Nontargeted quantitation of lipid classes using hydrophilic interaction liquid chromatography-electrospray ionization mass spectrometry with single internal standard and response factor approach

Toward Omics-Scale Quantitative Mass Spectrometry Imaging of Lipids in Brain Tissue Using a Multiclass Internal Standard Mixture

Mass spectrometry imaging (MSI) has accelerated our understanding of lipid metabolism and spatial distribution in tissues and cells. However, few MSI studies have approached lipid imaging quantitatively and those that have focused on a single lipid class. We overcome this limitation by using a multiclass internal standard (IS) mixture sprayed homogeneously over the tissue surface with concentrations that reflect those of endogenous lipids. This enabled quantitative MSI (Q-MSI) of 13 lipid classes and subclasses representing almost 200 sum-composition lipid species using both MALDI (negative ion mode) and MALDI-2 (positive ion mode) and pixel-wise normalization of each lipid species in a manner analogous to that widely used in shotgun lipidomics. The Q-MSI approach covered 3 orders of magnitude in dynamic range (lipid concentrations reported in pmol/mm2) and revealed subtle changes in distribution compared to data without normalization. The robustness of the method was evaluated by repeating experiments in two laboratories using both timsTOF and Orbitrap mass spectrometers with an ∼4-fold difference in mass resolution power. There was a strong overall correlation in the Q-MSI results obtained by using the two approaches. Outliers were mostly rationalized by isobaric interferences or the higher sensitivity of one instrument for a particular lipid species. These data provide insight into how the mass resolving power can affect Q-MSI data. This approach opens up the possibility of performing large-scale Q-MSI studies across numerous lipid classes and subclasses and revealing how absolute lipid concentrations vary throughout and between biological tissues.

Integrative deep learning framework predicts lipidomics-based investigation of preservatives on meat nutritional biomarkers and metabolic pathways

Preservatives are added as antimicrobial agents to extend the shelf life of meat. Adding preservatives to meat products can affect their flavor and nutrition. This review clarifies the effects of preservatives on metabolic pathways and network molecular transformations in meat products based on lipidomics, metabolomics and proteomics analyses. Preservatives change the nutrient content of meat products via altering ionic strength and pH to influence enzyme activity. Ionic strength in salt triggers muscle triglyceride hydrolysis by causing phosphorylation and lipid droplet splitting in adipose tissue hormone-sensitive lipase and triglyceride lipase. DisoLipPred exploiting deep recurrent networks and transfer learning can predict the lipid binding trend of each amino acid in the disordered region of input protein sequences, which could provide omics analyses of biomarkers metabolic pathways in meat products. While conventional meat quality assessment tools are unable to elucidate the intrinsic mechanisms and pathways of variables in the influences of preservatives on the quality of meat products, the promising application of omics techniques in food analysis and discovery through multimodal learning prediction algorithms of neural networks (e.g., deep neural network, convolutional neural network, artificial neural network) will drive the meat industry to develop new strategies for food spoilage prevention and control.

Determination of Polar Lipids in Wheat and Oat by a Complementary Approach of Hydrophilic Interaction Liquid Chromatography and Reversed-Phase High-Performance Liquid Chromatography Hyphenated with High-Resolution Mass Spectrometry

Cereals contain lipids that fulfill important physiological roles and are associated with stress in the plant. However, many of the specific biological roles of lipids are yet unknown. Comprehensive analysis of these polar lipid categories in whole grain wheat and oat, cereals highly relevant also in nutrition, was performed. Hydrophilic interaction liquid chromatography (HILIC) and reversed-phase high-performance liquid chromatography (RP-HPLC) coupled with high-resolution mass spectrometry using electrospray ionization in both positive and negative ionization mode was used. Exploiting the different separation mechanisms, HILIC was used as a screening method for straightforward lipid class assignment and enabled differentiation of isomeric lipid classes, like phosphatidylethanolamine and lyso-N-acylphosphatidylethanolamine, while RP-HPLC facilitated separation of constitutional isomers. In combination with data-dependent MS/MS experiments, 67 lipid species belonging to nine polar lipid classes could be identified. Furthermore, with both ionization modes, fatty acyl chains directly connected to the lipid headgroups could be assigned. This work focused on the four lipid classes N-acylphosphatidylethanolamines, acyl-monogalactosyldiacylglycerols, digalactosyldiacylglycerols, and monogalactosyldiacylglycerols as they were less studied in detail in the past. Applying the complementary approach, the relative lipid species compositions in these lipid classes was investigated in detail.

Profiling of branched chain and straight chain saturated fatty acids by ultra-high performance liquid chromatography-mass spectrometry

Branched chain fatty acids (BCFAs) are one of the important sub categories of fatty acids (FAs) which have unique functions in nature. They are commonly analyzed by GC-MS after derivatization to methyl esters (FAMEs). On the other hand, there is a lack of isomer-selective LC-MS methods which allow the distinction of different isomers with wide coverage of carbon chain length. In this work, a systematic retention and isomer selectivity study on seven commercially available UHPLC columns (six polysaccharide columns Chiralpak IA-U, IB-U, IC-U, ID-U, IG-U and IH-U; one Acquity UPLC CSH C18 column) was performed. Various experimental factors were evaluated including column temperatures, gradient profiles and flow rates to elucidate their effects on the separation ability of homologous series of BCFAs with distinct chain lengths, different branching types and branching positions. In general, IG-U outperformed the other columns in terms of isomer selectivity especially for the short and medium-chain BCFA isomers while RP C18 showed good potential in terms of selectivity for long-chain BCFA isomers. Furthermore, after the evaluation of the chromatographic retention pattern on the various columns and method optimization, we report a methodology for untargeted isomer-selective BCFA profiling without precolumn derivatization with UHPLC-ESI-MS/MS by quadrupole-time-of-flight instrument with SWATH acquisition. The best method provides selectivity for constitutional isomers of BCFAs covering distinct chain length (C5-C20) with different branching types (methyl or ethyl) and branching positions (2Me, 3Me, 4Me, 6Me, anteiso and iso-BCFAs) with an optimized LC condition on Acquity UPLC CSH C18 column. Finally, the optimized method was applied for the BCFAs profiling in lipid extracts of Staphylococcus aureus samples. Besides, pooled human platelets and pooled human plasma were evaluated as mammalian samples for presence of BCFAs as well. The new method showed strong potential for BCFA profiling in bacterial samples including different isomers anteiso and iso-BCFAs, which could be a useful tool for related subdisciplines in metabolomics and lipidomics in particular in combination with electron-activated dissociation MS. Compared to GC, the presented isomer selective LC methods can be also of great utility for preparative purposes. Equivalent (carbon) chain length numbers were calculated for RP18 and Chiralpak IG-U and compared to those of FAMEs obtained by GC.

Chewing the fat: How lipidomics is changing our understanding of human health and disease in 2022

Lipids are biological molecules that play vital roles in all living organisms. They perform many cellular functions, such as 1) forming cellular and subcellular membranes, 2) storing and using energy, and 3) serving as chemical messengers during intra- and inter-cellular signal transduction. The large-scale study of the pathways and networks of cellular lipids in biological systems is called "lipidomics" and is one of the fastest-growing omics technologies of the last two decades. With state-of-the-art mass spectrometry instrumentation and sophisticated data handling, clinical studies show how human lipid composition changes in health and disease, thereby making it a valuable medium to collect for clinical applications, such as disease diagnostics, therapeutic decision-making, and drug development. This review gives a comprehensive overview of current workflows used in clinical research, from sample collection and preparation to data and clinical interpretations. This is followed by an appraisal of applications in 2022 and a perspective on the exciting future of clinical lipidomics.

Bacterial catabolism of membrane phospholipids links marine biogeochemical cycles

In marine systems, the availability of inorganic phosphate can limit primary production leading to bacterial and phytoplankton utilization of the plethora of organic forms available. Among these are phospholipids that form the lipid bilayer of all cells as well as released extracellular vesicles. However, information on phospholipid degradation is almost nonexistent despite their relevance for biogeochemical cycling. Here, we identify complete catabolic pathways for the degradation of the common phospholipid headgroups phosphocholine (PC) and phosphorylethanolamine (PE) in marine bacteria. Using Phaeobacter sp. MED193 as a model, we provide genetic and biochemical evidence that extracellular hydrolysis of phospholipids liberates the nitrogen-containing substrates ethanolamine and choline. Transporters for ethanolamine (EtoX) and choline (BetT) are ubiquitous and highly expressed in the global ocean throughout the water column, highlighting the importance of phospholipid and especially PE catabolism in situ. Thus, catabolic activation of the ethanolamine and choline degradation pathways, subsequent to phospholipid metabolism, specifically links, and hence unites, the phosphorus, nitrogen, and carbon cycles.

Accurate quantification of TAGs to identify adulteration of edible oils by ultra-high performance liquid chromatography-quadrupole-time of flight-tandem mass spectrometry

Edible oils play important roles in biological functions, and triacylglycerols (TAGs) in edible oils are complex mixtures. This makes accurate TAGs quantitation quite difficult that bring economically motivated food adulteration. Herein, we demonstrated a strategy for accurate quantification of TAGs in edible oils, which could be applied in identification of olive oil adulteration. The results showed that the proposed strategy could significantly improve the accuracy of TAG content determination, reduce the relative error of fatty acids (FAs) content determination, and present a wider accurate quantitative range than that of gas chromatography-flame ionization detection. Most important, this strategy coupled with principal component analysis could be used to identify adulteration of high-priced olive oil with cheaper soybean oils, rapeseed oils or camellia oils at a lower concentration of 2%. These findings indicated that the proposed strategy could be regarded as a potential method for edible oils quality and authenticity analysis.

Omic-Scale High-Throughput Quantitative LC-MS/MS Approach for Circulatory Lipid Phenotyping in Clinical Research

Lipid analysis at the molecular species level represents a valuable opportunity for clinical applications due to the essential roles that lipids play in metabolic health. However, a comprehensive and high-throughput lipid profiling remains challenging given the lipid structural complexity and exceptional diversity. Herein, we present an 'omic-scale targeted LC-MS/MS approach for the straightforward and high-throughput quantification of a broad panel of complex lipid species across 26 lipid (sub)classes. The workflow involves an automated single-step extraction with 2-propanol, followed by lipid analysis using hydrophilic interaction liquid chromatography in a dual-column setup coupled to tandem mass spectrometry with data acquisition in the timed-selective reaction monitoring mode (12 min total run time). The analysis pipeline consists of an initial screen of 1903 lipid species, followed by high-throughput quantification of robustly detected species. Lipid quantification is achieved by a single-point calibration with 75 isotopically labeled standards representative of different lipid classes, covering lipid species with diverse acyl/alkyl chain lengths and unsaturation degrees. When applied to human plasma, 795 lipid species were measured with median intra- and inter-day precisions of 8.5 and 10.9%, respectively, evaluated within a single and across multiple batches. The concentration ranges measured in NIST plasma were in accordance with the consensus intervals determined in previous ring-trials. Finally, to benchmark our workflow, we characterized NIST plasma materials with different clinical and ethnic backgrounds and analyzed a sub-set of sera (n = 81) from a clinically healthy elderly population. Our quantitative lipidomic platform allowed for a clear distinction between different NIST materials and revealed the sex-specificity of the serum lipidome, highlighting numerous statistically significant sex differences.

Lipidomics analysis in drug discovery and development

Despite being a relatively new addition to the Omics' landscape, lipidomics is increasingly being recognized as an important tool for the identification of druggable targets and biochemical markers. In this review we present recent advances of lipid analysis in drug discovery and development. We cover current state of the art technologies which are constantly evolving to meet demands in terms of sensitivity and selectivity. A careful selection of important examples is then provided, illustrating the versatility of lipidomics analysis in the drug discovery and development process. Integration of lipidomics with other omics', stem-cell technologies, and metabolic flux analysis will open new avenues for deciphering pathophysiological mechanisms and the discovery of novel targets and biomarkers.

Antioxidant, Wound Healing Potential and In Silico Assessment of Naringin, Eicosane and Octacosane

1. Diabetic chronic wounds, mainly foot ulcers, constitute one of the most common complications of poorly managed diabetes mellitus. The most typical reasons are insufficient glycemic management, latent neuropathy, peripheral vascular disease, and neglected foot care. In addition, it is a common cause of foot osteomyelitis and amputation of the lower extremities. Patients are admitted in larger numbers attributable to chronic wounds compared to any other diabetic disease. In the United States, diabetes is currently the most common cause of non-traumatic amputations. Approximately five percent of diabetics develop foot ulcers, and one percent require amputation. Therefore, it is necessary to identify sources of lead with wound-healing properties. Redox imbalance due to excessive oxidative stress is one of the causes for the development of diabetic wounds. Antioxidants have been shown to decrease the progression of diabetic neuropathy by scavenging ROS, regenerating endogenous and exogenous antioxidants, and reversing redox imbalance. Matrix metalloproteinases (MMPs) play vital roles in numerous phases of the wound healing process. Antioxidant and fibroblast cell migration activity of Marantodes pumilum (MP) crude extract has previously been reported. Through their antioxidant, epithelialization, collagen synthesis, and fibroblast migration activities, the authors hypothesise that naringin, eicosane and octacosane identified in the MP extract may have wound-healing properties. 2. The present study aims to identify the bioactive components present in the dichloromethane (DCM) extract of M. pumilum and evaluate their antioxidant and wound healing activity. Bioactive components were identified using LCMS, HPTLC and GCMS. Excision wound on STZ-induced diabetic rat model, human dermal fibroblast (HDF) cell line and colorimetric antioxidant assays were used to evaluate wound healing and antioxidant activities, respectively. Molecular docking and pkCMS software would be utilised to predict binding energy and affinity, as well as ADME parameters. 3. Naringin (NAR), eicosane (EIC), and octacosane (OCT) present in MP displayed antioxidant action and wound excision closure. Histological examination HDF cell line demonstrates epithelialization, collagen production, fibroblast migration, polymorphonuclear leukocyte migration (PNML), and fibroblast movement. The results of molecular docking indicate a substantial attraction and contact between MMPs. pkCMS prediction indicates inadequate blood-brain barrier permeability, low toxicity, and absence of hepatotoxicity. 4. Wound healing properties of (NEO) naringin, eicosane and octacosane may be the result of their antioxidant properties and possible interactions with MMP.

Lipidomics-Paving the Road towards Better Insight and Precision Medicine in Rare Metabolic Diseases

Even though the application of Next-Generation Sequencing (NGS) has significantly facilitated the identification of disease-associated mutations, the diagnostic rate of rare diseases is still below 50%. This causes a diagnostic odyssey and prevents specific treatment, as well as genetic counseling for further family planning. Increasing the diagnostic rate and reducing the time to diagnosis in children with unclear disease are crucial for a better patient outcome and improvement of quality of life. In many cases, NGS reveals variants of unknown significance (VUS) that need further investigations. The delineation of novel (lipid) biomarkers is not only crucial to prove the pathogenicity of VUS, but provides surrogate parameters for the monitoring of disease progression and therapeutic interventions. Lipids are essential organic compounds in living organisms, serving as building blocks for cellular membranes, energy storage and signaling molecules. Among other disorders, an imbalance in lipid homeostasis can lead to chronic inflammation, vascular dysfunction and neurodegenerative diseases. Therefore, analyzing lipids in biological samples provides great insight into the underlying functional role of lipids in healthy and disease statuses. The method of choice for lipid analysis and/or huge assemblies of lipids (=lipidome) is mass spectrometry due to its high sensitivity and specificity. Due to the inherent chemical complexity of the lipidome and the consequent challenges associated with analyzing it, progress in the field of lipidomics has lagged behind other omics disciplines. However, compared to the previous decade, the output of publications on lipidomics has increased more than 17-fold within the last decade and has, therefore, become one of the fastest-growing research fields. Combining multiple omics approaches will provide a unique and efficient tool for determining pathogenicity of VUS at the functional level, and thereby identifying rare, as well as novel, genetic disorders by molecular techniques and biochemical analyses.

Two-dimensional liquid chromatography-mass spectrometry for lipidomics using off-line coupling of hydrophilic interaction liquid chromatography with 50 cm long reversed phase capillary columns

Comprehensive characterization of the lipidome remains a challenge requiring development of new analytical approaches to expand lipid coverage in complex samples. In this work, offline two-dimensional liquid chromatography-mass spectrometry was investigated for lipidomics from human plasma. Hydrophilic interaction liquid chromatography was implemented in the first dimension to fractionate lipid classes. Nine fractions were collected and subjected to a second-dimension separation utilizing 50 cm capillary columns packed with 1.7 µm C18 particles operated on custom-built instrumentation at 35 kpsi. Online coupling with time-of-flight mass spectrometry allowed putative lipid identification from precursor-mass based library searching. The method had good orthogonality (fractional coverage of ∼40%), achieved a peak capacity of approximately 1900 in 600 min, and detected over 1000 lipids from a 5 µL injection of a human plasma extract while consuming less than 3 mL of solvent. The results demonstrate the expected gains in peak capacity when employing long columns and two-dimensional separations and illustrate practical approaches for improving lipidome coverage from complex biological samples.

Determination of related substances in egg yolk lecithin by HPLC-CAD and characterization of its profiling by HPLC-Q-TOF-MS

A high-performance liquid chromatography (HPLC) method has been developed for the determination of related substances in egg yolk lecithin. Chromatographic separation was achieved using a gradient elution on a Waters Xbridge HILIC column maintained at 35 ℃. Mobile phase A was composed of water-acetonitrile (80:20, v/v, containing 5 mM ammonium acetate), and mobile phase B was composed of acetonitrile. Analytes were monitored by a charged aerosol detector (CAD) at 50 ℃. The novel HPLC-CAD method was selective and sensitive for the determination of related substances in egg yolk lecithin in its commercial bulk batches. It was also successfully validated by the International Council for Harmonisation (ICH) guidelines. The method will be a renewal of an old Chinese Pharmacopoeia method (2020 edition). Moreover, quadrupole time-of-flight mass spectrometry (Q-TOF-MS) was integrated with HPLC to investigate phospholipid species in egg yolk lecithin. This work provides comprehensive composition profiles of egg yolk lecithin, thereby accelerating the quality control, development, and application of egg yolk lecithin.

Role of lipidomics in assessing the functional lipid composition in breast milk

Breast milk is the ideal source of nutrients for infants in early life. Lipids represent 2–5% of the total breast milk composition and are a major energy source providing 50% of an infant’s energy intake. Functional lipids are an emerging class of lipids in breast milk mediating several different biological functions, health, and developmental outcome. Lipidomics is an emerging field that studies the structure and function of lipidome. It provides the ability to identify new signaling molecules, mechanisms underlying physiological activities, and possible biomarkers for early diagnosis and prognosis of diseases, thus laying the foundation for individualized, targeted, and precise nutritional management strategies. This emerging technique can be useful to study the major role of functional lipids in breast milk in several dimensions. Functional lipids are consumed with daily food intake; however, they have physiological benefits reported to reduce the risk of disease. Functional lipids are a new area of interest in lipidomics, but very little is known of the functional lipidome in human breast milk. In this review, we focus on the role of lipidomics in assessing functional lipid composition in breast milk and how lipid bioinformatics, a newly emerging branch in this field, can help to determine the mechanisms by which breast milk affects newborn health.

Temporal changes in the brain lipidome during neurodevelopment of Smith-Lemli-Opitz syndrome mice

Neurodevelopment is an intricately orchestrated program of cellular events that occurs with tight temporal and spatial regulation. While it is known that the development and proper functioning of the brain, which is the second most lipid rich organ behind adipose tissue, greatly rely on lipid metabolism and signaling, the temporal lipidomic changes that occur throughout the course of neurodevelopment have not been investigated. Smith-Lemli-Opitz syndrome is a metabolic disorder caused by genetic mutations in the DHCR7 gene, leading to defective 3β-hydroxysterol-Δ⁷-reductase (DHCR7), the enzyme that catalyzes the last step of the Kandutsch-Russell pathway of cholesterol synthesis. Due to the close regulatory relationship between sterol and lipid homeostasis, we hypothesize that altered or dysregulated lipid metabolism beyond the primary defect of cholesterol biosynthesis is present in the pathophysiology of SLOS. Herein, we applied our HILIC-IM-MS method and LiPydomics Python package to streamline an untargeted lipidomics analysis of developing mouse brains in both wild-type and Dhcr7-KO mice, identifying lipids at Level 3 (lipid species level: lipid class/subclass and fatty acid sum composition). We compared relative lipid abundances throughout development, from embryonic day 12.5 to postnatal day 0 and determined differentially expressed brain lipids between wild-type and Dhcr7-KO mice at specific developmental time points, revealing lipid metabolic pathways that are affected in SLOS beyond the cholesterol biosynthesis pathway, such as glycerolipid, glycerophospholipid, and sphingolipid metabolism. Implications of the altered lipid metabolic pathways in SLOS pathophysiology are discussed.

Comparison of untargeted and targeted perfluoroalkyl acids measured in adolescent girls

Quantitative biomonitoring (e.g., targeted analysis) has served as the gold standard for environmental exposure biomonitoring for several decades. Recent advancements to broaden exposomic research brought new semi-quantitative untargeted assays that capture a wide range of endogenous metabolites and exogenous exposures in a single assay for discovery, though usually at the expense of absolute quantitation. The high-resolution mass spectrometers (HRMS) typically used in untargeted workflows are sensitive and robust, but there do not yet exist comprehensive comparisons between environmental chemicals at population exposure levels measured using targeted and untargeted assays. Using liquid chromatography (LC)-HRMS, we measured per- and polyfluoroalkyl substances (PFAS) including perfluorohexane sulfonate (PFHxS), n-perfluorooctanoic acid (PFOA), n-perfluorooctanesulfonic acid (PFOS), and perfluorononanoic acid (PFNA) in plasma of 180 girls from New York City, and compared them to previously obtained targeted measures using correlation and rank order methods. We showed high agreement between the methods with Spearman Rhos ranging from 0.69 to 0.92 and weighted Kappa's from 0.62 to 0.82 for tertiles among the PFAS. This finding demonstrates that semi-quantitative data from untargeted assays designed for exposomics can be reliably used to estimate environmental exposures occurring in the general population, providing an economic alternative to targeted assays. We also describe an approach that can be used to compare relative quantitation measurements from an untargeted assay to traditional targeted measures to establish fit-for-purpose usability and validation. These results suggest that environmental exposure measures from untargeted assays can serve as reliable inputs into statistical analysis for discovery and for determining their resultant biological impacts. Future efforts to develop new statistical approaches for standardization and merging with targeted measures-toward harmonization-will further enhance the utility of untargeted assays in environmental epidemiology.

Application of hybrid surface technology for improving sensitivity and peak shape of phosphorylated lipids such as phosphatidic acid and phosphatidylserine

The use of hybrid surface technology (HST), applied to the metal surfaces of an ACQUITY™ UPLC™ system and column, designed to mitigate the chelation, poor peak shape and analyte loss seen with acidic phospholipids was investigated. Compared to a conventional system significant improvements in both sensitivity, recovery and peak shape were obtained following UPLC on a CSH C18 column when the HST was used for the analysis of lysophosphatidic acid (LPA), phosphatidic acid (PA), lysophosphatidylserine (LPS), phosphatidylserine (PS), phosphatidylinositol-monophosphates (PIP), ceramide phosphate (CerP) and sphingoid base phosphate (SPBP). The benefits in chromatographic performance provided by the HST were seen particularly at low concentrations of these analytes. The HST system and column reduced peak tailing by 65-80% and peak width by 70-86% for LPA and PA. Moreover, increased signal intensities of up to 12.7 times were observed for LPA with the HST approach compared to the equivalent untreated LC system and column. The application of this methodology to the analysis of chicken egg PA and brain porcine PS extracts were accompanied by similar improvements in data quality.

Achieving Absolute Molar Lipid Concentrations: A Phospholipidomics Cross-Validation Study

Standardization is essential in lipidomics and part of a huge community effort. However, with the still ongoing lack of reference materials, benchmarking quantification is hampered. Here, we propose traceable lipid class quantification as an important layer for the validation of quantitative lipidomics workflows. 31P nuclear magnetic resonance (NMR) and inductively coupled plasma (ICP)-mass spectrometry (MS) can use certified species-unspecific standards to validate shotgun or liquid chromatography (LC)-MS-based lipidomics approaches. We further introduce a novel lipid class quantification strategy based on lipid class separation and mass spectrometry using an all ion fragmentation (AIF) approach. Class-specific fragments, measured over a mass range typical for the lipid classes, are integrated to assess the lipid class concentration. The concept proved particularly interesting as low absolute limits of detection in the fmol range were achieved and LC-MS platforms are widely used in the field of lipidomics, while the accessibility of NMR and ICP-MS is limited. Using completely independent calibration strategies, the introduced validation scheme comprised the quantitative assessment of the complete phospholipid sub-ome, next to the individual lipid classes. Komagataella phaffii served as a prime example, showcasing mass balances and supporting the value of benchmarks for quantification at the lipid species level.

Quantitative non-targeted analysis: Bridging the gap between contaminant discovery and risk characterization

Chemical risk assessments follow a long-standing paradigm that integrates hazard, dose-response, and exposure information to facilitate quantitative risk characterization. Targeted analytical measurement data directly support risk assessment activities, as well as downstream risk management and compliance monitoring efforts. Yet, targeted methods have struggled to keep pace with the demands for data regarding the vast, and growing, number of known chemicals. Many contemporary monitoring studies therefore utilize non-targeted analysis (NTA) methods to screen for known chemicals with limited risk information. Qualitative NTA data has enabled identification of previously unknown compounds and characterization of data-poor compounds in support of hazard identification and exposure assessment efforts. In spite of this, NTA data have seen limited use in risk-based decision making due to uncertainties surrounding their quantitative interpretation. Significant efforts have been made in recent years to bridge this quantitative gap. Based on these advancements, quantitative NTA data, when coupled with other high-throughput data streams and predictive models, are poised to directly support 21st-century risk-based decisions. This article highlights components of the chemical risk assessment process that are influenced by NTA data, surveys the existing literature for approaches to derive quantitative estimates of chemicals from NTA measurements, and presents a conceptual framework for incorporating NTA data into contemporary risk assessment frameworks.

Non-targeted Lipidomics Using a Robust and Reproducible Lipid Separation Using UPLC with Charged Surface Hybrid Technology and High-Resolution Mass Spectrometry

Lipids play an important role in the energy storage, cellular signaling, and pathophysiology of diseases such as cancer, neurodegenerative diseases, infections, and diabetes. Due to high importance of diverse lipid classes in human health and disease, manipulating lipid abundance and composition is an important target for metabolic engineering. The extreme structural diversity of lipids in real biological samples is challenging for analytical techniques due to large difference in physicochemical properties of individual lipid species. This chapter describes lipidomic analysis of large sample sets requiring reliable and robust methodology. Rapid and robust methods facilitate the support of longitudinal studies allowing the transfer of methodology between laboratories. We describe a high-throughput reversed-phase LC-MS methodology using Ultra Performance Liquid Chromatography (UPLC^®) with charged surface hybrid technology and accurate mass detection for high-throughput non-targeted lipidomics. The methodology showed excellent specificity, robustness, and reproducibility for over 100 LC-MS injections.

Isolation and Identification of Non-Conjugated Linoleic Acid from Processed Panax ginseng Using LC-MS/MS and 1H-NMR

Black ginseng exhibits numerous pharmacological activities due to higher and more diverse ginsenosides than unprocessed white ginseng. The ginsenoside derivatives have been investigated in order to determine their chemical structures and pharmacological activities. We found a peak which was increased 10-fold but unidentified in the methanol extracts of a black ginseng product. The unknown peak was tracked and identified as linoleic acid rather than a ginsenoside derivative using liquid chromatography–tandem mass spectrometry (LC-MS/MS) and nuclear magnetic resonance (NMR) spectroscopy. NMR analysis confirmed no presence of conjugated linoleic acids. Ginsenoside profiles and linoleic acid contents in black ginseng products were quantified using LC-MS/MS. Linoleic acid content was more directly proportional to the number of applied thermal cycles in the manufacturing process than any ginsenosides.

Recommendations for good practice in MS-based lipidomics

In the last 2 decades, lipidomics has become one of the fastest expanding scientific disciplines in biomedical research. With an increasing number of new research groups to the field, it is even more important to design guidelines for assuring high standards of data quality. The Lipidomics Standards Initiative is a community-based endeavor for the coordination of development of these best practice guidelines in lipidomics and is embedded within the International Lipidomics Society. It is the intention of this review to highlight the most quality-relevant aspects of the lipidomics workflow, including preanalytics, sample preparation, MS, and lipid species identification and quantitation. Furthermore, this review just does not only highlights examples of best practice but also sheds light on strengths, drawbacks, and pitfalls in the lipidomic analysis workflow. While this review is neither designed to be a step-by-step protocol by itself nor dedicated to a specific application of lipidomics, it should nevertheless provide the interested reader with links and original publications to obtain a comprehensive overview concerning the state-of-the-art practices in the field.

Comparison of Kit-Based Metabolomics with Other Methodologies in a Large Cohort, towards Establishing Reference Values

Metabolic profiling is an omics approach that can be used to observe phenotypic changes, making it particularly attractive for biomarker discovery. Although several candidate metabolites biomarkers for disease expression have been identified in recent clinical studies, the reference values of healthy subjects have not been established. In particular, the accuracy of concentrations measured by mass spectrometry (MS) is unclear. Therefore, comprehensive metabolic profiling in large-scale cohorts by MS to create a database with reference ranges is essential for evaluating the quality of the discovered biomarkers. In this study, we tested 8700 plasma samples by commercial kit-based metabolomics and separated them into two groups of 6159 and 2541 analyses based on the different ultra-high-performance tandem mass spectrometry (UHPLC-MS/MS) systems. We evaluated the quality of the quantified values of the detected metabolites from the reference materials in the group of 2541 compared with the quantified values from other platforms, such as nuclear magnetic resonance (NMR), supercritical fluid chromatography tandem mass spectrometry (SFC-MS/MS) and UHPLC-Fourier transform mass spectrometry (FTMS). The values of the amino acids were highly correlated with the NMR results, and lipid species such as phosphatidylcholines and ceramides showed good correlation, while the values of triglycerides and cholesterol esters correlated less to the lipidomics analyses performed using SFC-MS/MS and UHPLC-FTMS. The evaluation of the quantified values by MS-based techniques is essential for metabolic profiling in a large-scale cohort.

Revealing Fatty Acid Heterogeneity in Staphylococcal Lipids with Isotope Labeling and RPLC-IM-MS

Up to 80% of the fatty acids in Staphylococcus aureus membrane lipids are branched, rather than straight-chain, fatty acids. The branched fatty acids (BCFAs) may have either an even or odd number of carbons, and the branch position may be at the penultimate carbon (iso) or the antepenultimate (anteiso) carbon of the tail. This results in two sets of isomeric fatty acid species with the same number of carbons that cannot be resolved by mass spectrometry. The isomer/isobar challenge is further complicated when the mixture of BCFAs and straight-chain fatty acids (SCFAs) are esterified into diacylated lipids such as the phosphatidylglycerol (PG) species of the S. aureus membrane. No conventional chromatographic method has been able to resolve diacylated lipids containing mixtures of SCFAs, anteiso-odd, iso-odd, and iso-even BCFAs. A major hurdle to method development in this area is the lack of relevant analytical standards for lipids containing BCFA isomers. The diversity of the S. aureus lipidome and its naturally high levels of BCFAs present an opportunity to explore the potential of resolving diacylated lipids containing BCFAs and SFCAs. Using our knowledge of lipid and fatty acid biosynthesis in S. aureus, we have used a stable-isotope-labeling strategy to develop and validate a 30 min C18 reversed-phase liquid chromatography method combined with traveling-wave ion mobility-mass spectrometry to provide resolution of diacylated lipids based on the number of BCFAs that they contain.

A versatile method to separate complex lipid mixtures using 1-butanol as eluent in a reverse-phase UHPLC-ESI-MS system

Simple, robust and versatile LC-MS based methods add to the rapid assessment of the lipidome of biological cells. Here we present a versatile RP-UHPLC-MS method using 1-butanol as the eluent, specifically designed to separate different highly hydrophobic lipids. This method is capable of separating different lipid classes of glycerophospholipid standards, in addition to phospholipids of the same class with a different acyl chain composition. The versatility of this method was demonstrated through analysis of lipid extracts of the bacterium Escherichia coli and the archaeon Sulfolobus acidocaldarius. In contrast to 2-propanol-based methods, the 1-butanol-based mobile phase is capable of eluting highly hydrophobic analytes such as cardiolipins, tetraether lipids and mycolic acids during the gradient instead of the isocratic purge phase, resulting in an enhanced separation of cardiolipins and extending the analytical range for RPLC.

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.

High Throughput Semiquantitative UHPSFC-MS/MS Lipid Profiling and Lipid Class Determination

High throughput and high-resolution lipid analyses are important for many biological model systems and research questions. This comprises both monitoring at the individual lipid species level and broad lipid classes. Here, we present a nontarget semiquantitative lipidomics workflow based on ultrahigh performance supercritical fluid chromatography (UHPSFC)-mass spectrometry (MS). The optimized chromatographic conditions enable the base-line separation of both nonpolar and polar classes in a single 7-minute run. Ionization efficiencies of lipid classes vary 10folds in magnitude and great care must be taken in a direct interpretation of raw data. Therefore, the inclusion of internal standards or experimentally determined Response factors (RF) are highly recommended for the conversion of raw abundances into (semi) quantitative data. We have deliberately developed an algorithm for automatic semiquantification of lipid classes by RF. The workflow was tested and validated using a bovine liver extract with satisfactory results. The RF corrected data provide a more representative relative lipid class determination, but also the interpretation of individual lipid species should be performed on RF corrected data. In addition, semiquantification can be improved by using internal or also external standards when more accurate quantitative data are of interest but this requires validation for all new sample types. The workflow established greatly extends the potential of nontarget UHPSFC–MS/MS based analysis.

UHPLC-Q-Orbitrap HRMS-based quantitative lipidomics reveals the chemical changes of phospholipids during thermal processing methods of Tan sheep meat

Thermal processing affects the lipid compositions of meat products. The study determined the effects of boiled, steamed and roasted processing methods on the lipidomics profiles of Tan sheep meat with a validated UPLC-Q-Orbitrap HRMS combined lipid screening strategy method. Combined with sphingolipid metabolism, the boiled approach was the suitable choice for atherosclerosis patients for more losses of sphingomyelin than ceramide in meat. The similarly less losses of phosphatidylcholine and lysophosphatidylcholine showed in glycerophospholipid metabolism implied that steamed Tan sheep meat was more suitable for the populations of elderly and infants. Furthermore, a total of 90 lipids with significant difference (VIP > 1) in 6 lipid subclasses (sphingomyelin, ceramide, lysophosphatidylcholine, phosphatidylcholine, phosphatidylethanolamines, triacylglycerol,) were quantified among raw and three types of thermal processed Tan sheep meat, further providing useful information for identification of meat products with different thermal processing methods (LOD with 0.14-0.31 μg kg^-1, LOQ with 0.39-0.90 μg kg^-1).

HIGH RESOLUTION MASS SPECTROMETRY IN LIPIDOMICS

The boost of research output in lipidomics during the last decade is tightly linked to improved instrumentation in mass spectrometry. Associated with this trend is the shift from low resolution-toward high-resolution lipidomics platforms. This review article summarizes the state of the art in the lipidomics field with a particular focus on the merits of high mass resolution. Following some theoretical considerations on the benefits of high mass resolution in lipidomics, it starts with a historical perspective on lipid analysis by sector instruments and moves further to today's instrumental approaches, including shotgun lipidomics, liquid chromatography-mass spectrometry, matrix-assisted laser desorption ionization-time-of-flight, and imaging lipidomics. Subsequently, several data processing and data analysis software packages are critically evaluated with all their pros and cons. Finally, this article emphasizes the importance and necessity of quality standards as the field evolves from its pioneering phase into a mature and robust omics technology and lists various initiatives for improving the applicability of lipidomics. © 2020 The Authors. Mass Spectrometry Reviews published by John Wiley & Sons Ltd. Mass Spec Rev.

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.

Global Lipidomics Profiling by a High Resolution LC-MS Platform

Lipidomics is the determination of big lipid assemblies by mass spectrometry. When using chromatography coupled high resolution mass spectrometry, lipids can be identified by exact mass, fragment spectra, and retention time. This protocol covers lipid extraction, LC-MS data acquisition by Orbitrap mass spectrometry and data processing by Lipid Data Analyzer, a custom developed open source software.

30 Years of research on ESI/MS response: Trends, contradictions and applications

The variation of ionization efficiency for different compounds has puzzled researchers since the invention of the electrospray mass spectrometry (ESI/MS). Ionization depends on the properties of the compound, eluent, matrix, and instrument. Despite significant research, some aspects have remained unclear. For example, research groups have reached contradicting conclusions regarding the ionization processes. One of the best-known is the significance of the logP value for predicting the ionization efficiency. In this tutorial review, we analyse the methodology used for ionization efficiency measurements as well as the most important trends observed in the data. Additionally, we give suggestions regarding the measurement methodology and modelling strategies to yield meaningful and consistent ionization efficiency data. Finally, we have collected a wide range of ionization efficiency values from the literature and evaluated the consistency of these data. We also make this collection available for everyone for downloading as well as for uploading additional and new ionization efficiency data. We hope this GitHub based ionization efficiency repository will allow a joined community effort to collect and unify the current knowledge about the ionization processes.

Same analytical method for both (bio)assay and zone isolation to identify/quantify bioactive compounds by quantitative nuclear magnetic resonance spectroscopy

Differing sensitivity is the main obstacle for a direct combination of HPTLC with NMR spectroscopy. A sufficient amount of the isolated compound zone must be provided by HPTLC for subsequent offline NMR detection (HPTLC//NMR). To fill the gap, a straightforward procedure was developed using the same analytical HPTLC system for both bioprofiling and isolation of bioactive zones from multicomponent mixtures. The HPTLC-effect-directed analysis (EDA) revealed several bioactive compounds in five botanical extracts, i.e. Salvia officinalis, Thymus vulgaris and Origanum vulgare, all Lamiaceae, and peels of red and green apples (Jonagored and Granny Smith, respectively), both Rosaceae. A tricky case study was designed to show how to deal with potentially coeluting bioactive structural isomers, e.g., ursolic (UA), oleanolic (OA) and betulinic acids (all C30H48O3), which are most difficult to identify and assign. A multipotent bioactive HPTLC zone showed the same hRF value and mass signal in HPTLCHRMS, though containing the coeluting structural isomers UA and OA. After zone isolation from the HPTLC plate, first the 1H NMR spectrum allowed to distinguish distinct allylic H-18 protons, i.e. 2.20 ppm for UA and 2.85 ppm for OA, and at the same time, to quantify the two isomers by using the PUlse Length-based CONcentration methodology (HPTLC//1H qNMR-PULCON). In case of a partial overlap of the diagnostic signal with that of the matrix, results were corroborated with those obtained by using the 1H deconvoluted or 2D 1H-13C Heteronuclear Single Quantum Coherence spectra. The comparison of the quantitative results showed a good correlation (R2 = 0.9718) between the two orthogonal methods HPTLC-Vis and HPTLC//1H qNMR-PULCON. A sufficient zone isolation from the HPTLC plate (mean isolation rate of 82%) for both UA and OA (0.27 - 4.67 mM) was achieved for HPTLC//qNMR, comparing the isolated bioactive compound zone with the respective zone in the botanical extract via HPTLC-Vis densitometry. The HPTLC-EDA-Vis//1H qNMR-PULCON procedure for bioprofiling and quantification/identification/confirmation of bioactive compounds in botanical extracts is considered as straightforward, eco-friendly (only 16 mL solvent required), simple (NMR calibration used over weeks) and reliable new alternative to the status quo of bioactivity-guided fractionation.

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

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

SWATH-MS for metabolomics and lipidomics: critical aspects of qualitative and quantitative analysis

INTRODUCTION

While liquid chromatography coupled to mass spectrometric detection in the selected reaction monitoring detection mode offers the best quantification sensitivity for omics, the number of target analytes is limited, must be predefined and specific methods developed. Data independent acquisition (DIA), including SWATH using quadrupole time of flight or orbitrap mass spectrometers and generic acquisition methods, has emerged as a powerful alternative technique for quantitative and qualitative analyses since it can cover a wide range of analytes without predefinition.

OBJECTIVES

Here we review the current state of DIA, SWATH-MS and highlight novel acquisition strategies for metabolomics and lipidomics and opportunities for data analysis tools.

METHOD

Different databases were searched for papers that report developments and applications of DIA and in particular SWATH-MS in metabolomics and lipidomics.

RESULTS

DIA methods generate digital sample records that can be mined retrospectively as further knowledge is gained and, with standardized acquisition schemes, used in multiple studies. The different chemical spaces of metabolites and lipids require different specificities, hence different acquisition and data processing approaches must be considered for their analysis.

CONCLUSIONS

Although the hardware and acquisition modes are well defined for SWATH-MS, a major challenge for routine use remains the lack of appropriate software tools capable of handling large datasets and large numbers of analytes.

Evaluation of lipid quantification accuracy using HILIC and RPLC MS on the example of NIST® SRM® 1950 metabolites in human plasma

Lipidomics analysis for large-scale studies aiming at the identification and quantification of natural lipidomes is often performed using LC-MS-based data acquisition. However, the choice of suitable LC-MS method for accurate lipid quantification remains a matter of debate. Here, we performed the systematic comparison between two HRAM-MS-based quantification workflows based on HILIC and RPLC MS by quantifying 191 lipids from five lipid classes in human blood plasma using deuterated standards in the "one ISTD-per-lipid class" approach. Lipid quantification was performed considering all necessary isotopic corrections, and obtained correction factors are illustrated. Concentrations of lipids in NIST® SRM® 1950 human blood plasma determined by the two methods were comparable for most of the studied lipid species except for highly unsaturated phosphatidylcholines (PC). A comparison of lipid concentrations to consensus values determined in a previously published multi-laboratory study illustrated possible "overestimation" of concentrations for these highly unsaturated lipids by HILIC MS. We evaluated the influence of lipid loading amounts as well as the difference between quantified lipid and internal standard concentrations on the HILIC MS quantification results. We conclude that both HILIC and RPLC HRAM-MS workflows can be equally used for accurate lysophosphatidylcholine (LPC), lysophosphatidylethanolamine (LPE), phosphatidylcholine (PC), phosphatidylethanolamine (PE), and sphingomyelin (SM) lipid quantification, despite significant differences in the concentration of highly unsaturated PC lipids which need to be addressed by establishing response factors to account for the differences in degree of lipid unsaturation. Graphical.

Strategies for Drawing Quantitative Conclusions from Nontargeted Liquid Chromatography-High-Resolution Mass Spectrometry Analysis

This Feature aims at giving an overview of different possibilities for quantitatively comparing the results obtained from LC-HRMS-based nontargeted analysis. More specifically, quantification via structurally similar internal standards, different isotope labeling strategies, radiolabeling, and predicted ionization efficiencies are reviewed.

Metabolite database for root, tuber, and banana crops to facilitate modern breeding in understudied crops

Roots, tubers, and bananas (RTB) are vital staples for food security in the world's poorest nations. A major constraint to current RTB breeding programmes is limited knowledge on the available diversity due to lack of efficient germplasm characterization and structure. In recent years large-scale efforts have begun to elucidate the genetic and phenotypic diversity of germplasm collections and populations and, yet, biochemical measurements have often been overlooked despite metabolite composition being directly associated with agronomic and consumer traits. Here we present a compound database and concentration range for metabolites detected in the major RTB crops: banana (Musa spp.), cassava (Manihot esculenta), potato (Solanum tuberosum), sweet potato (Ipomoea batatas), and yam (Dioscorea spp.), following metabolomics-based diversity screening of global collections held within the CGIAR institutes. The dataset including 711 chemical features provides a valuable resource regarding the comparative biochemical composition of each RTB crop and highlights the potential diversity available for incorporation into crop improvement programmes. Particularly, the tropical crops cassava, sweet potato and banana displayed more complex compositional metabolite profiles with representations of up to 22 chemical classes (unknowns excluded) than that of potato, for which only metabolites from 10 chemical classes were detected. Additionally, over 20% of biochemical signatures remained unidentified for every crop analyzed. Integration of metabolomics with the on-going genomic and phenotypic studies will enhance 'omics-wide associations of molecular signatures with agronomic and consumer traits via easily quantifiable biochemical markers to aid gene discovery and functional characterization.

Increased plasma phosphatidylcholine/lysophosphatidylcholine ratios in patients with Parkinson's disease

RATIONALE

Changes in lipid composition might be associated with the onset and progression of various neurodegenerative diseases. Herein, we investigated the changes in the plasma phosphatidylcholine (PC)/lysophosphatidylcholine (LPC) ratios in patients with Parkinson's disease (PD) in comparison with healthy subjects and their correlation with clinico-pathological features.

METHODS

The study included 10 controls and 25 patients with PD. All patients were assigned to groups based on clinico-pathological characteristics (gender, age at examination, duration of disease and Hoehn and Yahr (H&Y) stage). The analysis of the PC/LPC intensity ratios in plasma lipid extracts was performed using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry.

RESULTS

PD patients exhibited an increased PC/LPC intensity ratio in comparison with the control group of healthy subjects. Furthermore, the investigated ratio was shown to be correlated with clinico-pathological parameters, in particular with H&Y stage and disease duration. The PC/LPC intensity ratio in plasma samples of PD patients was found to be elevated in all examined H&Y stages and throughout the disease duration.

CONCLUSIONS

To our knowledge, this is the first study examining the PC/LPC ratios in plasma of patients with PD and illustrating their correlation with clinico-pathological features. Although the presented results may be considered as preliminary due to the limited number of participants, the observed alterations of PC/LPC ratios in plasma might be a first step in the characterization of plasma lipid changes in PD patients and an indicator of lipid reconfiguration.

Shared reference materials harmonize lipidomics across MS-based detection platforms and laboratories

Quantitative MS of human plasma lipids is a promising technology for translation into clinical applications. Current MS-based lipidomic methods rely on either direct infusion (DI) or chromatographic lipid separation methods (including reversed phase and hydrophilic interaction LC). However, the use of lipid markers in laboratory medicine is limited by the lack of reference values, largely because of considerable differences in the concentrations measured by different laboratories worldwide. These inconsistencies can be explained by the use of different sample preparation protocols, method-specific calibration procedures, and other experimental and data-reporting parameters, even when using identical starting materials. Here, we systematically investigated the roles of some of these variables in multiple approaches to lipid analysis of plasma samples from healthy adults by considering: 1) different sample introduction methods (separation vs. DI methods); 2) different MS instruments; and 3) between-laboratory differences in comparable analytical platforms. Each of these experimental variables resulted in different quantitative results, even with the inclusion of isotope-labeled internal standards for individual lipid classes. We demonstrated that appropriate normalization to commonly available reference samples (i.e., "shared references") can largely correct for these systematic method-specific quantitative biases. Thus, to harmonize data in the field of lipidomics, in-house long-term references should be complemented by a commonly available shared reference sample, such as NIST SRM 1950, in the case of human plasma.

Lipidomics by HILIC-Ion Mobility-Mass Spectrometry

Lipidomics is a rapidly growing field that enables the characterization of the entire lipidome in cells, tissues, or an organism. Changes in lipid metabolism and homeostasis caused by different disease states or drug treatments can be probed by lipidomics experiments, which can aid our understanding of normal physiology and disease pathology at the molecular level. While current technologies using liquid chromatography coupled with high-resolution mass spectrometry have greatly increased coverage of the lipidome, there are still limitations in resolving the large number of lipid species with similar masses in a narrow mass window. We recently reported that two orthogonal separation techniques, hydrophilic interaction liquid chromatography (HILIC) and ion mobility (IM), enhance the resolution of lipid species based on headgroup polarity and gas-phase size and shape, respectively, of various classes of glycerolipids, glycolipids, phospholipids, and sphingolipids. Here we describe the application of our HILIC-IM-MS lipidomics protocol to the analysis of lipid extracts derived from either tissues or cells, to identify significant changes in the lipidome in response to an internal or external stimulus, such as exposure to environmental chemicals.

From Samples to Insights into Metabolism: Uncovering Biologically Relevant Information in LC-HRMS Metabolomics Data

Untargeted metabolomics (including lipidomics) is a holistic approach to biomarker discovery and mechanistic insights into disease onset and progression, and response to intervention. Each step of the analytical and statistical pipeline is crucial for the generation of high-quality, robust data. Metabolite identification remains the bottleneck in these studies; therefore, confidence in the data produced is paramount in order to maximize the biological output. Here, we outline the key steps of the metabolomics workflow and provide details on important parameters and considerations. Studies should be designed carefully to ensure appropriate statistical power and adequate controls. Subsequent sample handling and preparation should avoid the introduction of bias, which can significantly affect downstream data interpretation. It is not possible to cover the entire metabolome with a single platform; therefore, the analytical platform should reflect the biological sample under investigation and the question(s) under consideration. The large, complex datasets produced need to be pre-processed in order to extract meaningful information. Finally, the most time-consuming steps are metabolite identification, as well as metabolic pathway and network analysis. Here we discuss some widely used tools and the pitfalls of each step of the workflow, with the ultimate aim of guiding the reader towards the most efficient pipeline for their metabolomics studies.

Lipidomics from sample preparation to data analysis: a primer

Lipids are amongst the most important organic compounds in living organisms, where they serve as building blocks for cellular membranes as well as energy storage and signaling molecules. Lipidomics is the science of the large-scale determination of individual lipid species, and the underlying analytical technology that is used to identify and quantify the lipidome is generally mass spectrometry (MS). This review article provides an overview of the crucial steps in MS-based lipidomics workflows, including sample preparation, either liquid–liquid or solid-phase extraction, derivatization, chromatography, ion-mobility spectrometry, MS, and data processing by various software packages. The associated concepts are discussed from a technical perspective as well as in terms of their application. Furthermore, this article sheds light on recent advances in the technology used in this field and its current limitations. Particular emphasis is placed on data quality assurance and adequate data reporting; some of the most common pitfalls in lipidomics are discussed, along with how to circumvent them.