"Lipidomics": Mass spectrometric and chemometric analyses of lipids

Lipidomic analyses reveal potential biomarkers for predicting death and heart failure after acute myocardial infarction

Background Acute myocardial infarction (AMI) and postmyocardial infarction heart failure (pMIHF) have high mortality rates worldwide. This study aimed to explore lipidomic profiles and identify potential biomarkers for the prediction of death and heart failure (HF) after AMI. Methods All serum samples were collected at Xuanwu Hospital, Capital Medical University, and their clinical characteristics and lipidomic profiles were analyzed in different groups. LC-MS/MS was used for lipidomic analyses, and underlying biomarkers were screened by receiver operating characteristic (ROC) curve analysis. Results Lipidomic analyses of the survival and nonsurvival groups revealed that the decrease of the content of SM (d18:1/22:0), PE (P-20:1/18:0), PC (18:2), LPE (18:2), PE (P-20:0/18:0), LPC (18:0) and PC (20:0/20:3) while increase of the content of PG (18:1/18:1) could increase the risk of death after AMI. In parallel, the lipidomic analysis of the HF and non-HF groups revealed that the decrease of the content of PC (20:3/20:4), LPC (20:3), LPC (18:0), LPC (18:2), LPC (20:0), LPC (18:3), LPE (16:1) and PC (18:2/20:3) could increase the risk of HF after AMI. Conclusion Several lipids could be potential biomarkers for the prediction of death and HF after AMI.

Diet Supplemented with Special Formula Milk Powder Promotes the Growth of the Brain in Rats

This investigation was to study the effects of different formula components on the brain growth of rats. Fifty male SD rats were randomly divided into five groups: a basic diet group; a 20% ordinary milk powder group; a 20% special milk powder group; a 30% ordinary milk powder group; and a 30% special milk powder group by weight. LC-MS was used to detect brain lipidomics. After 28 days of feeding, compared with the basic diet group, the brain/body weights of rats in the 30% ordinary milk powder group were increased. The serum levels of 5-HIAA in the 30% ordinary milk powder group were lower than in the 20% ordinary milk powder group. Compared with the basic diet group, the expressions of DLCL, MePC, PI, and GM1 were higher in the groups with added special milk powder, while the expressions of LPE, LdMePE, SM, and MGTG were higher in the groups with added ordinary milk powder. The expression of MBP was significantly higher in the 20% ordinary group. This study found that different formula components of infant milk powder could affect brain growth in SD rats. The addition of special formula infant milk powder may have beneficial effects on rat brains by regulating brain lipid expression.

Dexborneol Amplifies Pregabalin's Analgesic Effect in Mouse Models of Peripheral Nerve Injury and Incisional Pain

Pregabalin is a medication primarily used in the treatment of neuropathic pain and anxiety disorders, owing to its gabapentinoid properties. Pregabalin monotherapy faces limitations due to its variable efficacy and dose-dependent adverse reactions. In this study, we conducted a comprehensive investigation into the potentiation of pregabalin's analgesic effects by dexborneol, a neuroprotective bicyclic monoterpenoid compound. We performed animal experiments where pain models were induced using two methods: peripheral nerve injury, involving axotomy and ligation of the tibial and common peroneal nerves, and incisional pain through a longitudinal incision in the hind paw, while employing a multifaceted methodology that integrates behavioral pharmacology, molecular biology, neuromorphology, and lipidomics to delve into the mechanisms behind this potentiation. Dexborneol was found to enhance pregabalin's efficacy by promoting its transportation to the central nervous system, disrupting self-amplifying vicious cycles via the reduction of HMGB1 and ATP release, and exerting significant anti-oxidative effects through modulation of central lipid metabolism. This combination therapy not only boosted pregabalin's analgesic property but also notably decreased its side effects. Moreover, this therapeutic cocktail exceeded basic pain relief, effectively reducing neuroinflammation and glial cell activation-key factors contributing to persistent and chronic pain. This study paves the way for more tolerable and effective analgesic options, highlighting the potential of dexborneol as an adjuvant to pregabalin therapy.

Investigating the effects of dexamethasone on pulmonary surfactant lipids based on lipidomics studies

Dexamethasone, a glucocorticoid commonly used in pediatric patients, has potent anti-inflammatory and immunosuppressive properties. However, it is associated with side effects such as reduced lung function and decreased immunity. Pulmonary surfactant lipids are closely linked to lung disease and play a role in reducing surface tension, immune response and antiviral activity. The dysregulation of lipid metabolism is closely associated with lung disease. Hence, untargeted lipidomics may be instrumental in elucidating the effects of dexamethasone on pulmonary surfactant lipids. We obtained surfactant lipid samples from the bronchoalveolar lavage fluid of young mice injected subcutaneously with dexamethasone and conducted a comprehensive lipidomic analysis, comparing them with a control group. We observed a decrease in lipids, such as phosphatidylcholine, phosphatidylglycerol and phosphatidylethanolamine, and an increase in ceramide, fatty acid, diacylglycerol and monoglyceride, which may impact lung health. This study revealed the influence of dexamethasone on pulmonary surfactant lipids, offering new insights into adverse reactions in clinical settings.

Lipids and lipid metabolism in cellular senescence: Emerging targets for age-related diseases

Cellular senescence is a kind of cellular state triggered by endogenous or exogenous stimuli, which is mainly characterized by stable cell cycle arrest and complex senescence-associated secretory phenotype (SASP). Once senescent cells accumulate in tissues, they may eventually accelerate the progression of age-related diseases, such as atherosclerosis, osteoarthritis, chronic lung diseases, cancers, etc. Recent studies have shown that the disorders of lipid metabolism are not only related to age-related diseases, but also regulate the cellular senescence process. Based on existing research evidences, the changes in lipid metabolism in senescent cells are mainly concentrated in the metabolic processes of phospholipids, fatty acids and cholesterol. Obviously, the changes in lipid-metabolizing enzymes and proteins involved in these pathways play a critical role in senescence. However, the link between cellular senescence, changes in lipid metabolism and age-related disease remains to be elucidated. Herein, we summarize the lipid metabolism changes in senescent cells, especially the senescent cells that promote age-related diseases, as well as focusing on the role of lipid-related enzymes or proteins in senescence. Finally, we explore the prospect of lipids in cellular senescence and their potential as drug targets for preventing and delaying age-related diseases.

Lc-ms-based lipidomics analyses revealed changes in lipid profiles in Asian sea bass (Lates calcarifer) with dielectric barrier discharge (DBD) atmospheric plasma treatment

A comprehensive LC-MS-based lipidomics analysis of Asian sea bass (Lates calcarifer) muscle after dielectric barrier discharge (DBD) atmospheric plasma treatment was performed. Through the analysis, 1500 lipid species were detected, phosphatidylcholine (PC, 27.80%) was the most abundant lipid, followed by triglyceride (TG, 20.50%) and phosphatidylethanolamine (PE, 17.10%). Among them, 125 lipid species were detected and identified as differentially abundant lipids in Asian sea bass (ASB). PCA and OPLS-DA showed that ASB lipids changed significantly after DBD treatment. Moreover, glycerophospholipid metabolism was key metabolic pathways, as PC, PE, and lysophosphatidylcholine (LPC) were key lipid metabolites. The findings concerning fatty acids revealed that the saturated fatty acids (SFA) content of ASB after DBD treatment increased by 8.54%, while the content of monounsaturated fatty acids (MUFA) and polyunsaturated fatty acids (PUFA) decreased by 13.77% and 9.16%, respectively. Our study establishes a foundation for the lipid oxidation mechanism of ASB following DBD treatment.

Levels of lipid-derived gut microbial metabolites differ among plant matrices in an in vitro model of colon fermentation

This study explored differences in microbial lipid metabolites among sunflower seeds, soybeans, and walnuts. The matrices were subjected to in vitro digestion and colonic fermentation. Defatted digested materials and fiber/phenolics extracted therefrom were added to sunflower oil (SO) and also fermented. Targeted and untargeted lipidomics were employed to monitor and tentatively identify linoleic acid (LA) metabolites. Walnut fermentation produced the highest free fatty acids (FFAs), LA, and conjugated LAs (CLAs). Defatted digested walnuts added to SO boosted FFAs and CLAs production; the addition of fibre boosted CLAs, whereas the addition of phenolics only increased 9e,11z-CLA and 10e,12z-CLA. Several di-/tri-hydroxy-C18-FAs, reported as microbial LA metabolites for the first time, were annotated. Permutational multivariate analysis of variance indicated significant impacts of food matrix presence and type on lipidomics and C18-FAs. Our findings highlight how the food matrices affect CLA production from dietary lipids, emphasizing the role of food context in microbial lipid metabolism.

Cholesterol and low-density lipoprotein as a cause of psoriasis: Results from bidirectional Mendelian randomization

BACKGROUND

Psoriasis is an inflammatory disease that affects many people. However, the causal effect of lipid metabolism on psoriasis has not yet been verified. This study aimed to identify the genetic relationship between serum lipid levels and psoriasis.

METHODS

Bidirectional two-sample Mendelian randomization (MR) was used to analyse the causal relationship between cholesterol and psoriasis. The outcome of the forward causality test was psoriasis. In the analysis of reverse causality, psoriasis was exposed, and 79 single-nucleotide polymorphisms were detected in the genome-wide association study (GWASs) database from the IEU GWASs Project. MR-Egger regression, inverse variance-weighted, weighted median, weighted mode and simple mode were used for the MR analyses.

RESULTS

The level of triglyceride, lipase member N, chylomicrons, extremely large very low-density lipoprotein (VLDL) particles, high-density lipoprotein (HDL) cholesterol levels, cholesterol esters in large HDL, cholesterol esters in medium HDL and cholesterol esters in medium VLDL have not affected the development of psoriasis. However, total cholesterol, total free cholesterol, low-density lipoprotein (LDL) cholesterol levels, cholesterol esters in large VLDL and cholesterol esters in medium LDL were unidirectional causal effects on psoriasis.

CONCLUSION

Bidirectional two-sample MR analysis indicated that high levels of total cholesterol, total free cholesterol, LDL cholesterol, cholesterol esters in large VLDL and cholesterol esters in medium LDL are genetic risk factors for psoriasis.

From big data to big insights: statistical and bioinformatic approaches for exploring the lipidome

The goal of lipidomic studies is to provide a broad characterization of cellular lipids present and changing in a sample of interest. Recent lipidomic research has significantly contributed to revealing the multifaceted roles that lipids play in fundamental cellular processes, including signaling, energy storage, and structural support. Furthermore, these findings have shed light on how lipids dynamically respond to various perturbations. Continued advancement in analytical techniques has also led to improved abilities to detect and identify novel lipid species, resulting in increasingly large datasets. Statistical analysis of these datasets can be challenging not only because of their vast size, but also because of the highly correlated data structure that exists due to many lipids belonging to the same metabolic or regulatory pathways. Interpretation of these lipidomic datasets is also hindered by a lack of current biological knowledge for the individual lipids. These limitations can therefore make lipidomic data analysis a daunting task. To address these difficulties and shed light on opportunities and also weaknesses in current tools, we have assembled this review. Here, we illustrate common statistical approaches for finding patterns in lipidomic datasets, including univariate hypothesis testing, unsupervised clustering, supervised classification modeling, and deep learning approaches. We then describe various bioinformatic tools often used to biologically contextualize results of interest. Overall, this review provides a framework for guiding lipidomic data analysis to promote a greater assessment of lipidomic results, while understanding potential advantages and weaknesses along the way.

Combined quantitative lipidomics and back-propagation neural network approach to discriminate the breed and part source of lamb

The study successfully utilized an analytical approach that combined quantitative lipidomics with back-propagation neural networks to identify breed and part source of lamb using small-scale samples. 1230 molecules across 29 lipid classes were identified in longissimus dorsi and knuckle meat of both Tan sheep and Bahan crossbreed sheep. Applying multivariate statistical methods, 12 and 7 lipid molecules were identified as potential markers for breed and part identification, respectively. Stepwise linear discriminant analysis was applied to select 3 and 4 lipid molecules, respectively, for discriminating lamb breed and part sources, achieving correct rates of discrimination of 100 % and 95 %. Additionally, back-propagation neural network proved to be a superior method for identifying sources of lamb meat compared to other machine learning approaches. These findings indicate that integrating lipidomics with back-propagation neural network approach can provide an effective strategy to trace and certify lamb products, ensuring their quality and protecting consumer rights.

Using metabolomics and proteomics to identify the potential urine biomarkers for prediction and diagnosis of gestational diabetes

Gestational diabetes mellitus (GDM) is one of the most common metabolic complications during pregnancy, threatening both maternal and fetal health. Prediction and diagnosis of GDM is not unified. Finding effective biomarkers for GDM is particularly important for achieving early prediction, accurate diagnosis and timely intervention. Urine, due to its accessibility in large quantities, noninvasive collection and easy preparation, has become a good sample for biomarker identification. In recent years, a number of studies using metabolomics and proteomics approaches have identified differential expressed urine metabolites and proteins in GDM patients. In this review, we summarized these potential urine biomarkers for GDM prediction and diagnosis and elucidated their role in development of GDM.

Mass Spectrometry Strategies for O-Glycoproteomics

Glycoproteomics has accelerated in recent decades owing to numerous innovations in the analytical workflow. In particular, new mass spectrometry strategies have contributed to inroads in O-glycoproteomics, a field that lags behind N-glycoproteomics due to several unique challenges associated with the complexity of O-glycosylation. This review will focus on progress in sample preparation, enrichment strategies, and MS/MS techniques for the identification and characterization of O-glycoproteins.

Recent Analytical Methodologies in Lipid Analysis

Lipids represent a large group of biomolecules that are responsible for various functions in organisms. Diseases such as diabetes, chronic inflammation, neurological disorders, or neurodegenerative and cardiovascular diseases can be caused by lipid imbalance. Due to the different stereochemical properties and composition of fatty acyl groups of molecules in most lipid classes, quantification of lipids and development of lipidomic analytical techniques are problematic. Identification of different lipid species from complex matrices is difficult, and therefore individual analytical steps, which include extraction, separation, and detection of lipids, must be chosen properly. This review critically documents recent strategies for lipid analysis from sample pretreatment to instrumental analysis and data interpretation published in the last five years (2019 to 2023). The advantages and disadvantages of various extraction methods are covered. The instrumental analysis step comprises methods for lipid identification and quantification. Mass spectrometry (MS) is the most used technique in lipid analysis, which can be performed by direct infusion MS approach or in combination with suitable separation techniques such as liquid chromatography or gas chromatography. Special attention is also given to the correct evaluation and interpretation of the data obtained from the lipid analyses. Only accurate, precise, robust and reliable analytical strategies are able to bring complex and useful lipidomic information, which may contribute to clarification of some diseases at the molecular level, and may be used as putative biomarkers and/or therapeutic targets.

Research-based Analytical Procedures to Evaluate Diabetic Biomarkers and Related Parameters: In Vitro and In Vivo Methods

BACKGROUND

The degenerative tendency of diabetes leads to micro- and macrovascular complications due to abnormal levels of biochemicals, particularly in patients with poor diabetic control. Diabetes is supposed to be treated by reducing blood glucose levels, scavenging free radicals, and maintaining other relevant parameters close to normal ranges. In preclinical studies, numerous in vivo trials on animals as well as in vitro tests are used to assess the antidiabetic and antioxidant effects of the test substances. Since a substance that performs poorly in vitro won't perform better in vivo, the outcomes of in vitro studies can be utilized as a direct indicator of in vivo activities.

OBJECTIVE

The objective of the present study is to provide research scholars with a comprehensive overview of laboratory methods and procedures for a few selected diabetic biomarkers and related parameters.

METHOD

The search was conducted on scientific database portals such as ScienceDirect, PubMed, Google Scholar, BASE, DOAJ, etc. Conclusion: The development of new biomarkers is greatly facilitated by modern technology such as cell culture research, lipidomics study, microRNA biomarkers, machine learning techniques, and improved electron microscopies. These biomarkers do, however, have some usage restrictions. There is a critical need to find more accurate and sensitive biomarkers. With a few modifications, these biomarkers can be used with or even replace conventional markers of diabetes.

A lipidomic approach to bisphenol F-induced non-alcoholic fatty liver disease-like changes: altered lipid components in a murine model

Bisphenol A (BPA), a typical environmental endocrine disruptor, is an "obesogen" that can induce lipid accumulation in the liver. Highly similar in structure to BPA, bisphenol F (BPF) is becoming the dominant BPA substitute on the market, which attracts more and more attention due to its potential adverse effects. Recently, BPF exposure is found to cause non-alcoholic fatty liver disease (NAFLD)-like changes; however, the underlying toxic effects remain poorly understood. Therefore, in the current study, we focused on BPF-mediated lipid homeostasis, especially the alterations of lipid components and metabolism. In human serum, the BPF levels in healthy controls and NAFLD patients were assessed by ELISA, and BPF-induced disturbance of lipid metabolism was evaluated in mouse model via non-targeted lipomic methods with ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry. It suggested that BPF exposure was positively correlated with NAFLD severity and triglyceride level in patients. Based on the relationships, lipid metabolites were assessed in mouse livers between control and BPF-treated group, and it revealed that twenty-six lipid metabolites (including phospholipids, sphingolipids, and glycerides) were significantly changed in mouse livers. Phosphatidylcholine, phosphatidylethanolamine, and diglyceryl ester levels were lower than those in the control mice; hexose ceramide content in sphingolipids markedly increased in BPF-treated mouse livers. Noteworthily, the glycerophospholipid metabolic pathway was found to be the most pronounced in BPF-induced disturbance of lipid metabolism. Therefore, the current study, for the first time, is deciphering the BPF-induced lipid metabolic disturbance, which may provide novel intervention strategies for BPF-induced NAFLD-like changes.

Differences in the lipid metabolism profile and clinical characteristics between eosinophilic and non-eosinophilic acute exacerbation of chronic obstructive pulmonary disease

Objective: In this study, we aimed to investigate the differences in serum lipid metabolite profiles and their relationship with clinical characteristics between patients with eosinophilic and non-eosinophilic AECOPD. Methods: A total of 71 AECOPD patients were enrolled. Eosinophilic AECOPD was defined as blood EOS% ≥ 2% (n = 23), while non-eosinophilic AECOPD, as blood EOS< 2% (n = 48). Clinical data were collected, and serum lipid metabolism profiles were detected by liquid chromatography-mass spectrometry (LC-MS). The XCMS software package was used to pre-process the raw data, and then, lipid metabolite identification was achieved through a spectral match using LipidBlast library. Differences in lipid profiles and clinical features between eosinophilic and non-eosinophilic groups were analyzed by generalized linear regression. The least absolute shrinkage and selection operator (LASSO) was applied to screen the most characteristic lipid markers for the eosinophilic phenotype. Results: Eosinophilic AECOPD patients had less hypercapnic respiratory failures, less ICU admissions, a shorter length of stay in the hospital, and a lower fibrinogen level. In the lipid metabolism profiles, 32 significantly different lipid metabolites were screened through a t-test adjusted by using FDR (FDR-adjusted p < 0.05 and VIP> 1). Nine differential lipid metabolites were found to be associated with the three clinical features, namely, hypercapnia respiratory failure, ICU admission, and fibrinogen in further integration analysis. The species of triacylglycerol (TAG), phosphatidylcholine (PC), lysophosphatidylcholine (LPC), and diacylglyceryl trimethylhomoserine (DGTS) were high in these eosinophilic AECOPD. The LASSO was applied, and three lipid metabolites were retained, namely, LPC (16:0), TAG (17:0/17:2/17:2), and LPC (20:2). The logistic regression model was fitted using these three markers, and the area under the ROC curve of the model was 0.834 (95% CI: 0.740-0.929). Conclusion: Patients with eosinophilic AECOPD had a unique lipid metabolism status. Species of TAGs and LPCs were significantly increased in this phenotype and were associated with better clinical outcomes.

Autologous hematopoietic stem cell transplantation significantly alters circulating ceramides in peripheral blood of relapsing-remitting multiple sclerosis patients

BACKGROUND

The common inflammatory disease multiple sclerosis (MS) is a disease of the central nervous system. For more than 25 years autologous hematopoietic stem cell transplantation (AHSCT) has been used to treat MS. It has been shown to be highly effective in suppressing inflammatory activity in relapsing-remitting MS (RRMS) patients. This treatment is thought to lead to an immune system reset, inducing a new, more tolerant system; however, the precise mechanism behind the treatment effect in MS patients is unknown. In this study, the effect of AHSCT on the metabolome and lipidome in peripheral blood from RRMS patients was investigated.

METHODS

Peripheral blood samples were collected from 16 patients with RRMS at ten-time points over the five months course of AHSCT and 16 MS patients not treated with AHSCT. Metabolomics and lipidomics analysis were performed using liquid-chromatography high-resolution mass spectrometry. Mixed linear models, differential expression analysis, and cluster analysis were used to identify differentially expressed features and groups of features that could be of interest. Finally, in-house and in-silico libraries were used for feature identification, and enrichment analysis was performed.

RESULTS

Differential expression analysis found 657 features in the lipidomics dataset and 34 in the metabolomics dataset to be differentially expressed throughout AHSCT. The administration of cyclophosphamide during mobilization and conditioning was associated with decreased concentrations in glycerophosphoinositol species. Thymoglobuline administration was associated with an increase in ceramide and glycerophosphoethanolamine species. After the conditioning regimen, a decrease in glycerosphingoidlipids concentration was observed, and following hematopoietic stem cell reinfusion glycerophosphocholine concentrations decreased for a short period of time. Ceramide concentrations were strongly associated with leukocyte levels during the procedure. The ceramides Cer(d19:1/14:0) and Cer(d20:1/12:0) were found to be increased (P < .05) in concentration at the three-month follow-up compared to baseline. C16 ceramide, Cer(D18:2/16:0), and CerPE(d16:2(4E,6E)/22:0) were found to be significantly increased in concentration after AHSCT compared to prior to treatment as well as compared to newly diagnosed RRMS patients.

CONCLUSION

AHSCT had a larger impact on the lipids in peripheral blood compared to metabolites. The variation in lipid concentration reflects the transient changes in the peripheral blood milieu during the treatment, rather than the changes in the immune system that are assumed to be the cause of clinical improvement within RRMS patients treated with AHSCT. Ceramide concentrations were affected by AHSCT and associated with leukocyte counts and were altered three months after treatment, suggesting a long-lasting effect.

Lipidomics Workflow for Analyzing Lipid Profiles Using Multiple Reaction Monitoring (MRM) in Liver Homogenate of Mice with Non-alcoholic Steatohepatitis (NASH)

Non-alcoholic steatohepatitis (NASH) is a condition characterized by inflammation and hepatic injury/fibrosis caused by the accumulation of ectopic fats in the liver. Recent advances in lipidomics have allowed the identification and characterization of lipid species and have revealed signature patterns of various diseases. Here, we describe a lipidomics workflow to assess the lipid profiles of liver homogenates taken from a NASH mouse model. The protocol described below was used to extract and analyze the metabolites from the livers of mice with NASH by liquid chromatography-mass spectrometry (LC-MS); however, it can be applied to other tissue homogenate samples. Using this method, over 1,000 species of lipids from five classes can be analyzed in a single run on the LC-MS. Also, partial elucidation of the identity of neutral lipid (triacylglycerides and diacylglycerides) aliphatic chains can be performed with this simple LC-MS setup. Key features Over 1,000 lipid species (sphingolipids, cholesteryl esters, neutral lipids, phospholipids, fatty acids) are analyzed in one run. Analysis of liver lipids in non-alcoholic steatohepatitis (NASH) mouse model. Normal-phase chromatography coupled to a triple quadrupole mass spectrometer.

Untargeted Lipidomics and Chemometric Tools for the Characterization and Discrimination of Irradiated Camembert Cheese Analyzed by UHPLC-Q-Orbitrap-MS

In this work, an investigation using UHPLC-Q-Orbitrap-MS and multivariate statistics was conducted to obtain the lipid fingerprint of Camembert cheese and to explore its correlated variation with respect to X-ray irradiation treatment. A total of 479 lipids, categorized into 16 different lipid subclasses, were measured. Furthermore, the identification of oxidized lipids was carried out to better understand the possible phenomena of lipid oxidation related to this technological process. The results confirm that the lipidomic approach adopted is effective in implementing the knowledge of the effects of X-ray irradiation on food and evaluating its safety aspects. Furthermore, Partial Least Squares-Discriminant Analysis (PLS-DA) and Linear Discriminant Analysis (LDA) were applied showing high discriminating ability with excellent values of accuracy, specificity and sensitivity. Through the PLS-DA and LDA models, it was possible to select 40 and 24 lipids, respectively, including 3 ceramides (Cer), 1 hexosyl ceramide (HexCer), 1 lysophosphatidylcholine (LPC), 1 lysophosphatidylethanolamine (LPE), 3 phosphatidic acids (PA), 4 phosphatidylcholines (PC), 10 phosphatidylethanolamines (PE), 5 phosphatidylinositols (PI), 2 phosphatidylserines (PS), 3 diacylglycerols (DG) and 9 oxidized triacylglycerols (OxTG) as potential markers of treatment useful in food safety control plans.

Contrastive Learning-Based Embedder for the Representation of Tandem Mass Spectra

Tandem mass spectrometry (MS/MS) shows great promise in the research of metabolomics, providing an abundance of information on compounds. Due to the rapid development of mass spectrometric techniques, a large number of MS/MS spectral data sets have been produced from different experimental environments. The massive data brings great challenges into the spectral analysis including compound identification and spectra clustering. The core challenge in MS/MS spectral analysis is how to describe a spectrum more quantitatively and effectively. Recently, emerging deep-learning-based technologies have brought new opportunities to handle this challenge in which high-quality descriptions of MS/MS spectra can be obtained. In this study, we propose a novel contrastive learning-based method for the representation of MS/MS spectra, called CLERMS, which is based on transformer architecture. Specifically, an optimized model architecture equipped with a sinusoidal embedder and a novel loss function composed of InfoNCE loss and MSE loss has been proposed for the attainment of good embedding from the peak information and the metadata. We evaluate our method using a GNPS data set, and the results demonstrate that the learned embedding can not only distinguish spectra from different compounds but also reveal the structural similarity between them. Additionally, the comparison between our method and other methods on the performance of compound identification and spectra clustering shows that our method can achieve significantly better results.

Crosstalk between cholesterol metabolism and psoriatic inflammation

Psoriasis is a chronic autoinflammatory skin disease associated with multiple comorbidities, with a prevalence ranging from 2 to 3% in the general population. Decades of preclinical and clinical studies have revealed that alterations in cholesterol and lipid metabolism are strongly associated with psoriasis. Cytokines (tumor necrosis factor-α (TNF-α), interleukin (IL)-17), which are important in the pathogenesis of psoriasis, have been shown to affect cholesterol and lipid metabolism. Cholesterol metabolites and metabolic enzymes, on the other hand, influence not only the biofunction of keratinocytes (a primary type of cell in the epidermis) in psoriasis, but also the immune response and inflammation. However, the relationship between cholesterol metabolism and psoriasis has not been thoroughly reviewed. This review mainly focuses on cholesterol metabolism disturbances in psoriasis and their crosstalk with psoriatic inflammation.

Development and application of lipidomics for food research

Lipidomics is an emerging and promising omics derived from metabolomics to comprehensively analyze all of lipid molecules in biological matrices. The purpose of this chapter is to introduce the development and application of lipidomics for food research. First, three aspects of sample preparation are introduced: food sampling, lipid extraction, and transportation and storage. Second, five types of instruments for data acquisition are summarized: direct infusion-mass spectrometry (MS), chromatographic separation-MS, ion mobility-MS, MS imaging, and nuclear magnetic resonance spectroscopy. Third, data acquisition and analysis software are described for the lipidomics software development. Fourth, the application of lipidomics for food research is discussed such as food origin and adulteration analysis, food processing research, food preservation research, and food nutrition and health research. All the contents suggest that lipidomics is a powerful tool for food research based on its ability of lipid component profile analysis.

Mechanism of Cytotoxic Action of Gold Nanorods Photothermal Therapy for A549 Cell

Photothermal therapy has developed into an important field of tumor treatment research, and numerous studies have focused on the preparation of photothermal therapeutic agents, tumor targeting, diagnosis, and treatment integration. However, there are few studies on the mechanism of photothermal therapy acting on cancer cells. Here we investigated the metabolomics of lung cancer cell A549 during gold nanorod (GNR) photothermal treatment by high-resolution LC/MS, and several differential metabolites and corresponding metabolic pathways during photothermal therapy were found. The main differential metabolites contained 18-hydroxyoleate, beta-alanopine and cis-9,10-epoxystearic acid, and phosphorylcholine. Pathway analysis also showed metabolic changes involving cutin, suberine, and wax biosynthesis, pyruvate and glutamic acid synthesis, and choline metabolism. Analysis also showed that the photothermal process of GNRs may induce cytotoxicity by affecting pyruvate and glutamate synthesis, normal choline metabolism, and ultimately apoptosis.

Untargeted Lipidomic Profiling Reveals Lysophosphatidylcholine and Ceramide as Atherosclerotic Risk Factors in apolipoprotein E Knockout Mice

Despite the availability and use of numerous cholesterol-lowering drugs, atherosclerotic cardiovascular disease (ASCVD) remains the leading cause of mortality globally. Many researchers have focused their effort on identifying modified lipoproteins. However, lipid moieties such as lysophosphatidylcholine (LPC) and ceramide (CER) contribute to atherogenic events. LPC and CER both cause endothelial mitochondrial dysfunction, leading to fatty acid and triglyceride (TG) accumulation. In addition, they cause immune cells to differentiate into proinflammatory phenotypes. To uncover alternative therapeutic approaches other than cholesterol- and TG-lowering medications, we conducted untargeted lipidomic investigations to assess the alteration of lipid profiles in apolipoprotein E knockout (apoE^-/-) mouse model, with or without feeding a high-fat diet (HFD). Results indicated that, in addition to hypercholesterolemia and hyperlipidemia, LPC levels were two to four times higher in apoE^-/- mice compared to wild-type mice in C57BL/6 background, regardless of whether they were 8 or 16 weeks old. Sphingomyelin (SM) and CER were elevated three- to five-fold in apoE^-/- mice both at the basal level and after 16 weeks when compared to wild-type mice. After HFD treatment, the difference in CER levels elevated more than ten-fold. Considering the atherogenic properties of LPC and CER, they may also contribute to the early onset of atherosclerosis in apoE^-/- mice. In summary, the HFD-fed apoE^-/- mouse shows elevated LPC and CER contents and is a suitable model for developing LPC- and CER-lowering therapies.

Current Role of Modern Chromatography with Mass Spectrometry and Nuclear Magnetic Resonance Spectroscopy in the Investigation of Biomarkers of Endometriosis

Endometriosis has a wide range of clinical manifestations, and the disease course is unpredictable, making the diagnosis a challenging task. Despite significant advances in the pathophysiology of endometriosis and various proposed theories, the exact etiology is not fully understood and is still unknown. The most commonly used biomarker of endometriosis is CA-125, however, it is nonspecific and is applied for cancers diagnosis. Therefore, the development of reliable noninvasive diagnostic tests for the early diagnosis of endometriosis remains one of the top priorities. Omics technologies are very promising approaches for constructing diagnostic models and biomarker discovery. Their use can greatly facilitate the study of such a complex disease as endometriosis. Nowadays, powerful analytical platforms commonly used in omics, such as gas and liquid chromatography with mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy, have proven to be a promising tools for biomarker discovery. The aim of this review is to summarize the various features of the analytical approaches, practical challenges and features of gas and liquid chromatography with MS and NMR spectroscopy (including sample processing protocols, technological advancements, and methodology) used for profiling of metabolites, lipids, peptides and proteins in physiological fluids and tissues from patients with endometriosis. In addition, this report devotes special attention to the issue of how comprehensive analyses of these profiles can effectively contribute to the study of endometriosis. The search query included reports published between 2012 and 2022 years in PubMed, Web-of-Science, SCOPUS, Science Direct.

Metabolomics of Extracellular Vesicles: A Future Promise of Multiple Clinical Applications

Extracellular vesicles (EVs) can contain DNA, RNA, proteins and metabolic molecules from primary origins; they are coated with a phospholipid bilayer membrane and released by cells into the extracellular matrix. EVs can be obtained from various body liquids, including the blood, saliva, cerebrospinal fluid, and urine. As has been proved, EVs-mediated transfer of biologically active molecules is crucial for various physiological and pathological processes. Extensive investigations have already begun to explore the diagnosis and prognosis potentials for EVs. Furthermore, research has continued to recognize the critical role of nucleic acids and proteins in EVs. However, our understanding of the comprehensive effects of metabolites in these nanoparticles is currently limited and in its infancy. Therefore, we have attempted to summarize the recent research into the metabolomics of EVs in relation to potential clinical applications and discuss the problems and challenges that have occurred, to provide more guidance for the future development in this field.

Lysophosphatidylcholine: Potential Target for the Treatment of Chronic Pain

The bioactive lipid lysophosphatidylcholine (LPC), a major phospholipid component of oxidized low-density lipoprotein (Ox-LDL), originates from the cleavage of phosphatidylcholine by phospholipase A2 (PLA2) and is catabolized to other substances by different enzymatic pathways. LPC exerts pleiotropic effects mediated by its receptors, G protein-coupled signaling receptors, Toll-like receptors, and ion channels to activate several second messengers. Lysophosphatidylcholine (LPC) is increasingly considered a key marker/factor positively in pathological states, especially inflammation and atherosclerosis development. Current studies have indicated that the injury of nervous tissues promotes oxidative stress and lipid peroxidation, as well as excessive accumulation of LPC, enhancing the membrane hyperexcitability to induce chronic pain, which may be recognized as one of the hallmarks of chronic pain. However, findings from lipidomic studies of LPC have been lacking in the context of chronic pain. In this review, we focus in some detail on LPC sources, biochemical pathways, and the signal-transduction system. Moreover, we outline the detection methods of LPC for accurate analysis of each individual LPC species and reveal the pathophysiological implication of LPC in chronic pain, which makes it an interesting target for biomarkers and the development of medicine regarding chronic pain.

Mass Spectrometry Imaging of Lipids in the Scent Glands of Muskrat (Ondatra zibethicus) in Different Reproductive Statuses

As a typical seasonal breeding animal, male muskrats have a pair of scent glands that can emit musky odor substances to attract females during the breeding period. The present study aimed to visualize the differences in the distribution of lipids in the scent glands of muskrats during their different reproductive statuses by imaging mass spectrometry and quantitative real-time PCR (qRT-PCR). The results revealed remarkable differences in the expression and spatial distribution of lipids detected in the scent glands of muskrats during the different reproductive statuses. In addition, the expression levels of lipid molecules PC (32:0) and LysoPC (16:0) were found to be significantly higher in the breeding season than in the non-breeding season. Moreover, the mRNA expression levels of lipid synthesis enzyme Pemt and Pla2g4b were higher in the breeding season than in the non-breeding season, and there were positive correlations between the expression intensities of lipid molecules and the expression levels of Pemt and Pla2g4b. The present study investigates the changes and distribution of the endogenous lipid in the scent glands of muskrats and elucidates that the seasonal changes in the lipid metabolism may affect the functions of the scent glands in muskrats.

UHPLC-Q-Exactive Orbitrap MS/MS-based untargeted lipidomics reveals molecular mechanisms and metabolic pathways of lipid changes during golden pomfret (Trachinotus ovatus) fermentation

Fermented golden pomfret (a popular marine fish product) is prepared via spontaneous fermentation. However, no comprehensive analysis has been reported on its lipid composition and metabolism. Herein, UHPLC-MS/MS-based untargeted lipidomic analysis identified 998 lipids (six classes; 29 subclasses) in fermented golden pomfret, including glycerolipids (47.70%) and glycerophospholipids (32.06%). As fermentation proceeded, triglyceride and diglyceride contents increased and subsequently decreased, while that of poly-unsaturated fatty acid-containing lipids increased (including those with docosahexaenoic acid, eicosapentaenoic acid, and docosapentaenoic acid). Pathway enrichment analysis identified seven lipid-related metabolic pathways, with the glycerophospholipid pathway found to be the most pertinent. Moreover, the decreased abundance of phosphatidylethanolamines and phosphatidylcholines during fermentation results from their high unsaturated fatty acid (FA) content. Indeed, essential FA contents increase following fermentation, due to their occurrence as glycerolipid side chains. Collectively, the results of this study provide a theoretical reference for optimizing the quality of fermented fish products.

Role of hepatic lipid species in the progression of nonalcoholic fatty liver disease

Nonalcoholic fatty liver disease (NAFLD) has become the most common liver disease due to the global pandemic of metabolic diseases. Dysregulation of hepatic lipid metabolism plays a central role in the initiation and progression of NAFLD. With the advancement of lipidomics, an increasing number of lipid species and underlying mechanisms associating hepatic lipid components have been revealed. Therefore, the focus of this mini-review is to highlight the links between hepatic lipid species and their mechanisms mediating the pathogenesis of NAFLD. We first summarized the interplay between NAFLD and hepatic lipid disturbances. Next, we focused on reviewing the role of saturated fatty acids, cholesterol, oxidized phospholipids, and their respective intermediates in the pathogenesis of NAFLD. The mechanisms by which monounsaturated fatty acids and other pro-resolving mediators exert protective effects are also addressed. Finally, we further discussed the implication of different analysis approaches in lipidomic. Evolving insights into the pathophysiology of NAFLD will provide the opportunity for drug development.

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.

Lipid metabolism contribute to the pathogenesis of IgA Vasculitis

Background and objectives

The underlying mechanism of IgA vasculitis (IgAV) and IgA vasculitis with nephritis (IgAVN) remains unclear. Therefore, there are no accurate diagnostic methods. Lipid metabolism is related to many immune related diseases, so this study set out to explore the relationship of lipids and IgAV and IgAVN.

Methods

Fifty-eighth patients with IgAV and 28 healthy controls were recruited, which were divided into six separate pools to investigate the alterations of serum lipids according to the clinical characteristics: healthy controls group (HCs) and IgAV group (IgAVs), IgAVN group (IgAV-N) and IgAV without nephritis group (IgAV-C), initial IgAV group (IgAV0) and IgAV in treatment with glucocorticoids group (IgAV1).

Results

31 identified lipid ions significantly changed in IgAVs with p < 0.05, variable importance of the projection (VIP) > 1 and fold change (FC) > 1.5. All these 31 lipid ions belong to 6 classes: triacylglycerols (TG), phosphatidylethanolamine (PE), phosphatidylcholine (PC), phosphatidylserine, ceramide, and lysophosphatidylcholine. TG (16:0/18:1/22:6) +NH4 over 888875609.05, PC (32:1) +H over 905307459.90 and PE (21:4)-H less than 32236196.59 increased the risk of IgAV significantly (OR>1). PC (38:6) +H was significantly decreased (p < 0.05, VIP>1 and FC>1.5) in IgAVN. PC (38:6) less than 4469726623 conferred greater risks of IgAV (OR=45.833, 95%CI: 6.689~341.070).

Conclusion

We suggest that lipid metabolism may affect the pathogenesis of IgAV via cardiovascular disease, insulin resistance, cell apoptosis, and inflammation. The increase of TG(16:0/18:1/22:6) + NH4, and PC(32:1) + H as well as PE (21:4)-H allow a good prediction of IgAV. PE-to-PC conversion may participate in the damage of kidney in IgAV. PC (38:6) + H may be a potential biomarker for IgAVN.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13000-021-01185-1.

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.

Lipid analysis by ion mobility spectrometry combined with mass spectrometry: A brief update with a perspective on applications in the clinical laboratory

Ion mobility spectrometry (IMS) is an analytical technique where ions are separated in the gas phase based on their mobility through a buffer gas in the presence of an electric field. An ion passing through an IMS device has a characteristic collisional cross section (CCS) value that depends on the buffer gas used. IMS can be coupled with mass spectrometry (MS), which characterizes an ion based on a mass-to-charge ratio (m/z), to increase analytical specificity and provide further physicochemical information. In particular, IMS-MS is of ever-increasing interest for the analysis of lipids, which can be problematic to accurately identify and quantify in bodily fluids by liquid chromatography (LC) with MS alone due to the presence of isomers, isobars, and structurally similar analogs. IMS provides an additional layer of separation when combined with front-end LC approaches, thereby, enhancing peak capacity and analytical specificity. CCS (and also ion mobility drift time) can be plotted against m/z ion intensity and/or LC retention time in order to generate in-depth molecular profiles of a sample. Utilization of IMS-MS for routine clinical laboratory testing remains relatively unexplored, but areas do exist for potential implementation. A brief update is provided here on lipid analysis using IMS-MS with a perspective on some applications in the clinical laboratory.

Spectral entropy outperforms MS/MS dot product similarity for small-molecule compound identification

Compound identification in small-molecule research, such as untargeted metabolomics or exposome research, relies on matching tandem mass spectrometry (MS/MS) spectra against experimental or in silico mass spectral libraries. Most software programs use dot product similarity scores. Here we introduce the concept of MS/MS spectral entropy to improve scoring results in MS/MS similarity searches via library matching. Entropy similarity outperformed 42 alternative similarity algorithms, including dot product similarity, when searching 434,287 spectra against the high-quality NIST20 library. Entropy similarity scores proved to be highly robust even when we added different levels of noise ions. When we applied entropy levels to 37,299 experimental spectra of natural products, false discovery rates of less than 10% were observed at entropy similarity score 0.75. Experimental human gut metabolome data were used to confirm that entropy similarity largely improved the accuracy of MS-based annotations in small-molecule research to false discovery rates below 10%, annotated new compounds and provided the basis to automatically flag poor-quality, noisy spectra.

Fully Automatized Detection of Phosphocholine-Containing Lipids through an Isotopically Labeled Buffer Modification Workflow

High-resolution mass spectrometry is the foremost technique for qualitative and quantitative lipidomics analyses. Glycerophospholipids and sphingolipids, collectively termed polar lipids, are commonly investigated by hyphenated liquid chromatography-mass spectrometry (LC-MS) techniques that reduce aggregation effects and provide a greater dynamic range of detection sensitivity compared to shotgun lipidomics. However, automatic polar lipid identification is hindered by several isobaric and isomer mass overlaps, which cause software programs to often fail to correctly annotate the lipid species. In the present paper, a buffer modification workflow based on the use of labeled and unlabeled acetate ions in the chromatographic buffers was optimized by Box-Behnken design of the experiments and applied to the characterization of phosphocholine-containing lipids in human plasma samples. The contemporary generation of [M + CH₃COO]^-, [M + CD₃COO]^-, and [M - CH₃]^- coupled with a dedicated data processing workflow, which was specifically set up on Compound Discoverer software, allowed us to correctly determine adduct composition, molecular formulas, and grouping, as well as granting a lower false-positive rate and streamlining the manual validation step compared to commonly employed lipidomics platforms. The proposed workflow represents a robust yet easier alternative to the existing approaches for improving lipid annotation, as it does not require extensive sample pretreatment or prior isotopic enrichment or derivatization.

Mass Spectrometry Imaging for Glycome in the Brain

Glycans are diverse structured biomolecules that play crucial roles in various biological processes. Glycosylation, an enzymatic system through which various glycans are bound to proteins and lipids, is the most common and functionally crucial post-translational modification process. It is known to be associated with brain development, signal transduction, molecular trafficking, neurodegenerative disorders, psychopathologies, and brain cancers. Glycans in glycoproteins and glycolipids expressed in brain cells are involved in neuronal development, biological processes, and central nervous system maintenance. The composition and expression of glycans are known to change during those physiological processes. Therefore, imaging of glycans and the glycoconjugates in the brain regions has become a “hot” topic nowadays. Imaging techniques using lectins, antibodies, and chemical reporters are traditionally used for glycan detection. However, those techniques offer limited glycome detection. Mass spectrometry imaging (MSI) is an evolving field that combines mass spectrometry with histology allowing spatial and label-free visualization of molecules in the brain. In the last decades, several studies have employed MSI for glycome imaging in brain tissues. The current state of MSI uses on-tissue enzymatic digestion or chemical reaction to facilitate successful glycome imaging. Here, we reviewed the available literature that applied MSI techniques for glycome visualization and characterization in the brain. We also described the general methodologies for glycome MSI and discussed its potential use in the three-dimensional MSI in the brain.

Imaging lipids in biological samples with surface-assisted laser desorption/ionization mass spectrometry: A concise review of the last decade

Knowing the spatial location of the lipid species present in biological samples is of paramount importance for the elucidation of pathological and physiological processes. In this context, mass spectrometry imaging (MSI) has emerged as a powerful technology allowing the visualization of the spatial distributions of biomolecules, including lipids, in complex biological samples. Among the different ionization methods available, the emerging surface-assisted laser desorption/ionization (SALDI) MSI offers unique capabilities for the study of lipids. This review describes the specific advantages of SALDI-MSI for lipid analysis, including the ability to perform analyses in both ionization modes with the same nanosubstrate, the detection of lipids characterized by low ionization efficiency in MALDI-MS, and the possibilities of surface modification to improve the detection of lipids. The complementarity of SALDI and MALDI-MSI is also discussed. Finally, this review presents data processing strategies applied in SALDI-MSI of lipids, as well as examples of applications of SALDI-MSI in biomedical lipidomics.

Determination of inflammation-related lipids in depressive rats by on-line supercritical fluid extraction-supercritical fluid chromatography-tandem mass spectrometry

An on-line supercritical fluid extraction coupled with supercritical fluid chromatography-quadrupole tandem mass spectrometry method was developed to determine lipids related to inflammation in brain tissues of depressed rats. The analysis of 23 lipids from extraction to separation and detection only took 15 min and required 1 mg of brain tissue powder. The matrix effect of the on-line method for endogenous lipids was systematically investigated, and targeted lipids were quantified by matrix effect corrected calibration curves in this study. The on-line method was comprehensively optimized and evaluated. All calibration curves for lipids showed good linearity (correlation coefficient >0.99). The limits of detection and the limits of quantification were in the range of 0.0261-0.396 pg and 0.0791-1.20 pg. The recoveries and the matrix effect were in the range of 85.3-117.5% and 51.9-176.6%, respectively. The relative standard deviations of precision ranged from 2.7 to 14.2%, with accuracies higher than 87.2%. Compared with liquid-liquid extraction coupled with liquid chromatography-tandem mass spectrometry method, the on-line method obtained higher recovery and sensitivity with significantly reduced analytical time, manual operations, and sample amounts. Finally, this on-line method was applied to analyses of brain tissues of depressed rats. Six pro-inflammatory lipids increased in depressed rats, while six anti-inflammatory lipids decreased. Liquiritin and fluoxetine were presumed to promote a similar synthesis of anti-inflammatory lipids. Based on the results, this on-line method showed great promise in analyzing lipids in complex biological samples.

Targeted Lipidomics of Drosophila melanogaster During Development

Lipids play critical roles in developmental processes, and alterations in lipid metabolism are linked to a wide range of human diseases, including neurodegeneration, cancer, metabolic diseases, and microbial infections. Drosophila melanogaster, more commonly known as the fruit fly, is a powerful organism for developmental biology and human disease research. We have previously developed a comprehensive biochemical tool, based on liquid chromatography-mass spectrometry (LC-MS), to probe the dynamics of lipid remodeling during D. melanogaster development. This chapter introduces a step-by-step protocol for extracting and analyzing lipids across all developmental stages (embryo, larvae, pupa, and adult) of D. melanogaster. The targeted semi-quantitative approach offers a comprehensive coverage of more than 400 lipid species spanning the lipid classes, glycerophospholipids, sphingolipids, triacylglycerols, and sterols.

Mass spectrometry in the lipid study of cancer

Introduction: Cancer is a heterogeneous disease that exploits various metabolic pathways to meet the demand for increased energy and structural components. Lipids are biomolecules that play essential roles as high energy sources, mediators, and structural components of biological membranes. Accumulating evidence has established that altered lipid metabolism is a hallmark of cancer.Areas covered: Mass spectrometry (MS) is a label-free analytical tool that can simultaneously identify and quantify hundreds of analytes. To date, comprehensive lipid studies exclusively rely on this technique. Here, we reviewed the use of MS in the study of lipids in various cancers and discuss its instrumental limitations and challenges.Expert opinion: MS and MS imaging have significantly contributed to revealing altered lipid metabolism in a variety of cancers. Currently, a single MS approach cannot profile the entire lipidome because of its lack of sensitivity and specificity for all lipid classes. For the metabolic pathway investigation, lipid study requires the integration of MS with other molecular approaches. Future developments regarding the high spatial resolution, mass resolution, and sensitivity of MS instruments are warranted.

Urinary extracellular vesicles: A position paper by the Urine Task Force of the International Society for Extracellular Vesicles

Urine is commonly used for clinical diagnosis and biomedical research. The discovery of extracellular vesicles (EV) in urine opened a new fast-growing scientific field. In the last decade urinary extracellular vesicles (uEVs) were shown to mirror molecular processes as well as physiological and pathological conditions in kidney, urothelial and prostate tissue. Therefore, several methods to isolate and characterize uEVs have been developed. However, methodological aspects of EV separation and analysis, including normalization of results, need further optimization and standardization to foster scientific advances in uEV research and a subsequent successful translation into clinical practice. This position paper is written by the Urine Task Force of the Rigor and Standardization Subcommittee of ISEV consisting of nephrologists, urologists, cardiologists and biologists with active experience in uEV research. Our aim is to present the state of the art and identify challenges and gaps in current uEV-based analyses for clinical applications. Finally, recommendations for improved rigor, reproducibility and interoperability in uEV research are provided in order to facilitate advances in the field.

Mass Spectrometry-Based Lipidomics: An Overview

Over the last few decades, MS-based lipidomics has emerged as a powerful tool to study lipids in biological systems. This success is driven by the constant demand for complete and reliable data. The improvement of MS-based lipidomics will continue to be dependent on the advances in the technology of mass spectrometry and related techniques including separation and bioinformatics, and more importantly, on gaining insight into the knowledge of lipid chemistry essential to develop methodology for lipid analysis. It is hoped that the protocols in this book, collected from experts in their fields, can offer the beginner and the advanced user alike, useful tips toward successful lipidomic analysis.

The Function and Mechanism of Lipid Molecules and Their Roles in The Diagnosis and Prognosis of Breast Cancer

Lipids are essential components of cell structure and play important roles in signal transduction between cells and body metabolism. With the continuous development and innovation of lipidomics technology, many studies have shown that the relationship between lipids and cancer is steadily increasing, involving cancer occurrence, proliferation, migration, and apoptosis. Breast cancer has seriously affected the safety and quality of life of human beings worldwide and has become a significant public health problem in modern society, with an especially high incidence among women. Therefore, the issue has inspired scientific researchers to study the link between lipids and breast cancer. This article reviews the research progress of lipidomics, the biological characteristics of lipid molecules, and the relationship between some lipids and cancer drug resistance. Furthermore, this work summarizes the lipid molecules related to breast cancer diagnosis and prognosis, and then it clarifies their impact on the occurrence and development of breast cancer The discussion revolves around the current research hotspot long-chain non-coding RNAs (lncRNAs), summarizes and explains their impact on tumor lipid metabolism, and provides more scientific basis for future cancer research studies.