Retention behavior of lipids in reversed-phase ultrahigh-performance liquid chromatography-electrospray ionization mass spectrometry

The Lipidomics Reporting Checklist A framework for transparency of lipidomic experiments and repurposing resource data

The rapid increase in lipidomic studies has led to a collaborative effort within the community to establish standards and criteria for producing, documenting, and disseminating data. Creating a dynamic easy-to-use checklist that condenses key information about lipidomic experiments into common terminology will enhance the field's consistency, comparability, and repeatability. Here, we describe the structure and rationale of the established Lipidomics Minimal Reporting Checklist to increase transparency in lipidomics research.

FAIMS Shotgun Lipidomics for Enhanced Class- and Charge-State Separation Complemented by Automated Ganglioside Annotation

The analysis of gangliosides is extremely challenging, given their structural complexity, lack of reference standards, databases, and software solutions. Here, we introduce a fast 6 min high field asymmetric ion mobility spectrometry (FAIMS) shotgun lipidomics workflow, along with a dedicated software solution for ganglioside detection. By ramping FAIMS compensation voltages, ideal ranges for different ganglioside classes were obtained. FAIMS revealed both class- and charge-state separation behavior based on the glycan headgroup moiety. The number of sialic acids attached to the glycan moiety correlates positively with their preferred charge states, i.e., trisialylated gangliosides were mainly present as [M - 3H]3- ions, whereas [M - 4H]4- and [M - 5H]5- ions were observed for GQ1 and GP1. For data evaluation, we developed a shotgun/FAIMS extension for the open-source Lipid Data Analyzer (LDA), enabling automated annotation of gangliosides up to the molecular lipid species level. This extension utilized combined orthogonal fragmentation spectra from CID, HCD, and 213 nm UVPD ion activation methods and covers 29 ganglioside classes, including acetylated and fucosylated modifications. With our new workflow and software extension 117 unique gangliosides species were identified in porcine brain extracts. While conventional shotgun lipidomics favored the observation of singly charged ganglioside species, the utilization of FAIMS made multiply charged lipid species accessible, resulting in an increased number of detected species, primarily due to an improved signal-to-noise ratio arising from FAIMS charge state filtering. Therefore, this FAIMS-driven workflow, complemented by new software capabilities, offers a promising strategy for complex ganglioside and glycosphingolipid characterization in shotgun lipidomics.

Phospholipidomics in Clinical Trials for Brain Disorders: Advancing our Understanding and Therapeutic Potentials

Phospholipidomics is a specialized branch of lipidomics that focuses on the characterization and quantification of phospholipids. By using sensitive analytical techniques, phospholipidomics enables researchers to better understand the metabolism and activities of phospholipids in brain disorders such as Alzheimer's and Parkinson's diseases. In the brain, identifying specific phospholipid biomarkers can offer valuable insights into the underlying molecular features and biochemistry of these diseases through a variety of sensitive analytical techniques. Phospholipidomics has emerged as a promising tool in clinical studies, with immense potential to advance our knowledge of neurological diseases and enhance diagnosis and treatment options for patients. In the present review paper, we discussed numerous applications of phospholipidomics tools in clinical studies, with a particular focus on the neurological field. By exploring phospholipids' functions in neurological diseases and the potential of phospholipidomics in clinical research, we provided valuable insights that could aid researchers and clinicians in harnessing the full prospective of this innovative practice and improve patient outcomes by providing more potent treatments for neurological diseases.

LipiDex 2 Integrates MSn Tree-Based Fragmentation Methods and Quality Control Modules to Improve Discovery Lipidomics

As lipidomics experiments increase in scale and complexity, data processing tools must support workflows for new liquid chromatography-mass spectrometry (LC-MS) methods while simultaneously supporting quality controls to maximize the confidence in lipid identifications. LipiDex 2 improves lipidomics data processing algorithms from LipiDex 1 and introduces new tools for spectral matching and peak annotation functions, with improvements in speed and user-friendliness. In silico spectral library generation now supports tandem mass spectral (MSn) tree-based fragmentation methods, and the LipiDex 2 workflow fully integrates the fragmentation logic into the data processing steps to enable lipid identification at the appropriate level of structural resolution. Finally, LipiDex 2 features new modules for automated quality control checks that also allow users to visualize data quality in a data dashboard user interface.

Enhancing compound confidence in suspect and non-target screening through machine learning-based retention time prediction

The retention time (RT) of contaminants of emerging concern (CECs) in liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is crucial for database matching in non-targeted screening (NTS) analysis. In this study, we developed a machine learning (ML) model to predict RTs of CECs in NTS analysis. Using 1051 CEC standards, we evaluated Random Forest (RF), XGBoost, Support Vector Regression (SVR), and Artificial Neural Network (ANN) with molecular fingerprints and chemical descriptors to establish an optimal model. The SVR model utilizing chemical descriptors resulted in good predictive capacity with R2ext = 0.850 and r2 = 0.925. The model was further validated through laboratory NTS compound characterization. When applied to examine CEC occurrence in a large wastewater treatment plant, we identified 40 level S1 CECs (confirmed structure by reference standard) and 234 level S2 compounds (probable structure by library spectrum match). The model predicted RTs for level S2 compounds, leading to the classification of 153 level S2 compounds with high confidence (ΔRT <2 min). The model served as a robust filtering mechanism within the analytical framework. This study emphasizes the importance of predicted RTs in NTS analysis and highlights the potential of prediction models. Our research introduces a workflow that enhances NTS analysis by utilizing RT prediction models to determine compound confidence levels.

Lipid Wizard: Analysis Software for Comprehensive Two-Dimensional Liquid Chromatography-Mass Spectrometry-Based Lipid Profiling

Lipids play a significant role in life activities and participate in the biological system through different pathways. Although comprehensive two-dimensional liquid chromatography-mass spectrometry (2DLC-MS) has been developed to profile lipid abundance changes, lipid identification and quantification from 2DLC-MS data remain a challenge. We created Lipid Wizard, open-source software for lipid assignment and isotopic peak stripping of the 2DLC-MS data. Lipid Wizard takes the peak list deconvoluted from the 2DLC-MS data as input and assigns each isotopic peak to the lipids recorded in the LIPID MAPS database by precursor ion m/z matching. The matched lipids are then filtered by the first-dimension retention time (1D RT), followed by the second-dimension retention time (2D RT), where the 2D RT of each lipid is predicted using an equivalent carbon number (ECN) model. The remaining assigned lipids are used for isotopic peak stripping via an iterative linear regression. The performance of Lipid Wizard was tested using a set of lipid standards and then applied to study the lipid changes in the livers of mice (fat-1) fed with alcohol.

Monitoring stability indicating impurities and aldehyde content in lipid nanoparticle raw material and formulated drugs

Lipid nanoparticles (LNPs) are designed to protect and transport sensitive payloads or active pharmaceutical ingredients as part of new therapeutic modalities. As a multi-component particle, a high degree of quality control is necessary to ensure raw materials are free of critical impurities that could adversely impact the drug product. In this study, we demonstrate a reversed phase liquid chromatography method hyphenated with a single quadrupole mass spectrometer (RPLC-MS) as an alternative platform to methods that incorporate evaporative light scattering or charged aerosol detectors in the detection and quantitation of critical impurities associated with LNPs. The proposed RPLC-MS method offers an increase of up to 2 orders of magnitude in dynamic range and 3 orders of magnitude in sensitivity in the analysis of impurities associated with LNPs compared to conventional detectors. Access to complementary mass data enabled the detection and identification of stability indicating impurities as part of stress studies carried out on an ionizable lipid. In addition to confirmation of peak identity, complementary mass data was also used to assess residual aldehydes in raw material and formulated LNPs in accordance with regulatory guidance. Following derivatization using 2,4-dinitrophenylhydrazine, aldehyde content in the ionizable lipid raw material was determined to exceed the reporting threshold of 0.05% in 30% of the test cases. The experimental findings observed in this study demonstrate the utility of the proposed RPLC-MS method in the identification and monitoring of stability-indicating attributes associated with LNPs as part of current Good Manufacturing Practices for improved consumer safety in drug products.

Recent Analytical Methodologies in Lipid Analysis

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

Retention time prediction and MRM validation reinforce the biomarker identification of LC-MS based phospholipidomics

Dysfunctional lipid metabolism plays a crucial role in the development and progression of various diseases. Accurate measurement of lipidomes can help uncover the complex interactions between genes, proteins, and lipids in health and diseases. The prediction of retention time (RT) has become increasingly important in both targeted and untargeted metabolomics. However, the potential impact of RT prediction on targeted LC-MS based lipidomics is still not fully understood. Herein, we propose a simplified workflow for predicting RT in phospholipidomics. Our approach involves utilizing the fatty acyl chain length or carbon-carbon double bond (DB) number in combination with multiple reaction monitoring (MRM) validation. We found that our model's predictive capacity for RT was comparable to that of a publicly accessible program (QSRR Automator). Additionally, MRM validation helped in further mitigating the interference in signal recognition. Using this developed workflow, we conducted phospholipidomics of sorafenib resistant hepatocellular carcinoma (HCC) cell lines, namely MHCC97H and Hep3B. Our findings revealed an abundance of monounsaturated fatty acyl (MUFA) or polyunsaturated fatty acyl (PUFA) phospholipids in these cell lines after developing drug resistance. In both cell lines, a total of 29 lipids were found to be co-upregulated and 5 lipids were co-downregulated. Further validation was conducted on seven of the upregulated lipids using an independent dataset, which demonstrates the potential for translation of the established workflow or the lipid biomarkers.

Rapid quantification of 50 fatty acids in small amounts of biological samples for population molecular phenotyping

Efficient quantification of fatty-acid (FA) composition (fatty-acidome) in biological samples is crucial for understanding physiology and pathophysiology in large population cohorts. Here, we report a rapid GC-FID/MS method for simultaneous quantification of all FAs in numerous biological matrices. Within eight minutes, this method enabled simultaneous quantification of 50 FAs as fatty-acid methyl esters (FAMEs) in femtomole levels following the efficient transformation of FAs in all lipids including FFAs, cholesterol-esters, glycerides, phospholipids and sphingolipids. The method showed satisfactory inter-day and intra-day precision, stability and linearity (R2 > 0.994) within a concentration range of 2-3 orders of magnitude. FAs were then quantified in typical multiple biological matrices including human biofluids (urine, plasma) and cells, animal intestinal content and tissue samples. We also established a quantitative structure-retention relationship (QSRR) for analytes to accurately predict their retention time and aid their reliable identification. We further developed a novel no-additive retention index (NARI) with endogenous FAMEs reducing inter-batch variations to 15 seconds; such NARI performed better than the alkanes-based classical RI, making meta-analysis possible for data obtained from different batches and platforms. Collectively, this provides an inexpensive high-throughput analytical system for quantitative phenotyping of all FAs in 8-minutes multiple biological matrices in large cohort studies of pathophysiological effects.

Equivalent carbon number based targeted odd chain fatty acyl lipidomics reveals triacylglycerol profiling in clinical colon cancer

Odd chain fatty acids (OCFAs) present in very low level at nearly 1% of total fatty acids in human plasma and thus their functions were usually ignored. Recent epidemiological studies have shown that OCFAs are inversely associated with a variety of disease risks. However, the contribution of OCFAs incorporated into complex lipids remains elusive. Here, we developed a targeted odd chain fatty acyl containing lipidomics method based on equivalent carbon number and retention time prediction. The method displayed good reproducibility and robustness as shown by peak width at half height within 0.7 min and coefficient of variation (CV) under 20%. A total number of 776 lipid species with odd chain fatty acyl residues could be detected in the electrospray ionization (ESI) mode of reverse phase liquid chromatography-mass spectrometry, of which 309 lipids were further validated using multiple MRM transitiions. Using this method, we quantified odd chain fatty acyl containing lipidome in tissues from 12 colon cancer patients, revealing the remodeling of triacylglycerol (TAG). The dynamics of odd chain fatty acyl lipids were further consolidated by the association with genomic and proteomic feature of altered catabolism of branched chain amino acids and TAG endogenous synthesis in colon cancer. This lipidomics approach will be applicable for screening of dysregulated odd chain fatty acyl lipids, which enriches and improves the methods for diagnosis and prognosis evaluation of cancer using lipidomics.

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.

Lipidomic Characterization of Oocytes at Single-Cell Level Using Nanoflow Chromatography-Trapped Ion Mobility Spectrometry-Mass Spectrometry

Mass spectrometry (MS)-based lipidomic has become a powerful tool for studying lipids in biological systems. However, lipidome analysis at the single-cell level remains a challenge. Here, we report a highly sensitive lipidomic workflow based on nanoflow liquid chromatography and trapped ion mobility spectrometry (TIMS)-MS. This approach enables the high-coverage identification of lipidome landscape at the single-oocyte level. By using the proposed method, comprehensive lipid changes in porcine oocytes during their maturation were revealed. The results provide valuable insights into the structural changes of lipid molecules during porcine oocyte maturation, highlighting the significance of sphingolipids and glycerophospholipids. This study offers a new approach to the single-cell lipidomic.

Proteome Coverage after Simultaneous Proteo-Metabolome Liquid-Liquid Extraction

Proteomics and metabolomics are essential in systems biology, and simultaneous proteo-metabolome liquid-liquid extraction (SPM-LLE) allows isolation of the metabolome and proteome from the same sample. Since the proteome is present as a pellet in SPM-LLE, it must be solubilized for quantitative proteomics. Solubilization and proteome extraction are critical factors in the information obtained at the proteome level. In this study, we investigated the performance of two surfactants (sodium deoxycholate (SDC), sodium dodecyl sulfate (SDS)) and urea in terms of proteome coverage and extraction efficiency of an interphase proteome pellet generated by methanol-chloroform based SPM-LLE. We also investigated how the performance differs when the proteome is extracted from the interphase pellet or by direct cell lysis. We quantified 12 lipids covering triglycerides and various phospholipid classes, and 25 polar metabolites covering central energy metabolism in chloroform and methanol extracts. Our study reveals that the proteome coverages between the two surfactants and urea for the SPM-LLE interphase pellet were similar, but the extraction efficiencies differed significantly. While SDS led to enrichment of basic proteins, which were mainly ribosomal and ribonuclear proteins, urea was the most efficient extraction agent for simultaneous proteo-metabolome analysis. The results of our study also show that the performance of surfactants for quantitative proteomics is better when the proteome is extracted through direct cell lysis rather than an interphase pellet. In contrast, the performance of urea for quantitative proteomics was significantly better when the proteome was extracted from an interphase pellet than by direct cell lysis. We demonstrated that urea is superior to surfactants for proteome extraction from SPM-LLE interphase pellets, with a particularly good performance for the extraction of proteins associated with metabolic pathways. Data are available via ProteomeXchange with identifier PXD027338.

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.

Integrated metabolomics and network analysis reveal changes in lipid metabolisms of tripterygium glycosides tablets in rats with collagen-induced arthritis

Tripterygium glycosides tablets (TGT) are the commonly used preparation for rheumatoid arthritis (RA). However, the changes in TGT on RA are still unclear at the metabolic level. This study aimed to reveal the biological processes of TGT in collagen-induced arthritis (CIA) rats through integrated metabolomics and network analysis. First, the CIA model in rats was established, and the CIA rats were given three doses of TGT. Then, the endogenous metabolites in the serum from normal rats, CIA rats, and CIA rats treated with varying doses of TGT were detected by UHPLC-QTOF-MS/MS. Next, univariate and multivariate statistical analyses were performed to find the differential metabolites. Finally, differential metabolites, metabolic pathways, and hub genes were analyzed integrally to reveal the biological processes of TGT in CIA rats. The paw diameter, arthritis score, immunoglobulin G (IgG) concentration, CT image, and histological assay showed that TGT had evident therapeutic effects on CIA rats. Untargeted metabolomics revealed that TGT could ameliorate the down-regulation of lipid levels in CIA rats. Four key differential metabolites were found including LysoP(18:0), LysoPA(20:4), LysoPA(18:2), and PS(O-20:0/17:1). The glycerophospholipid metabolic pathway was perturbed in treating CIA with TGT. A total of 24 genes, including PLD1, LPCAT4, AGPAT1, and PLA2G4A, were found to be the hub genes of TGT in CIA rats. In conclusion, the integrated analysis provided a novel and holistic perspective on the biological processes of TGT in CIA rats, which could give helpful guidance for further TGT on RA. Future studies based on human samples are necessary.

Guidelines and considerations for building multidimensional libraries for untargeted MS-based metabolomics

INTRODUCTION

Feature annotation is crucial in untargeted metabolomics but remains a major challenge. The large pool of metabolites collected under various instrumental conditions is underrepresented in publicly available databases. Retention time (RT) and collision cross section (CCS) measurements from liquid chromatography ion mobility high-resolution mass spectrometers can be employed in addition to MS/MS spectra to improve the confidence of metabolite annotation. Recent advancements in machine learning focus on improving the accuracy of predictions for CCS and RT values. Therefore, high-quality experimental data are crucial to be used either as training datasets or as a reference for high-confidence matching.

METHODS

This manuscript provides an easy-to-use workflow for the creation of an in-house metabolite library, offers an overview of alternative solutions, and discusses the challenges and advantages of using open-source software. A total of 100 metabolite standards from various classes were analyzed and subjected to the described workflow for library generation.

RESULTS AND DISCUSSION

The outcome was an open-access available NIST format metabolite library (.msp) with multidimensional information. The library was used to evaluate CCS prediction tools, MS/MS spectra heterogeneities (e.g., multiple adducts, in-source fragmentation, radical fragment ions using collision-induced dissociation), and the reporting of RT.

Identification of new Omega-3 very long chain poly-unsaturated fatty acids in meibomian gland secretions

Three new very long chain polyunsaturated fatty acids (VLC PUFA) belonging to the omega-3 family have been identified in meibum samples collected by Schirmer strips. These VLC PUFA, namely FA (32:3), FA (34:3) and FA (36:3), were detected in O-acyl-ω-hydroxy fatty acids using a molecular network approach, and as free fatty acids. Identification was supported by retention time prediction model, exact mass determination and isotopic patterns. Double bond location was determined using cross metathesis reaction associated to tandem mass spectrometry. In meibum, synthesis of these VLC PUFA is likely to be mediated by elongation of very long chain fatty acids 4 enzyme. The biological role of these newly VLC PUFA and their occurrence in other tissues and biological fluids remains to be elucidated.

TurboPutative: A web server for data handling and metabolite classification in untargeted metabolomics

Untargeted metabolomics aims at measuring the entire set of metabolites in a wide range of biological samples. However, due to the high chemical diversity of metabolites that range from small to large and more complex molecules (i.e., amino acids/carbohydrates vs. phospholipids/gangliosides), the identification and characterization of the metabolome remain a major bottleneck. The first step of this process consists of searching the experimental monoisotopic mass against databases, thus resulting in a highly redundant/complex list of candidates. Despite the progress in this area, researchers are still forced to manually explore the resulting table in order to prioritize the most likely identifications for further biological interpretation or confirmation with standards. Here, we present TurboPutative (https://proteomics.cnic.es/TurboPutative/), a flexible and user-friendly web-based platform composed of four modules (Tagger, REname, RowMerger, and TPMetrics) that streamlines data handling, classification, and interpretability of untargeted LC-MS-based metabolomics data. Tagger classifies the different compounds and provides preliminary insights into the biological system studied. REname improves putative annotation handling and visualization, allowing the recognition of isomers and equivalent compounds and redundant data removal. RowMerger reduces the dataset size, facilitating the manual comparison among annotations. Finally, TPMetrics combines different datasets with feature intensity and relevant information for the researcher and calculates a score based on adduct probability and feature correlations, facilitating further identification, assessment, and interpretation of the results. The TurboPutative web application allows researchers in the metabolomics field that are dealing with massive datasets containing multiple putative annotations to reduce the number of these entries by 80%–90%, thus facilitating the extrapolation of biological knowledge and improving metabolite prioritization for subsequent pathway analysis. TurboPutative comprises a rapid, automated, and customizable workflow that can also be included in programmed bioinformatics pipelines through its RESTful API services. Users can explore the performance of each module through demo datasets supplied on the website. The platform will help the metabolomics community to speed up the arduous task of manual data curation that is required in the first steps of metabolite identification, improving the generation of biological knowledge.

Very long chain fatty acids

Very long chain fatty acids (VLCFAs) are important components of various lipid classes in most organisms, from bacteria to higher plants and mammals, including humans. VLCFAs, or very long chain polyunsaturated fatty acids (VLCPUFAs), can be defined as fatty acids with 23 or more carbon atoms in the molecule. The main emphasis in this review is on the analysis of these acids, including obtaining standards from natural sources or their synthesis. Furthermore, the occurrence and analysis of these compounds in both lower (bacteria, invertebrates) and higher organisms (flowering plants or mammals) are discussed in detail. Attention is paid to their biosynthesis, especially the elongation of very long chain fatty acids protein (ELOVL4). This review deals with papers describing these very interesting compounds, whose chemical, biochemical and biological properties have not been fully explored.

Development and Application of Feature-Based Molecular Networking for Phospholipidomics Analysis

Phospholipids are small but critical lipids in milk. Conventional lipidomics is a powerful method for the analysis of lipids in milk. Although the number of lipidomics software has drastically increased over the past five years, reducing false positives and obtaining structurally accurate annotations of phospholipids remain a significant challenge. In this study, we developed a rapid and accurate method for measuring a wide spectrum of phospholipids in milk. The developed approach that employed information-dependent acquisition (IDA) mode and feature-based molecular networking has exhibited better performance on data processing and lipid annotation when compared with sequential window acquisition of all theoretical mass spectra (SWATH) and MS-DIAL. This validated method was further evaluated using three kinds of sheep milk. A total of 150 phospholipids were identified, including rarely reported phospholipids containing ethers or vinyl ethers. The result indicated that phospholipids could be used as potential markers to distinguish sheep milk from different varieties and origins. The experimental and computational methods provide a rapid and reliable method for the investigation of phospholipids in milk.

An exploration into the use of Hansen solubility parameters for modelling reversed-phase chromatographic separations

The suitability of Hansen solubility parameters as descriptors for modelling analyte retention during reversed-phase chromatographic experiments was investigated. A novel theoretical model using Hansen solubility parameters as the basis for a complete mathematical derivation of the model was developed. The theoretical model also includes the cavitation volumes of the analytes, which were calculated using ab initio density functional theory methods. A set of three homologous phthalates was used for experimental data collection and subsequent model construction. The training error and the generalization error of the model were additionally evaluated using a range of chemically diverse analytes. Statistical evaluation of the results revealed that the model is suitable for analyte retention prediction but is limited to the analytes used in the model construction. Therefore, the resulting theoretical model cannot be easily generalized. A retention anomaly attributed to the column temperature and mobile phase composition was experimentally observed and mathematically investigated.

Equivalent Carbon Number and Interclass Retention Time Conversion Enhance Lipid Identification in Untargeted Clinical Lipidomics

Chromatography is often used as a method for reducing sample complexity prior to analysis by mass spectrometry, and the use of retention time (RT) is becoming increasingly popular to add valuable supporting information in lipid identification. The RT of lipids with the same headgroup in reversed-phase separation can be predicted using the equivalent carbon number (ECN) model. This model describes the effects of acyl chain length and degree of saturation on lipid RT. For the first time, we have found a robust correlation in the chromatographic separation of lipids with different headgroups that share the same fatty acid motive. This relationship can be exploited to perform interclass RT conversion (IC-RTC) by building a model from RT measurements from lipid standards that allows the prediction of RT of one lipid subclass based on another. Here, we utilize ECN modeling and IC-RTC to build a glycerophospholipid RT library with 517 entries based on 136 tandem mass spectrometry-characterized lipid RTs from NIST SRM-1950 plasma and lipid standards. The library was tested on a patient cohort undergoing coronary artery bypass grafting surgery (n = 37). A total of 156 unique circulating glycerophospholipids were identified, of which 52 (1 LPG, 24 PE, 5 PG, 18 PI, and 9 PS) were detected with IC-RTC, thereby demonstrating the utility of this technique for the identification of lipid species not found in commercial standards.

Regio- and Stereospecific Analysis of Triacylglycerols—A Brief Overview of the Challenges and the Achievements

The efforts to reveal, in detail, the molecular and intramolecular structures of one of the main lipid classes, namely, triacyl-sn-glycerols, which are now known to affect their specific and important role in all living organisms, are briefly overviewed. Some milestones of significance in the gradual but continuous development and improvement of the analytical methodology to identify the triacylglycerol regio- and stereoisomers in complex lipid samples are traced throughout the years: the use of chromatography based on different separation principles; the improvements in the chromatographic technique; the development and use of different detection techniques; the attempts to simplify and automatize the analysis without losing the accuracy of identification. The spectacular recent achievements of two- and multidimensional methods used as tools in lipidomics are presented.

Strategies for structure elucidation of small molecules based on LC–MS/MS data from complex biological samples

LC–MS/MS is a major analytical platform for metabolomics, which has become a recent hotspot in the research fields of life and environmental sciences. By contrast, structure elucidation of small molecules based on LC–MS/MS data remains a major challenge in the chemical and biological interpretation of untargeted metabolomics datasets. In recent years, several strategies for structure elucidation using LC–MS/MS data from complex biological samples have been proposed, these strategies can be simply categorized into two types, one based on structure annotation of mass spectra and for the other on retention time prediction. These strategies have helped many scientists conduct research in metabolite-related fields and are indispensable for the development of future tools. Here, we summarized the characteristics of the current tools and strategies for structure elucidation of small molecules based on LC–MS/MS data, and further discussed the directions and perspectives to improve the power of the tools or strategies for structure elucidation.

Optimization of a liquid chromatography-ion mobility-high resolution mass spectrometry platform for untargeted lipidomics and application to HepaRG cell extracts

Analytical methods to evaluate the lipidome of biological samples need to provide high data quality to ensure comprehensive profiling and reliable structural elucidation. In this perspective, liquid chromatography-high resolution mass spectrometry (LC-HRMS) is the state-of-the-art technique for lipidomic analysis of biological samples. There are thousands of lipids in most biological samples, and therefore separation methods before introduction to the mass spectrometer is key for relative quantitation and identification. Chromatographic methods differ across laboratories, without any consensus on the best methodologies. Therefore, we designed an experiment to determine the optimal LC methodology, and assessed the value of ion mobility for an additional dimension of separation. To apply an untargeted method for hypothesis generation focused on lipidomics, LC-HRMS parameters were optimized based on the measurement of 50 panel lipids covering key human metabolic pathways. Reversed-phase liquid chromatography columns were compared based on a quality scoring system considering the signal-to-noise ratio, peak shape, and retention factor. Furthermore, drift tube ion mobility spectrometry (DTIMS) was implemented to increase peak capacity and confidence during annotation by providing collision cross section (CCS) values for the analytes under investigation. However, hyphenating DTIMS to LC-HRMS may result in a reduced sensitivity due to impaired duty cycles. To increase the signal intensity, a Box-Behnken design (BBD) was used to optimize four key factors, i.e. drift entrance voltage, drift exit voltage, rear funnel entrance, and rear funnel exit voltages. Application of a maximized desirability function provided voltages for the above-mentioned parameters resulting in higher signal intensity compared to each combination of parameters used during the BBD. In addition, the influence of single pulse and Hadamard 4-bit multiplexed modes on signal intensity was explored and different trap filling and release times of ions were evaluated. The optimized LC-DTIM-HRMS platform was applied to extracts from HepaRG cells and resulted in 3912 high-quality features (<30% median relative standard deviation; n = 6, t = 24 h). From these features, 436 lipid species could be annotated (i.e., matching based on accurate mass <5 ppm, isotopic pattern, in-silico MS/MS fragmentation, and in-silico CCS database matching <3%). The application of LC-DTIM-HRMS for untargeted analysis workflows is growing and the platform optimization, as described here, can be used to guide the method development and CCS database comparison for high confidence lipid annotation.

Simple and Reproducible Derivatization with Benzoyl Chloride: Improvement of Sensitivity for Multiple Lipid Classes in RP-UHPLC/MS

The chemical derivatization of multiple lipid classes was developed using benzoyl chloride as a nonhazardous derivatization agent at ambient conditions. The derivatization procedure was optimized with standards for 4 nonpolar and 8 polar lipid classes and measured by reversed-phase ultrahigh-performance liquid chromatography-tandem mass spectrometry. The derivatization and nonderivatization approaches were compared on the basis of the calibration curves of 22 internal standards from 12 lipid classes. The new method decreased the limit of detection 9-fold for monoacylglycerols (0.9-1.0 nmol/mL), 6.5-fold for sphingoid base (0.2 nmol/mL), and 3-fold for diacylglycerols (0.9 nmol/mL). The sensitivity expressed by the ratio of calibration slopes was increased 2- to 10-fold for almost all investigated lipid classes and even more than 100-fold for monoacylglycerols. Moreover, the benzoylation reaction produces a more stable derivative of cholesterol in comparison to the easily in-source fragmented nonderivatized form and enabled the detection of fatty acids in a positive ion mode, which does not require polarity switching as for the nonderivatized form. The intralaboratory comparison with an additional operator without previous derivatization experiences shows the simplicity, robustness, and reproducibility. The stability of the derivatives was determined by periodical measurements during a one month period and five freeze/thaw cycles. The fully optimized derivatization method was applied to human plasma, which allows the detection of 169 lipid species from 11 lipid classes using the high confidence level of identification in reversed-phase (RP)-ultra high performance liquid chromatography (UHPLC)/mass spectrometry (MS). Generally, we detected more lipid species for monoacylglycerols, diacylglycerols, and sphingoid bases in comparison with previously reported papers without the derivatization.

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.

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.

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.

Lipidomic analysis using hydrophilic interaction liquid chromatography microgradient fractionation of total lipid extracts

Lipidomic samples are complex mixtures of structurally different species of a wide range of concentrations providing challenges in their characterization. In this work, we present a proof of concept for the application of a simple microgradient liquid chromatography device on the detailed analysis of lipid classes. Our lipidomic analysis is based on a lipid class microgradient fractionation of a total lipid extract using an in-house-prepared hydrophilic interaction liquid chromatography microcolumn followed by RP-LC/MS of the collected lipid class fractions. The final fractionation method uses a 40-mm-long microcolumn of 500 μm ID with silica stationary phase obtained from a commercially available chromatographic column and the microgradient of the mobile phase prepared in a microsyringe using methyl tert-butyl ether (MTBE) - methanol - water - ammonium acetate mixtures of various elution strengths. MTBE total lipid extract is directly separated by microgradient elution into lipid classes according to their polarity, which enables the collection of isolated fractions of most lipid classes. The method has been applied to the fractionation of porcine brain extract into nonpolar lipids, hexosylceramides, phosphoethanolamines, phosphocholines, sphingomyelins, and lysophosphocholines classes. Achieved repeatability, recovery, and advanced lipid coverage prove the applicability of the microgradient fractionation of total lipid extract for the comprehensive lipidomic analysis.

Retention dependences support highly confident identification of lipid species in human plasma by reversed-phase UHPLC/MS

Reversed-phase ultrahigh-performance liquid chromatography-mass spectrometry (RP-UHPLC/MS) method was developed with the aim to unambiguously identify a large number of lipid species from multiple lipid classes in human plasma. The optimized RP-UHPLC/MS method employed the C18 column with sub-2-μm particles with the total run time of 25 min. The chromatographic resolution was investigated with 42 standards from 18 lipid classes. The UHPLC system was coupled to high-resolution quadrupole-time-of-flight (QTOF) mass analyzer using electrospray ionization (ESI) measuring full-scan and tandem mass spectra (MS/MS) in positive- and negative-ion modes with high mass accuracy. Our identification approach was based on m/z values measured with mass accuracy within 5 ppm tolerance in the full-scan mode, characteristic fragment ions in MS/MS, and regularity in chromatographic retention dependences for individual lipid species, which provides the highest level of confidence for reported identifications of lipid species including regioisomeric and other isobaric forms. The graphs of dependences of retention times on the carbon number or on the number of double bond(s) in fatty acyl chains were constructed to support the identification of lipid species in homologous lipid series. Our list of identified lipid species is also compared with previous publications investigating human blood samples by various MS-based approaches. In total, we have reported more than 500 lipid species representing 26 polar and nonpolar lipid classes detected in NIST Standard reference material 1950 human plasma.

Non-aqueous reversed phase liquid chromatography with charged aerosol detection for quantitative lipid analysis with improved accuracy

There is a great need for efficient analysis of the composition of vegetable oils and fats, since it affects the physical and technical properties. However, due to the complex nature of these kind of samples, it is often difficult and costly. In the present study, we developed a Non-Aqueous Reversed-Phase HPLC method that can be used to separate and quantify different free fatty acids, fatty acid esters, monoacylglycerides, diacylglycerides and triacylglycerides, including regioisomers such as SOS/SSO and 1,2- and 1,3-diolein. Two 25 cm Nucleodur C18 Isis columns in series, sub-ambient column temperature and a mobile phase gradient composed of acetonitrile, acetic acid, isopropanol and heptane were used for the separation. The lipids were detected and quantified using a charged aerosol detector and it was found that the peak shape highly affected the detector response as well as the response uniformity, even when inverse gradient compensation was employed. Thus, calibration and determination of response factors were necessary for reliable quantification. A correlation between response factors and peak width at half peak height was found and used for quantification of non-calibrated components. A quantification approach was suggested including an appropriate selection of calibrated components, depending on sample composition and the accuracy required. It was shown in a complex oil sample that the reduced calibration approach, using only 6 instead of 33 calibrated components, resulted in virtually the same composition, but yielded a more accurate result compared to using relative area that neglects response factors. The method validation showed good reproducibility and accuracy, making it an excellent tool for extensive analysis of complex lipid mixtures.

A high throughput lipidomics method and its application in atrial fibrillation based on 96-well plate pretreatment and liquid chromatography-mass spectrometry

Successful applications of lipidomics in clinic need study large-scale samples, and the bottlenecks are in throughput and robustness of the lipid analytical method. Here, we report an untargeted lipidomics method by combining high throughput pretreatment in the 96-well plate with ultra-high performance liquid chromatography coupled to quadrupole time-of-flight tandem mass spectrometry. The developed method was validated to have satisfactory analytical characteristics in terms of linearity, repeatability and extraction recovery. It can be used to handle 96 samples simultaneously in 25 min and detect 441 lipids in plasma sample. Storage stability investigation on lipid extracts provided an operable procedure for large-scale sample analysis and demonstrated most lipids were stable in autosampler at 10 °C within 36 h and at -80 °C within 72 h after the pretreatment. To prove the usefulness, the method was employed to investigate abnormal plasma lipidome related to atrial fibrillation. A biomarker panel with the area under the curve (AUC) values of 0.831 and 0.745 was achieved in the discovery and external validation sets, respectively. These results showed that the developed method is applicable for large-scale biological sample handling and lipid analysis of plasma.

Evaluation and application of machine learning-based retention time prediction for suspect screening of pesticides and pesticide transformation products in LC-HRMS

Computational QSAR models have gradually been preferred for retention time prediction in data mining of emerging environmental contaminants using liquid chromatography coupled with mass spectrometry. Generally, the model performance relies on the components such as machine learning algorithms, chemical features, and example data. In this study, we evaluated the performances of four algorithms on three feature sets, using 321 and 77 pesticides as the training and validation sets, respectively. The results were varied with different combinations of algorithms on distinct feature sets. Two strategies including enhancing the complexity of chemical features and enlarging the size of the training set were proved to improve the results. XGBoost, Random Forest, and lightGBM algorithms exhibited the best results when built on a large-scale chemical descriptors, while the Keras algorithm preferred fingerprints. These four models have comparable prediction accuracies that at least 90% of pesticides in validation set can be successfully predicted with ΔRT <1.0 min. Meanwhile, a blended prediction strategy using average results from four models presented a better result than any single model. This strategy was used for assisting identification of pesticides and pesticide transformation products in 120 strawberry samples from a national survey of food contamination. Twenty pesticides and twelve pesticide transformation products were tentatively identified, where all pesticides and two pesticide transformation products (bifenazate diazene and spirotetramat-enol) were confirmed by standard materials. The outcome of this study suggested that retention time prediction is a valuable approach in compound identification when integrated with in silico MS² spectra and other MS identification strategies.

Novel Strategy for Mining and Identification of Acylcarnitines Using Data-Independent-Acquisition-Based Retention Time Prediction Modeling and Pseudo-Characteristic Fragmentation Ion Matching

It is a challenging work to screen, identify, and quantify acylcarnitines in complex biological samples. A method, based on the retention time (RT) prediction and data-independent acquisition strategies, was proposed for the large-scale identification of acylcarnitines using liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS). Relative cumulative eluotropic strength was introduced as a novel descriptor in building a linear prediction model, which not only solves the problem that acylcarnitines with long carbon chains cannot be well predicted in traditional models but also proves its robustness and transferability across instruments in two data sets that were acquired in distinct chromatography conditions. The accessibility of both predictive RT and MS² spectra of suspect features effectively reduced about 30% false-positive results, and consequently, 150 and 186 acylcarnitines were identified in the rat liver and human plasma (NIST SRM 1950), respectively. This method provides a new approach in large-scale analysis of acylcarnitine in lipidomic studies and can also be extended to the analysis of other lipids.

Enhancing Coverage of Phosphatidylinositol Species in Canola Through Specialised Liquid Chromatography-Mass Spectrometry Buffer Conditions

Phosphatidylinositols (PIs) constitute a minor class of phospholipid with wide-spread influence throughout various cellular functions. Monitoring the distribution of these lipids can therefore provide insight as to the state of cellular processes or reveal the development of various pathologies. The speciation of these compounds is often performed either as part of a comprehensive characterisation of lipids, or specifically targeted using the same methods, however, such methods were intended to maximise coverage of lipid classes rather than provide an in-depth analysis of any single class. In the particular case of PIs, the majority of reported molecular diversity is limited to a small proportion of the already minor class, as such the cursory glance enabled by such methods is insufficient. Therefore, this work compared the suitability of both established and novel LC-MS buffers with the aim of maximising the ionisation efficiency of PIs, in an attempt to enhance coverage of the class. Through experimentation, it was determined that a 0.25 mM ammonium fluoride buffer provided up to a 6-fold increase in signal intensity, and on average a 38-fold increase in the signal-to-noise ratio. Using these new conditions, 14 PI species, and 12 PI candidates were identified within a dilute lipid extract sourced from canola seed, compared to 0 species identified using the generalised method. As a result, it is suggested that this procedure has yielded the highest number of PI species identifications for a sample of this concentration. Methods which therefore intend to characterise PI species in dilute quantities, such as those extracted from mammalian cells, are henceforth provided with the means to conduct more comprehensive characterisations.

A UPLC-MRM-MS method for comprehensive profiling of Amadori compound-modified phosphatidylethanolamines in human plasma

Phosphatidylethanolamines (PEs) are targets of non-enzymatic glycation, a chemical process that occurs between glucose and primary amine-containing biomolecules. As the early-stage non-enzymatic glycation products of PE, Amadori-PEs are implicated in the pathogenesis of various diseases. However, only a few Amadori-PE molecular species have been identified so far; a comprehensive profiling of these glycated PE species is needed to establish their roles in disease pathology. Herein, based on our previous work using liquid chromatography-coupled neutral loss scanning and product ion scanning tandem mass spectrometry (LC-NLS-MS and LC-PIS-MS) in tandem, we extend identification of Amadori-PE to the low-abundance species, which is facilitated by using plasma lipids glycated in vitro. The confidence of identification is improved by high-resolution tandem mass spectrometry and chromatographic retention time regression. A LC-coupled multiple reaction monitoring mass spectrometry (LC-MRM-MS) assay is further developed for more sensitive quantitation of the Amadori compound-modified lipids. Using synthesized stable isotope-labeled Amadori lipids as internal standards, levels of 142 Amadori-PEs and 33 Amadori-LysoPEs are determined in the NIST human plasma standard reference material. These values may serve as an important reference for future investigations of Amadori-modified lipids in human diseases.

Chasing the Major Sphingolipids on Earth: Automated Annotation of Plant Glycosyl Inositol Phospho Ceramides by Glycolipidomics

Glycosyl inositol phospho ceramides (GIPCs) are the major sphingolipids on earth, as they account for a considerable fraction of the total lipids in plants and fungi, which in turn represent a large portion of the biomass on earth. Despite their obvious importance, GIPC analysis remains challenging due to the lack of commercial standards and automated annotation software. In this work, we introduce a novel GIPC glycolipidomics workflow based on reversed-phase ultra-high pressure liquid chromatography coupled to high-resolution mass spectrometry. For the first time, automated GIPC assignment was performed using the open-source software Lipid Data Analyzer (LDA), based on platform-independent decision rules. Four different plant samples (salad, spinach, raspberry, and strawberry) were analyzed and the results revealed 64 GIPCs based on accurate mass, characteristic MS2 fragments and matching retention times. Relative quantification using lactosyl ceramide for internal standardization revealed GIPC t18:1/h24:0 as the most abundant species in all plants. Depending on the plant sample, GIPCs contained mainly amine, N-acetylamine or hydroxyl residues. Most GIPCs revealed a Hex-HexA-IPC core and contained a ceramide part with a trihydroxylated t18:0 or a t18:1 long chain base and hydroxylated fatty acid chains ranging from 16 to 26 carbon atoms in length (h16:0-h26:0). Interestingly, four GIPCs containing t18:2 were observed in the raspberry sample, which was not reported so far. The presented workflow supports the characterization of different plant samples by automatic GIPC assignment, potentially leading to the identification of new GIPCs. For the first time, automated high-throughput profiling of these complex glycolipids is possible by liquid chromatography-high-resolution tandem mass spectrometry and subsequent automated glycolipid annotation based on decision rules.

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.

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.

Lipid Annotation by Combination of UHPLC-HRMS (MS), Molecular Networking, and Retention Time Prediction: Application to a Lipidomic Study of In Vitro Models of Dry Eye Disease

Annotation of lipids in untargeted lipidomic analysis remains challenging and a systematic approach needs to be developed to organize important datasets with the help of bioinformatic tools. For this purpose, we combined tandem mass spectrometry-based molecular networking with retention time (t_R) prediction to annotate phospholipid and sphingolipid species. Sixty-five standard compounds were used to establish the fragmentation rules of each lipid class studied and to define the parameters governing their chromatographic behavior. Molecular networks (MNs) were generated through the GNPS platform using a lipid standards mixture and applied to lipidomic study of an in vitro model of dry eye disease, i.e., human corneal epithelial (HCE) cells exposed to hyperosmolarity (HO). These MNs led to the annotation of more than 150 unique phospholipid and sphingolipid species in the HCE cells. This annotation was reinforced by comparing theoretical to experimental t_R values. This lipidomic study highlighted changes in 54 lipids following HO exposure of corneal cells, some of them being involved in inflammatory responses. The MN approach coupled to t_R prediction thus appears as a suitable and robust tool for the discovery of lipids involved in relevant biological processes.