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

Analysis of carbohydrates and glycoconjugates by matrix-assisted laser desorption/ionization mass spectrometry: An update for 2021-2022

The use of matrix-assisted laser desorption/ionization (MALDI) mass spectrometry for the analysis of carbohydrates and glycoconjugates is a well-established technique and this review is the 12th update of the original article published in 1999 and brings coverage of the literature to the end of 2022. As with previous review, this review also includes a few papers that describe methods appropriate to analysis by MALDI, such as sample preparation, even though the ionization method is not MALDI. The review follows the same format as previous reviews. It is divided into three sections: (1) general aspects such as theory of the MALDI process, matrices, derivatization, MALDI imaging, fragmentation, quantification and the use of computer software for structural identification. (2) Applications to various structural types such as oligo- and polysaccharides, glycoproteins, glycolipids, glycosides and biopharmaceuticals, and (3) other general areas such as medicine, industrial processes, natural products and glycan synthesis where MALDI is extensively used. Much of the material relating to applications is presented in tabular form. MALDI is still an ideal technique for carbohydrate analysis, particularly in its ability to produce single ions from each analyte and advancements in the technique and range of applications show little sign of diminishing.

Electroacupuncture at ST25 corrected gut microbial dysbiosis and SNpc lipid peroxidation in Parkinson's disease rats

Introduction

Parkinson's disease (PD) remains one kind of a complex, progressive neurodegenerative disease. Levodopa and dopamine agonists as widely utilized PD therapeutics have not shown significant positive long-term outcomes. Emerging evidences indicate that electroacupuncture (EA) have potential effects on the therapy of nervous system disorders, particularly PD, but its specific underlying mechanism(s) remains poorly understood, leading to the great challenge of clinical application and management. Previous study has shown that acupuncture ameliorates PD motor symptoms and dopaminergic neuron damage by modulating intestinal dysbiosis, but its intermediate pathway has not been sufficiently investigated.

Methods

A rat model of PD was induced using rotenone. The therapeutic effect of EA on PD was assessed using the pole and rotarod tests and immunohistostaining for tyrosine hydroxylase (TH) in the substantia nigra (SN) of brain. The role of gut microbiota was explored using 16S rRNA gene sequencing and metabonomic analysis. PICRUSt2 analysis, lipidomic analysis, LPS and inflammatory factor assays were used for subsequent exploration and validation. Correlation analysis was used to identify the key bacteria that EA regulates lipid metabolism to improve PD.

Results

The present study firstly reappeared the effects of EA on protecting motor function and dopaminergic neurons and modulation of gut microbial dysbiosis in rotenone-induced PD rat model. EA improved motor dysfunction (via the pole and rotarod tests) and protected TH+ neurons in PD rats. EA increased the abundance of beneficial bacteria such as Lactobacillus, Dubosiella and Bifidobacterium and decreased the abundance of Escherichia-Shigella and Morganella belonging to Pseudomonadota, suggesting that the modulation of gut microbiota by EA improving the symptoms of PD motility via alleviating LPS-induced inflammatory response and oxidative stress, which was also validated by various aspects such as microbial gene functional analysis, fecal metabolomics analysis, LPS and inflammatory factor assays and SNpc lipidomics analysis. Moreover, correlation analyses also verified strong correlations of Escherichia-Shigella and Morganella with motor symptoms and SNpc lipid peroxidation, explicating targets and intermediate pathways through which EA improve PD exercise symptom.

Conclusion

Our results indicate that the improvement of motor function in PD model by EA may be mediated in part by restoring the gut microbiota, which intermediate processes involve circulating endotoxins and inflammatory mediators, SNpc oxidative stress and lipid peroxidation. The gut-microbiome - brain axis may be a potential mechanism of EA treatment for the PD.

Lipidomics Analysis Deepen Understanding the Molecular Mechanisms in a Gouty Model Induced by Combination of MSU Crystals Injection and High-Fat Diet Feeding and the Intervention Mechanisms of Allopurinol

Background

Gouty arthritis (GA) is a common inflammatory disease caused by deposition of monosodium urate (MSU) crystals in diarthrodial joints. GA attacks commonly involved in joint with red, swollen, heat and pain, and often happened in unilateral foot-first metatarsophalangeal. Accumulated studies have proved that lipids play critical roles in biological processes and lipids biomarkers can substitute for the diagnosis of various diseases.

Methods

Herein, shotgun lipidomics was used to quantitatively analyze serum lipidomes of a gouty model which was induced by injecting MSU crystals and feeding high-fat diet with/without treatment with allopurinol. Meanwhile, ELISA kit was used to detect mouse serum levels of inflammatory cytokines (eg, tumor necrosis factor-α, interleukin 1 beta) and HE staining was used to observe the infiltration of inflammatory cells in the foot pad.

Results

A total of 9 types of serum lipids were detected in lipidomics by shotguns, and the result of NMDS' analysis demonstrated significant differences in lipids profiles between the control and model group. It is worth noting that lipid abnormality in GA (such as Ceramide (Cer), sphingomyelin (SM), 4-hydroxyalkenals (HNE), phosphatidylinositol (PI), ethanolamine glycerophospholipid (PE), etc.) is related with phospholipid and energy metabolism, and allopurinol treatment could correct the aberrant metabolism of lipid to some extent.

Conclusion

Our results indicated that various aberrant lipid metabolisms were present in the established gouty model, and allopurinol treatment could relief this aberrant metabolism of lipids to some degree.

Ultrahigh resolution lipid mass spectrometry imaging of high-grade serous ovarian cancer mouse models

No effective screening tools for ovarian cancer (OC) exist, making it one of the deadliest cancers among women. Considering that little is known about the detailed progression and metastasis mechanism of OC at a molecular level, it is crucial to gain more insights into how metabolic and signaling alterations accompany its development. Herein, we present a comprehensive study using ultra-high-resolution Fourier transform ion cyclotron resonance matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) to investigate the spatial distribution and alterations of lipids in ovarian tissues collected from double knockout (n = 4) and triple mutant mouse models (n = 4) of high-grade serous ovarian cancer (HGSOC). Lipids belonging to a total of 15 different classes were annotated and their abundance changes were compared to those in healthy mouse reproductive tissue (n = 4), mapping onto major lipid pathways involved in OC progression. From intermediate-stage OC to advanced HGSC, we provide direct visualization of lipid distributions and their biological links to inflammatory response, cellular stress, cell proliferation, and other processes. We also show the ability to distinguish tumors at different stages from healthy tissues via a number of highly specific lipid biomarkers, providing targets for future panels that could be useful in diagnosis.

Lipid metabolism analysis in esophageal cancer and associated drug discovery

Esophageal cancer is an upper gastrointestinal malignancy with a bleak prognosis. It is still being explored in depth due to its complex molecular mechanisms of occurrence and development. Lipids play a crucial role in cells by participating in energy supply, biofilm formation, and signal transduction processes, and lipid metabolic reprogramming also constitutes a significant characteristic of malignant tumors. More and more studies have found esophageal cancer has obvious lipid metabolism abnormalities throughout its beginning, progress, and treatment resistance. The inhibition of tumor growth and the enhancement of antitumor therapy efficacy can be achieved through the regulation of lipid metabolism. Therefore, we reviewed and analyzed the research results and latest findings for lipid metabolism and associated analysis techniques in esophageal cancer, and comprehensively proved the value of lipid metabolic reprogramming in the evolution and treatment resistance of esophageal cancer, as well as its significance in exploring potential therapeutic targets and biomarkers.

Ultrahigh Resolution Lipid Mass Spectrometry Imaging of High-Grade Serous Ovarian Cancer Mouse Models

No effective screening tools for ovarian cancer (OC) exist, making it one of the deadliest cancers among women. Considering little is known about the detailed progression and metastasis mechanism of OC at a molecular level, it is crucial to gain more insights on how metabolic and signaling alterations accompany its development. Herein, we present a comprehensive study using ultra-high-resolution Fourier transform ion cyclotron resonance matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) to investigate the spatial distribution and alterations of lipids in ovarian tissues collected from double knockout (n = 4) and a triple mutant mouse models (n = 4) of high-grade serous ovarian cancer (HGSC). Lipids belonging to a total of 15 different classes were annotated and their abundance changes compared to those in healthy mouse reproductive tissue (n = 4), mapping onto major lipid pathways involved in OC progression. From intermediate-stage OC to advanced HGSC, we provide a direct visualization of lipid distributions and their biological links to inflammatory response, cellular stress, cell proliferation, and other processes. We also show the ability to distinguish tumors at different stages from healthy tissues via a number of highly specific lipid biomarkers, providing targets for future panels that could be useful in diagnosis.

rMSIfragment: improving MALDI-MSI lipidomics through automated in-source fragment annotation

Matrix-Assisted Laser Desorption Ionization Mass Spectrometry Imaging (MALDI-MSI) spatially resolves the chemical composition of tissues. Lipids are of particular interest, as they influence important biological processes in health and disease. However, the identification of lipids in MALDI-MSI remains a challenge due to the lack of chromatographic separation or untargeted tandem mass spectrometry. Recent studies have proposed the use of MALDI in-source fragmentation to infer structural information and aid identification. Here we present rMSIfragment, an open-source R package that exploits known adducts and fragmentation pathways to confidently annotate lipids in MALDI-MSI. The annotations are ranked using a novel score that demonstrates an area under the curve of 0.7 in ROC analyses using HPLC-MS and Target-Decoy validations. rMSIfragment applies to multiple MALDI-MSI sample types and experimental setups. Finally, we demonstrate that overlooking in-source fragments increases the number of incorrect annotations. Annotation workflows should consider in-source fragmentation tools such as rMSIfragment to increase annotation confidence and reduce the number of false positives.

Methyl carbamates of phosphatidylethanolamines and phosphatidylserines reveal bacterial contamination in mitochondrial lipid extracts of mouse embryonic fibroblasts

The occurrence of methyl carbamates of phosphatidylethanolamines and phosphatidylserines in the lipid extract of mitochondria obtained from mouse embryonic fibroblasts was ascertained by hydrophilic interaction liquid chromatography with electrospray ionization single and multi-stage mass spectrometry, performed using sinergically a high resolution (quadrupole-Orbitrap) and a low resolution (linear ion trap) spectrometer. Two possible routes to the synthesis of methyl carbamates of phospholipids were postulated and evaluated: (i) a chemical transformation involving phosgene, occurring as a photooxidation by-product in the chloroform used for lipid extraction, and methanol, also used for the latter; (ii) an enzymatic methoxycarbonylation reaction due to an accidental bacterial contamination, that was unveiled subsequently on the murine mitochondrial sample. A specific lipid extraction performed on a couple of standard phosphatidyl-ethanolamines/-serines, based on purposely photo-oxidized chloroform and deuterated methanol, indicated route (i) as negligible in the specific case, thus highlighting the enzymatic route related to bacterial contamination as the most likely source of methyl carbamates. The unambiguous recognition of the latter might represent the starting point toward a better understanding of their generation in biological systems and a minimization of their occurrence when an artefactual formation is ascertained.

The unknown lipids project: harmonized methods improve compound identification and data reproducibility in an inter-laboratory untargeted lipidomics study

Untargeted lipidomics allows analysis of a broader range of lipids than targeted methods and permits discovery of unknown compounds. Previous ring trials have evaluated the reproducibility of targeted lipidomics methods, but inter-laboratory comparison of compound identification and unknown feature detection in untargeted lipidomics has not been attempted. To address this gap, five laboratories analyzed a set of mammalian tissue and biofluid reference samples using both their own untargeted lipidomics procedures and a common chromatographic and data analysis method. While both methods yielded informative data, the common method improved chromatographic reproducibility and resulted in detection of more shared features between labs. Spectral search against the LipidBlast in silico library enabled identification of over 2,000 unique lipids. Further examination of LC-MS/MS and ion mobility data, aided by hybrid search and spectral networking analysis, revealed spectral and chromatographic patterns useful for classification of unknown features, a subset of which were highly reproducible between labs. Overall, our method offers enhanced compound identification performance compared to targeted lipidomics, demonstrates the potential of harmonized methods to improve inter-site reproducibility for quantitation and feature alignment, and can serve as a reference to aid future annotation of untargeted lipidomics data.

PICA: Pixel Intensity Correlation Analysis for Deconvolution and Metabolite Identification in Mass Spectrometry Imaging

In-source fragmentation (ISF) is a naturally occurring phenomenon in various ion sources including soft ionization techniques such as matrix-assisted laser desorption/ionization (MALDI). It has traditionally been minimized as it makes the dataset more complex and often leads to mis-annotation of metabolites. Here, we introduce an approach termed PICA (for pixel intensity correlation analysis) that takes advantage of ISF in MALDI imaging to increase confidence in metabolite identification. In PICA, the extraction and association of in-source fragments to their precursor ion results in "pseudo-MS/MS spectra" that can be used for identification. We examined PICA using three different datasets, two of which were published previously and included validated metabolites annotation. We show that highly colocalized ions possessing Pearson correlation coefficient (PCC) ≥ 0.9 for a given precursor ion are mainly its in-source fragments, natural isotopes, adduct ions, or multimers. These ions provide rich information for their precursor ion identification. In addition, our results show that moderately colocalized ions (PCC < 0.9) may be structurally related to the precursor ion, which allows for the identification of unknown metabolites through known ones. Finally, we propose three strategies to reduce the total computation time for PICA in MALDI imaging. To conclude, PICA provides an efficient approach to extract and group ions stemming from the same metabolites in MALDI imaging and thus allows for high-confidence metabolite identification.

Lipidomics analysis facilitate insight into the molecular mechanisms of urate nephropathy in a gout model induced by combination of MSU crystals injection and high-fat diet feeding

Renal injury is one of the most common clinical manifestations of patients with hyperuricaemia/gout. The precise pathophysiological mechanism(s) for the renal injury is still unknown. Furthermore, it is also unclear whether the clinical therapies (e.g., colchicine and febuxostat) could prevent its progression or not. Lipids are involved in almost all of important biological processes and play critical roles in maintaining the renal functions. Herein, shotgun lipidomics was performed for class-targeted lipid analysis of cellular lipidomes in renal tissue of a gouty model induced by combination of monosodium urate crystals injection and high-fat diet feeding with/without treatment with either colchicine or febuxostat. Serum uric acid (UA), proinflammatory cytokines (i.e., TNF-α and IL-6), xanthine oxidase activity, footpad swelling, and pain threshold were determined to evaluate the gouty severity. Renal histopathological changes, blood urea nitrogen, creatinine, and kidney index were used to reflect renal injury. Lipidomics analysis revealed that altered triacylglycerol (TAG) profile, impaired mitochondrial function resulted by decreased tetra 18:2 cardiolipin, reduced 4-hydroxyalkenal (HNE) species, and elevated lysophospholipids were already present in the kidneys at early stage of renal injury, probably contributing to its occurrence and development. In addition to significantly reduce the UA level and relief the gouty severity, treatment with either colchicine or febuxostat could restore HNE bioavailability, thereby delaying the progression of renal injury. However, both of them could not recover the altered TAG profile and the impaired mitochondrial function, indicating that treatment with either of them could not completely prevent the development of renal injury in the gouty model.

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.

Addressing big data challenges in mass spectrometry-based metabolomics

Advancements in computer science and software engineering have greatly facilitated mass spectrometry (MS)-based untargeted metabolomics. Nowadays, gigabytes of metabolomics data are routinely generated from MS platforms, containing condensed structural and quantitative information from thousands of metabolites. Manual data processing is almost impossible due to the large data size. Therefore, in the "omics" era, we are faced with new challenges, the big data challenges of how to accurately and efficiently process the raw data, extract the biological information, and visualize the results from the gigantic amount of collected data. Although important, proposing solutions to address these big data challenges requires broad interdisciplinary knowledge, which can be challenging for many metabolomics practitioners. Our laboratory in the Department of Chemistry at the University of British Columbia is committed to combining analytical chemistry, computer science, and statistics to develop bioinformatics tools that address these big data challenges. In this Feature Article, we elaborate on the major big data challenges in metabolomics, including data acquisition, feature extraction, quantitative measurements, statistical analysis, and metabolite annotation. We also introduce our recently developed bioinformatics solutions for these challenges. Notably, all of the bioinformatics tools and source codes are freely available on GitHub (https://www.github.com/HuanLab), along with revised and regularly updated content.

Food glycomics: Dealing with unexpected degradation of oligosaccharides during sample preparation and analysis

This study reveals that unexpected degradation of food oligosaccharides can occur during conventional glycomics workflows, including sample preparation and analysis by liquid chromatography-mass spectrometry (LC-MS). With the present investigation, we aim to alert the scientific community of the susceptibility of specific glycosidic linkages to degradation induced by heat and acid. Key standard oligosaccharides representing the major types found in foods (3'-sialyllactose and 6'-sialyl-N-acetyllactosamine for milk, raffinose and stachyose for legumes) were selected as model systems and underwent each of the following treatments independently: (1) labeled with the derivatizing agent 1-aminopyrene-3,6,8-trisulfonic (APTS) (followed by analysis with a capillary electrophoresis system coupled with a fluorescence detector), (2) dried from an acetonitrile-water mixture containing 0.1% trifluoroacetic acid, and (3) injected into an LC-MS system. We demonstrated that both raffinose and stachyose degraded during APTS-labeling by the acid in the labeling reagents. We also discovered that during centrifugal evaporation at 37 °C, all of the four nonderivatized oligosaccharides tested were partially degraded. Additionally, when the LC-MS eluent contained 0.1% formic acid, 3'-sialyllactose, raffinose, and stachyose underwent extensive in-source fragmentation during analysis. Lastly, we identified a simple strategy that can reduce the probability of incorrect oligosaccharide identification resulting from extensive in-source fragmentation.

A Novel Approach to Characterize the Lipidome of Marine Archaeon Nitrosopumilus maritimus by Ion Mobility Mass Spectrometry

Archaea are differentiated from the other two domains of life by their biomolecular characteristics. One such characteristic is the unique structure and composition of their lipids. Characterization of the whole set of lipids in a biological system (the lipidome) remains technologically challenging. This is because the lipidome is innately complex, and not all lipid species are extractable, separable, or ionizable by a single analytical method. Furthermore, lipids are structurally and chemically diverse. Many lipids are isobaric or isomeric and often indistinguishable by the measurement of mass or even their fragmentation spectra. Here we developed a novel analytical protocol based on liquid chromatography ion mobility mass spectrometry to enhance the coverage of the lipidome and characterize the conformations of archaeal lipids by their collision cross-sections (CCSs). The measurements of ion mobility revealed the gas-phase ion chemistry of representative archaeal lipids and provided further insights into their attributions to the adaptability of archaea to environmental stresses. A comprehensive characterization of the lipidome of mesophilic marine thaumarchaeon, Nitrosopumilus maritimus (strain SCM1) revealed potentially an unreported phosphate- and sulfate-containing lipid candidate by negative ionization analysis. It was the first time that experimentally derived CCS values of archaeal lipids were reported. Discrimination of crenarchaeol and its proposed stereoisomer was, however, not achieved with the resolving power of the SYNAPT G2 ion mobility system, and a high-resolution ion mobility system may be required for future work. Structural and spectral libraries of archaeal lipids were constructed in non-vendor-specific formats and are being made available to the community to promote research of Archaea by lipidomics.

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.

Recommendations for good practice in MS-based lipidomics

In the last 2 decades, lipidomics has become one of the fastest expanding scientific disciplines in biomedical research. With an increasing number of new research groups to the field, it is even more important to design guidelines for assuring high standards of data quality. The Lipidomics Standards Initiative is a community-based endeavor for the coordination of development of these best practice guidelines in lipidomics and is embedded within the International Lipidomics Society. It is the intention of this review to highlight the most quality-relevant aspects of the lipidomics workflow, including preanalytics, sample preparation, MS, and lipid species identification and quantitation. Furthermore, this review just does not only highlights examples of best practice but also sheds light on strengths, drawbacks, and pitfalls in the lipidomic analysis workflow. While this review is neither designed to be a step-by-step protocol by itself nor dedicated to a specific application of lipidomics, it should nevertheless provide the interested reader with links and original publications to obtain a comprehensive overview concerning the state-of-the-art practices in the field.

Integration of Chemical Derivatization and in-Source Fragmentation Mass Spectrometry for High-Coverage Profiling of Submetabolomes

In-source fragmentation-based high-resolution mass spectrometry (ISF-HRMS) is a potential analytical technique, which is usually used to profile some specific compounds that can generate diagnostic neutral loss (NL) or fragment ion (FI) in ion source inherently. However, the ISF-HRMS method does not work for those compounds that cannot inherently produce diagnostic NL or FI in ion source. In this study, a derivatization-based in-source fragmentation-information-dependent acquisition (DISF-IDA) strategy was proposed for profiling the metabolites with easily labeled functional groups (submetabolomes) by liquid chromatography-electrospray ionization-quadrupole time-of-flight mass spectrometry (LC-ESI-Q-TOF MS). As a proof-of-concept study, 36 carboxylated compounds labeled with N,N-dimethylethylenediamine (DMED) were selected as model compounds to examine performance of DISF-IDA strategy in screening the carboxylated metabolites and acquiring their MSⁿ spectra. In ESI source, the DEMD-derived carboxylated compounds were fragmented to produce characteristic neutral losses of 45.0578, 63.0684, and/or 88.1000 Da that were further used as diagnostic features for screening the carboxylated metabolites by DISF-IDA-based LC-Q-TOF MS. Furthermore, high-resolution MSⁿ spectra of the model compounds were also obtained within a single run of DISF-IDA-based LC-Q-TOF MS analysis, which contributed to the improvement of the annotation confidence. To further verify its applicability, DISF-IDA strategy was used for profiling carboxylated submetabolome in mice feces. Using this strategy, a total of 351 carboxylated metabolites were detected from mice feces, of which 178 metabolites (51% of the total) were positively or putatively identified. Moreover, DISF-IDA strategy was also demonstrated to be applicable for profiling other submetabolomes with easily labeled functional groups such as amino, carbonyl, and cis-diol groups. Overall, our proposed DISF-IDA strategy is a promising technique for high-coverage profiling of submetabolomes with easily labeled functional groups in biological samples.

ISFrag: De Novo Recognition of In-Source Fragments for Liquid Chromatography-Mass Spectrometry Data

In-source fragmentation (ISF) is a naturally occurring phenomenon during electrospray ionization (ESI) in liquid chromatography-mass spectrometry (LC-MS) analysis. ISF leads to false metabolite annotation in untargeted metabolomics, prompting misinterpretation of the underlying biological mechanisms. Conventional metabolomic data cleaning mainly focuses on the annotation of adducts and isotopes, and the recognition of ISF features is mainly based on common neutral losses and the LC coelution pattern. In this work, we recognized three increasingly important patterns of ISF features, including (1) coeluting with their precursor ions, (2) being in the tandem MS (MS²) spectra of their precursor ions, and (3) sharing similar MS² fragmentation patterns with their precursor ions. Based on these patterns, we developed an R package, ISFrag, to comprehensively recognize all possible ISF features from LC-MS data generated from full-scan, data-dependent acquisition, and data-independent acquisition modes without the assistance of common neutral loss information or MS² spectral library. Tested using metabolite standards, we achieved a 100% correct recognition of level 1 ISF features and over 80% correct recognition for level 2 ISF features. Further application of ISFrag on untargeted metabolomics data allows us to identify ISF features that can potentially cause false metabolite annotation at an omics-scale. With the help of ISFrag, we performed a systematic investigation of how ISF features are influenced by different MS parameters, including capillary voltage, end plate offset, ion energy, and "collision energy". Our results show that while increasing energies can increase the number of real metabolic features and ISF features, the percentage of ISF features might not necessarily increase. Finally, using ISFrag, we created an ISF pathway to visualize the relationships between multiple ISF features that belong to the same precursor ion. ISFrag is freely available on GitHub (https://github.com/HuanLab/ISFrag).

Lipidomics Revealed Aberrant Metabolism of Lipids Including FAHFAs in Renal Tissue in the Progression of Lupus Nephritis in a Murine Model

Lupus nephritis (LN) is an inflammatory renal disease of patients with systemic lupus erythematosus with lots of immune complexes deposited in kidneys. Accumulated studies have demonstrated the close relationships among dyslipidaemia, inflammation, and autoimmune response, and oxidative stress in the patients. Lipids play numerous important roles in biological process and cellular functions. Herein, shotgun lipidomics was employed to quantitatively analyze cellular lipidomes in the renal tissue of MRL/lpr mice in the progression of LN (including pre-LN and LN state) with/without treated with glucocorticoids (GCs). The levels of cytokines (i.e., TNF-α (Tumor necrosis factor alpha) and IL-6 (Interleukin 6)) in the serum were measured by ELISA (enzyme-linked immunosorbent assay) kits. Renal histopathological changes and C3 deposition in the glomeruli of the mice were also determined. Lipidomics analysis revealed that the ectopic fat deposition and the aberrant metabolism of lipids that were relevant to oxidative stress (e.g., 4-hydroxyalkenal, ceramide, lysophospholipid species, etc.) always existed in the development of LN. Moreover, the anti-inflammatory FAHFA (fatty acid ester of hydroxyl fatty acid) species in the kidney tissue could largely reflect the severity of LN. Thus, they were a potential early biomarker for LN. In addition, the study also revealed that treatment with GCs could prevent the progression of LN, but greatly aggravate the aberrant metabolism of the lipids, particularly when used for a long time.

Obesity-Related Changes in Human Plasma Lipidome Determined by the Lipidyzer Platform

Obesity is an increasing public health concern both in the developed and developing countries. Previous studies have demonstrated that considerable alterations in lipid metabolism and consequently marked changes in lipid profile are associated with the onset and progression of obesity-related complications. To characterize the full spectrum of obesity-induced changes in lipid metabolism, direct infusion tandem mass spectrometry analysis is the most promising approach. To better understand which of the many lipid species are the most strongly associated with obesity, the aim of our work was to measure and profile plasma lipids in normal (n = 57), overweight (n = 31), and obese (n = 48) individuals randomly selected from samples of Hungarian general and Roma populations by using the targeted quantitative lipidomics platform, the Lipidyzer. Principal component and stepwise regression analyses were used to identify the most significant clusters and species of lipids by increasing body mass index (BMI). From the 18 clusters identified four key lipid species (PE P-16:0/20:3, TG 20:4_33:1, TG 22:6_36:4, TG 18:3_33:0) showed a strong significant positive and three others (Hex-Cer 18:1;O2/22:0, LPC 18:2, PC 18:1_18:1) significant negative association with BMI. Compared to individual lipid species alone, the lipid species ratio (LSR) we introduced showed an extremely strong, at least 9 orders of magnitude stronger, association with BMI. The LSR can be used as a sensitive and predictive indicator to monitor obesity-related alterations in human plasma and control the effectiveness of treatment of obesity associated non-communicable diseases.

An advanced method for propargylcholine phospholipid detection by direct-infusion MS

Phospholipids with a choline head group are an abundant component of cellular membranes and are involved in many important biological functions. For studies on the cell biology and metabolism of these lipids, traceable analogues where propargylcholine replaces the choline head group have proven useful. We present a novel method to analyze propargylcholine phospholipids by MS. The routine employs 1-radyl-2-lyso-sn-glycero-3-phosphopropargylcholines as labeled lysophosphatidylcholine precursors, which upon cellular conversion direct the traceable tag with superb specificity and efficiency to the primary target lipid class. Using azidopalmitate as a click-chemistry reporter, we introduce a highly specific, sensitive, and robust MS detection procedure for the propargylcholine phospholipids. In a first study, we apply the new technique to investigate choline phospholipid metabolism in brain endothelial cells. These experiments reveal differences in the metabolism of phosphatidylcholine and its pendant, ether phosphatidylcholine. The novel method described here opens a new, quantitative, and detailed view on propargylcholine phospholipid metabolism and will greatly facilitate future studies on choline phospholipid metabolism.