Optimizing the lipidomics workflow for clinical studies—practical considerations

Lipidomics by Nuclear Magnetic Resonance Spectroscopy and Liquid Chromatography-High-Resolution Mass Spectrometry in Osteosarcoma: A Pilot Study

Cancer is a complex disease that can also affect the younger population; however, it is responsible for a relatively high mortality rate of children and youth, especially in low- and middle-income countries (LMICs). Besides that, lipidomic studies in this age range are scarce. Therefore, we analyzed blood serum samples from young patients (12 to 35 years) with bone sarcoma (osteosarcoma) and compared their lipidomics to the ones from the control group of samples, named healthy control (HC group), using NMR and LC-MS techniques. Furthermore, differences in the lipidomic profiles between OS patients with and without metastasis indicate higher glycerophosphocholine (GPC) and glycerophospholipid (GPL) levels in osteosarcoma and increased cholesterol, choline, polyunsaturated fatty acids (PUFAs), and glycerols during the metastasis. These differences, detected in the peripheral blood, could be used as biomarkers for liquid biopsy.

Sphingolipid changes in mouse brain and plasma after mild traumatic brain injury at the acute phases

BACKGROUND

Traumatic brain injury (TBI) causes neuroinflammation and can lead to long-term neurological dysfunction, even in cases of mild TBI (mTBI). Despite the substantial burden of this disease, the management of TBI is precluded by an incomplete understanding of its cellular mechanisms. Sphingolipids (SPL) and their metabolites have emerged as key orchestrators of biological processes related to tissue injury, neuroinflammation, and inflammation resolution. No study so far has investigated comprehensive sphingolipid profile changes immediately following TBI in animal models or human cases. In this study, sphingolipid metabolite composition was examined during the acute phases in brain tissue and plasma of mice following mTBI.

METHODS

Wildtype mice were exposed to air-blast-mediated mTBI, with blast exposure set at 50-psi on the left cranium and 0-psi designated as Sham. Sphingolipid profile was analyzed in brain tissue and plasma during the acute phases of 1, 3, and 7 days post-TBI via liquid-chromatography-mass spectrometry. Simultaneously, gene expression of sphingolipid metabolic markers within brain tissue was analyzed using quantitative reverse transcription-polymerase chain reaction. Significance (P-values) was determined by non-parametric t-test (Mann-Whitney test) and by Tukey's correction for multiple comparisons.

RESULTS

In post-TBI brain tissue, there was a significant elevation of 1) acid sphingomyelinase (aSMase) at 1- and 3-days, 2) neutral sphingomyelinase (nSMase) at 7-days, 3) ceramide-1-phosphate levels at 1 day, and 4) monohexosylceramide (MHC) and sphingosine at 7-days. Among individual species, the study found an increase in C18:0 and a decrease in C24:1 ceramides (Cer) at 1 day; an increase in C20:0 MHC at 3 days; decrease in MHC C18:0 and increase in MHC C24:1, sphingomyelins (SM) C18:0, and C24:0 at 7 days. Moreover, many sphingolipid metabolic genes were elevated at 1 day, followed by a reduction at 3 days and an absence at 7-days post-TBI. In post-TBI plasma, there was 1) a significant reduction in Cer and MHC C22:0, and an increase in MHC C16:0 at 1 day; 2) a very significant increase in long-chain Cer C24:1 accompanied by significant decreases in Cer C24:0 and C22:0 in MHC and SM at 3 days; and 3) a significant increase of C22:0 in all classes of SPL (Cer, MHC and SM) as well as a decrease in Cer C24:1, MHC C24:1 and MHC C24:0 at 7 days.

CONCLUSIONS

Alterations in sphingolipid metabolite composition, particularly sphingomyelinases and short-chain ceramides, may contribute to the induction and regulation of neuroinflammatory events in the early stages of TBI, suggesting potential targets for novel diagnostic, prognostic, and therapeutic strategies in the future.

iLipidome: enhancing statistical power and interpretability using hidden biosynthetic interdependencies in the lipidome

Numerous biological processes and diseases are influenced by lipid composition. Advances in lipidomics are elucidating their roles, but analyzing and interpreting lipidomics data at the systems level remain challenging. To address this, we present iLipidome, a method for analyzing lipidomics data in the context of the lipid biosynthetic network, thus accounting for the interdependence of measured lipids. iLipidome enhances statistical power, enables reliable clustering and lipid enrichment analysis, and links lipidomic changes to their genetic origins. We applied iLipidome to investigate mechanisms driving changes in cellular lipidomes following supplementation of docosahexaenoic acid (DHA) and successfully identified the genetic causes of alterations. We further demonstrated how iLipidome can disclose enzyme-substrate specificity and pinpoint prospective glioblastoma therapeutic targets. Finally, iLipidome enabled us to explore underlying mechanisms of cardiovascular disease and could guide the discovery of early lipid biomarkers. Thus, iLipidome can assist researchers studying the essence of lipidomic data and advance the field of lipid biology.

Lipidomic studies revealing serological markers associated with the occurrence of retinopathy in type 2 diabetes

PURPOSE

The duration of type 2 diabetes mellitus (T2DM) and blood glucose levels have a significant impact on the development of T2DM complications. However, currently known risk factors are not good predictors of the onset or progression of diabetic retinopathy (DR). Therefore, we aimed to investigate the differences in the serum lipid composition in patients with T2DM, without and with DR, and search for potential serological indicators associated with the development of DR.

METHODS

A total of 622 patients with T2DM hospitalized in the Department of Endocrinology of the First Affiliated Hospital of Xi'an JiaoTong University were selected as the discovery set. One-to-one case-control matching was performed according to the traditional risk factors for DR (i.e., age, duration of diabetes, HbA1c level, and hypertension). All cases with comorbid chronic kidney disease were excluded to eliminate confounding factors. A total of 42 pairs were successfully matched. T2DM patients with DR (DR group) were the case group, and T2DM patients without DR (NDR group) served as control subjects. Ultra-performance liquid chromatography-mass spectrometry (LC-MS/MS) was used for untargeted lipidomics analysis on serum, and a partial least squares discriminant analysis (PLS-DA) model was established to screen differential lipid molecules based on variable importance in the projection (VIP) > 1. An additional 531 T2DM patients were selected as the validation set. Next, 1:1 propensity score matching (PSM) was performed for the traditional risk factors for DR, and a combined 95 pairings in the NDR and DR groups were successfully matched. The screened differential lipid molecules were validated by multiple reaction monitoring (MRM) quantification based on mass spectrometry.

RESULTS

The discovery set showed no differences in traditional risk factors associated with the development of DR (i.e., age, disease duration, HbA1c, blood pressure, and glomerular filtration rate). In the DR group compared with the NDR group, the levels of three ceramides (Cer) and seven sphingomyelins (SM) were significantly lower, and one phosphatidylcholine (PC), two lysophosphatidylcholines (LPC), and two SMs were significantly higher. Furthermore, evaluation of these 15 differential lipid molecules in the validation sample set showed that three Cer and SM(d18:1/24:1) molecules were substantially lower in the DR group. After excluding other confounding factors (e.g., sex, BMI, lipid-lowering drug therapy, and lipid levels), multifactorial logistic regression analysis revealed that a lower abundance of two ceramides, i.e., Cer(d18:0/22:0) and Cer(d18:0/24:0), was an independent risk factor for the occurrence of DR in T2DM patients.

CONCLUSION

Disturbances in lipid metabolism are closely associated with the occurrence of DR in patients with T2DM, especially in ceramides. Our study revealed for the first time that Cer(d18:0/22:0) and Cer(d18:0/24:0) might be potential serological markers for the diagnosis of DR occurrence in T2DM patients, providing new ideas for the early diagnosis of DR.

Plasma Sphingolipid Profile of Healthy Black and White Adults Differs Based on Their Parental History of Type 2 Diabetes

CONTEXT

Ceramides and sphingolipids have been linked to type 2 diabetes (T2D). The Ceramides and Sphingolipids as Predictors of Incident Dysglycemia (CASPID) study is designed to determine the association of plasma sphingolipids with the pathophysiology of human T2D.

OBJECTIVE

A comparison of plasma sphingolipids profiles in Black and White adults with (FH+) and without (FH-) family history of T2D.

DESIGN

We recruited 100 Black and White FH- (54 Black, 46 White) and 140 FH+ (75 Black, 65 White) adults. Fasting plasma levels of 58 sphingolipid species, including 18 each from 3 major classes (ceramides, monohexosylceramides, and sphingomyelins, all with 18:1 sphingoid base) and 4 long-chain sphingoid base-containing species, were measured by liquid chromatography/mass spectrometry.

RESULTS

Sphingomyelin was the most abundant sphingolipid in plasma (89% in FH-), and was significantly elevated in FH+ subjects (93%). Ceramides and monohexosylceramides comprised 5% and 6% of total sphingolipids in the plasma of FH- subjects, and were reduced significantly in FH+ subjects (3% and 4%, respectively). In FH+ subjects, most ceramide and monohexosylceramide species were decreased but sphingomyelin species were increased. The level of C18:1 species of all 3 classes was elevated in FH+ subjects.

CONCLUSION

Elevated levels of sphingomyelin, the major sphingolipids of plasma, and oleic acid-containing sphingolipids in healthy FH+ subjects compared with healthy FH- subjects may reflect heritable elements linking sphingolipids and the development of T2D.

Investigating the circulating sphingolipidome response to a single high-intensity interval training session within healthy females and males in their twenties (SphingoHIIT): Protocol for a randomised controlled trial

Introduction: Growing scientific evidence indicates that sphingolipids predict cardiometabolic risk, independently of and beyond traditional biomarkers such as low-density lipoprotein cholesterol. To date, it remains largely unknown if and how exercise, a simple, low-cost, and patient-empowering modality to optimise cardiometabolic health, influences sphingolipid levels. The SphingoHIIT study aims to assess the response of circulating sphingolipid species to a single session of high-intensity interval training (HIIT). Methods: This single-centre randomised controlled trial (RCT) will last 11 days per participant and aim to include 32 young and healthy individuals aged 20-29 (50% females). Participants will be randomly allocated to the HIIT (n= 16) or control groups (physical rest, n= 16). Participants will self-sample fasted dried blood spots for three consecutive days before the intervention (HIIT versus rest) to determine baseline sphingolipid levels. Dried blood spots will also be collected at five time points (2, 15, 30, 60min, and 24h) following the intervention (HIIT versus rest). To minimise the dietary influence, participants will receive a standardised diet for four days, starting 24 hours before the first dried blood sampling. For females, interventions will be timed to fall within the early follicular phase to minimise the menstrual cycle's influence on sphingolipid levels. Finally, physical activity will be monitored for the whole study duration using a wrist accelerometer. Ethics and dissemination: The Ethics Committee of Northwest and Central Switzerland approved this protocol (ID 2022-00513). Findings will be disseminated in scientific journals and meetings. Trial Registration The trial was registered on www.clinicaltrials.gov (NCT05390866, https://clinicaltrials.gov/ct2/show/NCT05390866) on May 25, 2022.

Biomarker Reproducibility Challenge: A Review of Non-Nucleotide Biomarker Discovery Protocols from Body Fluids in Breast Cancer Diagnosis

Breast cancer has now become the most commonly diagnosed cancer, accounting for one in eight cancer diagnoses worldwide. Non-invasive diagnostic biomarkers and associated tests are superlative candidates to complement or improve current approaches for screening, early diagnosis, or prognosis of breast cancer. Biomarkers detected from body fluids such as blood (serum/plasma), urine, saliva, nipple aspiration fluid, and tears can detect breast cancer at its early stages in a minimally invasive way. The advancements in high-throughput molecular profiling (omics) technologies have opened an unprecedented opportunity for unbiased biomarker detection. However, the irreproducibility of biomarkers and discrepancies of reported markers have remained a major roadblock to clinical implementation, demanding the investigation of contributing factors and the development of standardised biomarker discovery pipelines. A typical biomarker discovery workflow includes pre-analytical, analytical, and post-analytical phases, from sample collection to model development. Variations introduced during these steps impact the data quality and the reproducibility of the findings. Here, we present a comprehensive review of methodological variations in biomarker discovery studies in breast cancer, with a focus on non-nucleotide biomarkers (i.e., proteins, lipids, and metabolites), highlighting the pre-analytical to post-analytical variables, which may affect the accurate identification of biomarkers from body fluids.

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.

Phospholipids, the Masters in the Shadows during Healing after Acute Myocardial Infarction

Phospholipids are major components of cell membranes with complex structures, high heterogeneity and critical biological functions and have been used since ancient times to treat cardiovascular disease. Their importance and role were shadowed by the difficulty or incomplete available research methodology to study their biological presence and functionality. This review focuses on the current knowledge about the roles of phospholipids in the pathophysiology and therapy of cardiovascular diseases, which have been increasingly recognized. Used in singular formulation or in inclusive combinations with current drugs, phospholipids proved their positive and valuable effects not only in the protection of myocardial tissue, inflammation and fibrosis but also in angiogenesis, coagulation or cardiac regeneration more frequently in animal models as well as in human pathology. Thus, while mainly neglected by the scientific community, phospholipids present negligible side effects and could represent an ideal target for future therapeutic strategies in healing myocardial infarction. Acknowledging and understanding their mechanisms of action could offer a new perspective into novel therapeutic strategies for patients suffering an acute myocardial infarction, reducing the burden and improving the general social and economic outcome.

Circulating metabolomic and lipidomic changes in subjects with new-onset type 1 diabetes after optimization of glycemic control

AIMS

To uncover novel candidate metabolomic and lipidomic biomarkers in newly-diagnosed type 1 diabetes (T1DM) after achieving optimal glucose control.

METHODS

Comprehensive lipidomic and metabolomic analysis was performed in serum of 12 adults with T1DM at onset and after achieving optimal glycemic control (HbA1c < 7 %) (after 2-6 months).

RESULTS

After intensive therapy, subjects (mean age 25.2 years, 58.3 % men) showed decreases in blood glucose (p < 0.001), HbA1c [11.5 % (9.2-13.4) to 6.2 % (5.2 - 6.7); p < 0.001] and changes in 51 identified lipids. Among these changes, we found that triglycerides (TG) containing medium chain fatty acids (TG45:0, TG47:1), sphingomyelins (SM) (SM(d18:2/20:0), SM42:4)), and phosphatidylcholines (PC) (PC(O-26:2), PC(O-30:0), PC(O-32:0), PC(O-42:6), PC(O-44:5), PC(O-38:3), PC(O-33:0), PC(O-46:8), PC(O-44:6), PC(O-40:3), PC(O-42:4), PC(O-46:7), PC(O-46:6), PC(O-44:5), PC(O-42:3), PC(O-44:4)) decreased; whereas PC(35:1), PC(37:1) and TG containing longer chain fatty acids (TG(52:1), TG(55:7), TG(51:2), TG(53:3), TG52:2), TG(53:2), TG(57:3), TG(61:3), TG(61:2) increased. Further, dihydro O-acylceramide (18:1/18:0/16:0), diacylglycerophosphoethanolamine (PE(34:1)), diacylglycerophosphoinositol (PI(38:6), and dihydrosphingomyelins (dihydroSM(36:0), dihydroSM(40:0), dihydroSM(41:0), dihydroSM(42:0)) increased. Uric acid, mannitol, and mannitol-1-acetate levels also increased.

CONCLUSIONS

Our data uncovered potential favorable changes in the metabolism of glycerophospholipids, glycerolipids, and sphingolipids in new-onset T1DM after achieving optimal glycemic control. Further research on their potential role in developing diabetes-related complications is needed.

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.

A software-assisted untargeted liquid chromatography-mass spectrometry method for lipidomic profiling of human plasma samples

INTRODUCTION

In this paper, an analytical pipeline designed for untargeted lipidomic profiling in human plasma is proposed. The analytical pipeline was developed for case-control studies nested in prospective cohorts.

METHODS

The procedure consisted of isopropanol protein precipitation followed by reverse phase liquid chromatography coupled to high resolution mass spectrometry and software-assisted data processing. The compounds are putatively annotated by matching experimental mass spectrometry data with spectral library data using LipidSearch software. The lipid profile of a pool of plasma samples from 10 healthy volunteers was detected in both positive and negative polarity modes. The impact of the chosen polarity on the number and quality of the lipid identification has been evaluated.

RESULTS

More than 1000 lipids from 12 different classes were detected, 1150 in positive mode and 273 in negative mode. Nearly half of them were unambiguously identified by the software in positive mode, and about one-third in negative mode. The method repeatability was assessed on the plasma pool samples by means of variance components analysis. The intra- and inter-assay precision was measured for 10 lipids chosen among the most abundant found within the different lipid classes. The intra-assay coefficients of variation ranged from 2.56% to 4.56% while intra- and inter-day coefficients of variance never exceeded the 15% benchmark adopted. The lipidomic profiles of the 10 healthy volunteers were also investigated.

DISCUSSION

This method detects a wide range of lipids and reports their degree of identification. It is particularly fit and well-designed for large case-control epidemiologic studies.

Ion Mobility Mass Spectrometry Development and Applications

Although as an analytical method with high specificity and high sensitivity, mass spectrometry (MS) has a wide range of applications in many fields, it still needs other technologies as the assist and supplement to enhance the scope and capability of analysis. Coupling with ion mobility (IM) can make an enhancement effect in the field of pharmaceutical analysis as a supplementary method. The two-dimensional mass technology improves the confidence of compounds annotations while increasing peak capacity, with the gradual deepening of theoretical research on IM-MS, it has shown unique advantages in the complex analysis conditions. IM-MS owns great potential for improving the depth, range, dimension of in-depth drug research. In this review, the principle, instruments and methods, applications, advantages and limitations of IM-MS are described. Here, we also elaborate on the prospects in structural evaluation, separation, and identification of complex compounds for the drug discovery and development phase and the great advantages of macromolecules and omics.

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.

Sphingolipid Profiling: A Promising Tool for Stratifying the Metabolic Syndrome-Associated Risk

Metabolic syndrome (MetS) is a multicomponent risk condition that reflects the clustering of individual cardiometabolic risk factors related to abdominal obesity and insulin resistance. MetS increases the risk for cardiovascular diseases (CVD) and type 2 diabetes mellitus (T2DM). However, there still is not total clinical consensus about the definition of MetS, and its pathophysiology seems to be heterogeneous. Moreover, it remains unclear whether MetS is a single syndrome or a set of diverse clinical conditions conferring different metabolic and cardiovascular risks. Indeed, traditional biomarkers alone do not explain well such heterogeneity or the risk of associated diseases. There is thus a need to identify additional biomarkers that may contribute to a better understanding of MetS, along with more accurate prognosis of its various chronic disease risks. To fulfill this need, omics technologies may offer new insights into associations between sphingolipids and cardiometabolic diseases. Particularly, ceramides –the most widely studied sphingolipid class– have been shown to play a causative role in both T2DM and CVD. However, the involvement of simple glycosphingolipids remains controversial. This review focuses on the current understanding of MetS heterogeneity and discuss recent findings to address how sphingolipid profiling can be applied to better characterize MetS-associated risks.

Lipid Analysis of Follicular Fluids by UHPLC-ESI-HRMS Discovers Potential Biomarkers for Ovarian Hyperstimulation Syndrome

Ovarian hyperstimulation syndrome (OHSS) is a serious iatrogenic complication during ovarian stimulation. Even though the incidence of OHSS was relatively low in clinical practice, the consequence can be potentially devastating and life-threatening. Abnormal lipid metabolism may relate to the pathological development of OHSS, but there is still a research gap in the lipidomic research. So here in our study, an ultra-high-performance liquid chromatography coupled with electrospray ionization high-resolution mass spectrometry (UHPLC-ESI-HRMS) based lipidomic analysis was performed using follicular fluid samples obtained from 17 patients undergoing OHSS. The lipid profiles of OHSS patients were characterized by increased cholesterol ester (ChE) and decreased lysophosphatidylcholine (LPC), phosphatidylinositol (PI), sphingomyelin (SM), dimethylphosphatidylethanolamine (dMePE) and lysodimethylphosphatidylethanolamine (LdMePE). Totally 10 lipids including LPC(18:0), SM(d18:1/16:0), PC(18:0/18:1), PC(20:2/20:5), PC(16:0/18:1), TG(16:0/18:1/18:1), TG(16:0/18:2/18:2), TG(16:0/16:1/18:1), ChE(20:4) and TG(8:0/8:0/10:0) were selected as differential lipids. In conclusion, this study demonstrated the alteration of various lipids in OHSS patients, which suggested the key role of lipids during the development of OHSS and shed light on the further pathophysiological research of OHSS.

Clinical lipidomics: realizing the potential of lipid profiling

Dysregulation of lipid metabolism plays a major role in the etiology and sequelae of inflammatory disorders, cardiometabolic and neurological diseases, and several forms of cancer. Recent advances in lipidomic methodology allow comprehensive lipidomic profiling of clinically relevant biological samples, enabling researchers to associate lipid species and metabolic pathways with disease onset and progression. The resulting data serve not only to advance our fundamental knowledge of the underlying disease process but also to develop risk assessment models to assist in the diagnosis and management of disease. Currently, clinical applications of in-depth lipidomic profiling are largely limited to the use of research-based protocols in the analysis of population or clinical sample sets. However, we foresee the development of purpose-built clinical platforms designed for continuous operation and clinical integration-assisting health care providers with disease risk assessment, diagnosis, and monitoring. Herein, we review the current state of clinical lipidomics, including the use of research-based techniques and platforms in the analysis of clinical samples as well as assays already available to clinicians. With a primary focus on MS-based strategies, we examine instrumentation, analysis techniques, statistical models, prospective design of clinical platforms, and the possible pathways toward implementation of clinical lipidomics.

Untargeted UHPLC-ESI-QTOF-MS/MS analysis with targeted feature extraction at precursor and fragment level for profiling of the platelet lipidome with ex vivo thrombin-activation

Lipids play a major role in platelet signaling and activation. In this study, we analyzed the platelet lipidome in an untargeted manner by reversed-phase UHPLC for lipid species separation coupled to high-resolution QTOF-MS/MS in data-independent acquisition (DIA) mode with sequential window acquisition of all theoretical fragment ion mass spectra (SWATH) for compound detection. Lipid identification and peak picking was supported by the characteristic regular elution pattern of lipids differing in carbon and double bond numbers. It was primarily based on post-acquisition targeted feature extraction from the SWATH data. Multiple extracted ion chromatograms (EICs) from SWATH data of diagnostic ions on MS1 and MS2 level from both positive and negative ion mode allowed to distinguish between poorly resolved isomeric lipids based on their distinct fragment ions, which were used for relative quantification at a molecular lipid species level. It supports assay specificity for relative lipid quantitation via multiple quantifiably ions unlike to data-dependent acquisition methods which rely on precursor ions only. This approach was used to analyze human platelet samples. 457 lipids were annotated. Concentrations of lipids were estimated by stable isotope-labelled lipid class-specific internal standards as surrogate calibrants. Heatmaps of lipid concentrations in dependence on carbon and double bond numbers for the distinct lipid classes revealed a snapshot of the platelet lipidome in the resting state with lipid species distributions within classes supporting some functional interpretations. As expected, activation of the platelets by thrombin has led to significant alterations in the platelet lipidome as proven by univariate (volcano plot) and multivariate (PLS-DA) statistics. Several lipids were significantly up-regulated (lysophosphatidylinositols, oxylipins such as thromboxane B2 (TXB2), hydroxyheptadecatrienoic acid (HHT), hydroxyeicosatetraenoic acid (HETE), hydroxyoctadecadienoic acid (HODE), sphingoid-bases, (very) long chain saturated fatty acids) or down-regulated (lysophosphatidylethanolamines, polyunsaturated fatty acids, phosphatidylinositols). Several of them are well known as biomarkers of platelet activation while others may provide some further insights into pathways of platelet activation and platelet metabolism.

Metabolic View on Human Healthspan: A Lipidome-Wide Association Study

As ageing is a major risk factor for the development of non-communicable diseases, extending healthspan has become a medical and societal necessity. Precise lipid phenotyping that captures metabolic individuality could support healthspan extension strategies. This study applied ‘omic-scale lipid profiling to characterise sex-specific age-related differences in the serum lipidome composition of healthy humans. A subset of the COmPLETE-Health study, composed of 73 young (25.2 ± 2.6 years, 43% female) and 77 aged (73.5 ± 2.3 years, 48% female) clinically healthy individuals, was investigated, using an untargeted liquid chromatography high-resolution mass spectrometry approach. Compared to their younger counterparts, aged females and males exhibited significant higher levels in 138 and 107 lipid species representing 15 and 13 distinct subclasses, respectively. Percentage of difference ranged from 5.8% to 61.7% (females) and from 5.3% to 46.0% (males), with sphingolipid and glycerophophospholipid species displaying the greatest amplitudes. Remarkably, specific sphingolipid and glycerophospholipid species, previously described as cardiometabolically favourable, were found elevated in aged individuals. Furthermore, specific ether-glycerophospholipid and lyso-glycerophosphocholine species displayed higher levels in aged females only, revealing a more favourable lipidome evolution in females. Altogether, age determined the circulating lipidome composition, while lipid species analysis revealed additional findings that were not observed at the subclass level.

Coupling Machine Learning and Lipidomics as a Tool to Investigate Metabolic Dysfunction-Associated Fatty Liver Disease. A General Overview

Hepatic biopsy is the gold standard for staging nonalcoholic fatty liver disease (NAFLD). Unfortunately, accessing the liver is invasive, requires a multidisciplinary team and is too expensive to be conducted on large segments of the population. NAFLD starts quietly and can progress until liver damage is irreversible. Given this complex situation, the search for noninvasive alternatives is clinically important. A hallmark of NAFLD progression is the dysregulation in lipid metabolism. In this context, recent advances in the area of machine learning have increased the interest in evaluating whether multi-omics data analysis performed on peripheral blood can enhance human interpretation. In the present review, we show how the use of machine learning can identify sets of lipids as predictive biomarkers of NAFLD progression. This approach could potentially help clinicians to improve the diagnosis accuracy and predict the future risk of the disease. While NAFLD has no effective treatment yet, the key to slowing the progression of the disease may lie in predictive robust biomarkers. Hence, to detect this disease as soon as possible, the use of computational science can help us to make a more accurate and reliable diagnosis. We aimed to provide a general overview for all readers interested in implementing these methods.

Analytical challenges in human exposome analysis with focus on environmental analysis combined with metabolomics

Environmental factors, such as chemical exposures, are likely to play a crucial role in the development of several human chronic diseases. However, how the specific exposures contribute to the onset and progress of various diseases is still poorly understood. In part, this is because comprehensive characterization of the chemical exposome is a highly challenging task, both due to its complex dynamic nature as well as due to the analytical challenges. Herein, the analytical challenges in the field of exposome research are reviewed, with specific emphasis on the sampling, sample preparation, and analysis, as well as challenges in the compound identification. The primary focus is on the human chemical exposome, that is, exposures to mixtures of environmental chemicals and its impact on human metabolome. In order to highlight the recent progress in the exposome research in relation to human health and disease, selected examples of human exposome studies are presented.

Metabolic Signatures of the Exposome-Quantifying the Impact of Exposure to Environmental Chemicals on Human Health

Human health and well-being are intricately linked to environmental quality. Environmental exposures can have lifelong consequences. In particular, exposures during the vulnerable fetal or early development period can affect structure, physiology and metabolism, causing potential adverse, often permanent, health effects at any point in life. External exposures, such as the “chemical exposome” (exposures to environmental chemicals), affect the host’s metabolism and immune system, which, in turn, mediate the risk of various diseases. Linking such exposures to adverse outcomes, via intermediate phenotypes such as the metabolome, is one of the central themes of exposome research. Much progress has been made in this line of research, including addressing some key challenges such as analytical coverage of the exposome and metabolome, as well as the integration of heterogeneous, multi-omics data. There is strong evidence that chemical exposures have a marked impact on the metabolome, associating with specific disease risks. Herein, we review recent progress in the field of exposome research as related to human health as well as selected metabolic and autoimmune diseases, with specific emphasis on the impacts of chemical exposures on the host metabolome.

Ultrahigh-Resolution Mass Spectrometry-Based Platform for Plasma Metabolomics Applied to Type 2 Diabetes Research

Metabolomics-the endpoint of the omics cascade-is increasingly recognized as a preferred method for understanding the ultimate responses of biological systems to stress. Flow injection electrospray (FIE) mass spectrometry (MS) has advantages for untargeted metabolic fingerprinting due to its simplicity and capability for high-throughput screening but requires a high-resolution mass spectrometer to resolve metabolite features. In this study, we developed and validated a high-throughput and highly reproducible metabolomics platform integrating FIE with ultrahigh-resolution Fourier transform ion cyclotron resonance (FTICR) MS for analysis of both polar and nonpolar metabolite features from plasma samples. FIE-FTICR MS enables high-throughput detection of hundreds of metabolite features in a single mass spectrum without a front-end separation step. Using plasma samples from genetically identical obese mice with or without type 2 diabetes (T2D), we validated the intra and intersample reproducibility of our method and its robustness for simultaneously detecting alterations in both polar and nonpolar metabolite features. Only 5 min is needed to acquire an ultra-high resolution mass spectrum in either a positive or negative ionization mode. Approximately 1000 metabolic features were reproducibly detected and annotated in each mouse plasma group. For significantly altered and highly abundant metabolite features, targeted tandem MS (MS/MS) analyses can be applied to confirm their identity. With this integrated platform, we successfully detected over 300 statistically significant metabolic features in T2D mouse plasma as compared to controls and identified new T2D biomarker candidates. This FIE-FTICR MS-based method is of high throughput and highly reproducible with great promise for metabolomics studies toward a better understanding and diagnosis of human diseases.

Deep learning meets metabolomics: a methodological perspective

Deep learning (DL), an emerging area of investigation in the fields of machine learning and artificial intelligence, has markedly advanced over the past years. DL techniques are being applied to assist medical professionals and researchers in improving clinical diagnosis, disease prediction and drug discovery. It is expected that DL will help to provide actionable knowledge from a variety of 'big data', including metabolomics data. In this review, we discuss the applicability of DL to metabolomics, while presenting and discussing several examples from recent research. We emphasize the use of DL in tackling bottlenecks in metabolomics data acquisition, processing, metabolite identification, as well as in metabolic phenotyping and biomarker discovery. Finally, we discuss how DL is used in genome-scale metabolic modelling and in interpretation of metabolomics data. The DL-based approaches discussed here may assist computational biologists with the integration, prediction and drawing of statistical inference about biological outcomes, based on metabolomics data.

Advances in lipidomics

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

Plasma lipidomic biomarker analysis reveals distinct lipid changes in vascular dementia

Vascular dementia (VaD) is a complex neurocognitive disorder secondary to a variety of cerebrovascular lesions. Numerous studies have shown that lipid metabolism is involved in the pathobiology of the disease. We examined the plasma lipid profiles in VaD, with the expectation of identifying reliable lipid biomarkers for VaD. 49 VaD patients and 48 healthy controls were recruited from Bankstown-Lidcombe Hospital in Sydney, Australia. Lipids were extracted by single phase 1-butanol/methanol, and untargeted analysis was performed by liquid chromatography coupled-mass spectrometry (LC–MS/MS). Univariate analysis of variance was used to examine the differences in lipid classes and individual lipids between VaD and control groups. In an independent sample of 161 subjects from the Older Australian Twins Study (OATS), elastic net penalization for the generalized linear model (Glmnet) and Random Forest were applied to the lipid levels to subcategorise the sample into vascular cognitive impairment and controls. Most lipids belonging to the classes of ceramides (Cer), cholesterol esters (ChE) and phospholipids were significantly lower in VaD plasma, while glycerides were elevated compared to controls. Levels of ChE, Cer and the two lipid classes together achieved the best accuracy in discriminating VaD from controls, with more than 80% accuracy. The probable VaD group in the OATS sample predicted by the lipid levels showed greater impairment in most cognitive domains, especially attention and processing speed and executive function from controls but did not differ in white matter hyperintensities and DTI measures. As a conclusion, plasma lipids levels, in particular Cer and ChE, are abnormal in VaD and may help discriminate them from healthy controls. Understanding the basis of these differences may provide insights into the pathobiology of VaD.

Extracting Biological Insight from Untargeted Lipidomics Data

Lipidomics data generated using untargeted mass spectrometry techniques can offer great biological insight to metabolic status and disease diagnoses. As the community's ability to conduct large-scale studies with deep coverage of the lipidome expands, approaches to analyzing untargeted data and extracting biological insight are needed. Currently, the function of most individual lipids are not known; however, meaningful biological information can be extracted. Here, I will describe a step-by-step approach to identify patterns and trends in untargeted mass spectrometry lipidomics data to assist users in extracting information leading to a greater understanding of biological systems.

Recent development on liquid chromatography-mass spectrometry analysis of oxidized lipids

Polyunsaturated fatty acids (PUFAs) in the cellular membrane can be oxidized by various enzymes or reactive oxygen species (ROS) to form many oxidized lipids. These metabolites are highly bioactive, participating in a variety of physiological and pathophysiological processes. Mass spectrometry (MS), coupled with Liquid Chromatography, has been increasingly recognized as an indispensable tool for the analysis of oxidized lipids due to its excellent sensitivity and selectivity. We will give an update on the understanding of the molecular mechanisms related to generation of various oxidized lipids and recent progress on the development of LC-MS in the detection of these bioactive lipids derived from fatty acids, cholesterol esters, and phospholipids. The purpose of this review is to provide an overview of the formation mechanisms and technological advances in LC-MS for the study of oxidized lipids in human diseases, and to shed new light on the potential of using oxidized lipids as biomarkers and mechanistic clues of pathogenesis related to lipid metabolism. The key technical problems associated with analysis of oxidized lipids and challenges in the field will also discussed.

Lipidomic analysis reveals sphingomyelin and phosphatidylcholine species associated with renal impairment and all-cause mortality in type 1 diabetes

There is an urgent need for a better molecular understanding of the pathophysiology underlying development and progression of diabetic nephropathy. The aim of the current study was to identify novel associations between serum lipidomics and diabetic nephropathy. Non-targeted serum lipidomic analyses were performed with mass spectrometry in 669 individuals with type 1 diabetes. Cross-sectional associations of lipid species with estimated glomerular filtration rate (eGFR) and urinary albumin excretion were assessed. Moreover, associations with register-based longitudinal follow-up for progression to a combined renal endpoint including ≥30% decline in eGFR, ESRD and all-cause mortality were evaluated. Median follow-up time was 5.0–6.4 years. Adjustments included traditional risk factors and multiple testing correction. In total, 106 lipid species were identified. Primarily, alkyl-acyl phosphatidylcholines, triglycerides and sphingomyelins demonstrated cross-sectional associations with eGFR and macroalbuminuria. In longitudinal analyses, thirteen lipid species were associated with the slope of eGFR or albuminuria. Of these lipids, phosphatidylcholine and sphingomyelin species, PC(O-34:2), PC(O-34:3), SM(d18:1/24:0), SM(d40:1) and SM(d41:1), were associated with lower risk of the combined renal endpoint. PC(O-34:3), SM(d40:1) and SM(d41:1) were associated with lower risk of all-cause mortality while an SM(d18:1/24:0) was associated with lower risk of albuminuria group progression. We report distinct associations between lipid species and risk of renal outcomes in type 1 diabetes, independent of traditional markers of kidney function.

Plasma lipidome variation during the second half of the human lifespan is associated with age and sex but minimally with BMI

Recent advances in mass spectrometry-based techniques have inspired research into lipidomics, a subfield of ‘–omics’, which aims to identify and quantify large numbers of lipids in biological extracts. Although lipidomics is becoming increasingly popular as a screening tool for understanding disease mechanisms, it is largely unknown how the lipidome naturally varies by age and sex in healthy individuals. We aimed to identify cross-sectional associations of the human lipidome with ‘physiological’ ageing, using plasma from 100 subjects with an apolipoprotein E (APOE) E3/E3 genotype, and aged between 56 to 100 years. Untargeted analysis was performed by liquid chromatography coupled-mass spectrometry (LC-MS/MS) and data processing using LipidSearch software. Regression analyses confirmed a strong negative association of age with the levels of various lipid, which was stronger in males than females. Sex-related differences include higher LDL-C, HDL-C, total cholesterol, particular sphingomyelins (SM), and docosahexaenoic acid (DHA)-containing phospholipid levels in females. Surprisingly, we found a minimal relationship between lipid levels and body mass index (BMI). In conclusion, our results suggest substantial age and sex-related variation in the plasma lipidome of healthy individuals during the second half of the human lifespan. In particular, globally low levels of blood lipids in the ‘oldest old’ subjects over 95 years could signify a unique lipidome associated with extreme longevity.

Plasma lipid species at type 1 diabetes onset predict residual beta-cell function after 6 months

INTRODUCTION

The identification of metabolomic dysregulation appears promising for the prediction of type 1 diabetes and may also reveal metabolic pathways leading to beta-cell destruction. Recent studies indicate that regulation of multiple phospholipids precede the presence of autoantigens in the development of type 1 diabetes.

OBJECTIVES

We hypothesize that lipid biomarkers in plasma from children with recent onset type 1 diabetes will reflect their remaining beta-cell function and predict future changes in beta-cell function.

METHODS

We performed targeted lipidomic profiling by electrospray ionization tandem mass spectrometry to acquire comparative measures of 354 lipid species covering 25 lipid classes and subclasses in plasma samples from 123 patients < 17 years of age followed prospectively at 1, 3, 6 and 12 months after diagnosis. Lipidomic profiles were analysed using liner regression to investigate the relationship between plasma lipids and meal stimulated C-peptide levels at each time point. P-values were corrected for multiple comparisons by the method of Benjamini and Hochberg.

RESULTS

Linear regression analysis showed that the relative levels of cholesteryl ester, diacylglycerol and triacylglycerol at 1 month were associated to the change in c-peptide levels from 1 to 6 months (corrected p-values of 4.06E-03, 1.72E-02 and 1.72E02, respectively). Medium chain saturated and monounsaturated fatty acids were the major constituents of the di- and triacylglycerol species suggesting a link with increased lipogenesis.

CONCLUSION

These observations support the hypothesis of lipid disturbances as explanatory factors for residual beta-cell function in children with new onset type 1 diabetes.

Serum, plasma and erythrocyte membrane lipidomes in infants fed formula supplemented with bovine milk fat globule membranes

BACKGROUND

Supplementation of formula with bovine milk fat globule membranes has been shown to narrow the gap in immunological and cognitive development between breast-fed and formula-fed infants.

METHOD

In a double-blinded randomized controlled trial 160 formula-fed infants received an experimental formula (EF), supplemented with bovine milk fat globule membranes, or standard formula until 6 months of age. A breast-fed reference group was recruited. Lipidomic analyses were performed on plasma and erythrocyte membranes at 6 months and on serum at 4 and 12 months of age.

RESULTS

At 6 months of age, we observed a significant separation in the plasma lipidome between the two formula groups, mostly due to differences in concentrations of sphingomyelins (SM), phosphatidylcholines (PC), and ceramides, and in the erythrocyte membrane lipidome, mostly due to SMs, PEs and PCs. Already at 4 months, a separation in the serum lipidome was evident where SMs and PCs contributed. The separation was not detected at 12 months.

CONCLUSIONS

The effect of MFGM supplementation on the lipidome is likely part of the mechanisms behind the positive cognitive and immunological effects of feeding the EF previously reported in the same study population.

Lipid phenotyping of lung epithelial lining fluid in healthy human volunteers

BACKGROUND

Lung epithelial lining fluid (ELF)-sampled through sputum induction-is a medium rich in cells, proteins and lipids. However, despite its key role in maintaining lung function, homeostasis and defences, the composition and biology of ELF, especially in respect of lipids, remain incompletely understood.

OBJECTIVES

To characterise the induced sputum lipidome of healthy adult individuals, and to examine associations between different ELF lipid phenotypes and the demographic characteristics within the study cohort.

METHODS

Induced sputum samples were obtained from 41 healthy non-smoking adults, and their lipid compositions analysed using a combination of untargeted shotgun and liquid chromatography mass spectrometry methods. Topological data analysis (TDA) was used to group subjects with comparable sputum lipidomes in order to identify distinct ELF phenotypes.

RESULTS

The induced sputum lipidome was diverse, comprising a range of different molecular classes, including at least 75 glycerophospholipids, 13 sphingolipids, 5 sterol lipids and 12 neutral glycerolipids. TDA identified two distinct phenotypes differentiated by a higher total lipid content and specific enrichments of diacyl-glycerophosphocholines, -inositols and -glycerols in one group, with enrichments of sterols, glycolipids and sphingolipids in the other. Subjects presenting the lipid-rich ELF phenotype also had significantly higher BMI, but did not differ in respect of other demographic characteristics such as age or gender.

CONCLUSIONS

We provide the first evidence that the ELF lipidome varies significantly between healthy individuals and propose that such differences are related to weight status, highlighting the potential impact of (over)nutrition on lung lipid metabolism.

Comprehensive MS/MS profiling by UHPLC-ESI-QTOF-MS/MS using SWATH data-independent acquisition for the study of platelet lipidomes in coronary artery disease

A non-targeted lipidomics workflow based on C8 core-shell particle ultra high-performance liquid chromatography (UHPLC) hyphenated to ESI-QTOF-MS in data-independent acquisition (DIA) mode with sequential window acquisition of all theoretical fragment ion spectra (SWATH) was developed and applied to differential platelet lipidomics profiling of cardiovascular disease patients (stable angina pectoris (n = 10), ST-elevated myocardial infarction (n = 13)) against healthy controls (n = 10). DIA with SWATH generates comprehensive MS and MS/MS data throughout the entire chromatograms and all study samples. Hence, chromatograms can be extracted based on precursors or fragments which provided some benefits in terms of assay specificity in some cases. SWATH acquisition offers flexible experimental design with variable Q1 isolation windows. Liquid chromatography as well as SWATH settings were optimized to cover the lipidome of human platelets. The flexibility of the SWATH experiment design was utilized to implement target SWATH windows with narrow 5 Da Q1 precursor ion selection width (multiple reaction monitoring (MRM)-like SWATH windows) for the detection of low abundant oxidized phospholipids. Data processing was performed with MS-DIAL, and its feasibilities and caveats are discussed by illustrative examples. Thereby, identification of lipids is still a bottleneck in non-targeted lipidomics workflow. MS-DIAL, however, offers automatic identification via spectral matching using an in silico library. In total 1971 molecular features were detected cross the samples of which 611 were identified (total score >70%). The quality of the acquired data was validated with embedded quality control samples (n = 11). 80.3% of all features detected in the QC samples showed a coefficient of variation of below 30%. Multivariate statistics were used to visualize differences in the lipidome of distinct sample groups at a false discovery rate of 5%.

Dynamics of Plasma Lipidome in Progression to Islet Autoimmunity and Type 1 Diabetes - Type 1 Diabetes Prediction and Prevention Study (DIPP)

Type 1 diabetes (T1D) is one of the most prevalent autoimmune diseases among children in Western countries. Earlier metabolomics studies suggest that T1D is preceded by dysregulation of lipid metabolism. Here we used a lipidomics approach to analyze molecular lipids in a prospective series of 428 plasma samples from 40 children who progressed to T1D (PT1D), 40 children who developed at least a single islet autoantibody but did not progress to T1D during the follow-up (P1Ab) and 40 matched controls (CTR). Sphingomyelins were found to be persistently downregulated in PT1D when compared to the P1Ab and CTR groups. Triacylglycerols and phosphatidylcholines were mainly downregulated in PT1D as compared to P1Ab at the age of 3 months. Our study suggests that distinct lipidomic signatures characterize children who progressed to islet autoimmunity or overt T1D, which may be helpful in the identification of at-risk children before the initiation of autoimmunity.

Improving metabolome coverage and data quality: advancing metabolomics and lipidomics for biomarker discovery

This Feature Article highlights some of the key challenges within the field of metabolomics and examines what role separation and analytical sciences can play to improve the use of metabolomics in biomarker discovery and personalized medicine. Recent progress in four key areas is highlighted: (i) improving metabolite coverage, (ii) developing accurate methods for unstable metabolites including in vivo global metabolomics methods, (iii) advancing inter-laboratory studies and reference materials and (iv) improving data quality, standardization and quality control of metabolomics studies.

Lipidomic Profiles in Diabetes and Dementia

Lipids are a diverse class of hydrophobic and amphiphilic molecules which make up the bulk of most biological systems and are essential for human life. The role of lipids in health and disease has been recognized for many decades, as evidenced by the early identification of cholesterol as an important risk factor of heart disease and the development and introduction of statins as a one of the most successful therapeutic interventions to date. While several studies have demonstrated an increased risk of dementia, including Alzheimer's disease (AD), in those with diabetes mellitus, the nature of this risk is not well understood. Recent developments in the field of lipidomics, driven primarily by technological advances in high pressure liquid chromatography and particularly mass spectrometry, have enabled the detailed characterization of the many hundreds of individual lipid species in mammalian systems and their association with disease states. Diabetes mellitus and AD have received particular attention due to their prominence in Western societies as a result of the ongoing obesity epidemic and the aging populations. In this review, we examine how these lipidomic studies are informing on the relationship between lipid metabolism with diabetes and AD and how this may inform on the common pathological pathways that link diabetes risk with dementia.

Harmonizing lipidomics: NIST interlaboratory comparison exercise for lipidomics using SRM 1950-Metabolites in Frozen Human Plasma

As the lipidomics field continues to advance, self-evaluation within the community is critical. Here, we performed an interlaboratory comparison exercise for lipidomics using Standard Reference Material (SRM) 1950-Metabolites in Frozen Human Plasma, a commercially available reference material. The interlaboratory study comprised 31 diverse laboratories, with each laboratory using a different lipidomics workflow. A total of 1,527 unique lipids were measured across all laboratories and consensus location estimates and associated uncertainties were determined for 339 of these lipids measured at the sum composition level by five or more participating laboratories. These evaluated lipids detected in SRM 1950 serve as community-wide benchmarks for intra- and interlaboratory quality control and method validation. These analyses were performed using nonstandardized laboratory-independent workflows. The consensus locations were also compared with a previous examination of SRM 1950 by the LIPID MAPS consortium. While the central theme of the interlaboratory study was to provide values to help harmonize lipids, lipid mediators, and precursor measurements across the community, it was also initiated to stimulate a discussion regarding areas in need of improvement.

Validating Quantitative Untargeted Lipidomics Across Nine Liquid Chromatography-High-Resolution Mass Spectrometry Platforms

Liquid chromatography-mass spectrometry (LC-MS) methods are most often used for untargeted metabolomics and lipidomics. However, methods have not been standardized as accepted "best practice" documents, and reports lack harmonization with respect to quantitative data that enable interstudy comparisons. Researchers use a wide variety of high-resolution mass spectrometers under different operating conditions, and it is unclear if results would yield different biological conclusions depending on the instrument performance. To this end, we used 126 identical human plasma samples and 29 quality control samples from a nutritional intervention study. We investigated lipidomic data acquisitions across nine different MS instruments (1 single TOF, 1 Q/orbital ion trap, and 7 QTOF instruments). Sample preparations, chromatography conditions, and data processing methods were kept identical. Single-point internal standard calibrations were used to estimate absolute concentrations for 307 unique lipids identified by accurate mass, MS/MS spectral match, and retention times. Quantitative results were highly comparable between the LC-MS platforms tested. Using partial least-squares discriminant analysis (PLS-DA) to compare results between platforms, a 92% overlap for the most discriminating lipids based on variable importance in projection (VIP) scores was achieved for all lipids that were detected by at least two instrument platforms. Importantly, even the relative positions of individual samples on the PLS-DA projections were identical. The key for success in harmonizing results was to avoid ion saturation by carefully evaluating linear dynamic ranges using serial dilutions and adjusting the resuspension volume and/or injection volume before running actual study samples.

Lipidomic analysis of plasma in patients with lacunar infarction using normal-phase/reversed-phase two-dimensional liquid chromatography-quadrupole time-of-flight mass spectrometry

Stroke is a major cause of mortality and long-term disability worldwide. The study of biomarkers and pathogenesis is vital for early diagnosis and treatment of stroke. In the present study, a continuous-flow normal-phase/reversed-phase two-dimensional liquid chromatography-quadrupole time-of-flight mass spectrometry (NP/RP 2D LC-QToF/MS) method was employed to measure lipid species in human plasma, including healthy controls and lacunar infarction (LI) patients. As a result, 13 lipid species were demonstrated with significant difference between the two groups, and a "plasma biomarker model" including glucosylceramide (38:2), phosphatidylethanolamine (35:2), free fatty acid (16:1), and triacylglycerol (56:5) was finally established. This model was evaluated as an effective tool in that area under the receiver operating characteristic curve reached 1.000 in the discovery set and 0.947 in the validation set for diagnosing LI patients from healthy controls. Besides, the sensitivity and specificity of disease diagnosis in validation set were 93.3% and 96.6% at the best cutoff value, respectively. This study demonstrates the promising potential of NP/RP 2D LC-QToF/MS-based lipidomics approach in finding bio-markers for disease diagnosis and providing special insights into the metabolism of stroke induced by small vessel disease. Graphical abstract Flow-chart of the plasma biomarker model establishment through biomarker screening and validation.

The application of lipidomics to biomarker research and pathomechanisms in Alzheimer's disease

PURPOSE OF REVIEW

Alzheimer's disease is the most common cause of dementia. There are still no disease modifying treatments that can cure or slow disease progression. Recently, Alzheimer's disease researchers have attempted to improve early detection and diagnostic criteria for Alzheimer's disease, with the rationale that treatment of disease, or even prevention, may be more successful during the early preclinical stages of Alzheimer's disease when neurodegenerative damage is not as widespread. As the brain has a high lipid content, lipidomics may offer novel insights into the underlying pathogenesis of Alzheimer's disease. This review reports on recent developments in the relatively unexplored field of lipidomics in Alzheimer's disease, including novel biomarkers and pathomechanisms of Alzheimer's disease.

RECENT FINDINGS

Numerous biomarker panels involving phospholipids and sphingolipids have been proposed, indicating perturbed lipid metabolism in early stages of Alzheimer's disease. Future strategies targeting these metabolic changes through dietary supplementation could have therapeutic benefits in at-risk individuals.

SUMMARY

Dysregulated lipid metabolism could reflect pathological changes in synaptic function and neuronal membranes, leading to cognitive decline. However, extensive validation in large independent cohorts is required before lipid biomarkers can be used clinically to assess Alzheimer's disease risk and progression.