LC-MS-Based Lipidomics and Automated Identification of Lipids Using the LipidBlast In-Silico MS/MS Library

Investigating the effects of dexamethasone on pulmonary surfactant lipids based on lipidomics studies

Dexamethasone, a glucocorticoid commonly used in pediatric patients, has potent anti-inflammatory and immunosuppressive properties. However, it is associated with side effects such as reduced lung function and decreased immunity. Pulmonary surfactant lipids are closely linked to lung disease and play a role in reducing surface tension, immune response and antiviral activity. The dysregulation of lipid metabolism is closely associated with lung disease. Hence, untargeted lipidomics may be instrumental in elucidating the effects of dexamethasone on pulmonary surfactant lipids. We obtained surfactant lipid samples from the bronchoalveolar lavage fluid of young mice injected subcutaneously with dexamethasone and conducted a comprehensive lipidomic analysis, comparing them with a control group. We observed a decrease in lipids, such as phosphatidylcholine, phosphatidylglycerol and phosphatidylethanolamine, and an increase in ceramide, fatty acid, diacylglycerol and monoglyceride, which may impact lung health. This study revealed the influence of dexamethasone on pulmonary surfactant lipids, offering new insights into adverse reactions in clinical settings.

The manganese transporter SLC39A8 links alkaline ceramidase 1 to inflammatory bowel disease

The metal ion transporter SLC39A8 is associated with physiological traits and diseases, including blood manganese (Mn) levels and inflammatory bowel diseases (IBD). The mechanisms by which SLC39A8 controls Mn homeostasis and epithelial integrity remain elusive. Here, we generate Slc39a8 intestinal epithelial cell-specific-knockout (Slc39a8-IEC KO) mice, which display markedly decreased Mn levels in blood and most organs. Radiotracer studies reveal impaired intestinal absorption of dietary Mn in Slc39a8-IEC KO mice. SLC39A8 is localized to the apical membrane and mediates 54Mn uptake in intestinal organoid monolayer cultures. Unbiased transcriptomic analysis identifies alkaline ceramidase 1 (ACER1), a key enzyme in sphingolipid metabolism, as a potential therapeutic target for SLC39A8-associated IBDs. Importantly, treatment with an ACER1 inhibitor attenuates colitis in Slc39a8-IEC KO mice by remedying barrier dysfunction. Our results highlight the essential roles of SLC39A8 in intestinal Mn absorption and epithelial integrity and offer a therapeutic target for IBD associated with impaired Mn homeostasis.

An In Silico Database for Automated Feature Identification of High-Resolution Tandem Mass Spectrometry 13C-Trimethylation Enhancement Using Diazomethane (13C-TrEnDi)-Modified Lipid Data

13C-Trimethylation enhancement using diazomethane (13C-TrEnDi) is a chemical derivatization technique that uses 13C-labeled diazomethane to increase mass spectrometry (MS) signal intensities for phosphatidylcholine (PC) and phosphatidylethanolamine (PE) lipid classes, both of which are of major interest in biochemistry. In silico mass spectrometry databases have become mainstays in lipidomics experiments; however, 13C-TrEnDi-modified PC and PE species have altered m/z and fragmentation patterns from their native counterparts. To build a database of 13C-TrEnDi-modified PC and PE species, a lipid extract from nutritional yeast was derivatized and fragmentation spectra of modified PC and PE species were mined using diagnostic fragmentation filtering by searching 13C-TrEnDi-modified headgroups with m/z 199 (PC) and 202 (PE). Identities of 25 PC and 10 PE species were assigned after comparing to predicted masses from the Lipid Maps Structure Database with no false positive identifications observed; neutral lipids could still be annotated after derivatization. Collision energies from 16 to 52 eV were examined, resulting in three additional class-specific fragment ions emerging, as well as a combined sn-1/sn-2 fragment ion, allowing sum-composition level annotations to be assigned. Using the Lipid Blast templates, a NIST-compatible 13C-TrEnDi database was produced based on fragmentation spectra observed at 36 eV and tested on HEK 293T cell lipid extracts, identifying 47 PC and 24 PE species, representing a 1.8-fold and 2.2-fold increase in annotations, respectively. The 13C-TrEnDi database is freely available, MS vendor-independent, and widely compatible with MS data processing pipelines, increasing the throughput and accessibility of TrEnDi for lipidomics applications.

Expanding Lipidomic Coverage in Multisegment Injection-Nonaqueous Capillary Electrophoresis-Mass Spectrometry via a Convenient and Quantitative Methylation Strategy

Orthogonal separation techniques coupled to high-resolution mass spectrometry are required for characterizing the human lipidome, given its inherent chemical and structural complexity. However, electrophoretic separations remain largely unrecognized in contemporary lipidomics research compared to established chromatographic and ion mobility methods. Herein, we introduce a novel derivatization protocol based on 3-methyl-1-p-tolyltriazene (MTT) as a safer alternative to diazomethane for quantitative phospholipid (PL) methylation (∼90%), which enables their rapid analysis by multisegment injection-nonaqueous capillary electrophoresis-mass spectrometry (MSI-NACE-MS). Isobaric interferences and ion suppression effects were minimized by performing an initial reaction using 9-fluorenylmethyoxycarbonyl chloride prior to MTT and a subsequent back extraction in hexane. This charge-switch derivatization strategy expands lipidome coverage when using MSI-NACE-MS under positive ion mode with improved resolution, greater sensitivity, and higher throughput (∼3.5 min/sample), notably for zwitterionic PLs that are analyzed as their cationic phosphate methyl esters. Our method was validated by analyzing methyl-tert-butyl ether extracts of reference human plasma, which enabled a direct comparison of 48 phosphatidylcholine and 27 sphingomyelin species previously reported in an interlaboratory lipidomics harmonization study. The potential for plasma PL quantification by MSI-NACE-MS via a serial dilution of NIST SRM-1950 was also demonstrated based on estimation of relative response factors using their reported consensus concentrations. Moreover, lipid identification was supported by modeling predictable changes in the electrophoretic mobility for cationic PLs in conjunction with MS/MS. Overall, this work offers a practical derivatization protocol to expand lipidome coverage in CE-MS beyond the analysis of hydrophilic/polar metabolites under aqueous buffer conditions.

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.

Systematic untargeted UHPLC-Q-TOF-MS based lipidomics workflow for improved detection and annotation of lipid sub-classes in serum

INTRODUCTION AND OBJECTIVE

Taking into consideration the challenges of lipid analytics, present study aims to design the best high-throughput workflow for detection and annotation of lipids.

MATERIAL AND METHODS

Serum lipid profiling was performed on CSH-C18 and EVO-C18 columns using UHPLC Q-TOF-MS and generated lipid features were annotated based on m/z and fragment ion using different software.

RESULT AND DISCUSSION

Better detection of features was observed in CSH-C18 than EVO-C18 with enhanced resolution except for Glycerolipids (triacylglycerols) and Sphingolipids (sphingomyelin).

CONCLUSION

The study revealed an optimized untargeted Lipidomics-workflow with comprehensive lipid profiling (CSH-C18 column) and confirmatory annotation (LipidBlast).

Novel insights into the mechanisms of hard exudate in diabetic retinopathy: Findings of serum lipidomic and metabolomics profiling

Objective

Retinal hard exudates (HEs) result from lipoproteins leaking from capillaries into extracellular retinal space, and are related to decreased visual acuity in diabetic retinopathy (DR). This study aims to identify differential serum lipids and metabolites associated with HEs.

Materials and methods

A cross-sectional study was conducted Jul 2017 ∼ Mar 2021. We assessed the amount of HEs using standard ETDRS photographs for comparison. HEs severity was rated as "no or questionable", "moderate" or "severe". Serum samples were processed via high coverage pseudotargeted lipidomics analysis, and untargeted liquid chromatography coupled with time-of-flight mass spectrometry for metabolomics study, respectively. Weighted gene co-expression network analyses, partial least squares-discriminant analysis, and multi-receiver operating characteristic analysis were applied.

Results

A total of 167 patients were included. Discovery group: 116 eyes (116 patients). Validation group: 51 eyes (51 patients). 888 lipids were detected and divided into 18 modules (MEs), ME1 ∼ ME18. Lipids in ME1 significantly increased in patients with HEs in DR (NPDR and PDR combined), NPDR, and PDR, respectively. ME1 enriched to triglycerides (29%), ceramides (17%), and N-acylethanolamines (15%). A combined model of 20 lipids was the best to discriminate HEs, area under curve = 0.804, 95% confidence interval = 0.674-0.916. For metabolomics analysis, 19 metabolites and 13 pathways associated with HEs were identified. Taurine and hypotaurine metabolism, cysteine and methionine metabolism were closely related to HEs (P < 0.01).

Conclusions

The lipids and metabolites identified may serve as prediction biomarkers in the early stage of HEs in DR.

MAVEN2: An Updated Open-Source Mass Spectrometry Exploration Platform

MAVEN, an open-source software program for analysis of LC-MS metabolomics data, was originally released in 2010. As mass spectrometry has advanced in the intervening years, MAVEN has been periodically updated to reflect this advancement. This manuscript describes a major update to the program, MAVEN2, which supports LC-MS/MS analysis of metabolomics and lipidomics samples. We have developed algorithms to support MS/MS spectral matching and efficient search of large-scale fragmentation libraries. We explore the ability of our approach to separate authentic from spurious metabolite identifications using a set of standards spiked into water and yeast backgrounds. To support our improved lipid identification workflow, we introduce a novel in-silico lipidomics library covering major lipid classes and compare searches using our novel library to searches with existing in-silico lipidomics libraries. MAVEN2 source code and cross-platform application installers are freely available for download from GitHub under a GNU permissive license [ver 3], as are the in silico lipidomics libraries and corresponding code repository.

A Current Encyclopedia of Bioinformatics Tools, Data Formats and Resources for Mass Spectrometry Lipidomics

Mass spectrometry is a widely used technology to identify and quantify biomolecules such as lipids, metabolites and proteins necessary for biomedical research. In this study, we catalogued freely available software tools, libraries, databases, repositories and resources that support lipidomics data analysis and determined the scope of currently used analytical technologies. Because of the tremendous importance of data interoperability, we assessed the support of standardized data formats in mass spectrometric (MS)-based lipidomics workflows. We included tools in our comparison that support targeted as well as untargeted analysis using direct infusion/shotgun (DI-MS), liquid chromatography−mass spectrometry, ion mobility or MS imaging approaches on MS¹ and potentially higher MS levels. As a result, we determined that the Human Proteome Organization-Proteomics Standards Initiative standard data formats, mzML and mzTab-M, are already supported by a substantial number of recent software tools. We further discuss how mzTab-M can serve as a bridge between data acquisition and lipid bioinformatics tools for interpretation, capturing their output and transmitting rich annotated data for downstream processing. However, we identified several challenges of currently available tools and standards. Potential areas for improvement were: adaptation of common nomenclature and standardized reporting to enable high throughput lipidomics and improve its data handling. Finally, we suggest specific areas where tools and repositories need to improve to become FAIRer.

An Amish founder population reveals rare-population genetic determinants of the human lipidome

Identifying the genetic determinants of inter-individual variation in lipid species (lipidome) may provide deeper understanding and additional insight into the mechanistic effect of complex lipidomic pathways in CVD risk and progression beyond simple traditional lipids. Previous studies have been largely population based and thus only powered to discover associations with common genetic variants. Founder populations represent a powerful resource to accelerate discovery of previously unknown biology associated with rare population alleles that have risen to higher frequency due to genetic drift. We performed a genome-wide association scan of 355 lipid species in 650 individuals from the Amish founder population including 127 lipid species not previously tested. To the best of our knowledge, we report for the first time the lipid species associated with two rare-population but Amish-enriched lipid variants: APOB_rs5742904 and APOC3_rs76353203. We also identified novel associations for 3 rare-population Amish-enriched loci with several sphingolipids and with proposed potential functional/causal variant in each locus including GLTPD2_rs536055318, CERS5_rs771033566, and AKNA_rs531892793. We replicated 7 previously known common loci including novel associations with two sterols: androstenediol with UGT locus and estriol with SLC22A8/A24 locus. Our results show the double power of founder populations and detailed lipidome to discover novel trait-associated variants.

A GWAS of 355 lipid species in the Old Order Amish founder population reveals associations between Amish-enriched loci and several sphingolipids.

Untargeted Metabolomics Analysis of the Serum Metabolic Signature of Childhood Obesity

Obesity rates among children are growing rapidly worldwide, placing massive pressure on healthcare systems. Untargeted metabolomics can expand our understanding of the pathogenesis of obesity and elucidate mechanisms related to its symptoms. However, the metabolic signatures of obesity in children have not been thoroughly investigated. Herein, we explored metabolites associated with obesity development in childhood. Untargeted metabolomic profiling was performed on fasting serum samples from 27 obese Caucasian children and adolescents and 15 sex- and age-matched normal-weight children. Three metabolomic assays were combined and yielded 726 unique identified metabolites: gas chromatography–mass spectrometry (GC–MS), hydrophilic interaction liquid chromatography coupled to mass spectrometry (HILIC LC–MS/MS), and lipidomics. Univariate and multivariate analyses showed clear discrimination between the untargeted metabolomes of obese and normal-weight children, with 162 significantly differentially expressed metabolites between groups. Children with obesity had higher concentrations of branch-chained amino acids and various lipid metabolites, including phosphatidylcholines, cholesteryl esters, triglycerides. Thus, an early manifestation of obesity pathogenesis and its metabolic consequences in the serum metabolome are correlated with altered lipid metabolism. Obesity metabolite patterns in the adult population were very similar to the metabolic signature of childhood obesity. Identified metabolites could be potential biomarkers and used to study obesity pathomechanisms.

Genomics of Postprandial Lipidomics in the Genetics of Lipid-Lowering Drugs and Diet Network Study

Postprandial lipemia (PPL) is an important risk factor for cardiovascular disease. Inter-individual variation in the dietary response to a meal is known to be influenced by genetic factors, yet genes that dictate variation in postprandial lipids are not completely characterized. Genetic studies of the plasma lipidome can help to better understand postprandial metabolism by isolating lipid molecular species which are more closely related to the genome. We measured the plasma lipidome at fasting and 6 h after a standardized high-fat meal in 668 participants from the Genetics of Lipid-Lowering Drugs and Diet Network study (GOLDN) using ultra-performance liquid chromatography coupled to (quadrupole) time-of-flight mass spectrometry. A total of 413 unique lipids were identified. Heritable and responsive lipid species were examined for association with single-nucleotide polymorphisms (SNPs) genotyped on the Affymetrix 6.0 array. The most statistically significant SNP findings were replicated in the Amish Heredity and Phenotype Intervention (HAPI) Heart Study. We further followed up findings from GOLDN with a regional analysis of cytosine-phosphate-guanine (CpGs) sites measured on the Illumina HumanMethylation450 array. A total of 132 lipids were both responsive to the meal challenge and heritable in the GOLDN study. After correction for multiple testing of 132 lipids (α = 5 × 10-8/132 = 4 × 10-10), no SNP was statistically significantly associated with any lipid response. Four SNPs in the region of a known lipid locus (fatty acid desaturase 1 and 2/FADS1 and FADS2) on chromosome 11 had p < 8.0 × 10-7 for arachidonic acid FA(20:4). Those SNPs replicated in HAPI Heart with p < 3.3 × 10-3. CpGs around the FADS1/2 region were associated with arachidonic acid and the relationship of one SNP was partially mediated by a CpG (p = 0.005). Both SNPs and CpGs from the fatty acid desaturase region on chromosome 11 contribute jointly and independently to the diet response to a high-fat meal.

In-depth triacylglycerol profiling using MS3 Q-Trap mass spectrometry

Total triacylglycerol (TAG) level is a key clinical marker of metabolic and cardiovascular diseases. However, the roles of individual TAGs have not been thoroughly explored in part due to their extreme structural complexity. We present a targeted mass spectrometry-based method combining multiple reaction monitoring (MRM) and multiple stage mass spectrometry (MS³) for the comprehensive qualitative and semiquantitative profiling of TAGs. This method referred as TriP-MS3 - triacylglycerol profiling using MS³ - screens for more than 6,700 TAG species in a fully automated fashion. TriP-MS3 demonstrated excellent reproducibility (median interday CV ∼ 0.15) and linearity (median R² = 0.978) and detected 285 individual TAG species in human plasma. The semiquantitative accuracy of the method was validated by comparison with a state-of-the-art reverse phase liquid chromatography (RPLC)-MS (R² = 0.83), which is the most commonly used approach for TAGs profiling. Finally, we demonstrate the utility and the versatility of the method by characterizing the effects of a fatty acid desaturase inhibitor on TAG profiles in vitro and by profiling TAGs in Caenorhabditis elegans.

Autophagy Inhibition by Targeting PIKfyve Potentiates Response to Immune Checkpoint Blockade in Prostate Cancer

Multi-tyrosine kinase inhibitors (MTKIs) have thus far had limited success in the treatment of castration-resistant prostate cancer (CRPC). Here, we report a phase I-cleared orally bioavailable MTKI, ESK981, with a novel autophagy inhibitory property that decreased tumor growth in diverse preclinical models of CRPC. The anti-tumor activity of ESK981 was maximized in immunocompetent tumor environments where it upregulated CXCL10 expression through the interferon gamma pathway and promoted functional T cell infiltration, which resulted in enhanced therapeutic response to immune checkpoint blockade. Mechanistically, we identify the lipid kinase PIKfyve as the direct target of ESK981. PIKfyve-knockdown recapitulated ESK981's anti-tumor activity and enhanced the therapeutic benefit of immune checkpoint blockade. Our study reveals that targeting PIKfyve via ESK981 turns tumors from cold into hot through inhibition of autophagy, which may prime the tumor immune microenvironment in advanced prostate cancer patients and be an effective treatment strategy alone or in combination with immunotherapies.

Lipidomics in Biomarker Research

Lipids are natural substances found in all living organisms and involved in many biological functions. Imbalances in the lipid metabolism are linked to various diseases such as obesity, diabetes, or cardiovascular disease. Lipids comprise thousands of chemically distinct species making them a challenge to analyze because of their great structural diversity.Thanks to the technological improvements in the fields of chromatography, high-resolution mass spectrometry, and bioinformatics over the last years, it is now possible to perform global lipidomics analyses, allowing the concomitant detection, identification, and relative quantification of hundreds of lipid species. This review shall provide an insight into a general lipidomics workflow and its application in metabolic biomarker research.

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.

MetaboKit: a comprehensive data extraction tool for untargeted metabolomics

We have developed MetaboKit, a comprehensive software package for compound identification and relative quantification in mass spectrometry-based untargeted metabolomics analysis. In data dependent acquisition (DDA) analysis, MetaboKit constructs a customized spectral library with compound identities from reference spectral libraries, adducts, dimers, in-source fragments (ISF), MS/MS fragmentation spectra, and more importantly the retention time information unique to the chromatography system used in the experiment. Using the customized library, the software performs targeted peak integration for precursor ions in DDA analysis and for precursor and product ions in data independent acquisition (DIA) analysis. With its stringent identification algorithm requiring matches by both MS and MS/MS data, MetaboKit provides identification results with significantly greater specificity than the competing software packages without loss in sensitivity. The proposed MS/MS-based screening of ISFs also reduces the chance of unverifiable identification of ISFs considerably. MetaboKit's quantification module produced peak area values highly correlated with known concentrations in a DIA analysis of the metabolite standards at both MS1 and MS2 levels. Moreover, the analysis of Cdk1Liv-/- mouse livers showed that MetaboKit can identify a wide range of lipid species and their ISFs, and quantitatively reconstitute the well-characterized fatty liver phenotype in these mice. In DIA data, the MS1-level and MS2-level peak area data produced similar fold change estimates in the differential abundance analysis, and the MS2-level peak area data allowed for quantitative comparisons in compounds whose precursor ion chromatogram was too noisy for peak integration.

Colistin Treatment Affects Lipid Composition of Acinetobacter baumannii

Multidrug-resistant Acinetobacter baumannii (A. baumannii) causes severe and often fatal healthcare-associated infections due partly to antibiotic resistance. There are no studies on A. baumannii lipidomics of susceptible and resistant strains grown at lethal and sublethal concentrations. Therefore, we analyzed the impact of colistin resistance on glycerolipids’ content by using untargeted lipidomics on clinical isolate. Nine lipid sub-classes were annotated, including phosphatidylcholine, rarely detected in the bacterial membrane among 130 different lipid species. The other lipid sub-classes detected are phosphatidylethanolamine (PE), phosphatidylglycerol (PG), lysophosphatidylethanolamine, hemibismonoacylglycerophosphate, cardiolipin, monolysocardiolipin, diacylglycerol, and triacylglycerol. Under lethal and sublethal concentrations of colistin, significant reduction of PE was observed on the resistant and susceptible strain, respectively. Palmitic acid percentage was higher at colistin at low concentration but only for the susceptible strain. When looking at individual lipid species, the most abundant PE and PG species (PE 34:1 and PG 34:1) are significantly upregulated when the susceptible and the resistant strains are cultivated with colistin. This is, to date, the most exhaustive lipidomics data compilation of A. baumannii cultivated in the presence of colistin. This work is highlighting the plasma membrane plasticity used by this gram-negative bacterium to survive colistin treatment.

HIGH RESOLUTION MASS SPECTROMETRY IN LIPIDOMICS

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

Metabolic View on Human Healthspan: A Lipidome-Wide Association Study

As ageing is a major risk factor for the development of non-communicable diseases, extending healthspan has become a medical and societal necessity. Precise lipid phenotyping that captures metabolic individuality could support healthspan extension strategies. This study applied ‘omic-scale lipid profiling to characterise sex-specific age-related differences in the serum lipidome composition of healthy humans. A subset of the COmPLETE-Health study, composed of 73 young (25.2 ± 2.6 years, 43% female) and 77 aged (73.5 ± 2.3 years, 48% female) clinically healthy individuals, was investigated, using an untargeted liquid chromatography high-resolution mass spectrometry approach. Compared to their younger counterparts, aged females and males exhibited significant higher levels in 138 and 107 lipid species representing 15 and 13 distinct subclasses, respectively. Percentage of difference ranged from 5.8% to 61.7% (females) and from 5.3% to 46.0% (males), with sphingolipid and glycerophophospholipid species displaying the greatest amplitudes. Remarkably, specific sphingolipid and glycerophospholipid species, previously described as cardiometabolically favourable, were found elevated in aged individuals. Furthermore, specific ether-glycerophospholipid and lyso-glycerophosphocholine species displayed higher levels in aged females only, revealing a more favourable lipidome evolution in females. Altogether, age determined the circulating lipidome composition, while lipid species analysis revealed additional findings that were not observed at the subclass level.

Screening of lipid metabolism biomarkers in patients with coronary heart disease via ultra-performance liquid chromatography-high resolution mass spectrometry

Coronary heart disease (CHD) has a high mortality worldwide. This study aimed to screen lipid metabolism biomarkers in patients with coronary heart disease via ultra-performance liquid chromatography-high resolution mass spectrometry. Extraction and reconstitution solvents, liquid chromatographic and mass spectrometry conditions were optimized to detect more plasma lipid metabolites. In this study, the chromatographic and mass spectra characteristics of lipid metabolites were summarized. A total of 316 lipid metabolites were annotated via diagnostic fragment ion filtration, nitrogen rule filtration, and neutral loss filtration. Glycerophospholipid metabolism and sphingolipid metabolism were revealed as the main lipid disorders of CHD. This study provides a novel insight for high-throughput detection of lipid metabolites in plasma and provides a further understanding of the occurrence of CHD, which can provide valuable suggestions for the prevention of CHD.

High-Precision Automated Workflow for Urinary Untargeted Metabolomic Epidemiology

Urine is a noninvasive biofluid that is rich in polar metabolites and well suited for metabolomic epidemiology. However, because of individual variability in health and hydration status, the physiological concentration of urine can differ >15-fold, which can pose major challenges in untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics. Although numerous urine normalization methods have been implemented (e.g., creatinine, specific gravity-SG), most are manual and, therefore, not practical for population-based studies. To address this issue, we developed a method to measure SG in 96-well-plates using a refractive index detector (RID), which exhibited accuracy within 85-115% and <3.4% precision. Bland-Altman statistics showed a mean deviation of -0.0001 SG units (limits of agreement: -0.0014 to 0.0011) relative to a hand-held refractometer. Using this RID-based SG normalization, we developed an automated LC-MS workflow for untargeted urinary metabolomics in a 96-well-plate format. The workflow uses positive and negative ionization HILIC chromatography and acquires mass spectra in data-independent acquisition (DIA) mode at three collision energies. Five technical internal standards (tISs) were used to monitor data quality in each method, all of which demonstrated raw coefficients of variation (CVs) < 10% in the quality controls (QCs) and < 20% in the samples for a small cohort (n = 87 urine samples, n = 22 QCs). Application in a large cohort (n = 842 urine samples, n = 248 QCs) demonstrated CVQC < 5% and CVsamples < 16% for 4/5 tISs after signal drift correction by cubic spline regression. The workflow identified >540 urinary metabolites including endogenous and exogenous compounds. This platform is suitable for performing urinary untargeted metabolomic epidemiology and will be useful for applications in population-based molecular phenotyping.

A map of mass spectrometry-based in silico fragmentation prediction and compound identification in metabolomics

Metabolomics, the comprehensive study of the metabolome, and lipidomics-the large-scale study of pathways and networks of cellular lipids-are major driving forces in enabling personalized medicine. Complicated and error-prone data analysis still remains a bottleneck, however, especially for identifying novel metabolites. Comparing experimental mass spectra to curated databases containing reference spectra has been the gold standard for identification of compounds, but constructing such databases is a costly and time-demanding task. Many software applications try to circumvent this process by utilizing cutting-edge advances in computational methods-including quantum chemistry and machine learning-and simulate mass spectra by performing theoretical, so called in silico fragmentations of compounds. Other solutions concentrate directly on experimental spectra and try to identify structural properties by investigating reoccurring patterns and the relationships between them. The considerable progress made in the field allows recent approaches to provide valuable clues to expedite annotation of experimental mass spectra. This review sheds light on individual strengths and weaknesses of these tools, and attempts to evaluate them-especially in view of lipidomics, when considering complex mixtures found in biological samples as well as mass spectrometer inter-instrument variability.

Analytical challenges in human exposome analysis with focus on environmental analysis combined with metabolomics

Environmental factors, such as chemical exposures, are likely to play a crucial role in the development of several human chronic diseases. However, how the specific exposures contribute to the onset and progress of various diseases is still poorly understood. In part, this is because comprehensive characterization of the chemical exposome is a highly challenging task, both due to its complex dynamic nature as well as due to the analytical challenges. Herein, the analytical challenges in the field of exposome research are reviewed, with specific emphasis on the sampling, sample preparation, and analysis, as well as challenges in the compound identification. The primary focus is on the human chemical exposome, that is, exposures to mixtures of environmental chemicals and its impact on human metabolome. In order to highlight the recent progress in the exposome research in relation to human health and disease, selected examples of human exposome studies are presented.

Arsenate-Induced Changes in Bacterial Metabolite and Lipid Pools during Phosphate Stress

Agrobacterium tumefaciens GW4 is a heterotrophic arsenite-oxidizing bacterium with a high resistance to arsenic toxicity. It is now a model organism for studying the processes of arsenic detoxification and utilization. Previously, we demonstrated that under low-phosphate conditions, arsenate [As(V)] could enhance bacterial growth and be incorporated into biomolecules, including lipids. While the basic microbial As(V) resistance mechanisms have been characterized, global metabolic responses under low phosphate remain largely unknown. In the present work, the impacts of As(V) and low phosphate on intracellular metabolite and lipid profiles of GW4 were quantified using liquid chromatography-mass spectroscopy (LC-MS) in combination with transcriptional assays and the analysis of intracellular ATP and NADH levels. Metabolite profiling revealed that oxidative stress response pathways were altered and suggested an increase in DNA repair. Changes in metabolite levels in the tricarboxylic acid (TCA) cycle along with increased ATP are consistent with As(V)-enhanced growth of A. tumefaciens GW4. Lipidomics analysis revealed that most glycerophospholipids decreased in abundance when As(V) was available. However, several glycerolipid classes increased, an outcome that is consistent with maximizing growth via a phosphate-sparing phenotype. Differentially regulated lipids included phosphotidylcholine and lysophospholipids, which have not been previously reported in A. tumefaciens The metabolites and lipids identified in this study deepen our understanding of the interplay between phosphate and arsenate on chemical and metabolic levels.IMPORTANCE Arsenic is widespread in the environment and is one of the most ubiquitous environmental pollutants. Parodoxically, the growth of certain bacteria is enhanced by arsenic when phosphate is limited. Arsenate and phosphate are chemically similar, and this behavior is believed to represent a phosphate-sparing phenotype in which arsenate is used in place of phosphate in certain biomolecules. The research presented here uses a global approach to track metabolic changes in an environmentally relevant bacterium during exposure to arsenate when phosphate is low. Our findings are relevant for understanding the environmental fate of arsenic as well as how human-associated microbiomes respond to this common toxin.

Predicting changes in renal metabolism after compound exposure with a genome-scale metabolic model

The kidneys are metabolically active organs with importance in several physiological tasks such as the secretion of soluble wastes into the urine and synthesizing glucose and oxidizing fatty acids for energy in fasting (non-fed) conditions. Once damaged, the metabolic capability of the kidneys becomes altered. Here, we define metabolic tasks in a computational modeling framework to capture kidney function in an update to the iRno network reconstruction of rat metabolism using literature-based evidence. To demonstrate the utility of iRno for predicting kidney function, we exposed primary rat renal proximal tubule epithelial cells to four compounds with varying levels of nephrotoxicity (acetaminophen, gentamicin, 2,3,7,8-tetrachlorodibenzodioxin, and trichloroethylene) for six and twenty-four hours, and collected transcriptomics and metabolomics data to measure the metabolic effects of compound exposure. For the transcriptomics data, we observed changes in fatty acid metabolism and amino acid metabolism, as well as changes in existing markers of kidney function such as Clu (clusterin). The iRno metabolic network reconstruction was used to predict alterations in these same pathways after integrating transcriptomics data and was able to distinguish between select compound-specific effects on the proximal tubule epithelial cells. Genome-scale metabolic network reconstructions with coupled omics data can be used to predict changes in metabolism as a step towards identifying novel metabolic biomarkers of kidney function and dysfunction.

The plasma lipidome of the Quaker parrot (Myiopsitta monachus)

Dyslipidemias and lipid-accumulation disorders are common in captive parrots, in particular in Quaker parrots. Currently available diagnostic tests only measure a fraction of blood lipids and have overall problematic cross-species applicability. Comprehensively analyzing lipids in the plasma of parrots is the first step to better understand their lipid metabolism in health and disease, as well as to explore new lipid biomarkers. The plasma lipidome of 12 Quaker parrots was investigated using UHPLC-MS/MS with both targeted and untargeted methods. Targeted methods on 6 replicates measured 432 lipids comprised of sterol, cholesterol ester, bile acid, fatty acid, acylcarnitine, glycerolipid, glycerophospholipid, and sphingolipid panels. For untargeted lipidomics, precursor ion mass-to-charge ratios were matched to corresponding lipids using the LIPIDMAPS structure database and LipidBlast at the sum composition or acyl species level of information. Sterol lipids and glycerophospholipids constituted the majority of plasma lipids on a molar basis. The most common lipids detected with the targeted methods included free cholesterol, CE(18:2), CE(20:4) for sterol lipids; PC(36:2), PC(34:2), PC(34:1) for glycerophospholipids; TG(52:3), TG(54:4), TG(54:5), TG(52:2) for glycerolipids; SM(d18:1/16:0) for sphingolipids; and palmitic acid for fatty acyls. Over a thousand different lipid species were detected by untargeted lipidomics. Sex differences in the plasma lipidome were observed using heatmaps, principal component analysis, and discriminant analysis. This report presents the first comprehensive database of plasma lipid species in psittacine birds and paves the way for further research into blood lipid diagnostics and the impact of diet, diseases, and drugs on the parrot plasma lipidome.

Mid-gestation serum lipidomic profile associations with spontaneous preterm birth are influenced by body mass index

Spontaneous preterm birth (sPTB) is a major cause of infant morbidity and mortality. While metabolic changes leading to preterm birth are unknown, several factors including dyslipidemia and inflammation have been implicated and paradoxically both low (<18.5 kg/m²) and high (>30 kg/m²) body mass indices (BMIs) are risk factors for this condition. The objective of the study was to identify BMI-associated metabolic perturbations and potential mid-gestation serum biomarkers of preterm birth in a cohort of underweight, normal weight and obese women experiencing either sPTB or full-term deliveries (n = 102; n = 17/group). For this purpose, we combined untargeted metabolomics and lipidomics with targeted metabolic profiling of major regulators of inflammation and metabolism, including oxylipins, endocannabinoids, bile acids and ceramides. Women who were obese and had sPTB showed elevated oxidative stress and dyslipidemia characterized by elevated serum free fatty acids. Women who were underweight-associated sPTB also showed evidence of dyslipidemia characterized by elevated phospholipids, unsaturated triglycerides, sphingomyelins, cholesteryl esters and long-chain acylcarnitines. In normal weight women experiencing sPTB, the relative abundance of 14(15)-epoxyeicosatrienoic acid and 14,15-dihydroxyeicosatrienoic acids to other regioisomers were altered at mid-pregnancy. This phenomenon is not yet associated with any biological process, but may be linked to estrogen metabolism. These changes were differentially modulated across BMI groups. In conclusion, using metabolomics we observed distinct BMI-dependent metabolic manifestations among women who had sPTB. These observations suggest the potential to predict sPTB mid-gestation using a new set of metabolomic markers and BMI stratification. This study opens the door to further investigate the role of cytochrome P450/epoxide hydrolase metabolism in sPTB.

The Emerging and Diverse Roles of Bis(monoacylglycero) Phosphate Lipids in Cellular Physiology and Disease

Although understudied relative to many phospholipids, accumulating evidence suggests that bis(monoacylglycero)phosphate (BMP) is an important class of regulatory lipid that plays key roles in lysosomal integrity and function. BMPs are rare in most mammalian tissues, comprising only a few percent of total cellular lipid content, but are elevated in cell types such as macrophages that rely heavily on lysosomal function. BMPs are markedly enriched in endosomal and lysosomal vesicles compared to other organelles and membranous structures, and their unique sn-1:sn-1′ stereoconfiguration may confer stability within the hydrolytic lysosomal environment. BMP-enriched vesicles serve in endosomal-lysosomal trafficking and function as docking structures for the activation of lysosomal hydrolytic enzymes, notably those involved in the catabolic breakdown of sphingolipids. BMP levels are dysregulated in lysosomal storage disorders, phospholipidosis, metabolic diseases, liver and kidney diseases and neurodegenerative disorders. However, whether BMP alteration is a mediator or simply a marker of pathological states is unclear. Likewise, although BMP acyl chain composition may be altered with disease states, the functional significance of specific BMP species remains to be resolved. Newly developed tools for untargeted lipidomic analysis, together with a deeper understanding of enzymes mediating BMP synthesis and degradation, will help shed further light on the functional significance of BMPs in cellular physiology and pathology.

Lipidomic UPLC-MS/MS Profiles of Normal-Appearing White Matter Differentiate Primary and Secondary Progressive Multiple Sclerosis

Multiple sclerosis (MS) is a neurodegenerative inflammatory disease where an autoimmune response to components of the central nervous system leads to a loss of myelin and subsequent neurological deterioration. People with MS can develop primary or secondary progressive disease (PPMS, SPMS) and differentiation of the specific differences in the pathogenesis of these two courses, at the molecular level, is currently unclear. Recently, lipidomics studies using human biofluids, mainly plasma and cerebrospinal fluid, have highlighted a possible role for lipids in the initiation and progression of MS. However, there is a lack of lipidomics studies in MS on CNS tissues, such as normal-appearing white matter (NAWM), where local inflammation initially occurs. Herein, we developed an untargeted reverse phase ultra-performance liquid chromatography time of flight tandem mass spectrometry (RP-UPLC-TOF MS^E)-based workflow, in combination with multivariate and univariate statistical analysis, to assess significant differences in lipid profiles in brain NAWM from post-mortem cases of PPMS, SPMS and controls. Groups of eight control, nine PPMS and seven SPMS NAWM samples were used. Correlation analysis of the identified lipids by RP-UPLC-TOF MS^E was undertaken to remove those lipids that correlated with age, gender and post-mortem interval as confounding factors. We demonstrate that there is a significantly altered lipid profile of control cases compared with MS cases and that progressive disease, PPMS and SPMS, can be differentiated on the basis of the lipidome of NAWM with good sensitivity, specificity and prediction accuracy based on receiver operating characteristic (ROC) curve analysis. Metabolic pathway analysis revealed that the most altered lipid pathways between PPMS and SPMS were glycerophospholipid metabolism, glycerophosphatidyl inositol (GPI) anchor synthesis and linoleic acid metabolism. Further understanding of the impact of these lipid alterations described herein associated with progression will provide an increased understanding of the mechanisms underpinning progression and highlight possible new therapeutic targets.

A Metabolomics Workflow for Analyzing Complex Biological Samples Using a Combined Method of Untargeted and Target-List Based Approaches

In the highly dynamic field of metabolomics, we have developed a method for the analysis of hydrophilic metabolites in various biological samples. Therefore, we used hydrophilic interaction chromatography (HILIC) for separation, combined with a high-resolution mass spectrometer (MS) with the aim of separating and analyzing a wide range of compounds. We used 41 reference standards with different chemical properties to develop an optimal chromatographic separation. MS analysis was performed with a set of pooled biological samples human cerebrospinal fluid (CSF), and human plasma. The raw data was processed in a first step with Compound Discoverer 3.1 (CD), a software tool for untargeted metabolomics with the aim to create a list of unknown compounds. In a second step, we combined the results obtained with our internally analyzed reference standard list to process the data along with the Lipid Data Analyzer 2.6 (LDA), a software tool for a targeted approach. In order to demonstrate the advantages of this combined target-list based and untargeted approach, we not only compared the relative standard deviation (%RSD) of the technical replicas of pooled plasma samples (n = 5) and pooled CSF samples (n = 3) with the results from CD, but also with XCMS Online, a well-known software tool for untargeted metabolomics studies. As a result of this study we could demonstrate with our HILIC-MS method that all standards could be either separated by chromatography, including isobaric leucine and isoleucine or with MS by different mass. We also showed that this combined approach benefits from improved precision compared to well-known metabolomics software tools such as CD and XCMS online. Within the pooled plasma samples processed by LDA 68% of the detected compounds had a %RSD of less than 25%, compared to CD and XCMS online (57% and 55%). The improvements of precision in the pooled CSF samples were even more pronounced, 83% had a %RSD of less than 25% compared to CD and XCMS online (28% and 8% compounds detected). Particularly for low concentration samples, this method showed a more precise peak area integration with its 3D algorithm and with the benefits of the LDAs graphical user interface for fast and easy manual curation of peak integration. The here-described method has the advantage that manual curation for larger batch measurements remains minimal due to the target list containing the information obtained by an untargeted approach.

An improved detection and identification strategy for untargeted metabolomics based on UPLC-MS

Untargeted metabolomics provides a comprehensive investigation of metabolites and enables the discovery of biomarkers. Improvements in sample preparation, chromatographic separation and raw data processing procedure greatly enhance the metabolome coverage. In addition, database-dependent software identification is also essential, upon which enhances the identification confidence and benefits downstream biological analysis. Herein, we developed an improved detection and identification strategy for untargeted metabolomics based on UPLC-MS. In this work, sample preparation was optimized by considering chemical properties of different metabolites. Chromatographic separation was done by two different columns and MS detection was performed under positive and negative ion modes regarding to the different polarities of metabolites. According to the characteristics of the collected data, an improved identification and evaluation strategy was developed involving fragment simulation and MS/MS library search based on two commonly used databases, HMDB and METLIN. Such combination integrated information from different databases and was aimed to enhance identification confidence by considering the rationality of fragmentation, biological sources and functions comprehensively. In addition, decision tree analysis and lab-developed database were also introduced to assist the data processing and enhance the identification confidence. Finally, the feasibility of the developed strategy was validated by liver samples of obesity mice and controls. 238 metabolites were accurately detected, which was beneficial for the subsequent biomarker discovery and downstream pathway analysis. Therefore, the developed strategy remarkably facilitated the identification accuracy and the confirmation of metabolites in untargeted metabolomics.

A lipidome-wide association study of the lipoprotein insulin resistance index

Background

The lipoprotein insulin resistance (LPIR) score was shown to predict insulin resistance (IR) and type 2 diabetes (T2D) in healthy adults. However, the molecular basis underlying the LPIR utility for classification remains unclear.

Objective

To identify small molecule lipids associated with variation in the LPIR score, a weighted index of lipoproteins measured by nuclear magnetic resonance, in the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study (n = 980).

Methods

Linear mixed effects models were used to test the association between the LPIR score and 413 lipid species and their principal component analysis-derived groups. Significant associations were tested for replication with homeostatic model assessment-IR (HOMA-IR), a phenotype correlated with the LPIR score (r = 0.48, p <  0.001), in the Heredity and Phenotype Intervention (HAPI) Heart Study (n = 590).

Results

In GOLDN, 319 lipids were associated with the LPIR score (false discovery rate-adjusted p-values ranging from 4.59 × 10^− 161 to 49.50 × 10^− 3). Factors 1 (triglycerides and diglycerides/storage lipids) and 3 (mixed lipids) were positively (β = 0.025, p = 4.52 × 10^− 71 and β = 0.021, p = 5.84 × 10^− 41, respectively) and factor 2 (phospholipids/non-storage lipids) was inversely (β = − 0.013, p = 2.28 × 10^− 18) associated with the LPIR score. These findings were replicated for HOMA-IR in the HAPI Heart Study (β = 0.10, p = 1.21 × 10^− 02 for storage, β = − 0.13, p = 3.14 × 10^− 04 for non-storage, and β = 0.19, p = 8.40 × 10^− 07 for mixed lipids).

Conclusions

Non-storage lipidomics species show a significant inverse association with the LPIR metabolic dysfunction score and present a promising focus for future therapeutic and prevention studies.

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

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

Serum triglycerides in Alzheimer disease: Relation to neuroimaging and CSF biomarkers

OBJECTIVE

To investigate the association of triglyceride (TG) principal component scores with Alzheimer disease (AD) and the amyloid, tau, neurodegeneration, and cerebrovascular disease (A/T/N/V) biomarkers for AD.

METHODS

Serum levels of 84 TG species were measured with untargeted lipid profiling of 689 participants from the Alzheimer's Disease Neuroimaging Initiative cohort, including 190 cognitively normal older adults (CN), 339 with mild cognitive impairment (MCI), and 160 with AD. Principal component analysis with factor rotation was used for dimension reduction of TG species. Differences in principal components between diagnostic groups and associations between principal components and AD biomarkers (including CSF, MRI and [¹⁸F]fluorodeoxyglucose-PET) were assessed with a generalized linear model approach. In both cases, the Bonferroni method of adjustment was used to correct for multiple comparisons.

RESULTS

The 84 TGs yielded 9 principal components, 2 of which, consisting of long-chain, polyunsaturated fatty acid-containing TGs (PUTGs), were significantly associated with MCI and AD. Lower levels of PUTGs were observed in MCI and AD compared to CN. PUTG principal component scores were also significantly associated with hippocampal volume and entorhinal cortical thickness. In participants carrying the APOE ε4 allele, these principal components were significantly associated with CSF β-amyloid_1-42 values and entorhinal cortical thickness.

CONCLUSION

This study shows that PUTG component scores were significantly associated with diagnostic group and AD biomarkers, a finding that was more pronounced in APOE ε4 carriers. Replication in independent larger studies and longitudinal follow-up are warranted.

Laboratory Screening Protocol to Identify Novel Oleaginous Yeasts

Oleaginous microbes, which contain over 20% intracellular lipid, predominantly triacylglycerols (TG), by dry weight, have been discovered to have high oil content by many different protocols, ranging from simple staining to more complex chromatographic methods. In our laboratory, a methodical process was implemented to identify high oil yeasts, designed to minimize labor while optimizing success in identifying high oil strains among thousands of candidates. First, criteria were developed to select candidate yeast strains for analysis. These included observation of buoyancy of the yeast cell mass in 20% glycerol, and phylogenetic placement near known oleaginous species. A low-labor, semiquantitative Nile red staining protocol was implemented to screen numerous yeast cultures for high oil content in 96-well plates. Then, promising candidates were selected for more quantitative analysis. A more labor-intensive and quantitative gravimetric assay was implemented that gave consistent values for intracellular oil content for a broad range of yeast species. Finally, an LC-MS protocol was utilized to quantify and identify yeast triacylglycerols. This progressive approach was successful in identifying 30 new oleaginous yeast species, out of over 1000 species represented in the Phaff Yeast Culture Collection.

Laser Ablation Electrospray Ionization Hydrogen/Deuterium Exchange Ambient Mass Spectrometry Imaging

Identification and confirmation of known as well as unknown (bio)chemical entities in ambient mass spectrometry (MS) and MS imaging (MSI) mostly involve accurate mass determination, often in combination with MS/MS or MSⁿ work flows. To further improve structural assignment, additional molecular information is required. Here we present an ambient hydrogen/deuterium exchange (HDX) laser ablation electrospray ionization (LAESI) MS method in which, apart from the accurate mass and MS/MS data, the number of exchangeable protons in (un)known molecules is obtained. While eventually presenting ambient HDX-LAESI-MSI, samples were not preincubated with deuterated solvents, but instead HDX occurred following fusion of ablated sample material with microdroplets generated by ESI of deuterated solvents. Therefore, the degree of HDX was first studied following ablation of nondeuterated sample solutions of melamine and monosaccharides. From these experiments, it was concluded that the set-up used could provide meaningful HDX data in support of molecular structure elucidation by significantly reducing the number of structure options from a measured elemental composition. This reduction was demonstrated with an unknown accurate m/z value obtained in the analysis of an orange slice, reducing the possible number of molecular structures having the same elemental composition by 87% due to the number of H/D exchanges observed. Next, deuterated and nondeuterated MS/MS experiments showed the number of exchangeable protons in the substructures from deuterated neutral losses in the product ion spectra, confirming the compound to be arginine. Finally, the potential of ambient HDX-LAESI-MSI was demonstrated by the imaging of (secondary) plant metabolites in a Phalaenopsis petal.

Ultrahigh-Performance capillary liquid chromatography-mass spectrometry at 35 kpsi for separation of lipids

Improvements in sample preparation, separation, and mass spectrometry continue to expand the coverage in LC-MS based lipidomics. While longer columns packed with smaller particles in theory give higher separation performance compared to shorter columns, the implementation of this technology above commercial limits has been sparse due to difficulties in packing long columns and successfully operating instruments at ultrahigh pressures. In this work, a liquid chromatograph that operates up to 35 kpsi was investigated for the separation and identification of lipid species from human plasma. Capillary columns between 15-50 cm long were packed with 1.7 µm BEH C18 particles and evaluated for their ability to separate lipid isomers and complex lipid extracts from human plasma. Putative lipid class identifications were assigned using accurate mass and relative retention time data of the eluting peaks. Our findings indicate that longer columns packed and operated at 35 kpsi outperform shorter columns packed and run at lower pressures in terms of peak capacity and numbers of features identified. Packing columns with relatively high concentration slurries (200 mg/mL) while sonicating the column resulted in 6-34% increase in peak capacity for 50 cm columns compared to lower slurry concentrations and no sonication. For a given analysis time, 50 cm long columns operated at 35 kpsi provided a 20-95% increase in chromatographic peak capacity compared with 15 cm columns operated at 15 kpsi. Analysis times up to 4 h were evaluated, generating peak capacities up to 410 ± 5 (n = 3, measured at 4σ) and identifying 480 ± 85 lipids (n = 2). Importantly, the results also show a correlation between the peak capacity and the number of lipids identified from a human plasma extract. This correlation indicates that ionization suppression is a limiting factor in obtaining sufficient signal for identification by mass spectrometry. The result also shows that the higher resolution obtained by shallow gradients overcomes possible signal reduction due to broader, more dilute peaks in long gradients for improving detection of lipids in LC-MS. Lastly, longer columns operated at shallow gradients allowed for the best separation of both regional and geometrical isomers. These results demonstrate a system that enables the advantages of using longer columns packed and run at ultrahigh pressure for improving lipid separations and lipidome coverage.

Methods of the Analysis of Oxylipins in Biological Samples

Oxylipins are derivatives of polyunsaturated fatty acids and due to their important and diverse functions in the body, they have become a popular subject of studies. The main challenge for researchers is their low stability and often very low concentration in samples. Therefore, in recent years there have been developments in the extraction and analysis methods of oxylipins. New approaches in extraction methods were described in our previous review. In turn, the old analysis methods have been replaced by new approaches based on mass spectrometry (MS) coupled with liquid chromatography (LC) and gas chromatography (GC), and the best of these methods allow hundreds of oxylipins to be quantitatively identified. This review presents comparative and comprehensive information on the progress of various methods used by various authors to achieve the best results in the analysis of oxylipins in biological samples.

Concurrent lipidomics and proteomics on malignant plasma cells from multiple myeloma patients: Probing the lipid metabolome

Background

Multiple myeloma (MM) is a hematological malignancy characterized by the clonal expansion of malignant plasma cells. Though durable remissions are possible, MM is considered incurable, with relapse occurring in almost all patients. There has been limited data reported on the lipid metabolism changes in plasma cells during MM progression. Here, we evaluated the feasibility of concurrent lipidomics and proteomics analyses from patient plasma cells, and report these data on a limited number of patient samples, demonstrating the feasibility of the method, and establishing hypotheses to be evaluated in the future.

Methods

Plasma cells were purified from fresh bone marrow aspirates using CD138 microbeads. Proteins and lipids were extracted using a bi-phasic solvent system with methanol, methyl tert-butyl ether, and water. Untargeted proteomics, untargeted and targeted lipidomics were performed on 7 patient samples using liquid chromatography-mass spectrometry. Two comparisons were conducted: high versus low risk; relapse versus newly diagnosed. Proteins and pathways enriched in the relapsed group was compared to a public transcriptomic dataset from Multiple Myeloma Research Consortium reference collection (n = 222) at gene and pathways level.

Results

From one million purified plasma cells, we were able to extract material and complete untargeted (~6000 and ~3600 features in positive and negative mode respectively) and targeted lipidomics (313 lipids), as well as untargeted proteomics analysis (~4100 reviewed proteins). Comparative analyses revealed limited differences between high and low risk groups (according to the standard clinical criteria), hence we focused on drawing comparisons between the relapsed and newly diagnosed patients. Untargeted and targeted lipidomics indicated significant down-regulation of phosphatidylcholines (PCs) in relapsed MM. Although there was limited overlap of the differential proteins/transcripts, 76 significantly enriched pathways in relapsed MM were common between proteomics and transcriptomics data. Further evaluation of transcriptomics data for lipid metabolism network revealed enriched correlation of PC, ceramide, cardiolipin, arachidonic acid and cholesterol metabolism pathways to be exclusively correlated among relapsed but not in newly-diagnosed patients.

Conclusions

This study establishes the feasibility and workflow to conduct integrated lipidomics and proteomics analyses on patient-derived plasma cells. Potential lipid metabolism changes associated with MM relapse warrant further investigation.

High-throughput liquid chromatography mass-spectrometry-driven lipidomics discover metabolic biomarkers and pathways as promising targets to reveal the therapeutic effects of the Shenqi pill

Lipidomics, a branch of metabonomics, could provide a powerful technique for discovery of lipid molecules to reveal disease status and drug efficacy.

A Perspective and Framework for Developing Sample Type Specific Databases for LC/MS-Based Clinical Metabolomics

Metabolomics has the potential to greatly impact biomedical research in areas such as biomarker discovery and understanding molecular mechanisms of disease. However, compound identification (ID) remains a major challenge in liquid chromatography mass spectrometry-based metabolomics. This is partly due to a lack of specificity in metabolomics databases. Though impressive in depth and breadth, the sheer magnitude of currently available databases is in part what makes them ineffective for many metabolomics studies. While still in pilot phases, our experience suggests that custom-built databases, developed using empirical data from specific sample types, can significantly improve confidence in IDs. While the concept of sample type specific databases (STSDBs) and spectral libraries is not entirely new, inclusion of unique descriptors such as detection frequency and quality scores, can be used to increase confidence in results. These features can be used alone to judge the quality of a database entry, or together to provide filtering capabilities. STSDBs rely on and build upon several available tools for compound ID and are therefore compatible with current compound ID strategies. Overall, STSDBs can potentially result in a new paradigm for translational metabolomics, whereby investigators confidently know the identity of compounds following a simple, single STSDB search.

Metabolomics study of the prefrontal cortex in a rat model of attention deficit hyperactivity disorder reveals the association between cholesterol metabolism disorder and hyperactive behavior

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disease for which specific biomarkers and pathological mechanisms have yet to be identified. Methylphenidate (MPH) is commonly used to treat ADHD, but its therapeutic mechanisms and its impact on brain metabolites remain unclear. Metabolomics can help to discover biomarkers and identify pathophysiological mechanisms. We adopted an untargeted metabolomics approach based on gas chromatography-mass spectrometry to investigate the potential biomarkers and pathogenesis of ADHD. Ten Wistar-Kyoto (WKY) rats were chosen as healthy controls (vehicle, i.g.). Twenty young spontaneously hypertensive rats (SHR) were randomly allocated to the SHR group (vehicle, i.g.) and MPH group (2 mg/kg/day, i.g.). We identified 103 metabolites from the prefrontal cortex (PFC). Orthogonal partial least square-discriminate analysis showed the differential expression of these metabolites between the groups. Multivariate and univariate statistical analyses isolated 12 metabolites that differed significantly between the WKY and SHR groups: 3-hydroxymethylglutaric acid, 3-phosphoglyceric acid, adenosine monophosphate, cholesterol, lanosterol, and o-phosphoethanolamine; 3-hydroxymethylglutaric acid and cholesterol were reversed with MPH treatment. Pathway and enrichment analyses revealed that the altered metabolites belonged to the cholesterol metabolism pathways. ELISA and western blotting showed that the activity of 3-hydroxy-3-methyl-glutaryl-CoA reductase and the expression of sterol regulatory element-binding protein-2 and ATP-binding cassette transporter A1 were reduced in the PFC of the SHR; the latter two proteins were upregulated by MPH. In conclusion, metabolomics analysis identified potential biomarkers that influence cholesterol metabolism and may be implicated in the development of ADHD-like behavior. MPH can regulate cholesterol metabolism in the PFC of ADHD models. This study uncovered potential biomarkers and pathways involved in ADHD, providing new insight into its pathogenesis.

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.

Genome-Scale Characterization of Toxicity-Induced Metabolic Alterations in Primary Hepatocytes

Context-specific GEnome-scale metabolic Network REconstructions (GENREs) provide a means to understand cellular metabolism at a deeper level of physiological detail. Here, we use transcriptomics data from chemically-exposed rat hepatocytes to constrain a GENRE of rat hepatocyte metabolism and predict biomarkers of liver toxicity using the Transcriptionally Inferred Metabolic Biomarker Response algorithm. We profiled alterations in cellular hepatocyte metabolism following in vitro exposure to four toxicants (acetaminophen, carbon tetrachloride, 2,3,7,8-tetrachlorodibenzodioxin, and trichloroethylene) for six hour. TIMBR predictions were compared with paired fresh and spent media metabolomics data from the same exposure conditions. Agreement between computational model predictions and experimental data led to the identification of specific metabolites and thus metabolic pathways associated with toxicant exposure. Here, we identified changes in the TCA metabolites citrate and alpha-ketoglutarate along with changes in carbohydrate metabolism and interruptions in ATP production and the TCA Cycle. Where predictions and experimental data disagreed, we identified testable hypotheses to reconcile differences between the model predictions and experimental data. The presented pipeline for using paired transcriptomics and metabolomics data provides a framework for interrogating multiple omics datasets to generate mechanistic insight of metabolic changes associated with toxicological responses.

Proteomics reveals NNMT as a master metabolic regulator of cancer-associated fibroblasts

High-grade serous carcinoma has a poor prognosis, owing primarily to its early dissemination throughout the abdominal cavity. Genomic and proteomic approaches have provided snapshots of the proteogenomics of ovarian cancer1,2, but a systematic examination of both the tumour and stromal compartments is critical in understanding ovarian cancer metastasis. Here we develop a label-free proteomic workflow to analyse as few as 5,000 formalin-fixed, paraffin-embedded cells microdissected from each compartment. The tumour proteome was stable during progression from in situ lesions to metastatic disease; however, the metastasis-associated stroma was characterized by a highly conserved proteomic signature, prominently including the methyltransferase nicotinamide N-methyltransferase (NNMT) and several of the proteins that it regulates. Stromal NNMT expression was necessary and sufficient for functional aspects of the cancer-associated fibroblast (CAF) phenotype, including the expression of CAF markers and the secretion of cytokines and oncogenic extracellular matrix. Stromal NNMT expression supported ovarian cancer migration, proliferation and in vivo growth and metastasis. Expression of NNMT in CAFs led to depletion of S-adenosyl methionine and reduction in histone methylation associated with widespread gene expression changes in the tumour stroma. This work supports the use of ultra-low-input proteomics to identify candidate drivers of disease phenotypes. NNMT is a central, metabolic regulator of CAF differentiation and cancer progression in the stroma that may be therapeutically targeted.

Primed mesenchymal stem cells package exosomes with metabolites associated with immunomodulation

Mesenchymal stem cell (MSC) based therapies are currently being evaluated as a putative therapeutic in numerous human clinical trials. Recent reports have established that exosomes mediate much of the therapeutic properties of MSCs. Exosomes are nanovesicles which mediate intercellular communication, transmitting signals between cells which regulate a diverse range of biological processes. MSC-derived exosomes are packaged with numerous types of proteins and RNAs, however, their metabolomic and lipidomic profiles to date have not been well characterized. We previously reported that MSCs, in response to priming culture conditions that mimic the in vivo microenvironmental niche, substantially modulate cellular signaling and significantly increase the secretion of exosomes. Here we report that MSCs exposed to such priming conditions undergo glycolytic reprogramming, which homogenizes MSCs' metabolomic profile. In addition, we establish that exosomes derive from primed MSCs are packaged with numerous metabolites that have been directly associated with immunomodulation, including M2 macrophage polarization and regulatory T lymphocyte induction.