Mass spectrometry based lipidomics: an overview of technological platforms

Hollow Cathode Discharge Ionization Mass Spectrometry: Detection, Quantification and Gas Phase Ion-Molecule Reactions of Explosives and Related Compounds

Mass spectrometry (MS) has become an essential analytical method in every sector of science and technology. Because of its unique ability to provide direct molecular structure information on analytes, an extra method is rarely required. This review describes fabrication of a variable-pressure hollow cathode discharge (HCD) ion source for MS in detection, quantification and investigation of gas-phase ion molecule reactions of explosives and related compounds using air as a carrier gas. The HCD ion source has been designed in such a way that by altering the ion source pressures, the system can generate both HCD and conventional GD. This design enables for the selective detection and quantification of explosives at trace to ultra-trace levels. The pressure-dependent HCD ion source has also been used to investigate ion-molecule reactions in the gas phase of explosives and related compounds. The mechanism of ion formation in explosive reactions is also discussed.

Lipids as Emerging Biomarkers in Neurodegenerative Diseases

Biomarkers are molecules that can be used to observe changes in an individual's biochemical or medical status and provide information to aid diagnosis or treatment decisions. Dysregulation in lipid metabolism in the brain is a major risk factor for many neurodegenerative disorders, including frontotemporal dementia, Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis. Thus, there is a growing interest in using lipids as biomarkers in neurodegenerative diseases, with the anionic phospholipid bis(monoacylglycerol)phosphate and (glyco-)sphingolipids being the most promising lipid classes thus far. In this review, we provide a general overview of lipid biology, provide examples of abnormal lysosomal lipid metabolism in neurodegenerative diseases, and discuss how these insights might offer novel and promising opportunities in biomarker development and therapeutic discovery. Finally, we discuss the challenges and opportunities of lipid biomarkers and biomarker panels in diagnosis, prognosis, and/or treatment response in the clinic.

Early-stage mapping of macromolecular content in APPNL-F mouse model of Alzheimer's disease using nuclear Overhauser effect MRI

Non-invasive methods of detecting early-stage Alzheimer's disease (AD) can provide valuable insight into disease pathology, improving the diagnosis and treatment of AD. Nuclear Overhauser enhancement (NOE) MRI is a technique that provides image contrast sensitive to lipid and protein content in the brain. These macromolecules have been shown to be altered in Alzheimer's pathology, with early disruptions in cell membrane integrity and signaling pathways leading to the buildup of amyloid-beta plaques and neurofibrillary tangles. We used template-based analyzes of NOE MRI data and the characteristic Z-spectrum, with parameters optimized for increase specificity to NOE, to detect changes in lipids and proteins in an AD mouse model that recapitulates features of human AD. We find changes in NOE contrast in the hippocampus, hypothalamus, entorhinal cortex, and fimbria, with these changes likely attributed to disruptions in the phospholipid bilayer of cell membranes in both gray and white matter regions. This study suggests that NOE MRI may be a useful tool for monitoring early-stage changes in lipid-mediated metabolism in AD and other disorders with high spatial resolution.

A comprehensive analytical framework integrating liquid chromatography-tandem mass spectrometry metabolomics with chemometrics for metabolite profiling of lettuce varieties and discovery of antibacterial agents

This study comprehensively characterized the metabolite profiles of six lettuce varieties and established the correlation between the elucidated profiles and their antivirulence effects. A total of 195 metabolites were annotated using LC-QTOF-MS/MS metabolomics assisted by molecular networking and integrated with chemometrics. Red varieties (red longifolia and lolla rosa) demonstrated higher chlorogenic and chicoric acids suggesting their antioxidant properties. In parallel, amino acids and disaccharides were enriched in romaine longifolia rationalizing its palatable taste and nutritional potential, while crispa, capitata, and lolla bionda presented a high β-carboline alkaloid content. The antibacterial and antihemolytic potential of all varieties against methicillin-sensitive and methicillin-resistant Staphylococcus aureus was assessed and validated by prominent downregulation of α-hemolysin transcriptional levels in both strains. Moreover, correlation analysis revealed sesquiterpenes, β-carboline alkaloids, amino acids, and oxy-fatty acids as the main bioactives. Results emphasize lettuce significance as a functional food and nutraceutical source, and highlight varieties naturally rich in antibacterial agents to adapt breeding programs.

A Critical Overview of HPLC-MS-Based Lipidomics in Determining Triacylglycerol and Phospholipid in Foods

With the current advancement in mass spectrometry (MS)-based lipidomics, the knowledge of lipidomes and their diverse roles has greatly increased, enabling a deeper understanding of the action of bioactive lipid molecules in plant- and animal-based foods. This review provides in-depth information on the practical use of MS techniques in lipidomics, including lipid extraction, adduct formation, MS analysis, data processing, statistical analysis, and bioinformatics. Moreover, this contribution demonstrates the effectiveness of MS-based lipidomics for identifying and quantifying diverse lipid species, especially triacylglycerols and phospholipids, in foods. Further, it summarizes the wide applications of MS-based lipidomics in food science, such as for assessing food processing methods, detecting food adulteration, and measuring lipid oxidation in foods. Thus, MS-based lipidomics may be a useful method for identifying the action of individual lipid species in foods.

Lipidomic Approaches in Common and Rare Cerebrovascular Diseases: The Discovery of Unconventional Lipids as Novel Biomarkers

Stroke remains a major cause of death and disability worldwide. Identifying new circulating biomarkers able to distinguish and monitor common and rare cerebrovascular diseases that lead to stroke is of great importance. Biomarkers provide complementary information that may improve diagnosis, prognosis and prediction of progression as well. Furthermore, biomarkers can contribute to filling the gap in knowledge concerning the underlying disease mechanisms by pointing out novel potential therapeutic targets for personalized medicine. If many "conventional" lipid biomarkers are already known to exert a relevant role in cerebrovascular diseases, the aim of our study is to review novel "unconventional" lipid biomarkers that have been recently identified in common and rare cerebrovascular disorders using novel, cutting-edge lipidomic approaches.

Lipid Polyunsaturated Fatty Acid Chains in Mouse Kidneys Were Increased within 5 min of a Single High Dose Whole Body Irradiation

To understand the ultra-early reaction of normal organ lipids during irradiation, we investigated the response of lipids, including polyunsaturated fatty acid (PUFA) chains, which are particularly susceptible to damage by ROS, in mice's kidneys, lungs, brains, and livers within 5 min of single high-dose irradiation. In this study, we set up three groups of C56BL/6 male mice and conducted whole-body irradiation with 0 Gy, 10 Gy, and 20 Gy single doses. Kidney, lung, brain, and liver tissues were collected within 5 min of irradiation. PUFA-targeted and whole lipidomic analyses were conducted using liquid chromatography-tandem mass spectrometry (LC-MS/MS). The results showed that PUFA chains of kidney phosphatidylcholine (PC), phosphatidylethanolamine (PE), and triacylglycerol (TG) significantly increased within 5 min of 10 Gy and 20 Gy irradiation. The main components of increased PUFA chains in PC and PE were C18:2, C20:4, and C22:6, and in TG the main component was C18:2. The kidney lipidomes also showed significant changes from the perspective of lipid species, mainly dominated by an increase in PC, PE, TG, and signal lipids, while lipidomes of the lung, brain, and liver were slightly changed. Our results revealed that acute PUFA chains increase and other lipidomic changes in the kidney upon whole-body irradiation within 5 min of irradiation. The significantly increased lipids also showed a consistent preference for possessing PUFA chains. The lipidomic changes varied from organ to organ, which indicates that the response upon irradiation within a short time is tissue-specific.

A Comprehensive Insight and Mechanistic Understanding of the Lipidomic Alterations Associated With DCM

Dilated cardiomyopathy (DCM) is one of the major causes of heart failure characterized by the enlargement of the left ventricular cavity and contractile dysfunction of the myocardium. Lipids are the major sources of energy for the myocardium. Impairment of lipid homeostasis has a potential role in the pathogenesis of DCM. In this review, we have summarized the role of different lipids in the progression of DCM that can be considered as potential biomarkers. Further, we have also explained the mechanistic pathways followed by the lipid molecules in disease progression along with the cardioprotective role of certain lipids. As the global epidemiological status of DCM is alarming, it is high time to define some disease-specific biomarkers with greater prognostic value. We are proposing an adaptation of a system lipidomics-based approach to profile DCM patients in order to achieve a better diagnosis and prognosis of the disease.

In situ droplet-based on-tissue chemical derivatization for lipid isomer characterization using LESA

In this work, we present an in situ droplet-based derivatization method for fast tissue lipid profiling at multiple isomer levels. On-tissue derivatization for isomer characterization was achieved in a droplet delivered by the TriVersa NanoMate LESA pipette. The derivatized lipids were then extracted and analyzed by the automated chip-based liquid extraction surface analysis (LESA) mass spectrometry (MS) followed by tandem MS to produce diagnostic fragment ions to reveal the lipid isomer structures. Three reactions, i.e., mCPBA epoxidation, photocycloaddition catalyzed by the photocatalyst Ir[dF(CF₃)ppy]₂(dtbbpy)PF₆, and Mn(II) lipid adduction, were applied using the droplet-based derivatization to provide lipid characterization at carbon-carbon double-bond positional isomer and sn-positional isomer levels. Relative quantitation of both types of lipid isomers was also achieved based on diagnostic ion intensities. This method provides the flexibility of performing multiple derivatizations at different spots in the same functional region of an organ for orthogonal lipid isomer analysis using a single tissue slide. Lipid isomers were profiled in the cortex, cerebellum, thalamus, hippocampus, and midbrain of the mouse brain and 24 double-bond positional isomers and 16 sn-positional isomers showed various distributions in those regions. This droplet-based derivatization of tissue lipids allows fast profiling of multi-level isomer identification and quantitation and has great potential in tissue lipid studies requiring rapid sample-to-result turnovers.

Lipidomics and proteomics: An integrative approach for early diagnosis of dementia and Alzheimer's disease

Alzheimer's disease (AD) is the most common neurodegenerative disorder and considered to be responsible for majority of worldwide prevalent dementia cases. The number of patients suffering from dementia are estimated to increase up to 115.4 million cases worldwide in 2050. Hence, AD is contemplated to be one of the major healthcare challenge in current era. This disorder is characterized by impairment in various signaling molecules at cellular and nuclear level including aggregation of Aβ protein, tau hyper phosphorylation altered lipid metabolism, metabolites dysregulation, protein intensity alteration etc. Being heterogeneous and multifactorial in nature, the disease do not has any cure or any confirmed diagnosis before the onset of clinical manifestations. Hence, there is a requisite for early diagnosis of AD in order to downturn the progression/risk of the disorder and utilization of newer technologies developed in this field are aimed to provide an extraordinary assistance towards the same. The lipidomics and proteomics constitute large scale study of cellular lipids and proteomes in biological matrices at normal stage or any stage of a disease. The study involves high throughput quantification and detection techniques such as mass spectrometry, liquid chromatography, nuclear mass resonance spectroscopy, fluorescence spectroscopy etc. The early detection of altered levels of lipids and proteins in blood or any other biological matrices could aid in preventing the progression of AD and dementia. Therefore, the present review is designed to focus on the recent techniques and early diagnostic criteria for AD, revealing the role of lipids and proteins in this disease and their assessment through different techniques.

Lipid Spectrum Generator: A Simple Script for the Generation of Accurate In Silico Lipid Fragmentation Spectra

Due to the complexity of lipids in nature, the use of in silico generated spectral libraries to identify lipid species from mass spectral data has become an integral part of many lipidomic workflows. However, many in silico libraries are either limited in usability or their capacity to represent lipid species. Here, we introduce Lipid Spectrum Generator, an open-source in silico spectral library generator specifically designed to aid in the identification of lipids in liquid chromatography-tandem mass spectrometry analysis.

Isolation and Mass Spectrometry-Based Profiling of Major Lipids in Brown Adipose Tissue

Brown adipose tissue (BAT) is an important regulator of metabolic homeostasis through its role in adaptive thermogenesis and control of whole-body glucose metabolism. Lipids play multiple roles in BAT functions, including serving as a fuel source for thermogenesis, mediating inter-organelle cross talk, and acting as BAT-derived signaling molecules that influence systemic energy metabolism. Profiling of various lipids in BAT under distinct metabolic states could provide new insights into their roles in the biology of the thermogenic fat. In this chapter, we describe a step-by-step workflow starting from sample preparations to mass spectrometry-based analysis of fatty acids and phospholipids in BAT.

Targeted Lipidomics Analysis of Adipose and Skeletal Muscle Tissues by Multiple Reaction Monitoring Profiling

Lipid homeostasis is critical for maintaining normal cellular functions including membrane structural integrity, cell metabolism, and signal transduction. Adipose tissue and skeletal muscle are two major tissues involved in lipid metabolism. Adipose tissue can store excessive lipids in the form of triacylglyceride (TG), which can be hydrolyzed to release free fatty acids (FFAs) under insufficient nutrition states. In the highly energy-demanding skeletal muscle, lipids serve as oxidative substrates for energy production but can cause muscle dysfunction when overloaded. Lipids undergo fascinating cycles of biogenesis and degradation depending on physiological demands, while dysregulation of lipid metabolism has been increasingly recognized as a hallmark of diseases such as obesity and insulin resistance. Thus, it is important to understand the diversity and dynamics of lipid composition in adipose tissue and skeletal muscle. Here, we describe the use of multiple reaction monitoring profiling, based on lipid class and fatty acyl chain specific fragmentation, to explore various classes of lipids in skeletal muscle and adipose tissues. We provide a detailed method for exploratory analysis of acylcarnitine (AC), ceramide (Cer), cholesteryl ester (CE), diacylglyceride (DG), FFA, phosphatidylcholine (PC), phosphatidylethanolamine (PE), phosphatidylglycerol (PG), phosphatidylinositol (PI), phosphatidylserine (PS), sphingomyelin (SM), and TG. Characterization of lipid composition within adipose tissue and skeletal muscle under different physiological situations will provide biomarkers and therapeutic targets for obesity-related diseases.

Nutritional lipidomics for the characterization of lipids in food

Lipids represent one out of three major macronutrient classes in the human diet. It is estimated to account for about 15-20% of the total dietary intake. Triacylglycerides comprise the majority of them, estimated 90-95%. Other lipid classes include free fatty acids, phospholipids, cholesterol, and plant sterols as minor components. Various methods are used for the characterization of nutritional lipids, however, lipidomics approaches become increasingly attractive for this purpose due to their wide coverage, comprehensiveness and holistic view on composition. In this chapter, analytical methodologies and workflows utilized for lipidomics profiling of food samples are outlined with focus on mass spectrometry-based assays. The chapter describes common lipid extraction protocols, the distinct instrumental mass-spectrometry based analytical platforms for data acquisition, chromatographic and ion-mobility spectrometry methods for lipid separation, briefly mentions alternative methods such as gas chromatography for fatty acid profiling and mass spectrometry imaging. Critical issues of important steps of lipidomics workflows such as structural annotation and identification, quantification and quality assurance are discussed as well. Applications reported over the period of the last 5years are summarized covering the discovery of new lipids in foodstuff, differential profiling approaches for comparing samples from different origin, species, varieties, cultivars and breeds, and for food processing quality control. Lipidomics as a powerful tool for personalized nutrition and nutritional intervention studies is briefly discussed as well. It is expected that this field is significantly growing in the near future and this chapter gives a short insight into the power of nutritional lipidomics approaches.

Differential Kendrick's Plots as an Innovative Tool for Lipidomics in Complex Samples: Comparison of Liquid Chromatography and Infusion-Based Methods to Sample Differential Study

Lipidomics has developed rapidly over the past decade. Nontargeted lipidomics from biological samples remains a challenge due to the high structural diversity, the concentration range of lipids, and the complexity of biological samples. We introduce here the use of differential Kendrick's plots as a rapid visualization tool for a qualitative nontargeted analysis of lipids categories and classes from data generated by either liquid chromatography-mass spectrometry (LC-MS) or direct infusion (nESI-MS). Each lipid class is easily identified by comparison with the theoretical Kendrick plot pattern constructed from exact mass measurements and by using MSKendrickFilter, an in-house Python software. The lipids are identified with the LIPID MAPS database. In addition, in LC-MS, the software based on the Kendrick plots returns the retention time from all the lipids belonging to the same series. Lipid extracts from a yeast (Saccharomyces cerevisiae) are used as a model. An on/off case comparing Kendrick plots from two cell lines (prostate cancer cell lines treated or not with a DGAT2 inhibition) clearly shows the effect of the inhibition. Our study demonstrates the good performance of direct infusion as a fast qualitative screening method as well as for the analysis of chromatograms. A fast screening semiquantitative approach is also possible, while the targeted mode remains the golden standard for precise quantitative analysis.

Mitochondrial membrane models built from native lipid extracts: Interfacial and transport properties

The mitochondrion is an essential organelle enclosed by two membranes whose functionalities depend on their very specific protein and lipid compositions. Proteins from the outer mitochondrial membrane (OMM) are specialized in mitochondrial dynamics and mitophagy, whereas proteins of the inner mitochondrial membrane (IMM) have dedicated functions in cellular respiration and apoptosis. As for lipids, the OMM is enriched in glycerophosphatidyl choline but cardiolipin is exclusively found within the IMM. Though the lipid topology and distribution of the OMM and IMM are known since more than four decades, little is known about the interfacial and dynamic properties of the IMM and OMM lipid extracts. Here we build monolayers, supported bilayers and giant unilamellar vesicles (GUVs) of native OMM and IMM lipids extracts from porcine heart. Additionally, we perform a comparative analysis on the interfacial, phase immiscibility and mechanical properties of both types of extract. Our results show that IMM lipids form more expanded and softer membranes than OMM lipids, allowing a better understanding of the physicochemical and biophysical properties of mitochondrial membranes.

Tuberculostearic Acid-Containing Phosphatidylinositols as Markers of Bacterial Burden in Tuberculosis

One-fourth of the global human population is estimated to be infected with strains of the Mycobacterium tuberculosis complex (MTBC), the causative agent of tuberculosis (TB). Using lipidomic approaches, we show that tuberculostearic acid (TSA)-containing phosphatidylinositols (PIs) are molecular markers for infection with clinically relevant MTBC strains and signify bacterial burden. For the most abundant lipid marker, detection limits of ∼10² colony forming units (CFUs) and ∼10³ CFUs for bacterial and cell culture systems were determined, respectively. We developed a targeted lipid assay, which can be performed within a day including sample preparation—roughly 30-fold faster than in conventional methods based on bacterial culture. This indirect and culture-free detection approach allowed us to determine pathogen loads in infected murine macrophages, human neutrophils, and murine lung tissue. These marker lipids inferred from mycobacterial PIs were found in higher levels in peripheral blood mononuclear cells of TB patients compared to healthy individuals. Moreover, in a small cohort of drug-susceptible TB patients, elevated levels of these molecular markers were detected at the start of therapy and declined upon successful anti-TB treatment. Thus, the concentration of TSA-containing PIs can be used as a correlate for the mycobacterial burden in experimental models and in vitro systems and may prospectively also provide a clinically relevant tool to monitor TB severity.

Development of a High-Performance Thin-Layer Chromatography Method for the Quantification of Alkyl Glycerolipids and Alkenyl Glycerolipids from Shark and Chimera Oils and Tissues

Ether lipids are composed of alkyl lipids with an ether bond at the sn-1 position of a glycerol backbone and alkenyl lipids, which possess a vinyl ether bond at the sn-1 position of the glycerol. These ether glycerolipids are present either as polar glycerophospholipids or neutral glycerolipids. Before studying the biological role of molecular species of ether glycerolipids, there is a need to separate and quantify total alkyl and alkenyl glycerolipids from biological samples in order to determine any variation depending on tissue or physiopathological conditions. Here, we detail the development of the first high-performance thin-layer chromatography method for the quantification of total alkyl and alkenyl glycerolipids thanks to the separation of their corresponding alkyl and alkenyl glycerols. This method starts with a reduction of all lipids after extraction, resulting in the reduction of neutral and polar ether glycerolipids into alkyl and alkenyl glycerols, followed by an appropriate purification and, finally, the linearly ascending development of alkyl and alkenyl glycerols on high-performance thin-layer chromatography plates, staining, carbonization and densitometric analysis. Calibration curves were obtained with commercial alkyl and alkenyl glycerol standards, enabling the quantification of alkyl and alkenyl glycerols in samples and thus directly obtaining the quantity of alkyl and alkenyl lipids present in the samples. Interestingly, we found a differential quantity of these lipids in shark liver oil compared to chimera. We quantified alkyl and alkenyl glycerolipids in periprostatic adipose tissues from human prostate cancer and showed the feasibility of this method in other biological matrices (muscle, tumor).

Lipidomics in Understanding Pathophysiology and Pharmacologic Effects in Inflammatory Diseases: Considerations for Drug Development

The lipidome has a broad range of biological and signaling functions, including serving as a structural scaffold for membranes and initiating and resolving inflammation. To investigate the biological activity of phospholipids and their bioactive metabolites, precise analytical techniques are necessary to identify specific lipids and quantify their levels. Simultaneous quantification of a set of lipids can be achieved using high sensitivity mass spectrometry (MS) techniques, whose technological advancements have significantly improved over the last decade. This has unlocked the power of metabolomics/lipidomics allowing the dynamic characterization of metabolic systems. Lipidomics is a subset of metabolomics for multianalyte identification and quantification of endogenous lipids and their metabolites. Lipidomics-based technology has the potential to drive novel biomarker discovery and therapeutic development programs; however, appropriate standards have not been established for the field. Standardization would improve lipidomic analyses and accelerate the development of innovative therapies. This review aims to summarize considerations for lipidomic study designs including instrumentation, sample stabilization, data validation, and data analysis. In addition, this review highlights how lipidomics can be applied to biomarker discovery and drug mechanism dissection in various inflammatory diseases including cardiovascular disease, neurodegeneration, lung disease, and autoimmune disease.

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.

A new update of MALDI-TOF mass spectrometry in lipid research

Matrix-assisted laser desorption and ionization (MALDI) mass spectrometry (MS) is an indispensable tool in modern lipid research since it is fast, sensitive, tolerates sample impurities and provides spectra without major analyte fragmentation. We will discuss some methodological aspects, the related ion-forming processes and the MALDI MS characteristics of the different lipid classes (with the focus on glycerophospholipids) and the progress, which was achieved during the last ten years. Particular attention will be given to quantitative aspects of MALDI MS since this is widely considered as the most serious drawback of the method. Although the detailed role of the matrix is not yet completely understood, it will be explicitly shown that the careful choice of the matrix is crucial (besides the careful evaluation of the positive and negative ion mass spectra) in order to be able to detect all lipid classes of interest. Two developments will be highlighted: spatially resolved Imaging MS is nowadays well established and the distribution of lipids in tissues merits increasing interest because lipids are readily detectable and represent ubiquitous compounds. It will also be shown that a combination of MALDI MS with thin-layer chromatography (TLC) enables a fast spatially resolved screening of an entire TLC plate which makes the method competitive with LC/MS.

Current State of Fluid Lipid Biomarkers for Personalized Diagnostics and Therapeutics in Schizophrenia Spectrum Disorders and Related Psychoses: A Narrative Review

Schizophrenia spectrum disorders (SSD) are traditionally diagnosed and categorized through clinical assessment, owing to their complex heterogeneity and an insufficient understanding of their underlying pathology. However, disease progression and accurate clinical diagnosis become problematic when differentiating shared aspects amongst mental health conditions. Hence, there is a need for widely accessible biomarkers to identify and track the neurobiological and pathophysiological development of mental health conditions, including SSD. High-throughput omics applications involving the use of liquid chromatography-mass spectrometry (LC-MS) are driving a surge in biological data generation, providing systems-level insight into physiological and pathogenic conditions. Lipidomics is an emerging subset of metabolomics, largely underexplored amongst the omics systems. Lipid profiles in the brain are highly enriched with well-established functions, including maintenance, support, and signal transduction of neuronal signaling pathways, making them a prospective and exciting source of biological material for neuropsychiatric research. Importantly, changes in the lipid composition of the brain appear to extend into the periphery, as there is evidence that circulating lipid alterations correlate with alterations of psychiatric condition(s). The relative accessibility of fluid lipids offers a unique source to acquire a lipidomic "footprint" of molecular changes, which may support reliable diagnostics even at early disease stages, prediction of treatment response and monitoring of treatment success (theranostics). Here, we summarize the latest fluid lipidomics discoveries in SSD-related research, examining the latest strategies to integrate information into multi-systems overviews that generate new perspectives of SSD-related psychosis identification, development, and treatment.

The state of the art in plant lipidomics

Lipids are a group of compounds with diverse structures that perform several important functions in plants. To unravel and better understand their in vivo functions, plant biologists have been using various lipidomic technologies including liquid-chromatography (LC)-mass spectrometry (MS). However, there are still significant challenges in LC-MS based plant lipidomics, which need to be addressed. In this review, we provide an overview of the key developments in LC-MS based lipidomic approaches to detect and identify plant lipids with emphasis on areas that can be further improved. Given that the cellular lipidome is estimated to contain hundreds of thousands of lipids,^1,2 many of the lipid structures remain to be discovered. Furthermore, the plant lipidome is considered to be significantly more complex compared to that of mammals. Recent technical developments in mass spectrometry have made the detection of novel lipids possible; hence, approaches that can be used for plant lipid discovery are also discussed.

Plants Metabolome Study: Emerging Tools and Techniques

Metabolomics is now considered a wide-ranging, sensitive and practical approach to acquire useful information on the composition of a metabolite pool present in any organism, including plants. Investigating metabolomic regulation in plants is essential to understand their adaptation, acclimation and defense responses to environmental stresses through the production of numerous metabolites. Moreover, metabolomics can be easily applied for the phenotyping of plants; and thus, it has great potential to be used in genome editing programs to develop superior next-generation crops. This review describes the recent analytical tools and techniques available to study plants metabolome, along with their significance of sample preparation using targeted and non-targeted methods. Advanced analytical tools, like gas chromatography-mass spectrometry (GC-MS), liquid chromatography mass-spectroscopy (LC-MS), capillary electrophoresis-mass spectrometry (CE-MS), fourier transform ion cyclotron resonance-mass spectrometry (FTICR-MS) matrix-assisted laser desorption/ionization (MALDI), ion mobility spectrometry (IMS) and nuclear magnetic resonance (NMR) have speed up precise metabolic profiling in plants. Further, we provide a complete overview of bioinformatics tools and plant metabolome database that can be utilized to advance our knowledge to plant biology.

Lipidomic-based investigation into the therapeutic effects of polyene phosphatidylcholine and Babao Dan on rats with non-alcoholic fatty liver disease

In recent years, with the improvement of people's living standards, non-alcoholic fatty liver disease (NAFLD) has become the most common chronic liver disease in the world. In this paper, the metabolic disorders in Sprague Dawley (SD) rats were induced by a choline-deficient, l-amino acid-defined (CDAA) diet. The therapeutic effects of polyene phosphatidylcholine (PPC) and Babao Dan (BBD) on NAFLD were observed. Lipidomic analysis was performed using ultra-high-performance liquid chromatography-Orbitrap MS, and data analysis and lipid identification were performed using the software LipidSearch. Both PPC and BBD can reduce lipid accumulation in the liver and improve abnormal biochemical indicators in rats, including reduction of triglycerides, total cholesterol, alanine transaminase and aspartate transaminase in serum. In addition, lipids in rat serum were systematically analyzed by lipidomics. The lipidomic results showed that the most obvious lipids with abnormal metabolism in CDAA diet-induced rats were glycerides (triglycerides and diacylglycerols), phospholipids and cholesterol esters. Both BBD and PPC partly reversed the disturbance to lipids induced by the CDAA diet. PPC may be more effective than BBD in alleviating NAFLD because it has a better effect on inhibiting the abnormal accumulation of lipids and reducing the inflammatory reaction in the body.

Lipidomic profiling reveals distinct differences in plasma lipid composition in overweight or obese adolescent students

INTRODUCTION

The relationship between dyslipidemia and obesity has been widely reported, but the global lipid profiles associated with the development of obesity still need to be clarified. An investigation into the association between the lipidomic plasma profile and adolescent obesity may provide new insights into the development of obesity.

METHODS

Mass spectrometry coupled with liquid chromatography was applied to detect the global lipidome in the fasting plasma from 90 Chinese adolescents, including 34 obese adolescents, 26 overweight adolescents, and 30 adolescents with a normal body mass index (BMI). All participants underwent anthropometric measurements by using InBody. Clinical biochemical indicators were measured by Cobas Elecsys.

RESULTS

Both qualitative and quantitative analyses revealed a gradual change in plasma lipid features among obese students, exhibiting characteristics close to overweight students, but differing significantly from normal students. Compared with normal and overweight students, levels of triglyceride (TG), 18-hydroxycortisol, isohumulinone A, and 11-dihydro-12-norneoquassin were up-regulated in the obese group, while phosphatidylcholine (PC), phosphatidylethanolamine (PE), lysoPC (LPC), lysoPE (LPE), and phosphatidylinositol (PI) were significantly down-regulated in the obese group. Then, we conducted Venn diagrams and selected 8 significant metabolites from the 3 paired comparisons. Most of the selected features were significantly correlated with the anthropometric measurements.

CONCLUSIONS

This study demonstrated evidence for a relationship between the eight significant metabolites with obese adolescents. These lipid features may provide a basis for evaluating risk and monitoring the development of obesity.

Lipid Profiling of Alzheimer's Disease Brain Highlights Enrichment in Glycerol(phospho)lipid, and Sphingolipid Metabolism

Alzheimer's disease (AD) is reported to be closely linked with abnormal lipid metabolism. To gain a more comprehensive understanding of what causes AD and its subsequent development, we profiled the lipidome of postmortem (PM) human brains (neocortex) of people with a range of AD pathology (Braak 0-6). Using high-resolution mass spectrometry, we employed a semi-targeted, fully quantitative lipidomics profiling method (Lipidyzer) to compare the biochemical profiles of brain tissues from persons with mild AD (n = 15) and severe AD (AD; n = 16), and compared them with age-matched, cognitively normal controls (n = 16). Univariate analysis revealed that the concentrations of 420 lipid metabolites significantly (p < 0.05; q < 0.05) differed between AD and controls. A total of 49 lipid metabolites differed between mild AD and controls, and 439 differed between severe AD and mild AD. Interestingly, 13 different subclasses of lipids were significantly perturbed, including neutral lipids, glycerolipids, glycerophospholipids, and sphingolipids. Diacylglycerol (DAG) (14:0/14:0), triacylglycerol (TAG) (58:10/FA20:5), and TAG (48:4/FA18:3) were the most notably altered lipids when AD and control brains were compared (p < 0.05). When we compare mild AD and control brains, phosphatidylethanolamine (PE) (p-18:0/18:1), phosphatidylserine (PS) (18:1/18:2), and PS (14:0/22:6) differed the most (p < 0.05). PE (p-18:0/18:1), DAG (14:0/14:0), and PS (18:1/20:4) were identified as the most significantly perturbed lipids when AD and mild AD brains were compared (p < 0.05). Our analysis provides the most extensive lipid profiling yet undertaken in AD brain tissue and reveals the cumulative perturbation of several lipid pathways with progressive disease pathology. Lipidomics has considerable potential for studying AD etiology and identifying early diagnostic biomarkers.

Using machine learning approaches for multi-omics data analysis: A review

With the development of modern high-throughput omic measurement platforms, it has become essential for biomedical studies to undertake an integrative (combined) approach to fully utilise these data to gain insights into biological systems. Data from various omics sources such as genetics, proteomics, and metabolomics can be integrated to unravel the intricate working of systems biology using machine learning-based predictive algorithms. Machine learning methods offer novel techniques to integrate and analyse the various omics data enabling the discovery of new biomarkers. These biomarkers have the potential to help in accurate disease prediction, patient stratification and delivery of precision medicine. This review paper explores different integrative machine learning methods which have been used to provide an in-depth understanding of biological systems during normal physiological functioning and in the presence of a disease. It provides insight and recommendations for interdisciplinary professionals who envisage employing machine learning skills in multi-omics studies.

A Skin Lipidomics Study Reveals the Therapeutic Effects of Tanshinones in a Rat Model of Acne

Tanshinone (TAN), a class of bioactive components in traditional Chinese medicinal plant Salvia miltiorrhiza, has antibacterial and anti-inflammatory effects, can enhance blood circulation, remove blood stasis, and promote wound healing. For these reasons it has been developed as a drug to treat acne. The purpose of this study was to evaluate the therapeutic effects of TAN in rats with oleic acid-induced acne and to explore its possible mechanisms of action through the identification of potential lipid biomarkers. In this study, a rat model of acne was established by applying 0.5 ml of 80% oleic acid to rats' back skin. The potential metabolites and targets involved in the anti-acne effects of TAN were predicted using lipidomics. The results indicate that TAN has therapeutic efficacy for acne, as supported by the results of the histological analyses and biochemical index assays for interleukin (IL)-8, IL-6, IL-β and tumor necrosis factor alpha. The orthogonal projection of latent structure discriminant analysis score was used to analyze the lipidomic profiles between control and acne rats. Ninety-six potential biomarkers were identified in the skin samples of the acne rats. These biomarkers were mainly related to glycerophospholipid and sphingolipid metabolism, and the regulation of their dysfunction is thought to be a possible therapeutic mechanism of action of TAN on acne.

Methods of Lipidomic Analysis: Extraction, Derivatization, Separation, and Identification of Lipids

Lipidomics refers to the large-scale study of pathways and networks of cellular lipids in biological systems. A lipidomic analysis often involves the identification and quantification of the thousands of cellular lipid molecular species within a complex biological sample and therefore requires a well optimized method for lipid profiling. In this chapter, the methods for lipidomic analysis, including sample collection and preparation, lipid derivatization and separation, mass spectrometric identification of lipids, data processing and interpretation, and quality control, are overviewed.

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.

Effects on the liver lipidome of rat offspring prenatally exposed to bisphenol A

Bisphenol A (BPA) is a well-known endocrine disruptor that has obesogenic properties. We have previously reported sex- and age-dependent changes in hepatic transcriptome and proteome of several lipid homeostasis-related genes in rat offspring prenatally exposed to BPA. To further understand the impacts of prenatal BPA exposure, we analyzed lipidomic profiles in the postnatal day (PND) 21 and 60 rats using a high-resolution QTOF mass spectrometer coupled with a HPLC system. We found that the total lipid content was significantly decreased in PND21 females prenatally exposed to 5000 μg/kg bw/day of BPA. Levels of total fatty acids, acylcarnitines, and monoacylglycerols significantly increased in both female and male BPA-exposed rats at PND21. An elevation in total cholesterol esters and reductions in triacylglycerols and monogalactosyl diacylglycerols were found only in PND21 females prenatally exposed to BPA. Interestingly, opposite responses were observed for phospholipids and sphingolipids between PND21 females and males following BPA exposure. The effects on the body weight and total lipid content were mitigated in the latter stage, although the alterations of lipid profiles continued until PND60. A Data Integration Analysis for Biomarker discovery using Latent cOmponents (DIABLO) revealed a high correlation of the lipidome with our previously published transcriptome data. DIABLO also identified potential biomarkers of prenatal exposure to BPA; glycerol-3-phosphate dehydrogenase 1 (Gpd1) and glyceronephosphate O-acyltransferase (Gnpat), which are involved in the glycerophospholipid metabolism, in females and males, respectively. Collectively, we highlighted the sex- and age-dependent effects of prenatal BPA exposure on hepatic lipid homeostasis in rat offspring.

Single cell metabolomics using mass spectrometry: Techniques and data analysis

Mass spectrometry (MS) based techniques are gaining popularity for metabolomics research due to their high sensitivity, wide detection range, and capability of molecular identification. Utilizing such powerful technique to explore the cellular metabolism at the single cell level not only appreciates the subtle cell-to-cell difference (i.e., cell heterogeneity), but also gains biological merits corresponding to individual cells or small cell subpopulations. In this review article, we first briefly summarize recent advances in single cell MS experimental techniques, and then emphasize on the single cell metabolomics data analysis approaches. Through implementation of statistical analysis and more advanced data analysis methods, single cell metabolomics is expected to find more potential applications in the translational and clinical fields in the future.

Regional Lipid Expression Abnormalities Identified Using MALDI IMS Correspond to MRI-Defined White Matter Hyperintensities within Post-mortem Human Brain Tissues

Periventricular white matter hyperintensities (pvWMHs) are a neurological feature detected with magnetic resonance imaging that are clinically associated with an increased risk of stroke and dementia. pvWMHs represent white matter lesions characterized by regions of myelin and axon rarefaction and as such likely involve changes in lipid composition; however, these alterations remain unknown. Lipids are critical in determining cell function and survival. Perturbations in lipid expression have previously been associated with neurological disorders. Matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) is an emerging technique for untargeted, high-throughput investigation of lipid expression and spatial distribution in situ; however, the use of MALDI IMS has been previously been limited by the need for non-embedded, non-fixed, fresh-frozen samples. In the current study, we demonstrate the novel use of MALDI IMS to distinguish regional lipid abnormalities that correlate with magnetic resonance imaging (MRI) defined pvWMHs within ammonium formate washed, formalin-fixed human archival samples. MALDI IMS scans were conducted in positive or negative ion detection mode on tissues sublimated with 2,5-dihydroxybenzoic acid or 1,5-diaminonaphthalene matrices, respectively. Using a broad, untargeted approach to lipid analysis, we consistently detected 116 lipid ion species in 21 tissue blocks from 11 different post-mortem formalin-fixed human brains. Comparing the monoisotopic mass peaks of these lipid ions elucidated significant differences in lipid expression between pvWMHs and NAWM for 31 lipid ion species. Expanding our understanding of alterations in lipid composition will provide greater knowledge of molecular mechanisms underpinning ischemic white matter lesions and provides the potential for novel therapeutic interventions targeting lipid composition abnormalities.

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)1

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.

Standardizing and increasing the utility of lipidomics: a look to the next decade

Introduction: We present our views on the current application of mass spectrometry (MS) based lipidomics and how lipidomics can develop in the next decade to be most practical use to society. That is not to say that lipidomics has not already been of value. In-fact, in its earlier guise as metabolite profiling most of the pathways of steroid biosynthesis were uncovered and via focused lipidomics many inborn errors of metabolism are routinely clinically identified. However, can lipidomics be extended to improve biochemical understanding of, and to diagnose, the most prevalent diseases of the 21^st century? Areas covered: We will highlight the concept of 'level of identification' and the equally crucial topic of 'quantification'. Only by using a standardized language for these terms can lipidomics be translated to fields beyond academia. We will remind the lipid scientist of the value of chemical derivatization, a concept exploited since the dawn of lipid biochemistry. Expert opinion: Only by agreement of the concepts of identification and quantification and their incorporation in lipidomics reporting can lipidomics maximize its value.

Lipidomics of the brain, retina, and biofluids: from the biological landscape to potential clinical application in schizophrenia

Schizophrenia is a serious neuropsychiatric disorder, yet a clear pathophysiology has not been identified. To date, neither the objective biomarkers for diagnosis nor specific medications for the treatment of schizophrenia are clinically satisfactory. It is well accepted that lipids are essential to maintain the normal structure and function of neurons in the brain and that abnormalities in neuronal lipids are associated with abnormal neurodevelopment in schizophrenia. However, lipids and lipid-like molecules have been largely unexplored in contrast to proteins and their genes in schizophrenia. Compared with the gene- and protein-centric approaches, lipidomics is a recently emerged and rapidly evolving research field with particular importance for the study of neuropsychiatric disorders such as schizophrenia, in which even subtle aberrant alterations in the lipid composition and concentration of the neurons may disrupt brain functioning. In this review, we aimed to highlight the lipidomics of the brain, retina, and biofluids in both human and animal studies, discuss aberrant lipid alterations in correlation with schizophrenia, and propose future directions from the biological landscape towards potential clinical applications in schizophrenia. Recent studies are in support of the concept that aberrations in some lipid species [e.g. phospholipids, polyunsaturated fatty acids (PUFAs)] lead to structural alterations and, in turn, impairments in the biological function of membrane-bound proteins, the disruption of cell signaling molecule accessibility, and the dysfunction of neurotransmitter systems. In addition, abnormal lipidome alterations in biofluids are linked to schizophrenia, and thus they hold promise in the discovery of biomarkers for the diagnosis of schizophrenia.

Using stable isotope tracers to monitor membrane dynamics in C. elegans

Membranes within an animal are composed of phospholipids, cholesterol, and proteins that together form a dynamic barrier. The types of lipids that are found within a membrane bilayer impact its biophysical properties including its fluidity, permeability, and susceptibility to damage. While membrane composition is very stable in healthy adults, aberrant membrane structure is seen in a wide and varied array of diseases as well as during natural aging. Despite the wide-reaching impacts of membrane composition, there is relatively little known about how membrane landscape is established and maintained over time. In vivo biochemical modeling of membrane lipids is needed to understand how these molecules interact in their natural configurations. Here, we have described analytical methods that increase the capacity to map the dynamics of individual membrane phospholipids using different types of mass spectrometry. Specifically, we describe novel stable isotope (¹³C and ¹⁵N) strategies to quantify the turnover of dozens of fatty acid tails and intact phospholipids simultaneously.

Evaluation of cyclooxygenase oxylipins as potential biomarker for obesity-associated adipose tissue inflammation and type 2 diabetes using targeted multiple reaction monitoring mass spectrometry

INTRODUCTION

Obesity is associated with adipose tissue inflammation which in turn drives insulin resistance and the development of type 2 diabetes. Oxylipins are a collection of lipid metabolites, subdivided in different classes, which are involved in inflammatory cascades. They play important roles in regulating adipose tissue homeostasis and inflammation and are therefore putative biomarkers for obesity-associated adipose tissue inflammation and the subsequent risk of type 2 diabetes onset. The objective for this study is to design an assay for a specific oxylipin class and evaluate these as potential prognostic biomarker for obesity-associated adipose tissue inflammation and type 2 diabetes.

METHODS

An optimized workflow was developed to extract oxylipins from plasma using solid-phase extraction followed by analysis using ultra-high performance liquid chromatography coupled to a triple quadrupole mass spectrometer in multiple reaction monitoring mode. This workflow was applied to clinical plasma samples obtained from obese-type 2 diabetes patients and from lean and obese control subjects.

RESULTS

The assay was analytically validated and enabled reproducible analyses of oxylipins extracted from plasma with acceptable sensitivities. Analysis of clinical samples revealed discriminative values for four oxylipins between the type 2 diabetes patients and the lean and obese control subjects, viz. PGF_2α, PGE₂, 15-keto-PGE₂ and 13,14-dihydro-15-keto-PGE₂. The combination of PGF_2α and 15-keto-PGE₂ had the most predictive value to discriminate type 2 diabetic patients from lean and obese controls.

CONCLUSIONS

This proof-of-principle study demonstrates the potential value of oxylipins as biomarkers to discriminate obese individuals from obese-type 2 diabetes patients.

Combining Mass Spectrometry with Paternò-Büchi Reaction to Determine Double-Bond Positions in Lipids at the Single-Cell Level

Single cell MS (SCMS) techniques are under rapid development for molecular analysis of individual cells among heterogeneous populations. Lipids are basic cellular constituents playing essential functions in energy storage and the cellular signaling processes of cells. Unsaturated lipids are characterized with one or multiple carbon-carbon double (C═C) bonds, and they are critical for cell functions and human diseases. Characterizing unsaturated lipids in single cells allows for better understanding of metabolomic biomarkers and therapeutic targets of rare cells (e.g., cancer stem cells); however, these studies remain challenging. We developed a new technique using a micropipette needle, in which Paternò-Büchi (PB) reactions at C═C bond can be induced, to determine locations of C═C bonds in unsaturated lipids at the single-cell level. The micropipette needle is produced by combining a pulled glass capillary needle with a fused silica capillary. Cell lysis solvent and PB reagent (acetone or benzophenone) are delivered into the micropipette needle (tip size ≈ 15 um) through a fused silica capillary. The capillary needle plays multiple functions (i.e., single cell sampling probe, cell lysis container, microreactor, and nano-ESI emitter) in the experiments. Both regular (no reaction) and reactive (with PB reaction) SCMS analyses of the same cell can be achieved. C═C bond locations were determined from MS scan and MS/MS of PB products assisted by Python programs. This technique can potentially be used for other reactive SCMS studies to enhance molecular analysis for broad ranges of single cells.

Metabolite Structure Assignment Using In Silico NMR Techniques

A major challenge for metabolomic analysis is to obtain an unambiguous identification of the metabolites detected in a sample. Among metabolomics techniques, NMR spectroscopy is a sophisticated, powerful, and generally applicable spectroscopic tool that can be used to ascertain the correct structure of newly isolated biogenic molecules. However, accurate structure prediction using computational NMR techniques depends on how much of the relevant conformational space of a particular compound is considered. It is intrinsically challenging to calculate NMR chemical shifts using high-level DFT when the conformational space of a metabolite is extensive. In this work, we developed NMR chemical shift calculation protocols using a machine learning model in conjunction with standard DFT methods. The pipeline encompasses the following steps: (1) conformation generation using a force field (FF)-based method, (2) filtering the FF generated conformations using the ASE-ANI machine learning model, (3) clustering of the optimized conformations based on structural similarity to identify chemically unique conformations, (4) DFT structural optimization of the unique conformations, and (5) DFT NMR chemical shift calculation. This protocol can calculate the NMR chemical shifts of a set of molecules using any available combination of DFT theory, solvent model, and NMR-active nuclei, using both user-selected reference compounds and/or linear regression methods. Our protocol reduces the overall computational time by 2 orders of magnitude over methods that optimize the conformations using fully ab initio methods, while still producing good agreement with experimental observations. The complete protocol is designed in such a manner that makes the computation of chemical shifts tractable for a large number of conformationally flexible metabolites.

Analysis of Malassezia Lipidome Disclosed Differences Among the Species and Reveals Presence of Unusual Yeast Lipids

Malassezia yeasts are lipid dependent and part of the human and animal skin microbiome. However, they are also associated with a variety of dermatological conditions and even cause systemic infections. How these yeasts can live as commensals on the skin and switch to a pathogenic stage has long been a matter of debate. Lipids are important cellular molecules, and understanding the lipid metabolism and composition of Malassezia species is crucial to comprehending their biology and host-microbe interaction. Here, we investigated the lipid composition of Malassezia strains grown to the stationary phase in a complex Dixon medium broth. In this study, we perform a lipidomic analysis of a subset of species; in addition, we conducted a gene prediction analysis for the detection of lipid metabolic proteins. We identified 18 lipid classes and 428 lipidic compounds. The most commonly found lipids were triglycerides (TAG), sterol (CH), diglycerides (DG), fatty acids (FAs), phosphatidylcholine (PC), phosphatidylethanolamine (PE), ceramides, cholesteryl ester (CE), sphingomyelin (SM), acylcarnitine, and lysophospholipids. Particularly, we found a low content of CEs in Malassezia furfur, atypical M. furfur, and Malassezia pachydermatis and undetectable traces of these components in Malassezia globosa, Malassezia restricta, and Malassezia sympodialis. Remarkably, uncommon lipids in yeast, like diacylglyceryltrimethylhomoserine and FA esters of hydroxyl FAs, were found in a variable concentration in these Malassezia species. The latter are bioactive lipids recently reported to have antidiabetic and anti-inflammatory properties. The results obtained can be used to discriminate different Malassezia species and offer a new overview of the lipid composition of these yeasts. We could confirm the presence and the absence of certain lipid-biosynthesis genes in specific species. Further analyses are necessary to continue disclosing the complex lipidome of Malassezia species and the impact of the lipid metabolism in connection with the host interaction.

Ultrahigh-Resolution Mass Spectrometry Method for Resolving 13C-Enrichment Patterns in a Microalgal Lipidome

The analysis of 13C-labeled lipids by mass spectrometry is challenging due to the complexity from labeling the large number of carbon atoms in lipids. To further add to the complexity, different adducts can be produced during electrospray ionization and in-source fragmentation, which can create complex overlapping isotope patterns that can only be resolved using high-resolution mass spectrometry. Co-elution of lipids even after chromatographic separation also adds to the potential for overlapping mass spectra. Here, we describe a procedure that enables full 13C-labeled patterns to be resolved in complex microalgal lipid extracts as well a procedure that provides structural labeling information. Mass resolving powers of 240000 full width half-maximum (fwhm) and fast targeted MS/MS allowed the differentiation of isotopologues, adducts, and unresolved lipid species after chromatographic separation. This enabled the percentage of 13C enrichment to be calculated for each individual lipid species over a time series in the microalgal lipidome. The application of tandem mass spectrometry (MS/MS) also allowed the degree of labeling within the headgroup vs acyl chains to be determined, further adding to the detail of information collected. This information is particularly useful for studying lipid synthesis and remodeling processes and can be extended to other biological systems.

Comprehensive Characterization of Bovine Milk Lipids: Phospholipids, Sphingolipids, Glycolipids, and Ceramides

A comprehensive survey on phospholipids (PLs), sphingolipids, glycolipids, and ceramides (Cer) of bovine milk was conducted by liquid chromatography tandem-mass spectrometry. A total of 514 species belonging to 15 classes were identified by an automated search tool (LipidSearch) and a manual analysis of MS2 spectra. Over 60 species were detected for the classes of phosphatidylcholine (PC), phosphatidylinositol, phosphatidylserine, and sphingomyelin (SM), between 20 and 45 species for phosphatidylethanolamine (PE), phosphatidic acid (PA), and lactosylceramide (LacCer), and between 7 and 20 species for phosphatidylglycerol (PG), lyso-phosphatidylcholine, lyso-phosphatidylethanolamine, glucosylceramide (GluCer), PC-plasmalogen (PC-P), PE-plasmalogen, ganglioside, and Cer. The structural assignment for PL classes is straightforward based on class-specific product ion/neutral losses and fatty acid-related product ions. By contrast, structural determination of lipid classes containing a backbone of sphingoid bases (SM, GluCer, LacCer, ganglioside, and Cer) is much more challenging, and combining MS2/MS3 information acquired in both positive and negative modes is sometimes needed. The quantification of all 15 classes of lipids was performed at the species level. A large variation in abundance was observed across the lipid classes; PC, PE, and SM are the most abundant polar lipid classes, with concentrations reaching 120, 150, and 90 μmol/L, respectively, whereas PA, PG, and PC-P are present at very low levels (<1.5 μmol/L) and the remaining classes at an intermediate level (2-45 μmol/L). This is the first comprehensive characterization of minor lipid classes from bovine milk and the information provided can be used as a reference for studying milk lipids.

The Cardiac Lipidome in Models of Cardiovascular Disease

Cardiovascular disease (CVD) is the leading cause of death worldwide. There are numerous factors involved in the development of CVD. Among these, lipids have an important role in maintaining the myocardial cell structure as well as cardiac function. Fatty acids (FA) are utilized for energy, but also contribute to the pathogenesis of CVD and heart failure. Advances in mass spectrometry methods have enabled the comprehensive analysis of a plethora of lipid species from a single sample comprised of a heterogeneous population of lipid molecules. Determining cardiac lipid alterations in different models of CVD identifies novel biomarkers as well as reveals molecular mechanisms that underlie disease development and progression. This information could inform the development of novel therapeutics in the treatment of CVD. Herein, we provide a review of recent studies of cardiac lipid profiles in myocardial infarction, obesity, and diabetic and dilated cardiomyopathy models of CVD by methods of mass spectrometry analysis.

Comprehensive Characterization of Bovine Milk Lipids: Triglycerides

A comprehensive survey on triglycerides (TAGs) of bovine milk was conducted by a combination of exhaustive liquid chromatography (LC) separation, high-resolution mass spectrometry (MS) detection, and automated lipid molecular feature extraction. A total of 220 groups (a series of species having the same chemical formula and mass) and 3454 molecular species of TAGs were identified based on the accurate mass of the parent ion as well as MS2 information. Sixty-five different fatty acids (FAs) were found across these TAG species; C4:0, C6:0, C8:0, C10:0, C12:0, C14:0, C16:0, C18:0, and C18:1 were the most frequent FAs, whereas C11:3, C11:4, C27:0, C27:1, C28:0, and C28:1 were rare FAs in TAG molecules. The number of species identified represents only a small portion of total TAG molecules that can be theoretically synthesized from 65 FAs. Each TAG group contains on average 15-16 isomeric species (species with different FA composition), but positional isomers do not seem to be widespread. As the isomeric species cannot be completely resolved chromatographically, quantification of TAG was conducted at the group level. The most abundant TAG groups in bovine milk include TAG 34:0, TAG 36:0, TAG 38:1, TAG 38:0, and TAG 40:1. This study provides the most comprehensive milk TAG inventory so far that can be used as a reference for studying milk lipids.

Deriving Lipid Classification Based on Molecular Formulas

Despite instrument and algorithmic improvements, the untargeted and accurate assignment of metabolites remains an unsolved problem in metabolomics. New assignment methods such as our SMIRFE algorithm can assign elemental molecular formulas to observed spectral features in a highly untargeted manner without orthogonal information from tandem MS or chromatography. However, for many lipidomics applications, it is necessary to know at least the lipid category or class that is associated with a detected spectral feature to derive a biochemical interpretation. Our goal is to develop a method for robustly classifying elemental molecular formula assignments into lipid categories for an application to SMIRFE-generated assignments. Using a Random Forest machine learning approach, we developed a method that can predict lipid category and class from SMIRFE non-adducted molecular formula assignments. Our methods achieve high average predictive accuracy (>90%) and precision (>83%) across all eight of the lipid categories in the LIPIDMAPS database. Classification performance was evaluated using sets of theoretical, data-derived, and artifactual molecular formulas. Our methods enable the lipid classification of non-adducted molecular formula assignments generated by SMIRFE without orthogonal information, facilitating the biochemical interpretation of untargeted lipidomics experiments. This lipid classification appears insufficient for validating single-spectrum assignments, but could be useful in cross-spectrum assignment validation.

Insights Into Oxidized Lipid Modification in Barley Roots as an Adaptation Mechanism to Salinity Stress

Lipidomics is an emerging technology, which aims at the global characterization and quantification of lipids within biological matrices including biofluids, cells, whole organs and tissues. The changes in individual lipid molecular species in stress treated plant species and different cultivars can indicate the functions of genes affecting lipid metabolism or lipid signaling. Mass spectrometry-based lipid profiling has been used to track the changes of lipid levels and related metabolites in response to salinity stress. We have developed a comprehensive lipidomics platform for the identification and direct qualification and/or quantification of individual lipid species, including oxidized lipids, which enables a more systematic investigation of peroxidation of individual lipid species in barley roots under salinity stress. This new lipidomics approach has improved with an advantage of analyzing the composition of acyl chains at the molecular level, which facilitates to profile precisely the 18:3-containing diacyl-glycerophosphates and allowed individual comparison of lipids across varieties. Our findings revealed a general decrease in most of the galactolipids in plastid membranes, and an increase of glycerophospholipids and acylated steryl glycosides, which indicate that plastidial and extraplastidial membranes in barley roots ubiquitously tend to form a hexagonal II (HII) phase under salinity stress. In addition, salt-tolerant and salt-sensitive cultivars showed contrasting changes in the levels of oxidized membrane lipids. These results support the hypothesis that salt-induced oxidative damage to membrane lipids can be used as an indication of salt stress tolerance in barley.