Strategies to Improve/Eliminate the Limitations in Shotgun Lipidomics

Molecular networking-based mass spectral identification of Brucea javanica (L.) Merr. metabolites and their selective binding affinities for dengue virus enzymes

Brucea javanica, a valued traditional medicinal plant in Malaysia, known for its fever-treating properties yet remains underexplored for its potential antiviral properties against dengue. This study aims to simultaneously identify chemical classes and metabolites within B. javanica using molecular networking (MN), by Global Natural Product Social (GNPS), and SIRIUS in silico annotation. Liquid chromatography-mass spectrometry (LC-MS2)-based MN explores chemical diversity across four plant parts (leaves, roots, fruits, and stem bark), revealing diverse metabolites such as tryptophan-derived alkaloids, terpenoids, and octadecadenoids. Simultaneous LC-MS2 and MN analyses reveal a discriminative capacity for individual plant components, with roots accumulating tryptophan alkaloids, fruits concentrating quassinoids, leaves containing fusidanes, and stem bark primarily characterised by simple indoles. Subsequently, extracts were evaluated for dengue antiviral activity using adenosine triphosphate (ATP) and plaque assays, indicates potent efficacy in the dichloromethane (DCM) extract from roots (EC50 = 0.3 μg/mL, SI = 10). Molecular docking analysis of two major compounds; canthin-6-one (264) and 1-hydroxy-11-methoxycanthin-6-one (275) showed potential binding interactions with active sites of NS5 RNA-dependent RNA polymerase (RdRp) of dengue virus (DENV) protein. Subsequent in vitro evaluation revealed compounds 264 and 275 had a promising dengue antiviral activity with SI value of 63 and 1.85. These identified metabolites emerge as potential candidates for further evaluation in dengue antiviral activities.

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

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

Clinical advances in analytical profiling of signature lipids: implications for severe non-communicable and neurodegenerative diseases

BACKGROUND

Lipids play key roles in numerous biological processes, including energy storage, cell membrane structure, signaling, immune responses, and homeostasis, making lipidomics a vital branch of metabolomics that analyzes and characterizes a wide range of lipid classes. Addressing the complex etiology, age-related risk, progression, inflammation, and research overlap in conditions like Alzheimer's Disease, Parkinson's Disease, Cardiovascular Diseases, and Cancer poses significant challenges in the quest for effective therapeutic targets, improved diagnostic markers, and advanced treatments. Mass spectrometry is an indispensable tool in clinical lipidomics, delivering quantitative and structural lipid data, and its integration with technologies like Liquid Chromatography (LC), Magnetic Resonance Imaging (MRI), and few emerging Matrix-Assisted Laser Desorption Ionization- Imaging Mass Spectrometry (MALDI-IMS) along with its incorporation into Tissue Microarray (TMA) represents current advances. These innovations enhance lipidomics assessment, bolster accuracy, and offer insights into lipid subcellular localization, dynamics, and functional roles in disease contexts.

AIM OF THE REVIEW

The review article summarizes recent advancements in lipidomic methodologies from 2019 to 2023 for diagnosing major neurodegenerative diseases, Alzheimer's and Parkinson's, serious non-communicable cardiovascular diseases and cancer, emphasizing the role of lipid level variations, and highlighting the potential of lipidomics data integration with genomics and proteomics to improve disease understanding and innovative prognostic, diagnostic and therapeutic strategies.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Clinical lipidomic studies are a promising approach to track and analyze lipid profiles, revealing their crucial roles in various diseases. This lipid-focused research provides insights into disease mechanisms, biomarker identification, and potential therapeutic targets, advancing our understanding and management of conditions such as Alzheimer's Disease, Parkinson's Disease, Cardiovascular Diseases, and specific cancers.

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.

Investigating the Metabolic Heterogeneity of Cancer Cells Using Functional Single-Cell Selection and nLC Combined with Multinozzle Emitter Mass Spectrometry

Tumor metastasis and cancer recurrence are often a result of cell heterogeneity, where specific subpopulations of tumor cells may be resistant to radio- or chemotherapy. To investigate this physiological and phenotypic diversity, single-cell metabolomics provides a powerful approach at the chemical level, where distinct lipid profiles can be found in different tumor cells. Here, we established a highly sensitive platform using nanoflow liquid chromatography (nLC) combined with multinozzle emitter electrospray ionization mass spectrometry for more in-depth metabolomics profiling. Our platform identified 15 and 17 lipids from individual osteosarcoma (U2OS) and glioblastoma (GBM) cells when analyzing single-cell samples. Additionally, we used the functional single-cell selection (fSCS) pipeline to analyze the subpopulations of cells with a DNA damage response (DDR) in U2OS cells and fast migration in GBM cells. Specifically, we observed a down-regulation of polyunsaturated fatty acids (PUFAs) in U2OS cells undergoing DDR, such as fatty acids FA 20:3; O2 and FA 17:4; O3. Furthermore, ceramides (Cer 38:0; O3) and triglycerides (TG 36:0) were found to be down-regulated in fast-migrating GBM cells compared to the slow-migrating subpopulation. These findings suggest the potential roles of these metabolites and/or lipids in the cellular behavior of the subpopulations.

Shotgun-Based Mass Spectrometry Analysis of Phospholipid and Triacylglycerol Molecular Species and Eicosanoids in Salmon Muscle Tissue on Feeding Microbial Oil

The continuous growth of aquaculture places a growing demand on alternative sources of fish oil (FO). Certain microorganisms provide a sustainable replacement for FO due to their content of EPA and DHA, which are essential for fish health. Appreciable evidence shows that changes in feeding sources may alter the nutritional components of salmon; however, the influence of diets on lipid species remains unclear. In this study, the identification and semi-quantification of lipid molecular species in salmon muscle during feeding with a microbial oil (MO) were carried out by focusing on triacylglycerol (TAG) and diacyl-phospholipid using shotgun-based mass spectrometry analysis. DHA in the MO diet was efficiently incorporated into phospholipid structures on feeding, followed by accumulation in salmon muscle. The MO diet elevated the level of certain EPA-containing TAGs, such as TAG C52:5 (16:0_16:0_20:5) and TAG C54:6 (16:0_18:1_20:5), indicating that the MO diet may be an excellent source for enhancement of the abundance of ω3 lipids. Further, prostaglandins (PGs) PGE2 and PGF3α were identified and quantified for the first time in salmonid tissue.

Single-Cell Lipidomics Using Analytical Flow LC-MS Characterizes the Response to Chemotherapy in Cultured Pancreatic Cancer Cells

In this work, we demonstrate the development and first application of nanocapillary sampling followed by analytical flow liquid chromatography-mass spectrometry for single-cell lipidomics. Around 260 lipids were tentatively identified in a single cell, demonstrating remarkable sensitivity. Human pancreatic ductal adenocarcinoma cells (PANC-1) treated with the chemotherapeutic drug gemcitabine can be distinguished from controls solely on the basis of their single-cell lipid profiles. Notably, the relative abundance of LPC(0:0/16:0) was significantly affected in gemcitabine-treated cells, in agreement with previous work in bulk. This work serves as a proof of concept that live cells can be sampled selectively and then characterized using automated and widely available analytical workflows, providing biologically relevant outputs.

High-throughput analysis of lipidomic phenotypes of methicillin-resistant Staphylococcus aureus by coupling in situ 96-well cultivation and HILIC-ion mobility-mass spectrometry

Antimicrobial resistance is a major threat to human health as resistant pathogens spread globally, and the development of new antimicrobials is slow. Since many antimicrobials function by targeting cell wall and membrane components, high-throughput lipidomics for bacterial phenotyping is of high interest for researchers to unveil lipid-mediated pathways when dealing with a large number of lab-selected or clinical strains. However, current practice for lipidomic analysis requires the cultivation of bacteria on a large scale, which does not replicate the growth conditions for high-throughput bioassays that are normally carried out in 96-well plates, such as susceptibility tests, growth curve measurements, and biofilm quantitation. Analysis of bacteria grown under the same condition as other bioassays would better inform the differences in susceptibility and other biological metrics. In this work, a high-throughput method for cultivation and lipidomic analysis of antimicrobial-resistant bacteria was developed for standard 96-well plates exemplified by methicillin-resistant Staphylococcus aureus (MRSA). By combining a 30-mm liquid chromatography (LC) column with ion mobility (IM) separation, elution time could be dramatically shortened to 3.6 min for a single LC run without losing major lipid features. Peak capacity was largely rescued by the addition of the IM dimension. Through multi-linear calibration, the deviation of retention time can be limited to within 5%, making database-based automatic lipid identification feasible. This high-throughput method was further validated by characterizing the lipidomic phenotypes of antimicrobial-resistant mutants derived from the MRSA strain, W308, grown in a 96-well plate.

Role of untargeted omics biomarkers of exposure and effect for tobacco research

Tobacco research remains a clear priority to improve individual and population health, and has recently become more complex with emerging combustible and noncombustible tobacco products. The use of omics methods in prevention and cessation studies are intended to identify new biomarkers for risk, compared risks related to other products and never use, and compliance for cessation and reinitation. to assess the relative effects of tobacco products to each other. They are important for the prediction of reinitiation of tobacco use and relapse prevention. In the research setting, both technical and clinical validation is required, which presents a number of complexities in the omics methodologies from biospecimen collection and sample preparation to data collection and analysis. When the results identify differences in omics features, networks or pathways, it is unclear if the results are toxic effects, a healthy response to a toxic exposure or neither. The use of surrogate biospecimens (e.g., urine, blood, sputum or nasal) may or may not reflect target organs such as the lung or bladder. This review describes the approaches for the use of omics in tobacco research and provides examples of prior studies, along with the strengths and limitations of the various methods. To date, there is little consistency in results, likely due to small number of studies, limitations in study size, the variability in the analytic platforms and bioinformatic pipelines, differences in biospecimen collection and/or human subject study design. Given the demonstrated value for the use of omics in clinical medicine, it is anticipated that the use in tobacco research will be similarly productive.

Collision cross section measurement and prediction methods in omics

Omics studies such as metabolomics, lipidomics, and proteomics have become important for understanding the mechanisms in living organisms. However, the compounds detected are structurally different and contain isomers, with each structure or isomer leading to a different result in terms of the role they play in the cell or tissue in the organism. Therefore, it is important to detect, characterize, and elucidate the structures of these compounds. Liquid chromatography and mass spectrometry have been utilized for decades in the structure elucidation of key compounds. While prediction models of parameters (such as retention time and fragmentation pattern) have also been developed for these separation techniques, they have some limitations. Moreover, ion mobility has become one of the most promising techniques to give a fingerprint to these compounds by determining their collision cross section (CCS) values, which reflect their shape and size. Obtaining accurate CCS enables its use as a filter for potential analyte structures. These CCS values can be measured experimentally using calibrant-independent and calibrant-dependent approaches. Identification of compounds based on experimental CCS values in untargeted analysis typically requires CCS references from standards, which are currently limited and, if available, would require a large amount of time for experimental measurements. Therefore, researchers use theoretical tools to predict CCS values for untargeted and targeted analysis. In this review, an overview of the different methods for the experimental and theoretical estimation of CCS values is given where theoretical prediction tools include computational and machine modeling type approaches. Moreover, the limitations of the current experimental and theoretical approaches and their potential mitigation methods were discussed.

Improved Structural Characterization of Glycerophospholipids and Sphingomyelins with Real-Time Library Searching

In mass spectrometry-based lipidomics, complex lipid mixtures undergo chromatographic separation, are ionized, and are detected using tandem MS (MSn) to simultaneously quantify and structurally characterize eluting species. The reported structural granularity of these identified lipids is strongly reliant on the analytical techniques leveraged in a study. For example, lipid identifications from traditional collisionally activated data-dependent acquisition experiments are often reported at either species level or molecular species level. Structural resolution of reported lipid identifications is routinely enhanced by integrating both positive and negative mode analyses, requiring two separate runs or polarity switching during a single analysis. MS3+ can further elucidate lipid structure, but the lengthened MS duty cycle can negatively impact analysis depth. Recently, functionality has been introduced on several Orbitrap Tribrid mass spectrometry platforms to identify eluting molecular species on-the-fly. These real-time identifications can be leveraged to trigger downstream MSn to improve structural characterization with lessened impacts on analysis depth. Here, we describe a novel lipidomics real-time library search (RTLS) approach, which utilizes the lipid class of real-time identifications to trigger class-targeted MSn and to improve the structural characterization of phosphotidylcholines, phosphotidylethanolamines, phosphotidylinositols, phosphotidylglycerols, phosphotidylserine, and sphingomyelins in the positive ion mode. Our class-based RTLS method demonstrates improved selectivity compared to the current methodology of triggering MSn in the presence of characteristic ions or neutral losses.

Development of a Laser Microdissection-Coupled Quantitative Shotgun Lipidomic Method to Uncover Spatial Heterogeneity

Lipid metabolic disturbances are associated with several diseases, such as type 2 diabetes or malignancy. In the last two decades, high-performance mass spectrometry-based lipidomics has emerged as a valuable tool in various fields of biology. However, the evaluation of macroscopic tissue homogenates leaves often undiscovered the differences arising from micron-scale heterogeneity. Therefore, in this work, we developed a novel laser microdissection-coupled shotgun lipidomic platform, which combines quantitative and broad-range lipidome analysis with reasonable spatial resolution. The multistep approach involves the preparation of successive cryosections from tissue samples, cross-referencing of native and stained images, laser microdissection of regions of interest, in situ lipid extraction, and quantitative shotgun lipidomics. We used mouse liver and kidney as well as a 2D cell culture model to validate the novel workflow in terms of extraction efficiency, reproducibility, and linearity of quantification. We established that the limit of dissectible sample area corresponds to about ten cells while maintaining good lipidome coverage. We demonstrate the performance of the method in recognizing tissue heterogeneity on the example of a mouse hippocampus. By providing topological mapping of lipid metabolism, the novel platform might help to uncover region-specific lipidomic alterations in complex samples, including tumors.

Retinal Ganglion Cell Axon Fractionation

Retinal ganglion cell (RGC) axon regeneration in mammals can be stimulated through gene knockouts, pharmacological agents, and biophysical stimulation. Here we present a fractionation method to isolate regenerating RGC axons for downstream analysis using immunomagnetic separation of cholera toxin subunit B (CTB)-bound RGC axons. After optic nerve tissue dissection and dissociation, conjugated CTB is used to bind preferentially to regenerated RGC axons. Anti-CTB antibodies crosslinked to magnetic sepharose beads are used to isolate CTB-bound axons from a nonbound fraction of extracellular matrix and neuroglia. We provide a method of verifying fractionation by immunodetection of conjugated CTB and the RGC marker, Tuj1 (β-tubulin III). These fractions can be further analyzed with lipidomic methods, such as LC-MS/MS to gather fraction-specific enrichments.

Effects of Sustained Hyperglycemia on Skeletal Muscle Lipids in Healthy Subjects

CONTEXT

Sustained increases in plasma glucose promote skeletal muscle insulin resistance independent from obesity and dyslipidemia (ie, glucotoxicity). Skeletal muscle lipids are key molecular determinants of insulin action, yet their involvement in the development of glucotoxicity is unclear.

OBJECTIVE

To explore the impact of mild physiologic hyperglycemia on skeletal muscle lipids.

DESIGN

Single group pretest-posttest.

PARTICIPANTS

Healthy males and females with normal glucose tolerance.

INTERVENTIONS

72-hour glucose infusion raising plasma glucose by ~50 mg/dL.

MAIN OUTCOME MEASURES

Skeletal muscle lipids, insulin sensitivity, lipid oxidation.

RESULTS

Despite impairing insulin-mediated glucose disposal and suppressing fasting lipid oxidation, hyperglycemia did not alter either the content or composition of skeletal muscle triglycerides, diacylglycerides, or phospholipids. Skeletal muscle ceramides decreased after glucose infusion, likely in response to a reduction in free fatty acid concentrations.

CONCLUSIONS

Our results demonstrate that the major lipid pools in skeletal muscle are unperturbed by sustained increases in glucose availability and suggest that glucotoxicity and lipotoxicity drive insulin resistance through distinct mechanistic pathways.

Challenges and opportunities for prevention and removal of unwanted variation in lipidomic studies

Large 'omics studies are of particular interest to population and clinical research as they allow elucidation of biological pathways that are often out of reach of other methodologies. Typically, these information rich datasets are produced from multiple coordinated profiling studies that may include lipidomics, metabolomics, proteomics or other strategies to generate high dimensional data. In lipidomics, the generation of such data presents a series of unique technological and logistical challenges; to maximize the power (number of samples) and coverage (number of analytes) of the dataset while minimizing the sources of unwanted variation. Technological advances in analytical platforms, as well as computational approaches, have led to improvement of data quality - especially with regard to instrumental variation. In the small scale, it is possible to control systematic bias from beginning to end. However, as the size and complexity of datasets grow, it is inevitable that unwanted variation arises from multiple sources, some potentially unknown and out of the investigators control. Increases in cohort sizes and complexity has led to new challenges in sample collection, handling, storage, and preparation stages. If not considered and dealt with appropriately, this unwanted variation may undermine the quality of the data and reliability of any subsequent analysis. Here we review the various experimental phases where unwanted variation may be introduced and review general strategies and approaches to handle this variation, specifically addressing issues relevant to lipidomics studies.

RECOGNITION AND AVOIDANCE OF ION SOURCE-GENERATED ARTIFACTS IN LIPIDOMICS ANALYSIS

Lipid research is attracting more and more attention as various key roles and novel biological functions of lipids have been demonstrated and discovered in the organism. Mass spectrometry (MS)-based lipidomics approaches are the most powerful and effective tools for analysis of cellular lipidomes with very high sensitivity and specificity. However, the artifacts generated from in-source fragmentation are always present in all kinds of ion sources, even soft ionization techniques (i.e., electrospray ionization and matrix-assisted laser desorption/ionization [MALDI]). These artifacts can cause many problems for lipidomics, especially when the fragment ions correspond to/are isomeric species of other endogenous lipid species in complex biological samples. These commonly observed artifacts could lead to misannotation, false identification, and consequently, incorrect attribution of phenotypes, and will have negative impact on any MS-based lipidomics research including but not limited to biomarker discovery, drug development, etc. Liquid chromatography-MS, shotgun lipidomics, and MALDI-MS imaging are three representative lipidomics approaches in which ion source-generated artifacts are all manifested and are comprehensively summarized in this article. The strategies on how to avoid/reduce the artifacts of in-source fragmentation on lipidomics analysis are also discussed in detail. We believe that with the recognition and avoidance of ion source-generated artifacts, MS-based lipidomics approaches will provide better accuracy on comprehensive analysis of biological samples and will make greater contribution to the research on metabolism and translational/precision medicine (collectively termed functional lipidomics). © 2020 John Wiley & Sons Ltd. Mass Spec Rev.

Lipid Metabolite Biomarkers in Cardiovascular Disease: Discovery and Biomechanism Translation from Human Studies

Lipids represent a valuable target for metabolomic studies since altered lipid metabolism is known to drive the pathological changes in cardiovascular disease (CVD). Metabolomic technologies give us the ability to measure thousands of metabolites providing us with a metabolic fingerprint of individual patients. Metabolomic studies in humans have supported previous findings into the pathomechanisms of CVD, namely atherosclerosis, apoptosis, inflammation, oxidative stress, and insulin resistance. The most widely studied classes of lipid metabolite biomarkers in CVD are phospholipids, sphingolipids/ceramides, glycolipids, cholesterol esters, fatty acids, and acylcarnitines. Technological advancements have enabled novel strategies to discover individual biomarkers or panels that may aid in the diagnosis and prognosis of CVD, with sphingolipids/ceramides as the most promising class of biomarkers thus far. In this review, application of metabolomic profiling for biomarker discovery to aid in the diagnosis and prognosis of CVD as well as metabolic abnormalities in CVD will be discussed with particular emphasis on lipid metabolites.

Food Lipidomics for 155 Agricultural Plant Products

Lipids exhibit functional bioactivities based on their polar and acyl chain properties; humans obtain lipids from dietary plant product intake. Therefore, the identification of different molecular species facilitates the evaluation of biological functions and nutrition levels and new phenotype-modulating lipid structures. As a rapid screening strategy, we performed untargeted lipidomics for 155 agricultural products in 58 species from 23 plant families, wherein product-specific lipid diversities were shown using computational mass spectrometry. We characterized 716 lipid species, for which the profiles revealed the National Center for Biotechnology Information-established organismal classification and unique plant tissue metabotypes. Moreover, we annotated unreported subclasses in plant lipidology; e.g., triacylglycerol estolide (TG-EST) was detected in rice seeds (Oryza sativa) and several plant species. TG-EST is known as the precursor molecule producing the fatty acid ester of hydroxy fatty acid, which lowers ambient glycemia and improves glucose tolerance. Hence, our method can identify agricultural plant products containing valuable lipid ingredients.

Overview of Lipidomic Analysis of Triglyceride Molecular Species in Biological Lipid Extracts

Triglyceride (TG) is a class of neutral lipids, which functions as an energy storage depot and is important for cellular growth, metabolism, and function. The composition and content of TG molecular species are crucial factors for nutritional aspects in food chemistry and are directly associated with several diseases, including atherosclerosis, diabetes, obesity, stroke, etc. As a result of the complexities of aliphatic moieties and their different connections/locations to the glycerol backbone in TG molecules, accurate identification of individual TG molecular species and quantitative assessment of TG composition and content are particularly challenging, even at the current stage of lipidomics development. Herein, methods developed for analysis of TG species, such as liquid chromatography-mass spectrometry with a variety of columns and different mass spectrometric techniques, shotgun lipidomics approaches, and ion-mobility-based analysis, are reviewed. Moreover, the potential limitations of the methods are discussed. It is our sincere hope that the overviews and discussions can provide some insights for researchers to select an appropriate approach for TG analysis and can serve as the basis for those who would like to establish a methodology for TG analysis or develop a new method when novel tools become available. Biologically accurate analysis of TG species with an enabling method should lead us toward improving the nutritional quality, revealing the effects of TG on diseases, and uncovering the underlying biochemical mechanisms related to these diseases.

Metabolomics meets lipidomics: Assessing the small molecule component of metabolism

Metabolomics, including lipidomics, is emerging as a quantitative biology approach for the assessment of energy flow through metabolism and information flow through metabolic signaling; thus, providing novel insights into metabolism and its regulation, in health, healthy ageing and disease. In this forward-looking review we provide an overview on the origins of metabolomics, on its role in this postgenomic era of biochemistry and its application to investigate metabolite role and (bio)activity, from model systems to human population studies. We present the challenges inherent to this analytical science, and approaches and modes of analysis that are used to resolve, characterize and measure the infinite chemical diversity contained in the metabolome (including lipidome) of complex biological matrices. In the current outbreak of metabolic diseases such as cardiometabolic disorders, cancer and neurodegenerative diseases, metabolomics appears to be ideally situated for the investigation of disease pathophysiology from a metabolite perspective.

Untargeted Lipidomic Profiling of Dry Blood Spots Using SFC-HRMS

Lipids are essential cellular constituents that have many critical roles in physiological functions. They are notably involved in energy storage and cell signaling as second messengers, and they are major constituents of cell membranes, including lipid rafts. As a consequence, they are implicated in a large number of heterogeneous diseases, such as cancer, diabetes, neurological disorders, and inherited metabolic diseases. Due to the high structural diversity and complexity of lipid species, the presence of isomeric and isobaric lipid species, and their occurrence at a large concentration scale, a complete lipidomic profiling of biological matrices remains challenging, especially in clinical contexts. Using supercritical fluid chromatography coupled with high-resolution mass spectrometry, we have developed and validated an untargeted lipidomic approach to the profiling of plasma and blood. Moreover, we have tested the technique using the Dry Blood Spot (DBS) method and found that it allows for the easy collection of blood for analysis. To develop the method, we performed the optimization of the separation and detection of lipid species on pure standards, reference human plasma (SRM1950), whole blood, and DBS. These analyses allowed an in-house lipid data bank to be built. Using the MS-Dial software, we developed an automatic process for the relative quantification of around 500 lipids species belonging to the 6 main classes of lipids (including phospholipids, sphingolipids, free fatty acids, sterols, and fatty acyl-carnitines). Then, we compared the method using the published data for SRM 1950 and a mouse blood sample, along with another sample of the same blood collected using the DBS method. In this study, we provided a method for blood lipidomic profiling that can be used for the easy sampling of dry blood spots.

Mass spectrometry in the lipid study of cancer

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

Perspective on Emerging Mass Spectrometry Technologies for Comprehensive Lipid Structural Elucidation

Lipids and metabolites are of interest in many clinical and research settings because it is the metabolome that is increasingly recognized as a more dynamic and sensitive molecular measure of phenotype. The enormous diversity of lipid structures and the importance of biological structure-function relationships in a wide variety of applications makes accurate identification a challenging yet crucial area of research in the lipid community. Indeed, subtle differences in the chemical structures of lipids can have important implications in cellular metabolism and many disease pathologies. The speed, sensitivity, and molecular specificity afforded by modern mass spectrometry has led to its widespread adoption in the field of lipidomics on many different instrument platforms and experimental workflows. However, unambiguous and complete structural identification of lipids by mass spectrometry remains challenging. Increasingly sophisticated tandem mass spectrometry (MS/MS) approaches are now being developed and seamlessly integrated into lipidomics workflows to meet this challenge. These approaches generally either (i) alter the type of ion that is interrogated or (ii) alter the dissociation method in order to improve the structural information obtained from the MS/MS experiment. In this Perspective, we highlight recent advances in both ion type alteration and ion dissociation methods for lipid identification by mass spectrometry. This discussion is aimed to engage investigators involved in fundamental ion chemistry and technology developments as well as practitioners of lipidomics and its many applications. The rapid rate of technology development in recent years has accelerated and strengthened the ties between these two research communities. We identify the common characteristics and practical figures of merit of these emerging approaches and discuss ways these may catalyze future directions of lipid structural elucidation research.

Comparative nanometallomics as a new tool for nanosafety evaluation

Nanosafety evaluation is paramount since it is necessary not only for human health protection and environmental integrity but also as a cornerstone for industrial and regulatory bodies. The current nanometallomics did not cover non-metallic nanomaterials, which is an important part of nanomaterials. In this critical review, the concept of nanometallomics was expanded to incorporate all nanomaterials. The impacts on metal(loid) and metallo-biomolecular homeostasis by nanomaterials will be focused upon in nanometallomics study. Besides, the impacts on elemental and biomolecular homeostasis by metallo-nanomaterials are also considered as the research subjects of nanometallomics. Based on the new concept of nanometallomics, comparative nanometallomics was proposed as a new tool for nanosafety evaluation, which is high throughput and will be precise considering the nature of machine learning techniques. The perspectives of nanometallomics like metallo-wide association study and non-target nanometallomics were put forward.

Metabolomics and Lipidomics: Expanding the Molecular Landscape of Exercise Biology

Dynamic changes in circulating and tissue metabolites and lipids occur in response to exercise-induced cellular and whole-body energy demands to maintain metabolic homeostasis. The metabolome and lipidome in a given biological system provides a molecular snapshot of these rapid and complex metabolic perturbations. The application of metabolomics and lipidomics to map the metabolic responses to an acute bout of aerobic/endurance or resistance exercise has dramatically expanded over the past decade thanks to major analytical advancements, with most exercise-related studies to date focused on analyzing human biofluids and tissues. Experimental and analytical considerations, as well as complementary studies using animal model systems, are warranted to help overcome challenges associated with large human interindividual variability and decipher the breadth of molecular mechanisms underlying the metabolic health-promoting effects of exercise. In this review, we provide a guide for exercise researchers regarding analytical techniques and experimental workflows commonly used in metabolomics and lipidomics. Furthermore, we discuss advancements in human and mammalian exercise research utilizing metabolomic and lipidomic approaches in the last decade, as well as highlight key technical considerations and remaining knowledge gaps to continue expanding the molecular landscape of exercise biology.

Accurate quantification of lipid species affected by isobaric overlap in Fourier-transform mass spectrometry

Lipidomics data require consideration of ions with near-identical masses, which comprises among others the Type-II isotopic overlap. This overlap occurs in series of lipid species differing only by number of double bonds (DBs) mainly because of the natural abundance of 13C-atoms. High-resolution mass spectrometry, such as Fourier-transform mass spectrometry (FTMS), is capable of resolving Type-II overlap depending on mass resolving power. In this work, we evaluated FTMS quantification accuracy of lipid species affected by Type-II overlap. Spike experiments with lipid species pairs of various lipid classes were analyzed by flow injection analysis-FTMS. Accuracy of quantification was evaluated without and with Type-II correction (using relative isotope abundance) as well as utilizing the first isotopic peak (M+1). Isobaric peaks, which were sufficiently resolved, were most accurate without Type-II correction. In cases of partially resolved peaks, we observed peak interference causing distortions in mass and intensity, which is a well-described phenomenon in FTMS. Concentrations of respective species were more accurate when calculated from M+1. Moreover, some minor species, affected by considerable Type-II overlap, could only be quantified by M+1. Unexpectedly, even completely unresolved peaks were substantially overcorrected by Type-II correction because of peak interference. The described method was validated including intraday and interday precisions for human serum and fibroblast samples. Taken together, our results show that accurate quantification of lipid species by FTMS requires resolution-depended data analysis.

Chemical Derivatization-Aided High Resolution Mass Spectrometry for Shotgun Lipidome Analysis

Chemical derivatization coupled with nano-electrospray ionization (nESI) and ultra-high resolution accurate mass spectrometry (UHRAMS) is an established approach to overcome isobaric and isomeric mass interference limitations, and improve the analytical performance, of direct-infusion (i.e., "shotgun") lipidome analysis strategies for "sum composition" level identification and quantification of individual lipid species from within complex mixtures. Here, we describe a protocol for sequential functional group selective derivatization of aminophospholipids and O-alk-1'-enyl (i.e., plasmalogen) lipids, that when integrated into a shotgun lipidomics workflow involving deuterium-labeled internal lipid standard addition, monophasic lipid extraction, and nESI-UHRAMS analysis, enables the routine identification and quantification of >500 individual lipid species at the "sum composition" level, across four lipid categories and from >30 lipid classes and subclasses.

Systems biology approaches to study lipidomes in health and disease

Lipids have many important biological roles, such as energy storage sources, structural components of plasma membranes and as intermediates in metabolic and signaling pathways. Lipid metabolism is under tight homeostatic control, exhibiting spatial and dynamic complexity at multiple levels. Consequently, lipid-related disturbances play important roles in the pathogenesis of most of the common diseases. Lipidomics, defined as the study of lipidomes in biological systems, has emerged as a rapidly-growing field. Due to the chemical and functional diversity of lipids, the application of a systems biology approach is essential if one is to address lipid functionality at different physiological levels. In parallel with analytical advances to measure lipids in biological matrices, the field of computational lipidomics has been rapidly advancing, enabling modeling of lipidomes in their pathway, spatial and dynamic contexts. This review focuses on recent progress in systems biology approaches to study lipids in health and disease, with specific emphasis on methodological advances and biomedical applications.

Recent advances in analytical strategies for mass spectrometry-based lipidomics

Lipids are vital biological molecules and play multiple roles in cellular function of mammalian organisms such as cellular membrane anchoring, signal transduction, material trafficking and energy storage. Driven by the biological significance of lipids, lipidomics has become an emerging science in the field of omics. Lipidome in biological systems consists of hundreds of thousands of individual lipid molecules that possess complex structures, multiple categories, and diverse physicochemical properties assembled by different combinations of polar headgroups and hydrophobic fatty acyl chains. Such structural complexity poses a huge challenge for comprehensive lipidome analysis. Thanks to the great innovations in chromatographic separation techniques and the continuous advances in mass spectrometric detection tools, analytical strategies for lipidomics have been highly diversified so that the depth and breadth of lipidomics have been greatly enhanced. This review will present the current state of mass spectrometry-based analytical strategies including untargeted, targeted and pseudotargeted lipidomics. Recent typical applications of lipidomics in biomarker discovery, pathogenic mechanism and therapeutic strategy are summarized, and the challenges facing to the field of lipidomics are also discussed.

Sensitive analysis of fatty acid esters of hydroxy fatty acids in biological lipid extracts by shotgun lipidomics after one-step derivatization

Branched fatty acid esters of hydroxy fatty acids (FAHFAs) are an important family of endogenous lipids, possessing antidiabetic and anti-inflammatory functions. Therefore, analysis of FAHFAs in biological samples obtained under healthy and disease states can uncover underlying mechanisms of various relevant disorders (e.g., diabetes and autoimmune diseases). Up to now, due to their extremely low abundance, the determination of the changed levels of these species is still a huge challenge, even though great efforts have been made by utilizing liquid chromatography-tandem mass spectrometry with or without derivatization. Herein, we described a novel method for analysis of FAHFAs present in lipid extracts of biological examples after solid-phase extraction and chemical derivatization with one authentic FAHFA specie as an internal standard based on the principles of multi-dimensional mass spectrometry-based shotgun lipidomics. The approach possessed marked sensitivity, high specificity, and broad linear dynamic range of over 3 orders without obvious matrix effects. Moreover, after chemical derivatization, the molecular masses of FAHFAs shift from an overlapped region with ceramide species to a new region without overlaps, removing these contaminating signals from ceramides, and thereby reducing the false results of FAHFAs. Finally, this novel method was successfully applied for determining FAHFAs levels in varieties of representative biological samples, including plasma from lean and overweight/obese individuals of normoglycemia, and tissue samples (such as liver and white adipose tissue from diabetic (db/db) mice). We revealed significant alterations of FAHFAs in samples under patho(physio)logical conditions compared to their respective controls. Taken together, the developed method could greatly contribute to studying altered FAHFA levels under a variety of biological/biomedical conditions, and facilitate the understanding of these lipid species in the patho(physio)logical process.

Evaluation of Lipid In-Source Fragmentation on Different Orbitrap-based Mass Spectrometers

Natural lipidomes represent a complex mixture of lipid molecular species with a variety of biological and signaling functions. Modern mass spectrometry (MS)-based analytical platforms are often used to resolve the complexity of natural lipidomes. The quantitative transfer of lipid molecular species in the gas phase during the electrospray ionization required for MS analysis might be challenged by lipid in-source fragmentation (ISF) hampering their accurate identification and quantification. Here we evaluated the effect of transmission radio frequency (RF) levels and ion transfer temperatures (ITTs) on the analysis of four different lipids (ceramide, cholesteryl ester, phosphatidylethanolamine, and triacylglyceride) ionized in positive ion mode on three different Orbitrap-based platforms. ITT and RF levels were ramped in a systematic way to determine the best settings, allowing the most sensitive detection accompanied by the lowest ISF of a lipid. The extent of the ISF was shown to depend on the configurations of the transmission devices (S-lens vs letterbox/ion funnel) at defined RF and ITT levels for each studied lipid class. We provide here the recommendations for reducing the extent of lipid ISF without a significant loss in sensitivity for Q Exactive HF, Q Exactive HF-X, and Orbitrap Fusion Lumos platforms.

Ultra-High Mass Resolving Power, Mass Accuracy, and Dynamic Range MALDI Mass Spectrometry Imaging by 21-T FT-ICR MS

Detailed characterization of complex biological surfaces by matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) requires instrumentation that is capable of high mass resolving power, mass accuracy, and dynamic range. Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) offers the highest mass spectral performance for MALDI MSI experiments, and often reveals molecular features that are unresolved on lower performance instrumentation. Higher magnetic field strength improves all performance characteristics of FT-ICR; mass resolving power improves linearly, while mass accuracy and dynamic range improve quadratically with magnetic field strength. Here, MALDI MSI at 21T is demonstrated for the first time: mass resolving power in excess of 1 600 000 (at m/z 400), root-mean-square mass measurement accuracy below 100 ppb, and dynamic range per pixel over 500:1 were obtained from the direct analysis of biological tissue sections. Molecular features with m/z differences as small as 1.79 mDa were resolved and identified with high mass accuracy. These features allow for the separation and identification of lipids to the underlying structures of tissues. The unique molecular detail, accuracy, sensitivity, and dynamic range combined in a 21T MALDI FT-ICR MSI experiment enable researchers to visualize molecular structures in complex tissues that have remained hidden until now. The instrument described allows for future innovative, such as high-end studies to unravel the complexity of biological, geological, and engineered organic material surfaces with an unsurpassed detail.

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

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