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

A parallel reaction monitoring-mass spectrometric method for studying lipid biosynthesis in vitro using 13C16-palmitate as an isotope tracer

BACKGROUND

Palmitate, which is the end product of fatty acid synthase, is the key fatty acid for understanding of lipid biosynthetic process in mammalian cells. Mass spectrometry (MS) methodology using 13C-palmitate can trace the lipid biosynthesis such as glycerolipids, glycerophospholipids, and sphingolipids. However, due to the interferences of natural heavy isotopes, accurate measurement of 13C-labeled lipid species has been limited. Here we describe a high-throughput isotope tracing experiment to assess lipid biosynthesis using parallel reaction monitoring-MS (PRM-MS) with 13C16-palmitate as an isotope tracer.

RESULTS

The developed method can trace 14 13C16-labeled lipid classes without disturbance from the heavy isotope patterns of natural lipids. Lipid class-based separation was achieved through hydrophilic interaction liquid chromatography (HILIC) which allows facile identification of lipid, and PRM-MS was performed for accurate detection of the 13C16-labeled lipids. A fibroblast (NIH/3T3) cell line was used as an in vitro model, and the NIH/3T3 cells were treated with bovine serum albumin (BSA)-bound 13C16-palmitate. The isotopic disturbance from natural lipid was eliminated using 13C16-palmitate, rather than 13C1-palmitate, as an isotope tracer. After 24 h of incubation with 0.1 mmol/L of BSA-bound 13C16-palmitate in the fibroblasts, NIH/3T3 cells synthesized the 127 13C16-labeled lipid species of glycerolipids, glycerophospholipids, and sphingolipids. Finally, in the NIH/3T3 cells incubated for 1, 6, and 24 h after the treatment of the isotope tracer exhibited an increased profile of 13C16-labeled lipidome, depending on duration of incubation.

SIGNIFICANCE

The HILIC/PRM-MS method using 13C16-palmitate as an isotope tracer enables identification of 13C16-labeled lipid species by annotating 13C16-labeled position, including the 13C16-fatty acyl chain and 13C16-sphingolipid headgroup, without interference of natural heavy isotope patterns. This lipidomic flux analysis using PRM approach is expected to provide insights into assessment of isotope-labeled lipids.

Accurate Determination of Circulatory Lipids Using a Combination of HILIC-MRM and RPLC-PRM

Circulatory lipids are important markers for characterizing disease phenotypes; however, accurately determining lipid species remains a significant challenge in lipidomic analysis. Here, we present a novel analytical workflow for accurate lipidome characterization in human plasma using mass spectrometry (MS) through the integration of hydrophilic interaction liquid chromatography (HILIC) and reversed-phase liquid chromatography (RPLC). This workflow enables rapid screening of 1,966 lipid species across 18 lipid classes using HILIC-multiple reaction monitoring (MRM), which enables facile identification of lipid species by lipid class-based separations. In the NIST Standard Reference Material for Human Plasma (SRM 1950), 489 lipid species were identified using HILIC-MRM and subsequently analyzed with RPLC-parallel reaction monitoring (PRM) to resolve potential lipid isobars within the same lipid class. Notably, RPLC-PRM identified 70 additional lipidomic features in SRM 1950 that were not detectable with HILIC-MRM. Furthermore, a high correlation (Pearson correlation coefficient = 0.81) was observed regarding the concentrations of lipid species not carrying isobaric interferences in between HILIC-MRM and RPLC-PRM, indicating that the individual lipid concentrations measured by each platform can be integrated. The workflow was further applied to a cohort of 284 human plasma samples from chronic kidney disease (CKD) patients, successfully profiling lipidomic phenotypes across CKD subtypes. These findings demonstrate that combining HILIC-MRM and RPLC-PRM as complementary platforms enhances the accuracy and comprehensiveness of lipidomic analysis.

Clinical lipidomics reveals high individuality and sex specificity of circulatory lipid signatures: a prospective healthy population study

Lipid metabolism and circulatory lipid levels are tightly associated with the (cardio)metabolic health. Consequently, MS-based lipidomics has emerged as a powerful phenotyping tool in epidemiological, human population, and in clinical intervention studies. However, ensuring high-throughput and reproducible measurement of a wide panel of circulatory lipid species in large-scale studies poses a significant challenge. Here, we applied a recently developed quantitative LC-MS/MS lipidomics approach to a subset of 1,086 fasted plasma samples belonging to apparently healthy participants from prospective Lausanne population study. This high-coverage and high-throughput hydrophilic interaction liquid chromatography-based methodology allowed for the robust measurement of 782 circulatory lipid species spanning 22 lipid classes and six orders of magnitude-wide concentration range. This was achieved by combining semiautomated sample preparation using a stable isotope dilution approach and the alternate analysis of National Institute of Standards and Technology plasma reference material, as a quality control. Based on National Institute of Standards and Technology quality control analysis, median between-batch reproducibility was 8.5%, over the course of analysis of 13 independent batches comprising 1,086 samples collected from 364 individuals at three time points. Importantly, the biological variability, per lipid species, was significantly higher than the batch-to-batch analytical variability. Furthermore, the significantly lower between-subject (than within-subject) variability and unsupervised sample clustering demonstrated the high individuality and sex specificity of circulatory lipidome. The most prominent sex differences were reported for sphingomyelins and ether-linked phospholipids present in significantly higher concentrations in female plasma. The high individuality and sex specificity of circulatory lipidome constitute important pre-requisites for the application of lipidomics in next-generation metabolic health monitoring.

Skylite: Skyline-Based Lipid Isomer Retention Time Evaluation for Lipidomics in Metabolic Dysfunction-Associated Steatohepatitis

Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most prevalent liver disorder worldwide and can progress to steatohepatitis. Elevated de novo lipogenesis (DNL) is a key contributor to hepatic steatosis. Fatty acid (FA) desaturation produces several unsaturated lipid isomers that are structurally very similar but have diverse biological functions. However, due to their structural similarity, many conventional mass spectrometry approaches cannot detect such metabolic alterations. Thus, we introduce the Skylite (Skyline-based lipid isomer retention time evaluation) workflow using conventional liquid chromatography-mass spectrometry (LC-MS) to identify important isomer features. Retention times of isomeric phosphatidylcholines are compared with the well-characterized human plasma reference standard NIST 1950. Retention time trends correlate well with fixed-charge derivatized FA in liquid chromatography and ozone-induced dissociation mass spectrometry data. The interpretation is supported by double bond diagnostic fragments in LC-MS/MS experiments of epoxidized hydrolyzed fatty acids. We investigate hepatic lipid profiles, focusing on esterified fatty acids in two mouse models of metabolic dysfunction-associated steatohepatitis (MASH). Out of 37 phosphatidylcholine sum compositions, the workflow identifies 123 lipid features. Importantly, CCl4-induced and melanocortin-4 receptor knockout mice on a western diet (WD) have significantly higher levels of mead acid, branched-chain fatty acid, and n-7 PUFA incorporated into phosphatidylcholines. While the MASH mouse liver tissues contain notable amounts of n-7 PUFA, no n-10 PUFA were detected, potentially indicating a unique desaturation pattern. The screening for altered lipid isomer profiles bridges the gap between high-throughput analyses and specialized structure-resolved techniques.

Raman analysis of lipids in cells: Current applications and future prospects

Lipids play an important role in the regulation of cell life processes. Although there are various lipid detection methods, Raman spectroscopy, a non-invasive technique, provides the detailed chemical composition of lipid profiles without a complex sample preparation procedure and possesses greater potential in basic biology, clinical diagnosis and disease therapy. In this review, we summarized the characteristics and advantages of Raman-based techniques and their primary contribution to illustrating cellular lipid metabolism.

In-depth phospholipid profiling of plant-pathogenic bacteria after treatment with antimicrobial random peptide mixtures

BACKGROUND

The ability of plant-pathogenic bacteria to develop antimicrobial resistance against crop protection products represents a significant challenge. An alternative to conventional crop protecting products could be random peptide mixtures (RPMs), which potentially target the phospholipid-containing cell membrane. The randomized arrangement of the peptides minimizes the risk of bacterial resistance developing against the RPMs. However, not all plant-pathogenic bacteria exhibited growth inhibition after RPM treatment. Our prior studies revealed correlations between bacterial growth inhibition and changes in the fatty acid pattern following treatment. However, additional data on the intact phospholipid composition are essential to further understand and improve novel RPMs.

RESULTS

Accordingly, we developed an analytical setup for in-depth bacterial lipid membrane characterization based on two complementary methods in conjunction with chemometric data evaluation to study the impact of RPM treatment on phospholipid class and species level. An efficient phospholipid class quantitation using hydrophilic interaction liquid chromatography (HILIC)-based lipid class separation with uniform charged aerosol detection (CAD) revealed distinct differences in the class composition of six plant-pathogenic bacteria. Moreover, branched-chain fatty acid (BCFA)-comprising phospholipid profiling via liquid chromatography-tandem mass spectrometry (LC-MS/MS) provided additional lipid species information to classify the investigated bacteria based on the number of bound BCFA. The combination of these techniques served for a comprehensive characterization of the bacterial membrane adaptation to the RPM treatment, which showed some correlations with the inhibitory effects of the RPMs.

SIGNIFICANCE

In this proof-of-concept study, HILIC-CAD phospholipid quantitation and BCFA-comprising phospholipid profiling were introduced as complementary techniques for in-depth characterization of bacterial cell membranes as well as membrane adaptations at both phospholipid class and species level. Our developed analytical setup may facilitate future studies targeting in-depth characterization of bacterial lipid membranes.

Comparative lipid profiling reveals the differential response of distinct lipid subclasses in blast and blunt-induced mild traumatic brain injury

Head trauma from blast exposure is a growing health concern, particularly among active military personnel, and is considered the signature injury of the Gulf War. However, it remains elusive whether fundamental differences exist between blast-related Traumatic Brain Injuries (TBI) and TBI due to other mechanisms. Considering the importance of lipid metabolism associated with neuronal membrane integrity and its compromise during TBI, we sought to find changes in lipidomic profiling during blast or blunt (Stereotaxically Controlled Contusison-SCC)-mediated TBI. In the current study, we have developed the mild TBI (mTBI) model of blast (130 ± 10 kPa) and SCC (1.5 mm dorsal-ventral) on C57BL/6 mice, followed by the serum collection on days 1 and 7. Lipidomics was performed via ultra-high performance liquid chromatography (UHPLC) quadrupole time-of-flight mass spectrometry (qTOF-MS). Additionally, neurobehavioral outcomes were estimated using a revised neurobehavioral severity score for mice (mNSS-R) and an open field test (OFT). The study found that blast-exposed group exhibited more lipid dysregulation, as evidenced by a higher number of significant lipids and associated pathways at both time points. However, the comparative investigation further reveals eight significantly common lipids that can characterize the mTBI regardless of the manner of induction (blast or blunt). Besides, modulated neurobehavioral, locomotor and anxiety functions were also observed post-mTBI. The study illustrates the distinct systemic lipid metabolism intended to preserve the brain's lipid homeostasis post-mTBI. This approach may provide novel insights into lipid metabolism and identification of individual lipid species that aids in understanding the pathophysiology of mTBI.

Application of lipidomics in the study of traditional Chinese medicine

Lipidomics is an emerging discipline that systematically studies the various types, functions, and metabolic pathways of lipids within living organisms. This field compares changes in diseases or drug impact, identifying biomarkers and molecular mechanisms present in lipid metabolic networks across different physiological or pathological states. Through employing analytical chemistry within the realm of lipidomics, researchers analyze traditional Chinese medicine (TCM). This analysis aids in uncovering potential mechanisms for treating diverse physiopathological conditions, assessing drug efficacy, understanding mechanisms of action and toxicity, and generating innovative ideas for disease prevention and treatment. This manuscript assesses recent literature, summarizing existing lipidomics technologies and their applications in TCM research. It delineates the efficacy, mechanisms, and toxicity research related to lipidomics in Chinese medicine. Additionally, it explores the utilization of lipidomics in quality control research for Chinese medicine, aiming to expand the application of lipidomics within this field. Ultimately, this initiative seeks to foster the integration of traditional medicine theory with modern science and technology, promoting an organic fusion between the two domains.

Comprehensive, Quantitative Analysis of SRM 1950: the NIST Human Plasma Reference Material

Many analytical methods have been developed for performing targeted metabolomics. By combining multiple analytical techniques, comprehensive coverage of the metabolome can be achieved. We combined multiple analytical techniques to comprehensively and quantitatively characterize the widely studied NIST human plasma reference material, SRM 1950. Our goal was to provide a large, well-validated list of confident metabolite concentration values (i.e., benchmarks) to assist the metabolomics community in its calibration and comparison efforts. We used four analytical platforms: high-resolution NMR spectroscopy, direct injection tandem MS (DI-MS/MS), liquid chromatography tandem MS (LC-MS/MS), and inductively coupled plasma MS (ICP-MS). Eight validated analytical assays were run, yielding accurate quantitative measurements for 728 unique metabolites or metabolite species. Through computer-aided literature mining, we identified another 330 unique metabolites previously quantified in SRM 1950. We compared NIST-certified values along with literature-derived concentrations/ranges to the metabolite concentrations measured by our four platforms and eight assays. From these assays/platforms, we generated a list of high-confidence concentration values of 1058 metabolites or metabolite species in SRM 1950 including data for 60 amino acids/related compounds, 48 bile acids, 72 amines/sugars/alcohols, 21 metals, 8 catecholamines, 11 vitamins, 92 organic acids, 40 fatty acids/steroids/nucleobases/indole derivatives, 5 polyfluorinated compounds, 7 carotenoids, 39 acylcarnitines, 76 oxylipins, 13 sterols, and 566 lipids/lipid species. This data set represents the most complete quantitative characterization of SRM 1950. An online database (SRM1950-DB) containing 1058 plasma metabolites/metabolite species in SRM 1950, their structures, HMDB IDs, mass, chemical class, concentrations, references, and reliability is freely available at https://srm1950-data.wishartlab.com.

Recommendations for Accurate Lipid Annotation and Semi-absolute Quantification from LC-MS/MS Datasets

Recent developments in LC-MS instrumentation and analytical technologies together with bioinformatics tools supporting high-throughput processing of large omics datasets significantly enhanced our capabilities and efficiency of identification and quantification of lipids in diverse biological materials. However, each biological matrix is characterized by its unique lipid composition, thus requiring optimization of analytical and bioinformatics workflows for each studied lipidome. Here, we describe an integrated workflow for deep lipidome profiling, accurate annotation, and semi-absolute quantification of complex lipidomes based on reversed phase chromatography and high resolution mass spectrometry. This chapter provides details on selection of the optimal extraction protocol, acquisition of LC-MS/MS data for accurate annotation of lipid molecular species, and design of lipidome-specific mixtures of internal standards to assist quantitative analysis of complex, native lipidomes.

Advancements in Mass Spectrometry-Based Targeted Metabolomics and Lipidomics: Implications for Clinical Research

Targeted metabolomics and lipidomics are increasingly utilized in clinical research, providing quantitative and comprehensive assessments of metabolic profiles that underlie physiological and pathological mechanisms. These approaches enable the identification of critical metabolites and metabolic alterations essential for accurate diagnosis and precision treatment. Mass spectrometry, in combination with various separation techniques, offers a highly sensitive and specific platform for implementing targeted metabolomics and lipidomics in clinical settings. Nevertheless, challenges persist in areas such as sample collection, quantification, quality control, and data interpretation. This review summarizes recent advances in targeted metabolomics and lipidomics, emphasizing their applications in clinical research. Advancements, including microsampling, dynamic multiple reaction monitoring, and integration of ion mobility mass spectrometry, are highlighted. Additionally, the review discusses the critical importance of data standardization and harmonization for successful clinical implementation.

A Comprehensive Analysis of Liver Lipidomics Signature in Adults with Metabolic Dysfunction-Associated Steatohepatitis-A Pilot Study

Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) is the most common chronic liver disorder in Western countries, encompassing a range of conditions from steatosis to Metabolic Dysfunction-Associated Steatohepatitis (MASH), which can potentially progress to cirrhosis. Lipidomics approaches have revealed significant alterations in the hepatic lipidome associated with both steatosis and steatohepatitis, with these changes correlating with disease manifestation. While the transition from steatosis to MASH remains poorly understood, recent research indicates that both the quantity and quality of deposited lipids play a pivotal role in MASLD progression. In our study, we utilized untargeted and targeted analyses to identify intact lipids and fatty acids in liver biopsies from healthy controls and MASLD patients, categorized based on their histological findings. In total, 447 lipid species were identified, with 215 subjected to further statistical analysis. Univariate and multivariate analyses revealed alterations in triglyceride species and fatty acids, including FA 16:0, FA 16:1, FA 18:3 n6, the sum of MUFA, and the Δ9-desaturase activity ratio. This research provides insights into the connection between dysregulated lipid metabolism in the progression of MASLD, supporting previous findings. Further studies on lipid metabolism could improve risk assessment methods, particularly given the current limited understanding of the transition from steatosis to MASH.

Exploring environmental modifiers of LRRK2-associated Parkinson's disease penetrance: An exposomics and metagenomics pilot study on household dust

Pathogenic variants in the Leucine-rich repeat kinase 2 (LRRK2) gene are a primary monogenic cause of Parkinson's disease (PD). However, the likelihood of developing PD with inherited LRRK2 pathogenic variants differs (a phenomenon known as "reduced penetrance"), with factors including age and geographic region, highlighting a potential role for lifestyle and environmental factors in disease onset. To investigate this, household dust samples from four different groups of individuals were analyzed using metabolomics/exposomics and metagenomics approaches: PD+/LRRK2+ (PD patients with pathogenic LRRK2 variants; n = 11), PD-/LRRK2+ (individuals with pathogenic LRRK2 variants but without PD diagnosis; n = 8), iPD (PD of unknown cause; n = 11), and a matched, healthy control group (n = 11). The dust was complemented with metabolomics and lipidomics of matched serum samples, where available. A total of 1,003 chemicals and 163 metagenomic operational taxonomic units (mOTUs) were identified in the dust samples, of which ninety chemicals and ten mOTUs were statistically significant (ANOVA p-value < 0.05). Reduced levels of 2-benzothiazolesulfonic acid (BThSO3) were found in the PD-/LRRK2+ group compared to the PD+/LRRK2+ . Among the significant chemicals tentatively identified in dust, two are hazardous chemical replacements: Bisphenol S (BPS), and perfluorobutane sulfonic acid (PFBuS). Furthermore, various lipids were found altered in serum including different lysophosphatidylethanolamines (LPEs), and lysophosphatidylcholines (LPCs), some with higher levels in the PD+/LRRK2+ group compared to the control group. A cellular study on isogenic neurons generated from a PD+/LRRK2+ patient demonstrated that BPS negatively impacts mitochondrial function, which is implicated in PD pathogenesis. This pilot study demonstrates how non-target metabolomics/exposomics analysis of indoor dust samples complemented with metagenomics can prioritize relevant chemicals that may be potential modifiers of LRRK2 penetrance.

Tandem Mass Spectrometry in Untargeted Lipidomics: A Case Study of Peripheral Blood Mononuclear Cells

Peripheral blood mononuclear cells (PBMCs), including lymphocytes, are important components of the human immune system. These cells contain a diverse array of lipids, primarily glycerophospholipids (GPs) and sphingolipids (SPs), which play essential roles in cellular structure, signaling, and programmed cell death. This study presents a detailed analysis of GP and SP profiles in human PBMC samples using tandem mass spectrometry (MS/MS). Hydrophilic interaction liquid chromatography (HILIC) and electrospray ionization (ESI) coupled with linear ion-trap MS/MS were employed to investigate the diagnostic fragmentation patterns that aided in determining regiochemistry in complex lipid extracts. Specifically, the study explored the fragmentation patterns of various lipid species, including phosphatidylcholines (PCs), phosphatidylethanolamines (PEs), their plasmalogen and lyso forms, phosphatidylserines (PSs), phosphatidylinositols (PIs), phosphatidylglycerols (PGs), sphingomyelins (SMs), and dihexosylceramides (Hex2Cer). Our comprehensive analysis led to the characterization of over 200 distinct lipid species, significantly expanding our understanding of PBMC lipidome complexity. A freely available spreadsheet tool for simulating MS/MS spectra of GPs is provided, enhancing the accessibility and reproducibility of this research. This study advances our knowledge of PBMC lipidomes and establishes a robust analytical framework for future investigations in lipidomics.

Concordant inter-laboratory derived concentrations of ceramides in human plasma reference materials via authentic standards

In this community effort, we compare measurements between 34 laboratories from 19 countries, utilizing mixtures of labelled authentic synthetic standards, to quantify by mass spectrometry four clinically used ceramide species in the NIST (National Institute of Standards and Technology) human blood plasma Standard Reference Material (SRM) 1950, as well as a set of candidate plasma reference materials (RM 8231). Participants either utilized a provided validated method and/or their method of choice. Mean concentration values, and intra- and inter-laboratory coefficients of variation (CV) were calculated using single-point and multi-point calibrations, respectively. These results are the most precise (intra-laboratory CVs ≤ 4.2%) and concordant (inter-laboratory CVs < 14%) community-derived absolute concentration values reported to date for four clinically used ceramides in the commonly analyzed SRM 1950. We demonstrate that calibration using authentic labelled standards dramatically reduces data variability. Furthermore, we show how the use of shared RM can correct systematic quantitative biases and help in harmonizing lipidomics. Collectively, the results from the present study provide a significant knowledge base for translation of lipidomic technologies to future clinical applications that might require the determination of reference intervals (RIs) in various human populations or might need to estimate reference change values (RCV), when analytical variability is a key factor for recall during multiple testing of individuals.

Untargeted Blood Lipidomics Analysis in Critically Ill Pediatric Patients with Ventilator-Associated Pneumonia: A Pilot Study

This study aims to explore the diagnostic potential of blood lipid profiles in suspected ventilator-associated pneumonia (VAP). Early detection of VAP remains challenging for clinicians due to subjective clinical criteria and the limited effectiveness of current diagnostic tests. Blood samples from 20 patients, with ages between 6 months and 15 years, were collected at days 1, 3, 6, and 12, and an untargeted lipidomics analysis was performed using a Ultra high Pressure Liquid Chromatography hyphenated with High Resolution Mass Spectrometry UPLC-HRMS (TIMS-TOF/MS) platform. Patients were stratified based on modified pediatric clinical pulmonary index score (mCPIS) into high (mCPIS ≥ 6, n = 12) and low (mCPIS < 6, n = 8) VAP suspicion groups. With the untargeted lipid profiling, we were able to identify 144 lipid species from different lipid groups such as glycerophospholipids, glycerolipids, and sphingolipids, in the blood of children with VAP. Multivariate and univariate statistical analyses revealed a distinct distribution of blood lipid profiles between the studied groups, indicating the potential utility of lipid biomarkers in discriminating VAP presence. Additionally, specific lipids were associated with pharyngeal culture results, notably the presence of Klebsiella pneumoniae and Staphylococcus aureus, underscoring the importance of lipid profiling in identifying the microbial etiology of VAP.

Lipidomic-Based Algorithms Can Enhance Prediction of Obstructive Coronary Artery Disease

Lipidomics emerges as a promising research field with the potential to help in personalized risk stratification and improve our understanding on the functional role of individual lipid species in the metabolic perturbations occurring in coronary artery disease (CAD). This study aimed to utilize a machine learning approach to provide a lipid panel able to identify patients with obstructive CAD. In this posthoc analysis of the prospective CorLipid trial, we investigated the lipid profiles of 146 patients with suspected CAD, divided into two categories based on the existence of obstructive CAD. In total, 517 lipid species were identified, from which 288 lipid species were finally quantified, including glycerophospholipids, glycerolipids, and sphingolipids. Univariate and multivariate statistical analyses have shown significant discrimination between the serum lipidomes of patients with obstructive CAD. Finally, the XGBoost algorithm identified a panel of 17 serum biomarkers (5 sphingolipids, 7 glycerophospholipids, a triacylglycerol, galectin-3, glucose, LDL, and LDH) as totally sensitive (100% sensitivity, 62.1% specificity, 100% negative predictive value) for the prediction of obstructive CAD. Our findings shed light on dysregulated lipid metabolism's role in CAD, validating existing evidence and suggesting promise for novel therapies and improved risk stratification.

Predicting Non-Alcoholic Steatohepatitis: A Lipidomics-Driven Machine Learning Approach

Nonalcoholic fatty liver disease (NAFLD), nowadays the most prevalent chronic liver disease in Western countries, is characterized by a variable phenotype ranging from steatosis to nonalcoholic steatohepatitis (NASH). Intracellular lipid accumulation is considered the hallmark of NAFLD and is associated with lipotoxicity and inflammation, as well as increased oxidative stress levels. In this study, a lipidomic approach was used to investigate the plasma lipidome of 12 NASH patients, 10 Nonalcoholic Fatty Liver (NAFL) patients, and 15 healthy controls, revealing significant alterations in lipid classes, such as glycerolipids and glycerophospholipids, as well as fatty acid compositions in the context of steatosis and steatohepatitis. A machine learning XGBoost algorithm identified a panel of 15 plasma biomarkers, including HOMA-IR, BMI, platelets count, LDL-c, ferritin, AST, FA 12:0, FA 18:3 ω3, FA 20:4 ω6/FA 20:5 ω3, CAR 4:0, LPC 20:4, LPC O-16:1, LPE 18:0, DG 18:1_18:2, and CE 20:4 for predicting steatohepatitis. This research offers insights into the connection between imbalanced lipid metabolism and the formation and progression of NAFL D, while also supporting previous research findings. Future studies on lipid metabolism could lead to new therapeutic approaches and enhanced risk assessment methods, as the shift from isolated steatosis to NASH is currently poorly understood.

Electron-activated dissociation (EAD) for the complementary annotation of metabolites and lipids through data-dependent acquisition analysis and feature-based molecular networking, applied to the sentinel amphipod Gammarus fossarum

The past decades have marked the rise of metabolomics and lipidomics as the -omics sciences which reflect the most phenotypes in living systems. Mass spectrometry-based approaches are acknowledged for both quantification and identification of molecular signatures, the latter relying primarily on fragmentation spectra interpretation. However, the high structural diversity of biological small molecules poses a considerable challenge in compound annotation. Feature-based molecular networking (FBMN) combined with database searches currently sets the gold standard for annotation of large datasets. Nevertheless, FBMN is usually based on collision-induced dissociation (CID) data, which may lead to unsatisfying information. The use of alternative fragmentation methods, such as electron-activated dissociation (EAD), is undergoing a re-evaluation for the annotation of small molecules, as it gives access to additional fragmentation routes. In this study, we apply the performances of data-dependent acquisition mass spectrometry (DDA-MS) under CID and EAD fragmentation along with FBMN construction, to perform extensive compound annotation in the crude extracts of the freshwater sentinel organism Gammarus fossarum. We discuss the analytical aspects of the use of the two fragmentation modes, perform a general comparison of the information delivered, and compare the CID and EAD fragmentation pathways for specific classes of compounds, including previously unstudied species. In addition, we discuss the potential use of FBMN constructed with EAD fragmentation spectra to improve lipid annotation, compared to the classic CID-based networks. Our approach has enabled higher confidence annotations and finer structure characterization of 823 features, including both metabolites and lipids detected in G. fossarum extracts.

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.

Charge inversion under plasma-nanodroplet reaction conditions excludes Fischer esterification for unsaturated fatty acids: a chemical approach for type II isobaric overlap

Direct infusion ionization methods provide the highest throughput strategy for mass spectrometry (MS) analysis of low-volume samples. But the trade-off includes matrix effects, which can significantly reduce analytical performance. Herein, we present a novel chemical approach to tackle a special type of matrix effect, namely type II isobaric overlap. We focus on detailed investigation of a nanodroplet-based esterification chemistry for differentiating isotopologue [M + 2] signal due to unsaturated fatty acid (FA) from the monoisotopic signal from a saturated FA. The method developed involves the online fusion of nonthermal plasma with charged nanodroplets, enabling selective esterification of saturated FAs. We discovered that unsaturated FAs undergo spontaneous intramolecular reaction via a novel mechanism based on a carbocation intermediate to afford a protonated lactone moiety (resonance stabilized cyclic carbonium ion), whose mass is the same as the original protonated unsaturated FA. Therefore, the monoisotopic signal from any saturated FA can be selectively shifted away from the mass-to-charge position where the isobaric interference occurs to enable effective characterization by MS. The mechanism governing the spontaneous intramolecular reactions for unsaturated FAs was validated with DFT calculations, experimentation with standards, and isotope labeling. This novel insight achieved via the ultrafast plasma-nanodroplet reaction environment provides a potentially useful synthetic pathway to achieve catalyst-free lactone preparation. Analytically, we believe the performance of direct infusion MS can be greatly enhanced by combining our approach with prior sample enrichment steps for applications in biomedicine and food safety. Also, combination with portable mass spectrometers can improve the efficiency of field studies since front-end separation is not possible under such conditions.

Exploration and optimization of extraction, analysis and data normalization strategies for mass spectrometry-based DNA adductome mapping and modeling

Although interest in characterizing DNA damage by means of DNA adductomics has substantially grown, the field of DNA adductomics is still in its infancy, with room for optimization of methods for sample analysis, data processing and DNA adduct identification. In this context, the first objective of this study was to evaluate the use of hydrophilic interaction (HILIC) vs. reversed phase liquid chromatography (RPLC) coupled to high resolution mass spectrometry (HRMS) and thermal acidic vs. enzymatic hydrolysis of DNA followed by DNA adduct purification and enrichment using solid-phase extraction (SPE) or fraction collection for DNA adductome mapping. The second objective was to assess the use of total ion count (TIC) and median intensity (MedI) normalization compared to QC (quality control), iQC (internal QC) and quality control-based robust locally estimated scatterplot smoothing (LOESS) signal correction (QC-RLSC) normalization for processing of the acquired data. The results demonstrate that HILIC compared to RPLC allowed better modeling of the tentative DNA adductome, particularly in combination with thermal acidic hydrolysis and SPE (more valid models, with an average Q²(Y) and R²(Y) of 0.930 and 0.998, respectively). Regarding the need for data normalization and the management of (limited) system instability and signal drift, QC normalization outperformed TIC, MedI, iQC and LOESS normalization. As such, QC normalization can be put forward as the default data normalization strategy. In case of momentous signal drift and/or batch effects however, comparison to other normalization strategies (like e.g. LOESS) is recommended. In future work, further optimization of DNA adductomics may be achieved by merging of HILIC and RPLC datasets and/or application of 2D-LC, as well as the inclusion of Schiff base stabilization and/or fraction collection in the thermal acidic hydrolysis-SPE sample preparation workflow.

Evaluation of Lipid Extraction Protocols for Untargeted Analysis of Mouse Tissue Lipidome

Lipidomics refers to the full characterization of lipids present within a cell, tissue, organism, or biological system. One of the bottlenecks affecting reliable lipidomic analysis is the extraction of lipids from biological samples. An ideal extraction method should have a maximum lipid recovery and the ability to extract a broad range of lipid classes with acceptable reproducibility. The most common lipid extraction relies on either protein precipitation (monophasic methods) or liquid-liquid partitioning (bi- or triphasic methods). In this study, three monophasic extraction systems, isopropanol (IPA), MeOH/MTBE/CHCl3 (MMC), and EtOAc/EtOH (EE), alongside three biphasic extraction methods, Folch, butanol/MeOH/heptane/EtOAc (BUME), and MeOH/MTBE (MTBE), were evaluated for their performance in characterization of the mouse lipidome of six different tissue types, including pancreas, spleen, liver, brain, small intestine, and plasma. Sixteen lipid classes were investigated in this study using reversed-phase liquid chromatography/mass spectrometry. Results showed that all extraction methods had comparable recoveries for all tested lipid classes except lysophosphatidylcholines, lysophosphatidylethanolamines, acyl carnitines, sphingomyelines, and sphingosines. The recoveries of these classes were significantly lower with the MTBE method, which could be compensated by the addition of stable isotope-labeled internal standards prior to lipid extraction. Moreover, IPA and EE methods showed poor reproducibility in extracting lipids from most tested tissues. In general, Folch is the optimum method in terms of efficacy and reproducibility for extracting mouse pancreas, spleen, brain, and plasma. However, MMC and BUME methods are more favored when extracting mouse liver or intestine.

Improving Quantitative Accuracy in Nontargeted Lipidomics by Evaluating Adduct Formation

For large-scale lipidomic analyses, accurate and reproducible quantification of endogenous lipids is crucial for comparing results within and across studies. Many lipids present in liquid chromatography-electrospray ionization-mass spectrometry form various adducts with buffer components. The mechanisms and conditions that dictate adduct formation are still poorly understood. In a positive mode, neutral lipids like mono-, di-, and triacylglycerides and cholesteryl esters typically generate [M + NH4]+ adduct ions, although [M + Na]+, [M + K]+, and other (more complex) species can also be significantly abundant in MS1 precursor ion spectra. Variations in the ratios of these adducts (within and between matrices) can lead to dramatic inaccuracies during quantification. Here, we examine 48 unique diacylglycerol (DAG) species across 2366 mouse samples for eight matrix-specific data sets of plasma, liver, kidney, brain, heart muscle, gastrocnemius muscle, gonadal, and inguinal fat. Typically, no single adduct ion species accounted for more than 60% of the total observed abundance across each data set. Even within a single matrix, DAGs showed a high variability of adduct ratios. The ratio of [M + NH4]+ adduct ions was increased for longer-chain DAGs and for polyunsaturated DAGs, at the expense of reduced ratios of [M + Na]+ adducts. When using three deuterated internal DAG standards, we found that absolute concentrations were estimated with up to 70% error when only one adduct ion was used instead of all adducts combined. Importantly, when combining [M + NH4]+ and [M + Na]+ adduct ions, quantification results were within 5% accuracy compared to all adduct ions combined. Additional variance can be caused by other factors, such as instrument conditions or matrix effects.

Exploring the Impact of Organic Solvent Quality and Unusual Adduct Formation during LC-MS-Based Lipidomic Profiling

Liquid chromatography-mass spectrometry (LC-MS) is the key technique for analyzing complex lipids in biological samples. Various LC-MS modes are used for lipid separation, including different stationary phases, mobile-phase solvents, and modifiers. Quality control in lipidomics analysis is crucial to ensuring the generated data's reliability, reproducibility, and accuracy. While several quality control measures are commonly discussed, the impact of organic solvent quality during LC-MS analysis is often overlooked. Additionally, the annotation of complex lipids remains prone to biases, leading to potential misidentifications and incomplete characterization of lipid species. In this study, we investigate how LC-MS-grade isopropanol from different vendors may influence the quality of the mobile phase used in LC-MS-based untargeted lipidomic profiling of biological samples. Furthermore, we report the occurrence of an unusual, yet highly abundant, ethylamine adduct [M+46.0651]+ that may form for specific lipid subclasses during LC-MS analysis in positive electrospray ionization mode when acetonitrile is part of the mobile phase, potentially leading to lipid misidentification. These findings emphasize the importance of considering solvent quality in LC-MS analysis and highlight challenges in lipid annotation.

Plasma Sphingomyelin Disturbances: Unveiling Its Dual Role as a Crucial Immunopathological Factor and a Severity Prognostic Biomarker in COVID-19

SARS-CoV-2 infection triggers distinct patterns of disease development characterized by significant alterations in host regulatory responses. Severe cases exhibit profound lung inflammation and systemic repercussions. Remarkably, critically ill patients display a "lipid storm", influencing the inflammatory process and tissue damage. Sphingolipids (SLs) play pivotal roles in various cellular and tissue processes, including inflammation, metabolic disorders, and cancer. In this study, we employed high-resolution mass spectrometry to investigate SL metabolism in plasma samples obtained from control subjects (n = 55), COVID-19 patients (n = 204), and convalescent individuals (n = 77). These data were correlated with inflammatory parameters associated with the clinical severity of COVID-19. Additionally, we utilized RNAseq analysis to examine the gene expression of enzymes involved in the SL pathway. Our analysis revealed the presence of thirty-eight SL species from seven families in the plasma of study participants. The most profound alterations in the SL species profile were observed in patients with severe disease. Notably, a predominant sphingomyelin (SM d18:1) species emerged as a potential biomarker for COVID-19 severity, showing decreased levels in the plasma of convalescent individuals. Elevated SM levels were positively correlated with age, hospitalization duration, clinical score, and neutrophil count, as well as the production of IL-6 and IL-8. Intriguingly, we identified a putative protective effect against disease severity mediated by SM (d18:1/24:0), while ceramide (Cer) species (d18:1/24:1) and (d18:1/24:0)were associated with increased risk. Moreover, we observed the enhanced expression of key enzymes involved in the SL pathway in blood cells from severe COVID-19 patients, suggesting a primary flow towards Cer generation in tandem with SM synthesis. These findings underscore the potential of SM as a prognostic biomarker for COVID-19 and highlight promising pharmacological targets. By targeting sphingolipid pathways, novel therapeutic strategies may emerge to mitigate the severity of COVID-19 and improve patient outcomes.

Scrutinizing different ionization responses of polar lipids in a reversed-phase gradient by implementing a counter-gradient

Lipidomics studies strive for a comprehensive identification and quantification of lipids. While reversed phase (RP) liquid chromatography (LC) coupled to high resolution mass spectrometry (MS) offers unrivalled selectivity and thus is the preferred method for lipid identification, accurate lipid quantification remains challenging. The widely adopted one-point lipid class specific quantification (one internal standard per lipid class) suffers from the fact that ionization of internal standard and target lipid occurs under different solvent composition as a consequence of chromatographic separation. To address this issue, we established a dual flow injection and chromatography setup that allows to control solvent conditions during ionization enabling isocratic ionization while running a RP gradient through the use of a counter-gradient. Using this dual LC pump platform, we investigated the impact of solvent conditions within a RP gradient on ionization response and arising quantification biases. Our results confirmed that changing solvent composition significantly influences ionization response. Quantification of human plasma (SRM 1950) lipids under gradient and isocratic ionization conditions further confirmed these findings as significant differences between the two conditions were found for the majority of lipids. While the quantity of sphingomyelins with >40 C atoms was consistently overestimated under gradient ionization, isocratic ionization improved their recovery compared to consensus values. However, the limitation of consensus values was demonstrated as overall only small changes in z-score were observed because of high uncertainties of the consensus values. Furthermore, we observed a trueness bias between gradient and isocratic ionization when quantifying a panel of lipid species standards which is highly dependent on lipid class and ionization mode. Uncertainty calculations under consideration of the trueness bias as RP gradient uncertainty revealed that especially ceramides with >40 C atoms had a high bias leading to total combined uncertainties of up to 54%. The assumption of isocratic ionization significantly decreases total measurement uncertainty and highlights the importance of studying the trueness bias introduced by a RP gradient to reduce quantification uncertainty.

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

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

Comparison of Data-Dependent Acquisition, Data-Independent Acquisition, and Parallel Reaction Monitoring in Trapped Ion Mobility Spectrometry-Time-of-Flight Tandem Mass Spectrometry-Based Lipidomics

The parallel accumulation-serial fragmentation (PASEF) approach based on trapped ion mobility spectrometry (TIMS) enables mobility-resolved fragmentation and a higher number of fragments in the same time period compared to conventional MS/MS experiments. Furthermore, the ion mobility dimension offers novel approaches for fragmentation. Using parallel reaction monitoring (prm), the ion mobility dimension allows a more accurate selection of precursor windows, while using data-independent aquisition (dia) spectral quality is improved through ion-mobility filtering. Owing to favorable implementation in proteomics, the transferability of these PASEF modes to lipidomics is of great interest, especially as a result of the high complexity of analytes with similar fragments. However, these novel PASEF modes have not yet been thoroughly evaluated for lipidomics applications. Therefore, data-dependent acquisition (dda)-, dia-, and prm-PASEF were compared using hydrophilic interaction liquid chromatography (HILIC) for phospholipid class separation in human plasma samples. Results show that all three PASEF modes are generally suitable for usage in lipidomics. Although dia-PASEF achieves a high sensitivity in generating MS/MS spectra, the fragment-to-precursor assignment for lipids with both, similar retention time as well as ion mobility, was difficult in HILIC-MS/MS. Therefore, dda-PASEF is the method of choice to investigate unknown samples. However, the best data quality was achieved by prm-PASEF, owing to the focus on fragmentation of specified targets. The high selectivity and sensitivity in generating MS/MS spectra of prm-PASEF could be a potential alternative for targeted lipidomics, e.g., in clinical applications.

Multimodal plasma metabolomics and lipidomics in elucidating metabolic perturbations in tuberculosis patients with concurrent type 2 diabetes

Type 2 diabetes mellitus (DM) poses a major burden for the treatment and control of tuberculosis (TB). Characterization of the underlying metabolic perturbations in DM patients with TB infection would yield insights into the pathophysiology of TB-DM, thus potentially leading to improvements in TB treatment. In this study, a multimodal metabolomics and lipidomics workflow was applied to investigate plasma metabolic profiles of patients with TB and TB-DM. Significantly different biological processes and biomarkers in TB-DM vs. TB were identified using a data-driven, knowledge-based framework. Changes in metabolic and signaling pathways related to carbohydrate and amino acid metabolism were mainly captured by amide HILIC column metabolomics analysis, while perturbations in lipid metabolism were identified by the C18 metabolomics and lipidomics analysis. Compared to TB, TB-DM exhibited elevated levels of bile acids and molecules related to carbohydrate metabolism, as well as the depletion of glutamine, retinol, lysophosphatidylcholine, and phosphatidylcholine. Moreover, arachidonic acid metabolism was determined as a potential important factor in the interaction between TB and DM pathophysiology. In a correlation network of the significantly altered molecules, among the central nodes, chenodeoxycholic acid was robustly associated with TB and DM. Fatty acid (22:4) was a component of all significant modules. In conclusion, the integration of multimodal metabolomics and lipidomics provides a thorough picture of the metabolic changes associated with TB-DM. The results obtained from this comprehensive profiling of TB patients with DM advance the current understanding of DM comorbidity in TB infection and contribute to the development of more effective treatment.

Four-dimensional trapped ion mobility spectrometry lipidomics for high throughput clinical profiling of human blood samples

Lipidomics encompassing automated lipid extraction, a four-dimensional (4D) feature selection strategy for confident lipid annotation as well as reproducible and cross-validated quantification can expedite clinical profiling. Here, we determine 4D descriptors (mass to charge, retention time, collision cross section, and fragmentation spectra) of 200 lipid standards and 493 lipids from reference plasma via trapped ion mobility mass spectrometry to enable the implementation of stringent criteria for lipid annotation. We use 4D lipidomics to confidently annotate 370 lipids in reference plasma samples and 364 lipids in serum samples, and reproducibly quantify 359 lipids using level-3 internal standards. We show the utility of our 4D lipidomics workflow for high-throughput applications by reliable profiling of intra-individual lipidome phenotypes in plasma, serum, whole blood, venous and finger-prick dried blood spots.

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

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

Guiding the choice of informatics software and tools for lipidomics research applications

Progress in mass spectrometry lipidomics has led to a rapid proliferation of studies across biology and biomedicine. These generate extremely large raw datasets requiring sophisticated solutions to support automated data processing. To address this, numerous software tools have been developed and tailored for specific tasks. However, for researchers, deciding which approach best suits their application relies on ad hoc testing, which is inefficient and time consuming. Here we first review the data processing pipeline, summarizing the scope of available tools. Next, to support researchers, LIPID MAPS provides an interactive online portal listing open-access tools with a graphical user interface. This guides users towards appropriate solutions within major areas in data processing, including (1) lipid-oriented databases, (2) mass spectrometry data repositories, (3) analysis of targeted lipidomics datasets, (4) lipid identification and (5) quantification from untargeted lipidomics datasets, (6) statistical analysis and visualization, and (7) data integration solutions. Detailed descriptions of functions and requirements are provided to guide customized data analysis workflows.

Comprehensive lipid profiles investigation reveals host metabolic and immune alterations during anti-tuberculosis treatment: Implications for therapeutic monitoring

In this study, we investigated the lipidome of tuberculosis patients during standard chemotherapy to discover biosignatures that could aid therapeutic monitoring. UPLC-QToF MS was used to analyze 82 baseline and treatment plasma samples of patients with pulmonary tuberculosis. Subsequently, a data-driven and knowledge-based workflow, including robust annotation, statistical analysis, and functional analysis, was applied to assess lipid profiles during treatment. Overall, the lipids species from 17 lipid subclasses were significantly altered by anti-tuberculosis chemotherapy. Cholesterol ester (CE), monoacylglycerols, and phosphatidylcholine (PC) were upregulated, whereas triacylglycerols, sphingomyelin, and ether-linked phosphatidylethanolamines (PE O-) were downregulated. Notably, PCs demonstrated a clear upward expression pattern during tuberculosis treatment. Several lipid species were identified as potential biomarkers for therapeutic monitoring, such as PC(42:6), PE(O-40:5), CE(24:6), and dihexosylceramide Hex2Cer(34:2;2 O). Functional and lipid gene enrichment analysis revealed alterations in pathways related to lipid metabolism and host immune responses. In conclusion, this study provides a foundation for the use of lipids as biomarkers for clinical management of tuberculosis.

Nutritional lipidomics for the characterization of lipids in food

Lipids represent one out of three major macronutrient classes in the human diet. It is estimated to account for about 15-20% of the total dietary intake. Triacylglycerides comprise the majority of them, estimated 90-95%. Other lipid classes include free fatty acids, phospholipids, cholesterol, and plant sterols as minor components. Various methods are used for the characterization of nutritional lipids, however, lipidomics approaches become increasingly attractive for this purpose due to their wide coverage, comprehensiveness and holistic view on composition. In this chapter, analytical methodologies and workflows utilized for lipidomics profiling of food samples are outlined with focus on mass spectrometry-based assays. The chapter describes common lipid extraction protocols, the distinct instrumental mass-spectrometry based analytical platforms for data acquisition, chromatographic and ion-mobility spectrometry methods for lipid separation, briefly mentions alternative methods such as gas chromatography for fatty acid profiling and mass spectrometry imaging. Critical issues of important steps of lipidomics workflows such as structural annotation and identification, quantification and quality assurance are discussed as well. Applications reported over the period of the last 5years are summarized covering the discovery of new lipids in foodstuff, differential profiling approaches for comparing samples from different origin, species, varieties, cultivars and breeds, and for food processing quality control. Lipidomics as a powerful tool for personalized nutrition and nutritional intervention studies is briefly discussed as well. It is expected that this field is significantly growing in the near future and this chapter gives a short insight into the power of nutritional lipidomics approaches.

Analytical and computational workflow for in-depth analysis of oxidized complex lipids in blood plasma

Lipids are a structurally diverse class of biomolecules which can undergo a variety of chemical modifications. Among them, lipid (per)oxidation attracts most of the attention due to its significance in the regulation of inflammation, cell proliferation and death programs. Despite their apparent regulatory significance, the molecular repertoire of oxidized lipids remains largely elusive as accurate annotation of lipid modifications is complicated by their low abundance and often unknown, biological context-dependent structural diversity. Here, we provide a workflow based on the combination of bioinformatics and LC-MS/MS technologies to support identification and relative quantification of oxidized complex lipids in a modification type- and position-specific manner. The developed methodology is used to identify epilipidomics signatures of lean and obese individuals with and without type 2 diabetes. The characteristic signature of lipid modifications in lean individuals, dominated by the presence of modified octadecanoid acyl chains in phospho- and neutral lipids, is drastically shifted towards lipid peroxidation-driven accumulation of oxidized eicosanoids, suggesting significant alteration of endocrine signalling by oxidized lipids in metabolic disorders.

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.

Ion Mobility Shift Reagents for Lipid Double Bonds Based on Paternò-Büchi Photoderivatization with Halogenated Acetophenones

The Paternò-Büchi (PB) reaction is a cycloaddition reaction between a carbon-carbon double bond (C═C) and a photochemically excited carbonyl-containing compound. The constrained ring formed between the C═C bond and the PB reagent is more susceptible to fragmentation by collision-induced dissociation, which facilitates identification of the C═C position within the fatty acyl tails of lipids. Although the original PB reaction using acetone had a low yield of derivatized lipids and therefore a low yield of diagnostic ions, a new generation of PB reagents based on halogenated acetophenones has improved the reaction yield substantially. In this study, we investigated the use of halogenated PB reagents and ion mobility to improve the identification of PB-derivatized lipids by shifting them out of the densely populated lipid region of ion mobility-mass spectrometry (IM-MS) space. Several halogenated PB reagents containing fluorine, chlorine and bromine were investigated for their ability to decrease the collision cross-section (CCS) values of derivatized lipids and yield sufficient intensity for both the derivatized lipid and its diagnostic ions. We found that 4'-chloro-2',6'-difluoroacetophenone (CDFAP) displayed the best performance, with an average decrease in CCS of 4.4% and yield of derivatized lipids and diagnostic ions comparable to the trifluorinated acetophenone reagent proposed by the Xia group. The unique isotope pattern resulting from the chlorine substituent aided in identification of the derivatized lipids and their diagnostic ions, as well. We further demonstrate that derivatization with CDFAP preserves the separation of lipids classes in IM-MS space.

Ion mobility mass spectrometry in the omics era: Challenges and opportunities for metabolomics and lipidomics

Researchers worldwide are taking advantage of novel, commercially available, technologies, such as ion mobility mass spectrometry (IM-MS), for metabolomics and lipidomics applications in a variety of fields including life, biomedical, and food sciences. IM-MS provides three main technical advantages over traditional LC-MS workflows. Firstly, in addition to mass, IM-MS allows collision cross-section values to be measured for metabolites and lipids, a physicochemical identifier related to the chemical shape of an analyte that increases the confidence of identification. Second, IM-MS increases peak capacity and the signal-to-noise, improving fingerprinting as well as quantification, and better defining the spatial localization of metabolites and lipids in biological and food samples. Third, IM-MS can be coupled with various fragmentation modes, adding new tools to improve structural characterization and molecular annotation. Here, we review the state-of-the-art in IM-MS technologies and approaches utilized to support metabolomics and lipidomics applications and we assess the challenges and opportunities in this growing field.

Comprehensive lipid and lipid-related gene investigations of host immune responses to characterize metabolism-centric biomarkers for pulmonary tuberculosis

Despite remarkable success in the prevention and treatment of tuberculosis (TB), it remains one of the most devastating infectious diseases worldwide. Management of TB requires an efficient and timely diagnostic strategy. In this study, we comprehensively characterized the plasma lipidome of TB patients, then selected candidate lipid and lipid-related gene biomarkers using a data-driven, knowledge-based framework. Among 93 lipids that were identified as potential biomarker candidates, ether-linked phosphatidylcholine (PC O–) and phosphatidylcholine (PC) were generally upregulated, while free fatty acids and triglycerides with longer fatty acyl chains were downregulated in the TB group. Lipid-related gene enrichment analysis revealed significantly altered metabolic pathways (e.g., ether lipid, linolenic acid, and cholesterol) and immune response signaling pathways. Based on these potential biomarkers, TB patients could be differentiated from controls in the internal validation (random forest model, area under the curve [AUC] 0.936, 95% confidence interval [CI] 0.865–0.992). PC(O-40:4), PC(O-42:5), PC(36:0), and PC(34:4) were robust biomarkers able to distinguish TB patients from individuals with latent infection and healthy controls, as shown in the external validation. Small changes in expression were identified for 162 significant lipid-related genes in the comparison of TB patients vs. controls; in the random forest model, their utilities were demonstrated by AUCs that ranged from 0.829 to 0.956 in three cohorts. In conclusion, this study introduced a potential framework that can be used to identify and validate metabolism-centric biomarkers.

Circulating Exosome Cargoes Contain Functionally Diverse Cancer Biomarkers: From Biogenesis and Function to Purification and Potential Translational Utility

Although diagnostic and therapeutic treatments of cancer have tremendously improved over the past two decades, the indolent nature of its symptoms has made early detection challenging. Thus, inter-disciplinary (genomic, transcriptomic, proteomic, and lipidomic) research efforts have been focused on the non-invasive identification of unique "silver bullet" cancer biomarkers for the design of ultra-sensitive molecular diagnostic assays. Circulating tumor biomarkers, such as CTCs and ctDNAs, which are released by tumors in the circulation, have already demonstrated their clinical utility for the non-invasive detection of certain solid tumors. Considering that exosomes are actively produced by all cells, including tumor cells, and can be found in the circulation, they have been extensively assessed for their potential as a source of circulating cell-specific biomarkers. Exosomes are particularly appealing because they represent a stable and encapsulated reservoir of active biological compounds that may be useful for the non-invasive detection of cancer. T biogenesis of these extracellular vesicles is profoundly altered during carcinogenesis, but because they harbor unique or uniquely combined surface proteins, cancer biomarker studies have been focused on their purification from biofluids, for the analysis of their RNA, DNA, protein, and lipid cargoes. In this review, we evaluate the biogenesis of normal and cancer exosomes, provide extensive information on the state of the art, the current purification methods, and the technologies employed for genomic, transcriptomic, proteomic, and lipidomic evaluation of their cargoes. Our thorough examination of the literature highlights the current limitations and promising future of exosomes as a liquid biopsy for the identification of circulating tumor biomarkers.

Supercritical Fluid Chromatography Coupled with Drift Time Ion Mobility Quadrupole Time-of-Flight Mass Spectrometry as a Tool for Lipid Characterization of HepG2 Cells

Lipidomic studies are often conducted using shotgun mass spectrometry (MS) or reversed-phase liquid chromatography coupled with MS (LC–MS). However, chromatographic separation offers several advantages such as an additional identification parameter (retention time), lower ion suppression, and separation of isobaric species. In contrast, quantification is more difficult because ion suppression is not the same over the whole analysis, and as a consequence more standards are needed to compensate for this. Supercritical fluid chromatography (SFC) offers orthogonal separation compared to reversed-phase LC. While the separation of lipids in reversed-phase LC is mainly based on the length of the carbon chain and the number of double bonds, lipids in SFC are mainly separated according to their lipid classes, which simplifies quantification with standards. In this study, SFC coupled with drift time ion mobility quadrupole time-of-flight mass spectrometry (DTIMS-QTOF-MS)was used to characterize the HepG2 lipidome.

A High Throughput Lipidomics Method Using Scheduled Multiple Reaction Monitoring

Lipid compositions of cells, tissues, and bio-fluids are complex, with varying concentrations and structural diversity making their identification challenging. Newer methods for comprehensive analysis of lipids are thus necessary. Herein, we propose a targeted-mass spectrometry based lipidomics screening method using a combination of variable retention time window and relative dwell time weightage. Using this method, we identified more than 1000 lipid species within 24-min. The limit of detection varied from the femtomolar to the nanomolar range. About 883 lipid species were detected with a coefficient of variance <30%. We used this method to identify plasma lipids altered due to vitamin B12 deficiency and found a total of 18 lipid species to be altered. Some of the lipid species with ω-6 fatty acid chains were found to be significantly increased while ω-3 decreased in vitamin B12 deficient samples. This method enables rapid screening of a large number of lipid species in a single experiment and would substantially advance our understanding of the role of lipids in biological processes.

Tricky Isomers—The Evolution of Analytical Strategies to Characterize Plasmalogens and Plasmanyl Ether Lipids

Typically, glycerophospholipids are represented with two esterified fatty acids. However, by up to 20%, a significant proportion of this lipid class carries an ether-linked fatty alcohol side chain at the sn-1 position, generally referred to as ether lipids, which shape their specific physicochemical properties. Among those, plasmalogens represent a distinct subgroup characterized by an sn-1 vinyl-ether double bond. The total loss of ether lipids in severe peroxisomal defects such as rhizomelic chondrodysplasia punctata indicates their crucial contribution to diverse cellular functions. An aberrant ether lipid metabolism has also been reported in multifactorial conditions including Alzheimer’s disease. Understanding the underlying pathological implications is hampered by the still unclear exact functional spectrum of ether lipids, especially in regard to the differentiation between the individual contributions of plasmalogens (plasmenyl lipids) and their non-vinyl-ether lipid (plasmanyl) counterparts. A primary reason for this is that exact identification and quantification of plasmalogens and other ether lipids poses a challenging and usually labor-intensive task. Diverse analytical methods for the detection of plasmalogens have been developed. Liquid chromatography–tandem mass spectrometry is increasingly used to resolve complex lipid mixtures, and with optimized parameters and specialized fragmentation strategies, discrimination between ethers and plasmalogens is feasible. In this review, we recapitulate historic and current methodologies for the recognition and quantification of these important lipids and will discuss developments in this field that can contribute to the characterization of plasmalogens in high structural detail.

Reference materials for MS-based untargeted metabolomics and lipidomics: a review by the metabolomics quality assurance and quality control consortium (mQACC)

INTRODUCTION

The metabolomics quality assurance and quality control consortium (mQACC) is enabling the identification, development, prioritization, and promotion of suitable reference materials (RMs) to be used in quality assurance (QA) and quality control (QC) for untargeted metabolomics research.

OBJECTIVES

This review aims to highlight current RMs, and methodologies used within untargeted metabolomics and lipidomics communities to ensure standardization of results obtained from data analysis, interpretation and cross-study, and cross-laboratory comparisons. The essence of the aims is also applicable to other 'omics areas that generate high dimensional data.

RESULTS

The potential for game-changing biochemical discoveries through mass spectrometry-based (MS) untargeted metabolomics and lipidomics are predicated on the evolution of more confident qualitative (and eventually quantitative) results from research laboratories. RMs are thus critical QC tools to be able to assure standardization, comparability, repeatability and reproducibility for untargeted data analysis, interpretation, to compare data within and across studies and across multiple laboratories. Standard operating procedures (SOPs) that promote, describe and exemplify the use of RMs will also improve QC for the metabolomics and lipidomics communities.

CONCLUSIONS

The application of RMs described in this review may significantly improve data quality to support metabolomics and lipidomics research. The continued development and deployment of new RMs, together with interlaboratory studies and educational outreach and training, will further promote sound QA practices in the community.

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

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

HMDB 5.0: the Human Metabolome Database for 2022

The Human Metabolome Database or HMDB (https://hmdb.ca) has been providing comprehensive reference information about human metabolites and their associated biological, physiological and chemical properties since 2007. Over the past 15 years, the HMDB has grown and evolved significantly to meet the needs of the metabolomics community and respond to continuing changes in internet and computing technology. This year's update, HMDB 5.0, brings a number of important improvements and upgrades to the database. These should make the HMDB more useful and more appealing to a larger cross-section of users. In particular, these improvements include: (i) a significant increase in the number of metabolite entries (from 114 100 to 217 920 compounds); (ii) enhancements to the quality and depth of metabolite descriptions; (iii) the addition of new structure, spectral and pathway visualization tools; (iv) the inclusion of many new and much more accurately predicted spectral data sets, including predicted NMR spectra, more accurately predicted MS spectra, predicted retention indices and predicted collision cross section data and (v) enhancements to the HMDB's search functions to facilitate better compound identification. Many other minor improvements and updates to the content, the interface, and general performance of the HMDB website have also been made. Overall, we believe these upgrades and updates should greatly enhance the HMDB's ease of use and its potential applications not only in human metabolomics but also in exposomics, lipidomics, nutritional science, biochemistry and clinical chemistry.

The state of the art in plant lipidomics

Lipids are a group of compounds with diverse structures that perform several important functions in plants. To unravel and better understand their in vivo functions, plant biologists have been using various lipidomic technologies including liquid-chromatography (LC)-mass spectrometry (MS). However, there are still significant challenges in LC-MS based plant lipidomics, which need to be addressed. In this review, we provide an overview of the key developments in LC-MS based lipidomic approaches to detect and identify plant lipids with emphasis on areas that can be further improved. Given that the cellular lipidome is estimated to contain hundreds of thousands of lipids,^1,2 many of the lipid structures remain to be discovered. Furthermore, the plant lipidome is considered to be significantly more complex compared to that of mammals. Recent technical developments in mass spectrometry have made the detection of novel lipids possible; hence, approaches that can be used for plant lipid discovery are also discussed.