of 48 CE dogs compared with 68 healthy dogs. In CE patients, the levels of stearic (p < 0.0001), dihomo-gamma-linolenic, eicosapentaenoic (p = 0.02), and docosahexaenoic (p = 0.02) acids were significantly higher, and those of palmitic (p < 0.0001) and linoleic (p = 0.0006) acids were significantly lower. Non-responder dogs presented higher percentages of vaccenic acid (p = 0.007), compared to those of dogs that responded to diagnostic trials. These results suggest that lipidomic status may reflect the "gut health", and the non-invasive analysis of RBC membrane might have the potential to become a candidate biomarker in the evaluation of dogs affected by CE.

Gonadal lipidomics profile of an ovoviviparity teleost, black rockfish, during gonadal development

In order to study the variation of gonad lipidomics during reproductive cycle, black rockfish was employed as the research model in the present study. Using histology, lipidomics, and qPCR, the profile of gonad lipidomics and the expression levels of related genes during different developmental stages were detected and analyzed to show the potential regulatory network of lipid metabolism. Based on Ultra High-Performance Liquid Tandem Chromatography Quadrupole Time of Flight Mass Spectrometry (UHPLC-QTOFMS), four significant differential glycerophospholipid metabolic pathways including phosphatidylcholine (PC), phosphatidylethanolamine (PE), phosphatidylserine (PS), and phosphatidic acid (PA) were enriched by KEGG. Pathway-related enzyme-coding genes, including phosphatidylserine decarboxylase (pisd), phosphatidylserine synthase (ptdss1, ptdss2), and phospholipase D (pld1, pld2) were identified from the whole genome data and confirmed by cloning. The expression profiles of these genes were tested by qPCR in the tissues and gonads in developmental stages, and we found that pisd, pld, and ptdss genes were all downregulated through the developmental process in the brain of male, and the latter two genes were upregulated in the liver and testis at stage IV, which were the opposite trend observed in the female. Thus, our findings would be helpful in further understanding the substance metabolism and regulation during gonad development in ovoviviparity teleosts.

Plasma Lipidomics Profiling Reveals Biomarkers for Papillary Thyroid Cancer Diagnosis

The objective of this study was to identify potential biomarkers and possible metabolic pathways of malignant and benign thyroid nodules through lipidomics study. A total of 47 papillary thyroid carcinomas (PTC) and 33 control check (CK) were enrolled. Plasma samples were collected for UPLC-Q-TOF MS system detection, and then OPLS-DA model was used to identify differential metabolites. Based on classical statistical methods and machine learning, potential biomarkers were characterized and related metabolic pathways were identified. According to the metabolic spectrum, 13 metabolites were identified between PTC group and CK group, and a total of five metabolites were obtained after further screening. Its metabolic pathways were involved in glycerophospholipid metabolism, linoleic acid metabolism, alpha-linolenic acid metabolism, glycosylphosphatidylinositol (GPI)—anchor biosynthesis, Phosphatidylinositol signaling system and the metabolism of arachidonic acid metabolism. The metabolomics method based on PROTON nuclear magnetic resonance (NMR) had great potential for distinguishing normal subjects from PTC. GlcCer(d14:1/24:1), PE-NME (18:1/18:1), SM(d16:1/24:1), SM(d18:1/15:0), and SM(d18:1/16:1) can be used as potential serum markers for the diagnosis of PTC.

Extending the Lipidome Coverage by Combining Different Mass Spectrometric Platforms: An Innovative Strategy to Answer Chemical Food Safety Issues

From a general public health perspective, a strategy combining non-targeted and targeted lipidomics MS-based approaches is proposed to identify disrupted patterns in serum lipidome upon growth promoter treatment in pigs. Evaluating the relative contributions of the platforms involved, the study aims at investigating the potential of innovative analytical approaches to highlight potential chemical food safety threats. Serum samples collected during an animal experiment involving control and treated pigs, whose food had been supplemented with ractopamine, were extracted and characterised using three MS strategies: Non-targeted RP LC-HRMS; the targeted Lipidyzer™ platform (differential ion mobility associated with shotgun lipidomics) and a homemade LC-HRMS triglyceride platform. The strategy enabled highlighting specific lipid profile patterns involving various lipid classes, mainly in relation to cholesterol esters, sphingomyelins, lactosylceramide, phosphatidylcholines and triglycerides. Thanks to the combination of non-targeted and targeted MS approaches, various compartments of the pig serum lipidome could be explored, including commonly characterised lipids (Lipidyzer™), triglyceride isomers (Triglyceride platform) and unique lipid features (non-targeted LC-HRMS). Thanks to their respective characteristics, the complementarity of the three tools could be demonstrated for public health purposes, with enhanced coverage, level of characterization and applicability.

UPLC-Q-TOF/MS based Untargeted Metabolite and Lipid Analysis on Premature Ovarian Insufficiency Plasma Samples

Background:

Metabolomics is one of the main areas to understand cellular process at molecular level by analyzing metabolites. In recent years metabolomics has emerged as a key tool to understand molecular basis of diseases, to find diagnostic and prognostic biomarkers and develop new treatment opportunities and drug molecules.

Objective:

In this study, untargeted metabolite and lipid analysis were performed to identify potential biomarkers on premature ovarian insufficiency plasma samples. 43 POI subject plasma samples were compared with 32 healthy subject plasma samples.

Methods:

Plasma samples were pooled and extracted using chloroform:methanol:water (3:3:1 v/v/v) mixture. Agilent 6530 LC/MS Q-TOF instrument equipped with ESI source was used for analysis. A C18 column (Agilent Zorbax 1.8 μM, 50 x 2.1 mm) was used for separation of the metabolites and lipids. XCMS, an “R software” based freeware program, was used for peak picking, grouping and comparing the findings. Isotopologue Parameter Optimization (IPO) software was used to optimize XCMS parameters. The analytical methodology and data mining process were validated according to the literature.

Results:

83 metabolite peaks and 213 lipid peaks were found to be in semi-quantitatively and statistically different (fold change >1.5, p <0.05) between the POI plasma samples and control subjects.

Conclusion:

According to the results, two groups were successfully separated through principal component analysis. Among the peaks, phenyl alanine, decanoyl-L-carnitine, 1-palmitoyl lysophosphatidylcholine and PC(O-16:0/2:0) were identified through auto MS/MS and matched with human metabolome database and proposed as plasma biomarker for POI and monitoring the patients in treatment period.

Serum lipid profiling analysis and potential marker discovery for ovarian cancer based on liquid chromatography-Mass spectrometry

Low early diagnosis rate and unclear pathogenesis are the primary reasons for the high mortality of epithelial ovarian cancer (EOC). Lipidomics is a powerful tool for marker discovery and mechanism explanation. Hence, a ultra high-performance liquid chromatography-mass spectrometry based non-targeted lipidomics analysis was performed to acquire lipid profiling of 153 serum samples including healthy control (HC, n = 50), benign ovarian tumor (BOT, n = 41), and EOC (n = 62) to reveal lipid disturbance, then differential lipids were verified in another sample set including 187 sera. Significant lipid disturbance occurred in BOT and EOC, fatty acid, lyso-phosphatidylcholine, and lyso-phosphatidylethanolamine were observed to be increased in BOT and EOC subjects, while phosphatidylcoline, ether phosphatidylcoline (PC-O), ether phosphatidylethanolamine (PE-O), and sphingomyelin significantly decreased. Compared with BOT, PC-Os and PE-Os presented a greater reduction in EOC, and serum ceramide increased only in EOC. Moreover, potential markers consisting of 4 lipids were defined and validated for EOC diagnosis. High areas under the curve (0.854∼0.865 and 0.903∼0.923 for distinguishing EOC and early EOC from non-cancer, respectively) as well as good specificity and sensitivity were obtained. This study not only revealed the characteristics of lipid metabolism in EOC, but also provided a potential marker pattern for aiding EOC diagnosis.

Analytical performance of Raman spectroscopy in assaying biochemical components in human serum

Chronic non-infectious diseases are important to research as they are the main causes of death in Brazil and worldwide. One very important chronic non-infectious disease is cardiovascular disease, whose risk factors (diabetes, dyslipidemia, and renal failure) can be detected through assessments of serum biochemical components. The objective of this study was to evaluate the analytical performance of Raman spectroscopy for analysis of lipid profile (total cholesterol, triglycerides, and HDL cholesterol), non-protein nitrogenous compounds (urea and creatinine), and glucose in 242 human serum samples. Models to discriminate and quantify the samples were developed using the predicted concentration by quantitative regression model based on partial least squares (PLS). The analytical error for the "leave-one-out" cross-validation based on the predicted PLS concentration was 10.5 mg/dL for total cholesterol, 21.4 mg/dL for triglyceride, 13.0 mg/dL for HDL cholesterol, 4.9 mg/dL for urea, 0.21 mg/dL for creatinine, and 15.4 mg/dL for glucose. The Kappa coefficient indicate very good agreement for cholesterol (0.83), good for triglyceride (0.77), urea (0.70) and creatinine (0.66), and fair for HDL cholesterol (0.38) and glucose (0.30). The results of the analytical performance demonstrated that Raman spectroscopy can be considered an important methodology to screen the population, especially for serum triglycerides and cholesterol.

Application of comprehensive lipidomics to biomarker research on adverse drug reactions

Lipidomics is a relatively new field of omics that focuses on lipids, one of the major categories of metabolites. Owing to their various functions, lipids are considered suitable targets for biomarker development; in addition, lipidomics analysis of adverse drug reactions (ADRs) has been conducted recently. In this review, I have summarized information on comprehensive lipidomics, which involves the analysis of global lipids in a non-targeted manner. Mass spectrometry-based platforms are currently the dominant lipidomics platform owing to their versatile features. I have also summarized the application of lipidomics in biomarker research on ADRs caused by therapeutic drugs in humans and rodents. Additionally, general concerns in and emerging approaches of lipidomics research on ADR have been highlighted. Although biomarkers identified using the lipidomics analysis of ADRs have not been qualified, reported candidates will be evaluated for clinical application. In addition, novel biomarker candidates will be developed via classical and new approaches exemplified in this review.

"Modeling-Prediction" Strategy for Deep Profiling of Lysophosphatidic Acids by Liquid Chromatography-Mass Spectrometry: Exploration Biomarkers of Breast Cancer

Lysophosphatidic acids (LPAs) are important bioactive phospholipids consisting of various species involved in a wide array of physiological and pathological processes. However, LPAs were rarely identified in untargeted lipidomics studies because of the incompatibility with analytical methods. Moreover, in targeted studies, the coverages of LPAs remained unsatisfactorily low due to the limitation of reference standards. Herein, a "modeling-prediction" workflow for deep profiling of LPAs by liquid chromatography-mass spectrometry was developed. Multiple linear regression models of qualitative and quantitative parameters were established according to features of fatty acyl tails of the commercial standards to predict the corresponding parameters for unknown LPAs. Then 72 multiple reaction monitoring (MRM) transitions were monitored simultaneously and species of LPA 14:0, LPA 16:1, LPA 18:3, LPA 20:3 and LPA 20:5 were firstly characterized and quantified in plasma. Finally, the workflow was applied to explore the changes of LPAs in plasma of breast cancer patients compared with healthy volunteers. Multi-LPAs indexes with strong diagnostic ability for breast cancer were identified successfully using Student's t- test, orthogona partial least-squares discrimination analysis (OPLS-DA) and logistic regression- receiver operating characteristic (ROC) curve analysis. The proposed workflow with high sensitivity, high accuracy, high coverage and reliable identification would be a powerful complement to untargeted lipidomics and shed a light on the analysis of other lipids.

137Cs γ Ray and 28Si Irradiation Induced Murine Hepatocellular Carcinoma Lipid Changes in Liver Assessed by MALDI-MSI Combined with Spatial Shrunken Centroid Clustering Algorithm: A Pilot Study

Characterization of lipids by matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) is of great interest because not only are lipids important structural molecules in both the cell and internal organelle membranes, but they are also important signaling molecules. MALDI-MSI combined with spatial image segmentation has been previously used to identify tumor heterogeneities within tissues with distinct anatomical regions such as the brain. However, there has been no systematic study utilizing MALDI-MSI combined with spatial image segmentation to assess the tumor microenvironment in the liver. Here, we present that image segmentation can be used to evaluate the tumor microenvironment in the liver. In particular, to better understand the molecular mechanisms of irradiation-induced hepatic carcinogenesis, we used MALDI-MSI in the negative ion mode to identify lipid changes 12 months post exposure to low dose ²⁸Si and ¹³⁷Cs γ ray irradiation. We report here the changes in the lipid profiles of male C3H/HeNCrl mice liver tissues after exposure to irradiation and analyzed using the spatial shrunken centroid clustering algorithm. These findings provide valuable information as astronauts will be exposed to high-charge high-energy (HZE) particles and low-energy γ-ray irradiation during deep space travel. Even at low doses, exposure to these irradiations can lead to cancer. Previous studies infer that irradiation of mice with low-dose HZE particles induces oxidative damage and microenvironmental changes that are thought to play roles in the pathophysiology of hepatocellular carcinoma.

Enhancing detection and characterization of lipids using charge manipulation in electrospray ionization-tandem mass spectrometry

Heightened awareness regarding the implication of disturbances in lipid metabolism with respect to prevalent human-related pathologies demands analytical techniques that provide unambiguous structural characterization and accurate quantitation of lipids in complex biological samples. The diversity in molecular structures of lipids along with their wide range of concentrations in biological matrices present formidable analytical challenges. Modern mass spectrometry (MS) offers an unprecedented level of analytical power in lipid analysis, as many advancements in the field of lipidomics have been facilitated through novel applications of and developments in electrospray ionization tandem mass spectrometry (ESI-MS/MS). ESI allows for the formation of intact lipid ions with little to no fragmentation and has become widely used in contemporary lipidomics experiments due to its sensitivity, reproducibility, and compatibility with condensed-phase modes of separation, such as liquid chromatography (LC). Owing to variations in lipid functional groups, ESI enables partial chemical separation of the lipidome, yet the preferred ion-type is not always formed, impacting lipid detection, characterization, and quantitation. Moreover, conventional ESI-MS/MS approaches often fail to expose diverse subtle structural features like the sites of unsaturation in fatty acyl constituents or acyl chain regiochemistry along the glycerol backbone, representing a significant challenge for ESI-MS/MS. To overcome these shortcomings, various charge manipulation strategies, including charge-switching, have been developed to transform ion-type and charge state, with aims of increasing sensitivity and selectivity of ESI-MS/MS approaches. Importantly, charge manipulation approaches afford enhanced ionization efficiency, improved mixture analysis performance, and access to informative fragmentation channels. Herein, we present a critical review of the current suite of solution-based and gas-phase strategies for the manipulation of lipid ion charge and type relevant to ESI-MS/MS.

Uncovering the mechanism of Astragali Radix against nephrotic syndrome by intergrating lipidomics and network pharmacology

BACKGROUND

Astragali Radix (AR), a common Traditional Chinese Medicine (TCM), is commonly used for treating nephrotic syndrome (NS) in China. At present, the research on the efficacy of AR against NS is relative clearly, but there are fewer researches on the mechanism.

PURPOSE

The aim of this study was to evaluate the potential beneficial effects of AR in an adriamycin-induced nephropathy rat model, as well as investigate the possible mechanisms of action and potential lipid biomarkers.

METHODS

In this work, a rat model of NS was established by two injections of ADR (3.5 + 1 mg/kg) into the tail vein. The potential metabolites and targets involved in the anti-NS effects of AR were predicted by lipidomics coupled with the network pharmacology approach, and the crucial metabolite and protein were further validated by western blotting and ELISA.

RESULTS

The results showed that 22 metabolites such as l-carnitine, LysoPC (20:3), and SM (d18:1/16:0) were associated with renal injury. Moreover, SMPD1, CPT1A and LCAT were predicted as lipids linked targets of AR against NS, whilst glycerophospholipid, sphingolipid and fatty acids metabolism were involved as key pathways of AR against NS. Besides, AR could play a critical role in NS by improving oxidative stress, inhibiting apoptosis and reducing inflammation. Interestingly, our results indicated that key metabolite l-carnitine and target CPT1 were one of the important metabolites and targets for AR to exert anti-NS effects.

CONCLUSION

In summary, this study offered a new understanding of the protection mechanism of AR against NS by network pharmacology and lipidomic method.

Lipidomic analysis reveals therapeutic effects of Yijin-Tang on high-fat/high-cholesterol diet-induced obese mice

BACKGROUND

In Traditional Korean and Chinese medicine, the herbal remedy Yijin (Erchen)-Tang (YJT) is widely used to treat obesity-related disorders, and its therapeutic potential has been demonstrated in numerous studies. However, the systemic effect of YJT on obesity status and change of lipid metabolism by YJT still remains unknown.

PURPOSE

This study aimed to investigate the therapeutic mechanism of the YJT on obesity by using lipidomics.

METHODS

To evaluate the effects of treatment with YJT on obesity, C57BL/6 J mice were fed a high-fat and high-cholesterol (HFHC, 40% fat and 1% cholesterol) diet for 8 weeks and treated them with YJT for an additional 6 weeks. We then performed untargeted lipidomic analysis using ultra-performance liquid chromatography/quadrupole-time-of-flight mass spectrometry mass spectroscopy coupled with multivariate statistical analysis.

RESULTS

YJT ameliorated obesity induced systemic inflammation and improved insulin resistance. Additionally, YJT protected against HFHC-diet-induced hepatic inflammation. To explore specific changes in lipid metabolism associated with the therapeutic effects of YJT, we performed untargeted lipid profiling of the plasma. Partial least squares-discriminant analysis (PLS-DA) score plots showed that YJT altered the lipid metabolic pattern of HFHC mice. In particular, ceramides and triglycerides with saturated fatty acids and monounsaturated fatty acids were significantly changed by YJT, which were significantly associated with insulin resistance, the AGE-RAGE signaling pathway in diabetic complications and adipocytokine signaling pathway in pathway enrichment analysis. Thus, we analyzed the changes in adipocytes and adipokine caused by YJT, and confirmed that YJT alleviated adipocytes inflammation and macrophage infiltration, and reversed HFHC-induced alterations in leptin and adiponectin levels in adipose tissue and plasma.

CONCLUSION

These data suggest that YJT ameliorates obesity-induced systemic inflammation and insulin resistance by regulating lipid metabolism, and demonstrated that lipidomic profiling is a useful method to investigate the therapeutic effects of herbal decoctions in traditional Korean and Chinese medicine.

Advances in lipidomics

The present article examines recently published literature on lipids, mainly focusing on research involving glycero-, glycerophospho- and sphingo-lipids. The primary aim is identification of distinct profiles in biologic lipidomic systems by ultra-high-performance liquid chromatography (UHPLC) coupled with mass spectrometry (MS, tandem MS) with multivariate data analysis. This review specifically targets lipid biomarkers and disease pathway mechanisms in humans and artificial targets. Different specimen matrices such as primary blood derivatives (plasma, serum, erythrocytes, and blood platelets), faecal matter, urine, as well as biologic tissues (liver, lung and kidney) are highlighted.

UHPLC Q-Exactive MS-based spleen metabolomics and lipidomics to explore the effect mechanisms of Danggui Buxue Decoction in anemia mice

Danggui Buxue Decoction (DBD), a famous traditional Chinese medicine (TCM), is often used to treat anemia in China. However, its underlying therapeutic mechanism is unclear. Through the analysis of body weight, spleen and thymus indexes, peripheral blood routine and pathological section of femur, it was obviously that DBD could significantly improve acetylphenylhydrazine (APH) + cyclophosphamide (CTX) induced anemia mice in the present work. Ultra high performance liquid chromatography coupled with quadrupole - Exactive mass spectrometry (UHPLC Q-Exactive MS) based metabolomics and lipidomics was further utilized to screen out differential spleen metabolites associated with DBD treatment. A total of 26 differential metabolites including 8 polar metabolites and 18 lipids were firstly obtained to relate with anemia mice. 7 polar metabolites and 10 lipids among them were reversed by DBD, which the regulation of pyrimidine metabolism and glycerophospholipid metabolism were mainly associated to the anti-anemia effect of DBD based on MetaboAnalyst analysis. Through random forest analysis (RF), ROC analysis and pearson matrix correlation, three metabolites, cytosine, uracil and PC (o-16:1(9Z)/20:0), were further screened out as the potential pharmacodynamic biomarkers associated with the efficacy of DBD. This study provided a methodological reference for the study of the mechanism of TCM.

Identification of neurotoxicity markers induced by realgar exposure in the mouse cerebral cortex using lipidomics

Realgar is a traditional Chinese medicine containing arsenic and has neurotoxicity. This study used realgar exposure mice model, neurobehavioral tests, analytical chemistry, molecular biology and nontargeted lipidomics to explore the mechanism of realgar damages the nervous system. The arsenic contained in realgar passed through the BBB and accumulated in the brain. Neurons, synapses and myelin showed abnormal changes in the cerebral cortex. The number of autophagosomes were incresed as well as levels of MDA, Lp-PLA2, and cPLA2 but the CAT level was significant reduced. Finally, the cognition and memory of mice were decreased. Nontargeted lipidomics detected 34 lipid subclasses including 1603 lipid molecules. The levels of the LPC and LPE were significantly increased. Under the condition of variable importance for the projection (VIP)>1 and P < 0.05, only 28 lipid molecules satisfied the criteria. The lipid molecular markers SM (d36:2), PE (18:2/22:6) and PE (36:3) which were filtered by receiver operating characteristic (ROC) curve (AUC>0.8 or AUC<0.2) were used to identify the neurotoxicity induced by realgar. Therefore, realgar induces neurotoxicity through exacerbating oxidative damage and lipid dysfunction. Providing research basis for the clinical diagnosis and treatment of realgar-induced neurotoxicity.

Lipidomic Profiling for Serum Biomarkers in Mice Exposed to Ionizing Radiation

Radiation biodosimeters are required urgently for fast and accurate evaluation of absorbed dose for irradiated individuals. Lipidomics has appeared as a credible technique for identification and quantification of lipid for researching biomarker of diseases. We performed a lipidomic profile on mice serum at time points of 6, 24, and 72 hours after 0, 2, 5.5, 7, and 8 Gy irradiation to select radiation-responsive lipids and conducted Kyoto Encyclopedia of Genes and Genome pathway enrichment analysis to recognize the pathways and network changes. Then, Pearson correlation analysis was performed to evaluate the feasibility of radiation-responsive lipids to estimate radiation dose. Seven radiation-responsive lipids including PC (18:2/18:2), PC (18:0/18:2), Lyso PC 18:1, PC (18:0/20:4), SM (D18:0/24:1), PC (16:0/18:1), and Lyso PC 18:2 were identified in which glycerophospholipid metabolism presented as the most significant pathway, and they all presented good linear correlation with the irradiated dose. This study identified 7 radiation-responsive lipids in mice serum and certificate their feasibility of dose estimation as biodosimeters.

Lipid Fingerprinting of Different Material Sources by UPLC-Q-Exactive Orbitrap/MS Approach and Their Zebrafish-Based Activities Comparison

Lipids are considered to be critical contributors to nutrition, structural function, metabolic function, and other biological roles. They vary from one biological source to another. Here, the lipids from three common sources (soybean, egg yolk, and shrimp head) were comprehensively compared and characterized using the lipidomics approach, together with the UPLC-Q-Exactive Orbitrap/MS method. A total of 3027 lipid structures containing 778 fatty acids, 750 glycerolipids, 1283 glycerophospholipids, 200 sphingolipids, and 16 sterol lipids were first identified and quantified in these resources. The characteristic lipid species with significant differences among groups were determined by lipidomics analysis. Besides, the antithrombotic, antioxidant, and anti-inflammatory activities were evaluated based on the zebrafish model. The correlation between differential lipids and activities was also analyzed. Our comprehensive lipidomics profiling and bioactivities of lipids from different sources in vivo can provide evidence for their future applications.

Dietary citrus peel essential oil ameliorates hypercholesterolemia and hepatic steatosis by modulating lipid and cholesterol homeostasis

Integration of lipidomics and gene expression analysis provided new insights into in-depth mechanistic understanding of the effects of dietary CPEO.

Microarray and Mass Spectrometry-Based Methodology for Lipid Profiling of Tissues and Cell Cultures

The recent developments in mass spectrometry have revealed the importance of lipids as biomarkers in the context of different diseases and as indicators of the cell's homeostasis. However, further advances are required to unveil the complex relationships between lipid classes and lipid species with proteins. Here, we present a new methodology that combines microarrays with mass spectrometry to obtain the lipid fingerprint of samples of a different nature in a standardized and fast way, with minimal sample consumption. As a proof of concept, we use the methodology to obtain the lipid fingerprint of 20 rat tissues and to create a lipid library for tissue classification. Then, we combine those results with immunohistochemistry and enzymatic assays to unveil the relationship between some lipid species and two enzymes. Finally, we demonstrate the performance of the methodology to explore changes in lipid composition of the nucleus accumbens from mice subjected to two lipid diets.

A novel 8.7-kb mitochondrial genome deletion accurately detects endometriosis in the plasma of symptomatic women

Aim: To evaluate an 8.7-kb mitochondrial DNA (mtDNA) deletion as a potential biomarker of endometriosis. Materials & methods: We tested the diagnostic accuracy of the 8.7-kb deletion real-time PCR assay using 182 prospectively collected blood samples from females presenting with symptoms of endometriosis in a case-control format. Results: The assay differentiated between endometriosis and controls (area under curve: 0.74-0.89) with a statistically significant difference (p < 0.05) in 8.7-kb deletion levels measured for all disease subtypes and stages. No correlation was seen between 8.7-kb deletion levels and participant or specimen age, hormone status or menstrual phase. Conclusion: The diagnostic accuracy of the 8.7-kb deletion for endometriosis suggests potential utility in the clinic to improve patient management.

Skeletal Muscle Lipidomics as a New Tool to Determine Altered Lipid Homeostasis in Fish Exposed to Urban and Industrial Wastewaters

This work applies ultrahigh performance liquid chromatography coupled with high-resolution mass spectrometry (UPLC-HRMS) to characterize for the first time the lipidome of the skeletal muscle of two fish species (Barbus meridionalis, Squalius laietanus) collected in a Mediterranean River affected by urban and industrial wastewater outflows. The untargeted analysis allowed a clear separation of the lipidome of fish from polluted and reference sites; phosphatidylcholines (PCs), phosphatidylethanolamines (PEs), and their lyso and ether-linked forms were among the distinctive features. The targeted analysis consistently detected a decrease in PC-plasmalogens (36:4, 36:6, 38:6) and highly unsaturated PCs (36:5, 36:6, 38:6, 40:6, 40:7) and an increase in plasmanyl-PCs (36:5, 38:5), lyso-PCs (16:1, 18:1, 22:4) and cholesteryl esters (CEs) (16:0, 18:0, 20:4) in fish from polluted sites. These lipid profiles were indicative of oxidative stress and dysregulation of cholesterol homeostasis in fish from polluted sites. This methodology represents a promising tool for the development of novel noninvasive diagnostic methods based on muscle tissue biopsies to assess the effects of water pollution in wildlife.

Generating Fatty Acid Profiles in the Gas Phase: Fatty Acid Identification and Relative Quantitation Using Ion/Ion Charge Inversion Chemistry

Representing the most fundamental lipid class, fatty acids (FA) play vital biological roles serving as energy sources, cellular signaling molecules, and key architectural components of complex lipids. Direct infusion electrospray ionization spectrometry, also known as shotgun lipidomics, has emerged as a rapid and powerful toolbox for lipid analysis. While shotgun lipidomics can be a sensitive approach to FA detection, the diverse molecular structure of FA presents challenges for unambiguous identification and the relative quantification of isomeric contributors. In particular, pinpointing double bond position(s) in unsaturated FA and determining the relative contribution of double bond isomers has limited the application of the shotgun approach. Recently, we reported the use of gas-phase ion/ion reactions to facilitate the identification of FA. Briefly, singly deprotonated FA anions undergo charge inversion when reacted in the gas phase with tris-phenanthroline magnesium dications by forming [FA - H + MgPhen]+ complex ions. These charge-inverted FA complex cations fragment upon ion-trap collision-induced dissociation (CID) to generate product ion spectra unique to individual FA isomers. Herein, we report the development of a mass spectral library comprised of [FA - H + MgPhen]+ product ion spectra. The developed FA library permits confident FA identification, including polyunsaturated FA isomers. Furthermore, we demonstrate the ability to determine relative contributions of isomeric FA using multiple linear regression analysis paired with gas-phase ion/ion reactions. We successfully applied the presented method to generate a FA profile for bovine liver phospholipidome based entirely on gas-phase chemistries.

Visualized Networking of Co-Regulated Lipids in Human Blood Based on High-Throughput Screening Data: Implications for Exposure Assessment

Exposure to environmental chemicals could disturb lipidome homeostasis in biotas. Comprehensive identification and interpretation of lipid molecules in biological samples are of great importance to elucidate the potential changes in lipid homeostasis upon exposure to various environmental stimuli. In this study, a total of 156 human blood samples were collected including 108 general citizens (control group) and 48 employees in a municipal solid waste incineration (MSWI) plant (occupational exposure group). More than 1500 lipid molecules, belonging to five lipid classes, were screened in the blood samples by UPLC-QTOF-MS in the MS^E acquisition mode. All of the coupled compounds with correlation coefficients ( R) of 0.7 or higher were selected for automated network correlation analysis. A global visual network was automatically produced from thousands of coregulated lipid species in the blood samples. In the automatically produced molecular network, the distributions of the major correlated lipids were in accordance with their metabolic pathways in the KEGG map. Different lipidomic profiles in the blood samples from the two groups of people were easily observed by this visualization technique. Among the intrinsic lipid classes, glycererides and sterol lipids might represent the most sensitively affected lipids upon exposure to various pollutants emitted from the MSWI plant. The visualized network of coregulated lipids identified in human blood presents a new approach for interpreting the metabolic relationships among the thousands of metabolites identified in toxicological and epidemiological studies.