Common cases of improper lipid annotation using high-resolution tandem mass spectrometry data and corresponding limitations in biological interpretation

Lipidomics analysis reveals new insights into crisp grass carp associated with meat texture

Feeding faba beans to grass carp could crisp its muscle texture to avoid softening, the relationship between texture formation throughout the crisping process and the critical lipids regulating the fish quality has not yet been clarified. Herein, an 60-day nutritional trial and untargeted lipidomic analysis was used to study the changes of lipids in crisp grass carp dorsal muscle. A total of 1036 lipids were remarkably different between ordinary and crisp grass carp. The concentrations of the LPC, LPE, PG, Cer, Hex2Cer, SM, MG and MGMG were positively correlated with hardness and springiness, and the CL, TG, PMe, WE, dMePE and AcCa were negative correlation. High content of lipids involved in storage in ordinary grass carp, such as glycerophospholipids, polyunsaturated and saturated fatty acid content. In contrast, high content of membrane components in crisp grass carp, such as monounsaturated fatty acid, sphingolipid and glycerolipids content, and the distribution of PUFA in lipid molecules was related to lipid biosynthesis. This study might provide some insights into improved knowledge of the association between meat texture and lipid molecules in fish fed with faba bean.

Recent additions and access to a multidimensional lipidomic database containing liquid chromatography, ion mobility spectrometry, and tandem mass spectrometry information

The importance of lipids in biology continues to grow with their recent linkages to more diseases and conditions, microbiome fluctuations, and environmental exposures. These associations have motivated researchers to evaluate lipidomic changes in numerous matrices and studies. Lipidomic analyses, however, present numerous challenges as lipid species have broad chemistries that require different extraction methods and instrumental analyses to evaluate and separate their many isomers and isobars. Increasing knowledge about different lipid characteristics is therefore crucial for improving their separation and identification. Here, we present a multidimensional database for lipids analyzed on a platform combining reversed-phase liquid chromatography, drift tube ion mobility spectrometry, collision-induced dissociation, and mass spectrometry (RPLC-DTIMS-CID-MS). This platform and the different separation characteristics it provides enables more confident lipid annotations when compared to traditional tandem mass spectrometry platforms, especially when analyzing highly isomeric molecules such as lipids. This database expands on our previous publication containing only human plasma and bronchoalveolar lavage fluid lipids and provides experimental RPLC retention times, IMS collision cross section (CCS) values, and m/z information for 877 unique lipids from additional biofluids and tissues. Specifically, the database contains 1504 precursor [M + H]+, [M + NH4]+, [M + Na]+, [M-H]-, [M-2H]2-, [M + HCOO]-, and [M + CH3COO]- ion species and their associated CID fragments which are commonly targeted in clinical and environmental studies, in addition to being present in the chloroform layer of Folch extractions. Furthermore, this multidimensional RPLC-DTIMS-CID-MS database spans 5 lipid categories (fatty acids, sterols, sphingolipids, glycerolipids, and glycerophospholipids) and 24 lipid classes. We have also created a webpage (tarheels.live/bakerlab/databases/) to enhance the accessibility of this resource which will be populated regularly with new lipids as we identify additional species and integrate novel standards.

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.

Challenges and perspectives for naming lipids in the context of lipidomics

INTRODUCTION

Lipids are key compounds in the study of metabolism and are increasingly studied in biology projects. It is a very broad family that encompasses many compounds, and the name of the same compound may vary depending on the community where they are studied.

OBJECTIVES

In addition, their structures are varied and complex, which complicates their analysis. Indeed, the structural resolution does not always allow a complete level of annotation so the actual compound analysed will vary from study to study and should be clearly stated. For all these reasons the identification and naming of lipids is complicated and very variable from one study to another, it needs to be harmonized.

METHODS & RESULTS

In this position paper we will present and discuss the different way to name lipids (with chemoinformatic and semantic identifiers) and their importance to share lipidomic results.

CONCLUSION

Homogenising this identification and adopting the same rules is essential to be able to share data within the community and to map data on functional networks.

Blood-Derived Lipid and Metabolite Biomarkers in Cardiovascular Research from Clinical Studies: A Recent Update

The primary prevention, early detection, and treatment of cardiovascular disease (CVD) have been long-standing scientific research goals worldwide. In the past decades, traditional blood lipid profiles have been routinely used in clinical practice to estimate the risk of CVDs such as atherosclerotic cardiovascular disease (ASCVD) and as treatment targets for the primary prevention of adverse cardiac events. These blood lipid panel tests often fail to fully predict all CVD risks and thus need to be improved. A comprehensive analysis of molecular species of lipids and metabolites (defined as lipidomics and metabolomics, respectively) can provide molecular insights into the pathophysiology of the disease and could serve as diagnostic and prognostic indicators of disease. Mass spectrometry (MS) and nuclear magnetic resonance (NMR)-based lipidomics and metabolomics analysis have been increasingly used to study the metabolic changes that occur during CVD pathogenesis. In this review, we provide an overview of various MS-based platforms and approaches that are commonly used in lipidomics and metabolomics workflows. This review summarizes the lipids and metabolites in human plasma/serum that have recently (from 2018 to December 2022) been identified as promising CVD biomarkers. In addition, this review describes the potential pathophysiological mechanisms associated with candidate CVD biomarkers. Future studies focused on these potential biomarkers and pathways will provide mechanistic clues of CVD pathogenesis and thus help with the risk assessment, diagnosis, and treatment of CVD.

Early whole-body mutant huntingtin lowering averts changes in proteins and lipids important for synapse function and white matter maintenance in the LacQ140 mouse model

Expansion of a triplet repeat tract in exon 1 of the HTT gene causes Huntington's disease (HD). The mutant HTT protein (mHTT) has numerous aberrant interactions with diverse, pleiomorphic effects. Lowering mHTT is a promising approach to treat HD, but it is unclear when lowering should be initiated, how much is necessary, and what duration should occur to achieve benefits. Furthermore, the effects of mHTT lowering on brain lipids have not been assessed. Using a mHtt-inducible mouse model, we analyzed mHtt lowering initiated at different ages and sustained for different time-periods. mHTT protein in cytoplasmic and synaptic compartments of the striatum was reduced 38-52%; however, there was minimal lowering of mHTT in nuclear and perinuclear regions where aggregates formed at 12 months of age. Total striatal lipids were reduced in 9-month-old LacQ140 mice and preserved by mHtt lowering. Subclasses important for white matter structure and function including ceramide (Cer), sphingomyelin (SM), and monogalactosyldiacylglycerol (MGDG), contributed to the reduction in total lipids. Phosphatidylinositol (PI), phosphatidylserine (PS), and bismethyl phosphatidic acid (BisMePA) were also changed in LacQ140 mice. Levels of all subclasses except ceramide were preserved by mHtt lowering. mRNA expression profiling indicated that a transcriptional mechanism contributes to changes in myelin lipids, and some but not all changes can be prevented by mHtt lowering. Our findings suggest that early and sustained reduction in mHtt can prevent changes in levels of select striatal proteins and most lipids, but a misfolded, degradation-resistant form of mHTT hampers some benefits in the long term.

Ozone-enabled fatty acid discovery reveals unexpected diversity in the human lipidome

Fatty acid isomers are responsible for an under-reported lipidome diversity across all kingdoms of life. Isomers of unsaturated fatty acids are often masked in contemporary analysis by incomplete separation and the absence of sufficiently diagnostic methods for structure elucidation. Here, we introduce a comprehensive workflow, to discover unsaturated fatty acids through coupling liquid chromatography and mass spectrometry with gas-phase ozonolysis of double bonds. The workflow encompasses semi-automated data analysis and enables de novo identification in complex media including human plasma, cancer cell lines and vernix caseosa. The targeted analysis including ozonolysis enables structural assignment over a dynamic range of five orders of magnitude, even in instances of incomplete chromatographic separation. Thereby we expand the number of identified plasma fatty acids two-fold, including non-methylene-interrupted fatty acids. Detection, without prior knowledge, allows discovery of non-canonical double bond positions. Changes in relative isomer abundances reflect underlying perturbations in lipid metabolism.

Non-targeted analysis (NTA) and suspect screening analysis (SSA): a review of examining the chemical exposome

Non-targeted analysis (NTA) and suspect screening analysis (SSA) are powerful techniques that rely on high-resolution mass spectrometry (HRMS) and computational tools to detect and identify unknown or suspected chemicals in the exposome. Fully understanding the chemical exposome requires characterization of both environmental media and human specimens. As such, we conducted a review to examine the use of different NTA and SSA methods in various exposure media and human samples, including the results and chemicals detected. The literature review was conducted by searching literature databases, such as PubMed and Web of Science, for keywords, such as "non-targeted analysis", "suspect screening analysis" and the exposure media. Sources of human exposure to environmental chemicals discussed in this review include water, air, soil/sediment, dust, and food and consumer products. The use of NTA for exposure discovery in human biospecimen is also reviewed. The chemical space that has been captured using NTA varies by media analyzed and analytical platform. In each media the chemicals that were frequently detected using NTA were: per- and polyfluoroalkyl substances (PFAS) and pharmaceuticals in water, pesticides and polyaromatic hydrocarbons (PAHs) in soil and sediment, volatile and semi-volatile organic compounds in air, flame retardants in dust, plasticizers in consumer products, and plasticizers, pesticides, and halogenated compounds in human samples. Some studies reviewed herein used both liquid chromatography (LC) and gas chromatography (GC) HRMS to increase the detected chemical space (16%); however, the majority (51%) only used LC-HRMS and fewer used GC-HRMS (32%). Finally, we identify knowledge and technology gaps that must be overcome to fully assess potential chemical exposures using NTA. Understanding the chemical space is essential to identifying and prioritizing gaps in our understanding of exposure sources and prior exposures. IMPACT STATEMENT: This review examines the results and chemicals detected by analyzing exposure media and human samples using high-resolution mass spectrometry based non-targeted analysis (NTA) and suspect screening analysis (SSA).

Cancer metabolites: promising biomarkers for cancer liquid biopsy

Cancer exerts a multitude of effects on metabolism, including the reprogramming of cellular metabolic pathways and alterations in metabolites that facilitate inappropriate proliferation of cancer cells and adaptation to the tumor microenvironment. There is a growing body of evidence suggesting that aberrant metabolites play pivotal roles in tumorigenesis and metastasis, and have the potential to serve as biomarkers for personalized cancer therapy. Importantly, high-throughput metabolomics detection techniques and machine learning approaches offer tremendous potential for clinical oncology by enabling the identification of cancer-specific metabolites. Emerging research indicates that circulating metabolites have great promise as noninvasive biomarkers for cancer detection. Therefore, this review summarizes reported abnormal cancer-related metabolites in the last decade and highlights the application of metabolomics in liquid biopsy, including detection specimens, technologies, methods, and challenges. The review provides insights into cancer metabolites as a promising tool for clinical applications.

High Throughput LC-MS Platform for Large Scale Screening of Bioactive Polar Lipids in Human Plasma and Serum

Lipids play a key role in many biological processes, and their accurate measurement is critical to unraveling the biology of diseases and human health. A high throughput HILIC-based (LC-MS) method for the semiquantitative screening of over 2000 lipids, based on over 4000 MRM transitions, was devised to produce an accessible and robust lipidomic screen for phospholipids in human plasma/serum. This methodology integrates many of the advantages of global lipid analysis with those of targeted approaches. Having used the method as an initial "wide class" screen, it can then be easily adapted for a more targeted analysis and quantification of key, dysregulated lipids. Robustness was assessed using 1550 continuous injections of plasma extracts onto a single column and via the evaluation of columns from 5 different batches of stationary phase. Initial screens in positive (239 lipids, 431 MRM transitions) and negative electrospray ionization (ESI) mode (232 lipids, 446 MRM transitions) were assessed for reproducibility, sensitivity, and dynamic range using analysis times of 8 min. The total number of lipids monitored using these screening methods was 433 with an overlap of 38 lipids in both modes. A polarity switching method for accurate quantification, using the same LC conditions, was assessed for intra- and interday reproducibility, accuracy, dynamic range, stability, carryover, dilution integrity, and matrix interferences and found to be acceptable. This polarity switching method was then applied to lipids important in the stratification of human prostate cancer samples.

Pan-cancer analysis of pre-diagnostic blood metabolite concentrations in the European Prospective Investigation into Cancer and Nutrition

BACKGROUND

Epidemiological studies of associations between metabolites and cancer risk have typically focused on specific cancer types separately. Here, we designed a multivariate pan-cancer analysis to identify metabolites potentially associated with multiple cancer types, while also allowing the investigation of cancer type-specific associations.

METHODS

We analysed targeted metabolomics data available for 5828 matched case-control pairs from cancer-specific case-control studies on breast, colorectal, endometrial, gallbladder, kidney, localized and advanced prostate cancer, and hepatocellular carcinoma nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. From pre-diagnostic blood levels of an initial set of 117 metabolites, 33 cluster representatives of strongly correlated metabolites and 17 single metabolites were derived by hierarchical clustering. The mutually adjusted associations of the resulting 50 metabolites with cancer risk were examined in penalized conditional logistic regression models adjusted for body mass index, using the data-shared lasso penalty.

RESULTS

Out of the 50 studied metabolites, (i) six were inversely associated with the risk of most cancer types: glutamine, butyrylcarnitine, lysophosphatidylcholine a C18:2, and three clusters of phosphatidylcholines (PCs); (ii) three were positively associated with most cancer types: proline, decanoylcarnitine, and one cluster of PCs; and (iii) 10 were specifically associated with particular cancer types, including histidine that was inversely associated with colorectal cancer risk and one cluster of sphingomyelins that was inversely associated with risk of hepatocellular carcinoma and positively with endometrial cancer risk.

CONCLUSIONS

These results could provide novel insights for the identification of pathways for cancer development, in particular those shared across different cancer types.

A narrative review of metabolomics in the era of "-omics": integration into clinical practice for inborn errors of metabolism

BACKGROUND AND OBJECTIVE

Traditional targeted metabolomic investigations identify a pre-defined list of analytes in samples and have been widely used for decades in the diagnosis and monitoring of inborn errors of metabolism (IEMs). Recent technological advances have resulted in the development and maturation of untargeted metabolomics: a holistic, unbiased, analytical approach to detecting metabolic disturbances in human disease. We aim to provide a summary of untargeted metabolomics [focusing on tandem mass spectrometry (MS-MS)] and its application in the field of IEMs.

METHODS

Data for this review was identified through a literature search using PubMed, Google Scholar, and personal repositories of articles collected by the authors. Findings are presented within several sections describing the metabolome, the current use of targeted metabolomics in the diagnostic pathway of patients with IEMs, the more recent integration of untargeted metabolomics into clinical care, and the limitations of this newly employed analytical technique.

KEY CONTENT AND FINDINGS

Untargeted metabolomic investigations are increasingly utilized in screening for rare disorders, improving understanding of cellular and subcellular physiology, discovering novel biomarkers, monitoring therapy, and functionally validating genomic variants. Although the untargeted metabolomic approach has some limitations, this "next generation metabolic screening" platform is becoming increasingly affordable and accessible.

CONCLUSIONS

When used in conjunction with genomics and the other promising "-omic" technologies, untargeted metabolomics has the potential to revolutionize the diagnostics of IEMs (and other rare disorders), improving both clinical and health economic outcomes.

Impacts of Gestational FireMaster 550 Exposure on the Neonatal Cortex Are Sex Specific and Largely Attributable to the Organophosphate Esters

INTRODUCTION

Flame retardants (FRs) are common bodily and environmental pollutants, creating concern about their potential toxicity. We and others have found that the commercial mixture FireMaster® 550 (FM 550) or its individual brominated (BFR) and organophosphate ester (OPFR) components are potential developmental neurotoxicants. Using Wistar rats, we previously reported that developmental exposure to FM 550 or its component classes produced sex- and compound-specific effects on adult socioemotional behaviors. The underlying mechanisms driving the behavioral phenotypes are unknown.

METHODS

To further mechanistic understanding, here we conducted transcriptomics in parallel with a novel lipidomics approach using cortical tissues from newborn siblings of the rats in the published behavioral study. Inclusion of lipid composition is significant because it is rarely examined in developmental neurotoxicity studies. Pups were gestationally exposed via oral dosing to the dam to FM 550 or the BFR or OPFR components at environmentally relevant doses.

RESULTS

The neonatal cortex was highly sexually dimorphic in lipid and transcriptome composition, and males were more significantly impacted by FR exposure. Multiple adverse modes of action for the BFRs and OPFRs on neurodevelopment were identified, with the OPFRs being more disruptive than the BFRs via multiple mechanisms including dysregulation of mitochondrial function and disruption of cholinergic and glutamatergic systems. Disrupted mitochondrial function by environmental factors has been linked to a higher risk of autism spectrum disorders and neurodegenerative disorders. Impacted lipid classes included ceramides, sphingomyelins, and triacylglycerides. Robust ceramide upregulation in the OPFR females could suggest a heightened risk of brain metabolic disease.

CONCLUSIONS

This study reveals multiple mechanisms by which the components of a common FR mixture are developmentally neurotoxic and that the OPFRs may be the compounds of greatest concern.

Utilizing Skyline to analyze lipidomics data containing liquid chromatography, ion mobility spectrometry and mass spectrometry dimensions

Lipidomics studies suffer from analytical and annotation challenges because of the great structural similarity of many of the lipid species. To improve lipid characterization and annotation capabilities beyond those afforded by traditional mass spectrometry (MS)-based methods, multidimensional separation methods such as those integrating liquid chromatography, ion mobility spectrometry, collision-induced dissociation and MS (LC-IMS-CID-MS) may be used. Although LC-IMS-CID-MS and other multidimensional methods offer valuable hydrophobicity, structural and mass information, the files are also complex and difficult to assess. Thus, the development of software tools to rapidly process and facilitate confident lipid annotations is essential. In this Protocol Extension, we use the freely available, vendor-neutral and open-source software Skyline to process and annotate multidimensional lipidomic data. Although Skyline ( https://skyline.ms/skyline.url ) was established for targeted processing of LC-MS-based proteomics data, it has since been extended such that it can be used to analyze small-molecule data as well as data containing the IMS dimension. This protocol uses Skyline's recently expanded capabilities, including small-molecule spectral libraries, indexed retention time and ion mobility filtering, and provides a step-by-step description for importing data, predicting retention times, validating lipid annotations, exporting results and editing our manually validated 500+ lipid library. Although the time required to complete the steps outlined here varies on the basis of multiple factors such as dataset size and familiarity with Skyline, this protocol takes ~5.5 h to complete when annotations are rigorously verified for maximum confidence.

Combining Micropunch Histology and Multidimensional Lipidomic Measurements for In-Depth Tissue Mapping

While decades of technical and analytical advancements have been utilized to discover novel lipid species, increase speciation, and evaluate localized lipid dysregulation at subtissue, cellular, and subcellular levels, many challenges still exist. One limitation is that the acquisition of both in-depth spatial information and comprehensive lipid speciation is extremely difficult, especially when biological material is limited or lipids are at low abundance. In neuroscience, for example, it is often desired to focus on only one brain region or subregion, which can be well under a square millimeter for rodents. Herein, we evaluate a micropunch histology method where cortical brain tissue at 2.0, 1.25, 1.0, 0.75, 0.5, and 0.25 mm diameter sizes and 1 mm depth was collected and analyzed with multidimensional liquid chromatography, ion mobility spectrometry, collision induced dissociation, and mass spectrometry (LC-IMS-CID-MS) measurements. Lipid extraction was optimized for the small sample sizes, and assessment of lipidome coverage for the 2.0 to 0.25 mm diameter sizes showed a decline from 304 to 198 lipid identifications as validated by all 4 analysis dimensions (~35% loss in coverage for ~88% less tissue). While losses were observed, the ~200 lipids and estimated 4630 neurons contained within the 0.25 punch still provided in-depth characterization of the small tissue region. Furthermore, while localization routinely achieved by mass spectrometry imaging (MSI) and single cell analyses is greater, this diameter is sufficiently small to isolate subcortical, hypothalamic, and other brain regions in adult rats, thereby increasing the coverage of lipids within relevant spatial windows without sacrificing speciation. Therefore, micropunch histology enables in-depth, region-specific lipid evaluations, an approach that will prove beneficial to a variety of lipidomic applications.

Lipidomic Profiling Reveals the Reducing Lipid Accumulation Effect of Dietary Taurine in Groupers (Epinephelus coioides)

A lipidomic analysis was conducted to provide the first detailed overview of lipid molecule profiles in response to dietary lipid and taurine and associations of liver lipid-lowering effects of dietary taurine with lipid molecular species and the positional distributions of fatty acids in the liver of juvenile orange-spotted groupers (Epinephelus coioides). The results indicated that the liver was more sensitive to varied dietary lipid and taurine contents than the muscle with regard to lipid molecules. A total of 131 differential lipid molecules (DLMs) were observed in the liver of groupers when dietary taurine was increased from 0 to 1% at 15% lipid, among which all the up and down-regulated DLMs are phospholipids (PLs) and triglycerides (TGs), respectively. The liver content of TGs containing 18:2n-6 attached at the sn-2 and sn-3 positions on the glycerol backbone increased with increasing dietary lipid from 10 to 15% but decreased with increasing dietary taurine from 0 to 1%. Therefore, dietary taurine can not only reduce lipid accumulation through decreasing the contents of TGs containing 18:2n-6 at the sn-2 and sn-3 positions but also enhance the anti-inflammatory capacity and health status of groupers. This study will also provide a new insight into the function of taurine in farmed fish.

An actionable annotation scoring framework for gas chromatography-high-resolution mass spectrometry

Omics-based technologies have enabled comprehensive characterization of our exposure to environmental chemicals (chemical exposome) as well as assessment of the corresponding biological responses at the molecular level (eg, metabolome, lipidome, proteome, and genome). By systematically measuring personal exposures and linking these stimuli to biological perturbations, researchers can determine specific chemical exposures of concern, identify mechanisms and biomarkers of toxicity, and design interventions to reduce exposures. However, further advancement of metabolomics and exposomics approaches is limited by a lack of standardization and approaches for assigning confidence to chemical annotations. While a wealth of chemical data is generated by gas chromatography high-resolution mass spectrometry (GC-HRMS), incorporating GC-HRMS data into an annotation framework and communicating confidence in these assignments is challenging. It is essential to be able to compare chemical data for exposomics studies across platforms to build upon prior knowledge and advance the technology. Here, we discuss the major pieces of evidence provided by common GC-HRMS workflows, including retention time and retention index, electron ionization, positive chemical ionization, electron capture negative ionization, and atmospheric pressure chemical ionization spectral matching, molecular ion, accurate mass, isotopic patterns, database occurrence, and occurrence in blanks. We then provide a qualitative framework for incorporating these various lines of evidence for communicating confidence in GC-HRMS data by adapting the Schymanski scoring schema developed for reporting confidence levels by liquid chromatography HRMS (LC-HRMS). Validation of our framework is presented using standards spiked in plasma, and confident annotations in outdoor and indoor air samples, showing a false-positive rate of 12% for suspect screening for chemical identifications assigned as Level 2 (when structurally similar isomers are not considered false positives). This framework is easily adaptable to various workflows and provides a concise means to communicate confidence in annotations. Further validation, refinements, and adoption of this framework will ideally lead to harmonization across the field, helping to improve the quality and interpretability of compound annotations obtained in GC-HRMS.

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.

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.

Mass Spectrometry-Based Zebrafish Toxicometabolomics: A Review of Analytical and Data Quality Challenges

Metabolomics has achieved great progress over the last 20 years, and it is currently considered a mature research field. As a result, the number of applications in toxicology, biomarker, and drug discovery has also increased. Toxicometabolomics has emerged as a powerful strategy to provide complementary information to study molecular-level toxic effects, which can be combined with a wide range of toxicological assessments and models. The zebrafish model has gained importance in recent decades as a bridging tool between in vitro assays and mammalian in vivo studies in the field of toxicology. Furthermore, as this vertebrate model is a low-cost system and features highly conserved metabolic pathways found in humans and mammalian models, it is a promising tool for toxicometabolomics. This short review aims to introduce zebrafish researchers interested in understanding the effects of chemical exposure using metabolomics to the challenges and possibilities of the field, with a special focus on toxicometabolomics-based mass spectrometry. The overall goal is to provide insights into analytical strategies to generate and identify high-quality metabolomic experiments focusing on quality management systems (QMS) and the importance of data reporting and sharing.

Metabolomic Laboratory-Developed Tests: Current Status and Perspectives

Laboratory-developed tests (LDTs) are a subset of in vitro diagnostic devices, which the US Food and Drug Administration defines as "tests that are manufactured by and used within a single laboratory". The review describes the emergence and history of LDTs. The current state and development prospects of LDTs based on metabolomics are analyzed. By comparing LDTs with the scientific metabolomics study of human bio samples, the characteristic features of metabolomic LDT are shown, revealing its essence, strengths, and limitations. The possibilities for further developments and scaling of metabolomic LDTs and their potential significance for healthcare are discussed. The legal aspects of LDT regulation in the United States, European Union, and Singapore, demonstrating different approaches to this issue, are also provided. Based on the data presented in the review, recommendations were made on the feasibility and ways of further introducing metabolomic LDTs into practice.

Untargeted Lipidomics for Determining Cellular and Subcellular Responses in Zebrafish (Danio rerio) Liver Cells Following Exposure to Complex Mixtures in U.S. Streams

Surface waters often contain a variety of chemical contaminants potentially capable of producing adverse outcomes in both humans and wildlife due to impacts from industrial, urban, and agricultural activity. Here, we report the results of a zebrafish liver (ZFL) cell-based lipidomics approach to assess the potential ecotoxicological effects of complex contaminant mixtures using water collected from eight impacted streams across the United States mainland and Puerto Rico. We initially characterized the ZFL lipidome using high resolution mass spectrometry, resulting in the annotation of 508 lipid species covering 27 classes. We then identified lipid changes induced by all streamwater samples (nonspecific stress indicators) as well as those unique to water samples taken from specific streams. Subcellular impacts were classified based on organelle-specific lipid changes, including increased lipid saturation (endoplasmic reticulum stress), elevated bis(monoacylglycero)phosphate (lysosomal overload), decreased ubiquinone (mitochondrial dysfunction), and elevated ether lipids (peroxisomal stress). Finally, we demonstrate how these results can uniquely inform environmental monitoring and risk assessments of surface waters.

Untargeted lipidomics reveals metabolic responses to different dietary n-3 PUFA in juvenile swimming crab (Portunus trituberculatus)

While tissue fatty acid compositions reflect that of the dietary lipid source, little information is available on how dietary oils modify lipid class and molecular species profiles in hepatopancreas of crustacean. Herein, an 8-week nutritional trial and untargeted lipidomic analysis were used to investigate the impacts of dietary n-3 PUFA lipid sources including fish oil, krill oil and linseed oil on the lipidomic characteristics of hepatopancreas of swimming crab (Portunus trituberculatus). Dietary krill oil significantly increased distribution of 20:5n-3 and 22:6n-3 at sn-2 in phosphatidylcholine and phosphatidylethanolamine compared to fish oil. Fish oil intake promoted the deposition of 20:5n-3 and 22:6n-3 at sn-1,2,3 in triglyceride compared to linseed oil, which significantly increased the specific accumulation of 18:3n-3 at sn-1,3 in triglyceride and sn-2 in phosphatidylcholine and phosphatidylethanolamine. The study revealed metabolic responses to different dietary n-3 PUFA in swimming crab, which provided novel insight into the lipid nutrition of crustacean.

Lipid remodeling in response to methionine stress in MDA-MBA-468 triple-negative breast cancer cells

Methionine (Met) is an essential amino acid and critical precursor to the cellular methyl donor S-adenosylmethionine. Unlike nontransformed cells, cancer cells have a unique metabolic requirement for Met and are unable to proliferate in growth media where Met is replaced with its metabolic precursor, homocysteine. This metabolic vulnerability is common among cancer cells regardless of tissue origin and is known as "methionine dependence", "methionine stress sensitivity", or the Hoffman effect. The response of lipids to Met stress, however, is not well-understood. Using mass spectroscopy, label-free vibrational microscopy, and next-generation sequencing, we characterize the response of lipids to Met stress in the triple-negative breast cancer cell line MDA-MB-468 and its Met stress insensitive derivative, MDA-MB-468res-R8. Lipidome analysis identified an immediate, global decrease in lipid abundances with the exception of triglycerides and an increase in lipid droplets in response to Met stress specifically in MDA-MB-468 cells. Furthermore, specific gene expression changes were observed as a secondary response to Met stress in MDA-MB-468, resulting in a downregulation of fatty acid metabolic genes and an upregulation of genes in the unfolded protein response pathway. We conclude that the extensive changes in lipid abundance during Met stress is a direct consequence of the modified metabolic profile previously described in Met stress-sensitive cells. The changes in lipid abundance likely results in changes in membrane composition inducing the unfolded protein response we observe.

Multi-omics integration in biomedical research - A metabolomics-centric review

Recent advances in high-throughput technologies have enabled the profiling of multiple layers of a biological system, including DNA sequence data (genomics), RNA expression levels (transcriptomics), and metabolite levels (metabolomics). This has led to the generation of vast amounts of biological data that can be integrated in so-called multi-omics studies to examine the complex molecular underpinnings of health and disease. Integrative analysis of such datasets is not straightforward and is particularly complicated by the high dimensionality and heterogeneity of the data and by the lack of universal analysis protocols. Previous reviews have discussed various strategies to address the challenges of data integration, elaborating on specific aspects, such as network inference or feature selection techniques. Thereby, the main focus has been on the integration of two omics layers in their relation to a phenotype of interest. In this review we provide an overview over a typical multi-omics workflow, focusing on integration methods that have the potential to combine metabolomics data with two or more omics. We discuss multiple integration concepts including data-driven, knowledge-based, simultaneous and step-wise approaches. We highlight the application of these methods in recent multi-omics studies, including large-scale integration efforts aiming at a global depiction of the complex relationships within and between different biological layers without focusing on a particular phenotype.

High Confidence Shotgun Lipidomics Using Structurally Selective Ion Mobility-Mass Spectrometry

Ion mobility (IM) is a gas phase separation strategy that can either supplement or serve as a high-throughput alternative to liquid chromatography (LC) in shotgun lipidomics. Incorporating the IM dimension in untargeted lipidomics workflows can help resolve isomeric lipids, and the collision cross section (CCS) values obtained from the IM measurements can provide an additional molecular descriptor to increase lipid identification confidence. This chapter provides a broad overview of an untargeted ion mobility-mass spectrometry (IM-MS) workflow using a commercial drift tube ion mobility-quadrupole-time-of-flight mass spectrometer (IM-QTOF) for high confidence lipidomics.

From Prevention to Disease Perturbations: A Multi-Omic Assessment of Exercise and Myocardial Infarctions

While a molecular assessment of the perturbations and injury arising from diseases is essential in their diagnosis and treatment, understanding changes due to preventative strategies is also imperative. Currently, complex diseases such as cardiovascular disease (CVD), the leading cause of death worldwide, suffer from a limited understanding of how the molecular mechanisms taking place following preventive measures (e.g., exercise) differ from changes occurring due to the injuries caused from the disease (e.g., myocardial infarction (MI)). Therefore, this manuscript assesses lipidomic changes before and one hour after exercise treadmill testing (ETT) and before and one hour after a planned myocardial infarction (PMI) in two separate patient cohorts. Strikingly, unique lipidomic perturbations were observed for these events, as could be expected from their vastly different stresses on the body. The lipidomic results were then combined with previously published metabolomic characterizations of the same patients. This integration provides complementary insights into the exercise and PMI events, thereby giving a more holistic understanding of the molecular changes associated with each.

Coupled Mass-Spectrometry-Based Lipidomics Machine Learning Approach for Early Detection of Clear Cell Renal Cell Carcinoma

A discovery-based lipid profiling study of serum samples from a cohort that included patients with clear cell renal cell carcinoma (ccRCC) stages I, II, III, and IV (n = 112) and controls (n = 52) was performed using ultraperformance liquid chromatography coupled to quadrupole-time-of-flight mass spectrometry and machine learning techniques. Multivariate models based on support vector machines and the LASSO variable selection method yielded two discriminant lipid panels for ccRCC detection and early diagnosis. A 16-lipid panel allowed discriminating ccRCC patients from controls with 95.7% accuracy in a training set under cross-validation and 77.1% accuracy in an independent test set. A second model trained to discriminate early (I and II) from late (III and IV) stage ccRCC yielded a panel of 26 compounds that classified stage I patients from an independent test set with 82.1% accuracy. Thirteen species, including cholic acid, undecylenic acid, lauric acid, LPC(16:0/0:0), and PC(18:2/18:2), identified with level 1 exhibited significantly lower levels in samples from ccRCC patients compared to controls. Moreover, 3α-hydroxy-5α-androstan-17-one 3-sulfate, cis-5-dodecenoic acid, arachidonic acid, cis-13-docosenoic acid, PI(16:0/18:1), PC(16:0/18:2), and PC(O-16:0/20:4) contributed to discriminate early from late ccRCC stage patients. The results are auspicious for early ccRCC diagnosis after validation of the panels in larger and different cohorts.

Analysis of the Association between Fatigue and the Plasma Lipidomic Profile of Inflammatory Bowel Disease Patients

Inflammatory bowel disease (IBD) is a chronic, relapsing noninfectious inflammatory condition of the intestinal tract with two main phenotypes, ulcerative colitis (UC) and Crohn's disease (CD), and globally increasing incidence and prevalence. Nearly 80% of the IBD patients with active disease and 50% of those with inactive disease suffer fatigue with significant impairment of their quality of life. Fatigue has been associated with multiple factors in IBD patients but, in most cases, no direct cause can be identified, and risk factors in clinically quiescent IBD are contradictory. Furthermore, as the assessment of fatigue is subjective, there is an unmet clinical need for fatigue biomarkers. In this explorative study, we analyzed the plasma lipidomic profiles of 47 quiescent UC and CD patients (23 fatigued, 24 nonfatigued) using ultraperformance liquid chromatography-time-of-flight mass spectrometry (UPLC-TOFMS). The results showed changes in lipids associated with fatigue and IBD. Significantly decreased levels of phosphatidylcholines, plasmanyls, sphingomyelins, lysophosphatidylcholines, phosphatidylethanolamines, phosphatidylinositols, phosphatidylserines, and eicosanoids were observed in patients with fatigue. Network and metabolic pathway analysis indicated a dysregulation of the arachidonic acid and glycerophospholipid metabolisms and the sphingolipid pathway. The protein-metabolite interaction network showed interactions between functionally related metabolites and proteins, displaying 40 disease-associated hidden proteins including ABDH4, GLTP, and LCAT.

Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources

As researchers are increasingly able to collect data on a large scale from multiple clinical and omics modalities, multi-omics integration is becoming a critical component of metabolomics research. This introduces a need for increased understanding by the metabolomics researcher of computational and statistical analysis methods relevant to multi-omics studies. In this review, we discuss common types of analyses performed in multi-omics studies and the computational and statistical methods that can be used for each type of analysis. We pinpoint the caveats and considerations for analysis methods, including required parameters, sample size and data distribution requirements, sources of a priori knowledge, and techniques for the evaluation of model accuracy. Finally, for the types of analyses discussed, we provide examples of the applications of corresponding methods to clinical and basic research. We intend that our review may be used as a guide for metabolomics researchers to choose effective techniques for multi-omics analyses relevant to their field of study.

Environmental lipidomics: understanding the response of organisms and ecosystems to a changing world

BACKGROUND

Understanding the interaction between organisms and the environment is important for predicting and mitigating the effects of global phenomena such as climate change, and the fate, transport, and health effects of anthropogenic pollutants. By understanding organism and ecosystem responses to environmental stressors at the molecular level, mechanisms of toxicity and adaptation can be determined. This information has important implications in human and environmental health, engineering biotechnologies, and understanding the interaction between anthropogenic induced changes and the biosphere. One class of molecules with unique promise for environmental science are lipids; lipids are highly abundant and ubiquitous across nearly all organisms, and lipid profiles often change drastically in response to external stimuli. These changes allow organisms to maintain essential biological functions, for example, membrane fluidity, as they adapt to a changing climate and chemical environment. Lipidomics can help scientists understand the historical and present biofeedback processes in climate change and the biogeochemical processes affecting nutrient cycles. Lipids can also be used to understand how ecosystems respond to historical environmental changes with lipid signatures dating back to hundreds of millions of years, which can help predict similar changes in the future. In addition, lipids are direct targets of environmental stressors, for example, lipids are easily prone to oxidative damage, which occurs during exposure to most toxins.

AIM OF REVIEW

This is the first review to summarize the current efforts to comprehensively measure lipids to better understand the interaction between organisms and their environment. This review focuses on lipidomic applications in the arenas of environmental toxicology and exposure assessment, xenobiotic exposures and health (e.g., obesity), global climate change, and nutrient cycles. Moreover, this review summarizes the use of and the potential for lipidomics in engineering biotechnologies for the remediation of persistent compounds and biofuel production.

KEY SCIENTIFIC CONCEPT

With the preservation of certain lipids across millions of years and our ever-increasing understanding of their diverse biological roles, lipidomic-based approaches provide a unique utility to increase our understanding of the contemporary and historical interactions between organisms, ecosystems, and anthropogenically-induced environmental changes.

Lipidomic study of cell lines reveals differences between breast cancer subtypes

Breast cancer (BC) is the most prevalent type of cancer in women in western countries. BC mortality has not declined despite early detection by screening, indicating the need for better informed treatment decisions. Therefore, a novel noninvasive diagnostic tool for BC would give the opportunity of subtype-specific treatment and improved prospects for the patients. Heterogeneity of BC tumor subtypes is reflected in the expression levels of enzymes in lipid metabolism. The aim of the study was to investigate whether the subtype defined by the transcriptome is reflected in the lipidome of BC cell lines. A liquid chromatography mass spectrometry (LC-MS) platform was applied to analyze the lipidome of six cell lines derived from human BC cell lines representing different BC subtypes. We identified an increased abundance of triacylglycerols (TG) ≥ C-48 with moderate or multiple unsaturation in fatty acyl chains and down-regulated ether-phosphatidylethanolamines (PE) (C-34 to C-38) in cell lines representing estrogen receptor and progesterone receptor positive tumor subtypes. In a cell line representing HER2-overexpressing tumor subtype an elevated expression of TG (≤ C-46), phosphatidylcholines (PC) and PE containing short-chained (≤ C-16) saturated or monounsaturated fatty acids were observed. Increased abundance of PC ≥ C-40 was found in cell lines of triple negative BC subtype. In addition, differences were detected in lipidomes within these previously defined subtypes. We conclude that subtypes defined by the transcriptome are indeed reflected in differences in the lipidome and, furthermore, potentially biologically relevant differences may exist within these defined subtypes.

Lipid Annotator: Towards Accurate Annotation in Non-Targeted Liquid Chromatography High-Resolution Tandem Mass Spectrometry (LC-HRMS/MS) Lipidomics Using A Rapid and User-Friendly Software

Lipidomics has great promise in various applications; however, a major bottleneck in lipidomics is the accurate and comprehensive annotation of high-resolution tandem mass spectral data. While the number of available lipidomics software has drastically increased over the past five years, the reduction of false positives and the realization of obtaining structurally accurate annotations remains a significant challenge. We introduce Lipid Annotator, which is a user-friendly software for lipidomic analysis of data collected by liquid chromatography high-resolution tandem mass spectrometry (LC-HRMS/MS). We validate annotation accuracy against lipid standards and other lipidomics software. Lipid Annotator was integrated into a workflow applying an iterative exclusion MS/MS acquisition strategy to National Institute of Standards and Technology (NIST) SRM 1950 Metabolites in Frozen Human Plasma using reverse phase LC-HRMS/MS. Lipid Annotator, LipidMatch, and MS-DIAL produced consensus annotations at the level of lipid class for 98% and 96% of features detected in positive and negative mode, respectively. Lipid Annotator provides percentages of fatty acyl constituent species and employs scoring algorithms based on probability theory, which is less subjective than the tolerance and weighted match scores commonly used by available software. Lipid Annotator enables analysis of large sample cohorts and improves data-processing throughput as compared to previous lipidomics software.

Lipidomics analysis of juveniles' blue mussels (Mytilus edulis L. 1758), a key economic and ecological species

Blue mussels (Mytilus edulis L. 1758) are important components of coastal ecosystems and in the economy of rural and coastal areas. The understanding of their physiological processes at key life stages is important both within food production systems and in the management of wild populations. Lipids are crucial molecules for bivalve growth, but their diversity and roles have not been fully characterised. In this study, traditional lipid profiling techniques, such as fatty acid (FA) and lipid class analysis, are combined to untargeted lipidomics to elucidate the lipid metabolism in newly settled spat fed on a range of diets. The evaluated diets included single strains treatments (Cylindrotheca fusiformis CCAP 1017/2 -CYL, Isochrysis galbana CCAP 927/1- ISO, Monodopsis subterranean CCAP 848/1 -MONO, Nannochloropsis oceanica CCAP 849/10- NANNO) and a commercial algae paste (SP). Spat growth was influenced by the diets, which, according to their efficacy were ranked as follows: ISO>NANNO/CYL>SP>MONO. A higher triacylglycerols (TG) content, ranging from 4.23±0.82 μg mgashfree Dry weight (DW)-1 at the beginning of the trial (T0) to 51±15.3 μg mgashfreeDW-1 in ISO, characterised significant growth in the spat, whereas, a reduction of TG (0.3±0.08 μg mgashfreeDW-1 in MONO), mono unsaturated FA-MUFA (from 8.52±1.02 μg mgFAashfreeDW-1 at T0 to 2.81±1.02 μg mgFAashfreeDW-1 in MONO) and polyunsaturated FA-PUFA (from 17.57±2.24 μg mgFAashfreeDW-1 at T0 to 6.19±2.49 μg mgFAashfreeDW-1 in MONO) content characterised poor performing groups. Untargeted lipidomics evidenced how the availability of dietary essential PUFA did not influence only neutral lipids but also the membrane lipids, with changes in lipid molecular species in relation to the essential PUFA provided via the diet. Such changes have the potential to affect spat production cycle and their ability to respond to the surrounding environment. This study evidenced the advantages of coupling different lipid analysis techniques, as each technique disclosed relevant information on nutritional requirements of M. edulis juveniles, expanding the existing knowledge on the physiology of this important species.

Ozone-Induced Cleavage of Endocyclic C═C Double Bonds within Steroid Epimers Produces Unique Gas-Phase Conformations

Herein we demonstrate the first application of ozone-induced cleavage of endocyclic C═C double bonds for improved steroid isomer separation using ion mobility-mass spectrometry. Steroids represent a challenging biomolecular class for ion mobility (IM) separations due to their structural rigidity and subtle stereochemical differences. In this work, we compare the effects of ozonolysis on the relative mobilities of a model stereoisomer pair, testosterone and epitestosterone. A solution-phase ozonolysis approach is used due to its simplicity, relatively low cost, and potential for rapid, online analysis. Despite the presence of solvent-based addition products, we observe that these steroids undergo an ozone-based cleavage resulting in unique, stable gas-phase conformations. The resulting resolution between testosterone and epitestosterone, with collision cross section values of 176.6 and 193.3 Ų, respectively, demonstrates a significant improvement in comparison with previous IM-based approaches. The significantly smaller conformation observed for epitestosterone is stabilized by a three-point interaction between the oxygen-containing functional groups and a sodium ion; this same conformation cannot be sterically achieved by testosterone. Identification of this specific structural difference is strengthened by experimental results showing the disappearance of this conformation following in-source water loss, which eliminates the potential for that three-point interaction. Computational modeling of the lowest energy gas-phase structures for these ozone products corroborates the experimental results. In conclusion, this approach provides tremendous potential as a rapid IM separation method for steroid isomers and other endocyclic C═C double bond containing molecules.

"Lipidomics": Mass spectrometric and chemometric analyses of lipids

Lipids are ubiquitous in the human organism and play essential roles as components of cell membranes and hormones, for energy storage or as mediators of cell signaling pathways. As crucial mediators of the human metabolism, lipids are also involved in metabolic diseases, cardiovascular and renal diseases, cancer and/or hepatological and neurological disorders. With rapidly growing evidence supporting the impact of lipids on both the genesis and progression of these diseases as well as patient wellbeing, the characterization of the human lipidome has gained high interest and importance in life sciences and clinical diagnostics within the last 15 years. This is mostly due to technically advanced molecular identification and quantification methods, mainly based on mass spectrometry. Mass spectrometry has become one of the most powerful tools for the identification of lipids. New lipidic mediators or biomarkers of diseases can be analysed by state-of-the art mass spectrometry techniques supported by sophisticated bioinformatics and biostatistics. The lipidomic approach has developed dramatically in the realm of life sciences and clinical diagnostics due to the available mass spectrometric methods and in particular due to the adaptation of biostatistical methods in recent years. Therefore, the current knowledge of lipid extraction methods, mass-spectrometric approaches, biostatistical data analysis, including workflows for the interpretation of lipidomic high-throughput data, are reviewed in this manuscript.

Lipidomics: Current state of the art in a fast moving field

Lipids are essential for all facets of life. They play three major roles: energy metabolism, structural, and signaling. They are dynamic molecules strongly influenced by endogenous and exogenous factors including genetics, diet, age, lifestyle, drugs, disease and inflammation. As precision medicine starts to become mainstream, there is a huge burgeoning interest in lipids and their potential to act as unique biomarkers or prognostic indicators. Lipids comprise a large component of all metabolites (around one-third), and our expanding knowledge about their dynamic behavior is fueling the hope that mapping their regulatory biochemical pathways on a systems level will revolutionize our ability to prevent, diagnose, and stratify major human diseases. Up to now, clinical lipid measurements have consisted primarily of total cholesterol or triglycerides, as a measure for cardiovascular risk and response to lipid lowering drugs. Nowadays, we are able to measure thousands of individual lipids that make up the lipidome. nuclear magnetic resonance spectrometry (NMR) metabolomics is also being increasingly used in large cohort studies where it can report on total levels of selected lipid classes, and relative levels of fatty acid saturation. To support the application of lipidomics research, LIPID MAPS was established in 2003, and since then has gone on to become the go-to resource for several lipid databases, lipid drawing tools, data deposition, and more recently lipidomics informatics tools, and a lipid biochemistry encyclopedia, LipidWeb. Alongside this, the recently established Lipidomics Standards Initiative plays a key role in standardization of lipidomics methodologies. This article is categorized under: Laboratory Methods and Technologies > Metabolomics Analytical and Computational Methods > Analytical Methods.

Mapping Unsaturation in Human Plasma Lipids by Data-Independent Ozone-Induced Dissociation

Over 1500 different lipids have been reported in human plasma at the sum composition level. Yet the number of unique lipids present is surely higher, once isomeric contributions from double bond location(s) and fatty acyl regiochemistry are considered. In order to resolve this ambiguity, herein, we describe the incorporation of ozone-induced dissociation (OzID) into data-independent shotgun lipidomics workflows on a high-resolution hybrid quadrupole-Orbitrap platform. In this configuration, [M + Na]⁺ ions generated by electrospray ionization of a plasma lipid extract were transmitted through the quadrupole in 1 Da segments. Reaction of mass-selected lipid ions with ozone in the octopole collision cell yielded diagnostic ions for each double bond position. The increased ozone concentration in this region significantly improved ozonolysis efficiency compared with prior implementations on linear ion-trap devices. This advancement translates into increased lipidome coverage and improvements in duty cycle for data-independent MS/MS analysis using shotgun workflows. Grouping all precursor ions with a common OzID neutral loss enables straightforward classification of the lipidome by unsaturation position (with respect to the methyl terminus). Two-dimensional maps obtained from this analysis provide a powerful visualization of structurally related lipids and lipid isomer families within plasma. Global profiling of lipid unsaturation in plasma extracts reveals that most unsaturated lipids are present as isomeric mixtures. These new insights provide a unique picture of underlying metabolism that could in the future provide novel indicators of health and disease.

Identification of potential agents for thymoma by integrated analyses of differentially expressed tumour-associated genes and molecular docking experiments

Thymoma, derived from the epithelial cells of the thymus, is a rare malignant tumour type. Following diagnosis with thymoma, patients generally undergo surgical treatment. However, patients with advanced-stage disease are only candidates for chemotherapy and have poor survival. Therefore, it is urgently required to explore effective chemotherapeutic agents for the treatment of thymoma. In the present study, a Bioinformatics analysis was performed to identify novel drugs for thymoma. Differentially expressed genes (DEGs) in thymoma were obtained by Gene Expression Profiling Interactive Analysis. Subsequently, these genes were processed by Connectivity Map analysis to identify suitable compounds. In addition, Metascape software was used to verify drug and target binding. Molecular docking technology was used to verify drug and target binding. Finally, absorption, distribution, metabolism and excretion parameters in the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform database were used for drug screening and for evaluation of the potential clinical value. In total, 2,447 DEGs, including 2,204 upregulated and 243 downregulated genes, were identified from 118 thymoma patients and 339 normal samples. The top 10 drugs displaying the most significant negative correlations were fulvestrant, hesperetin, zidovudine, hydrocortisone, rolitetracycline, ellipticine, sirolimus, quinisocaine, oestradiol (estradiol) and harmine. The predicted targets of these drugs were then confirmed. The score for the association between estrogen receptor 1 (ESR1) and fulvestrant was 0.99. According to the molecular docking analysis, the total scores for the interaction between ESR1 were 10.26, and those for the interaction between tamoxifen and ESR1 were 6.60. The oral bioavailability (%), drug-likeness and drug half-life for hesperetin were 70.31, 0.27 and 15.78, respectively; those for oestradiol were 53.56, 0.32 and 3.50, respectively; and those for harmine were 56.80, 0.13 and 5.04, respectively. In conclusion, several potential therapeutic drugs for thymoma were identified in the present study. The results suggested that the compounds, including fulvestrant, estradiol, hesperetin and ellipticine, represent the most likely drugs for the treatment of thymoma. Future studies should focus on testing these novel compounds in vitro and in vivo.

Challenges in Identifying the Dark Molecules of Life

Metabolomics is the study of the metabolome, the collection of small molecules in living organisms, cells, tissues, and biofluids. Technological advances in mass spectrometry, liquid- and gas-phase separations, nuclear magnetic resonance spectroscopy, and big data analytics have now made it possible to study metabolism at an omics or systems level. The significance of this burgeoning scientific field cannot be overstated: It impacts disciplines ranging from biomedicine to plant science. Despite these advances, the central bottleneck in metabolomics remains the identification of key metabolites that play a class-discriminant role. Because metabolites do not follow a molecular alphabet as proteins and nucleic acids do, their identification is much more time consuming, with a high failure rate. In this review, we critically discuss the state-of-the-art in metabolite identification with specific applications in metabolomics and how technologies such as mass spectrometry, ion mobility, chromatography, and nuclear magnetic resonance currently contribute to this challenging task.

Increased throughput and ultra-high mass resolution in DESI FT-ICR MS imaging through new-generation external data acquisition system and advanced data processing approaches

Desorption electrospray ionisation-mass spectrometry imaging (DESI-MSI) is a powerful imaging technique for the analysis of complex surfaces. However, the often highly complex nature of biological samples is particularly challenging for MSI approaches, as options to appropriately address molecular complexity are limited. Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) offers superior mass accuracy and mass resolving power, but its moderate throughput inhibits broader application. Here we demonstrate the dramatic gains in mass resolution and/or throughput of DESI-MSI on an FT-ICR MS by developing and implementing a sophisticated data acquisition and data processing pipeline. The presented pipeline integrates, for the first time, parallel ion accumulation and detection, post-processing absorption mode Fourier transform and pixel-by-pixel internal re-calibration. To achieve that, first, we developed and coupled an external high-performance data acquisition system to an FT-ICR MS instrument to record the time-domain signals (transients) in parallel with the instrument’s built-in electronics. The recorded transients were then processed by the in-house developed computationally-efficient data processing and data analysis software. Importantly, the described pipeline is shown to be applicable even to extremely large, up to 1 TB, imaging datasets. Overall, this approach provides improved analytical figures of merits such as: (i) enhanced mass resolution at no cost in experimental time; and (ii) up to 4-fold higher throughput while maintaining a constant mass resolution. Using this approach, we not only demonstrate the record 1 million mass resolution for lipid imaging from brain tissue, but explicitly show such mass resolution is required to resolve the complexity of the lipidome.

Metabolomics biotechnology, applications, and future trends: a systematic review

Given the highly increased incidence of human diseases, a better understanding of the related mechanisms regarding endogenous metabolism is urgently needed. Mass spectrometry-based metabolomics has been used in a variety of disease research areas. However, the deep research of metabolites remains a difficult and lengthy process. Fortunately, mass spectrometry is considered to be a universal tool with high specificity and sensitivity and is widely used around the world. Mass spectrometry technology has been applied to various basic disciplines, providing technical support for the discovery and identification of endogenous substances in living organisms. The combination of metabolomics and mass spectrometry is of great significance for the discovery and identification of metabolite biomarkers. The mass spectrometry tool could further improve and develop the exploratory research of the life sciences. This mini review discusses metabolomics biotechnology with a focus on recent applications of metabolomics as a powerful tool to elucidate metabolic disturbances and the related mechanisms of diseases.

Given the highly increased incidence of human diseases, a better understanding of the related mechanisms regarding endogenous metabolism is urgently needed.

Optimization of Electrospray Ionization Source Parameters for Lipidomics To Reduce Misannotation of In-Source Fragments as Precursor Ions

Lipidomics requires the accurate annotation of lipids in complex samples to enable determination of their biological relevance. We demonstrate that unintentional in-source fragmentation (ISF, common in lipidomics) generates ions that have identical masses to other lipids. Lysophosphatidylcholines (LPC), for example, generate in-source fragments with the same mass as free fatty acids and lysophosphatidylethanolamines (LPE). The misannotation of in-source fragments as true lipids is particularly insidious in complex matrixes since most masses are initially unannotated and comprehensive lipid standards are unavailable. Indeed, we show such LPE/LPC misannotations are incorporated in the data submitted to the National Institute of Standards and Technology (NIST) interlaboratory comparison exercise. Computer simulations exhaustively identified potential misannotations. The selection of in-source fragments of highly abundant lipids as features, instead of the correct recognition of trace lipids, can potentially lead to (i) missing the biologically relevant lipids (i.e., a false negative) and/or (ii) incorrect assignation of a phenotype to an incorrect lipid (i.e., false positive). When ISF is not eliminated in the negative ion mode, ∼40% of the 100 most abundant masses corresponding to unique phospholipids measured in plasma were artifacts from ISF. We show that chromatographic separation and ion intensity considerations assist in distinguishing precursor ions from in-source fragments, suggesting ISF may be especially problematic when complex samples are analyzed via shotgun lipidomics. We also conduct a systematic evaluation of electrospray ionization (ESI) source parameters on an Exactive equipped with a heated electrospray ionization (HESI-II) source with the objective of obtaining uniformly appropriate source conditions for a wide range of lipids, while, at the same time, reducing in-source fragmentation.

MS-based lipidomics of human blood plasma: a community-initiated position paper to develop accepted guidelines

Human blood is a self-regenerating lipid-rich biological fluid that is routinely collected in hospital settings. The inventory of lipid molecules found in blood plasma (plasma lipidome) offers insights into individual metabolism and physiology in health and disease. Disturbances in the plasma lipidome also occur in conditions that are not directly linked to lipid metabolism; therefore, plasma lipidomics based on MS is an emerging tool in an array of clinical diagnostics and disease management. However, challenges exist in the translation of such lipidomic data to clinical applications. These relate to the reproducibility, accuracy, and precision of lipid quantitation, study design, sample handling, and data sharing. This position paper emerged from a workshop that initiated a community-led process to elaborate and define a set of generally accepted guidelines for quantitative MS-based lipidomics of blood plasma or serum, with harmonization of data acquired on different instrumentation platforms across independent laboratories as an ultimate goal. We hope that other fields may benefit from and follow such a precedent.

NIST lipidomics workflow questionnaire: an assessment of community-wide methodologies and perspectives

INTRODUCTION

Efforts to harmonize lipidomic methodologies have been limited within the community. Here, we aimed to capitalize on the recent National Institute of Standards and Technology lipidomics interlaboratory comparison exercise by implementing a questionnaire that assessed current methodologies, quantitation strategies, standard operating procedures (SOPs), and quality control activities employed by the lipidomics community.

OBJECTIVES

Lipidomics is a rapidly developing field with diverse applications. At present, there are no community-vetted methods to assess measurement comparability or data quality. Thus, a major impetus of this questionnaire was to profile current efforts, highlight areas of need, and establish future objectives in an effort to harmonize lipidomics workflows.

METHODS

The 54-question survey inquired about laboratory demographics, lipidomic methodologies and SOPs, analytical platforms, quantitation, reference materials, quality control procedures, and opinions regarding challenges existing within the community.

RESULTS

A total of 125 laboratories participated in the questionnaire. A broad overview of results highlighted a wide methodological diversity within current lipidomic workflows. The impact of this diversity on lipid measurement and quantitation is currently unknown and needs to be explored further. While some laboratories do incorporate SOPs and quality control activities, these concepts have not been fully embraced by the community. The top five perceived challenges within the lipidomics community were a lack of standardization amongst methods/protocols, lack of lipid standards, software/data handling and quantification, and over-reporting/false positives.

CONCLUSION

The questionnaire provided an overview of current lipidomics methodologies and further promoted the need for community-accepted guidelines and protocols. The questionnaire also served as a platform to help determine and prioritize metrological issues to be investigated.

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

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