LipidMS: An R Package for Lipid Annotation in Untargeted Liquid Chromatography-Data Independent Acquisition-Mass Spectrometry Lipidomics

Pseudotargeted lipidomics analysis of scoparone on glycerophospholipid metabolism in non-alcoholic steatohepatitis mice by LC-MRM-MS

As the inflammatory subtype of nonalcoholic fatty liver disease (NAFLD), the progression of nonalcoholic steatohepatitis (NASH) is associated with disorders of glycerophospholipid metabolism. Scoparone is the major bioactive component in Artemisia capillaris which has been widely used to treat NASH in traditional Chinese medicine. However, the underlying mechanisms of scoparone against NASH are not yet fully understood, which hinders the development of effective therapeutic agents for NASH. Given the crucial role of glycerophospholipid metabolism in NASH progression, this study aimed to characterize the differential expression of glycerophospholipids that is responsible for scoparone's pharmacological effects and assess its efficacy against NASH. Liquid chromatography-multiple reaction monitoring-mass spectrometry (LC-MRM-MS) was performed to get the concentrations of glycerophospholipids, clarify mechanisms of disease, and highlight insights into drug discovery. Additionally, pathologic findings also presented consistent changes in high-fat diet-induced NASH model, and after scoparone treatment, both the levels of glycerophospholipids and histopathology were similar to normal levels, indicating a beneficial effect during the observation time. Altogether, these results refined the insights on the mechanisms of scoparone against NASH and suggested a route to relieve NASH with glycerophospholipid metabolism. In addition, the current work demonstrated that a pseudotargeted lipidomic platform provided a novel insight into the potential mechanism of scoparone action.

Relationship between Plasma Lipid Profile and Cognitive Status in Early Alzheimer Disease

Alzheimer disease (AD) is a heterogeneous and complex disease in which different pathophysiological mechanisms are involved. This heterogenicity can be reflected in different atrophy patterns or clinical manifestations. Regarding biochemical pathways involved in early AD, lipid metabolism plays an important role; therefore, lipid levels have been evaluated as potential AD diagnosis biomarkers, and their levels could be related to different AD clinical manifestations. Therefore, the aim of this work is to study AD lipid profiles from early AD patients and evaluate their clinical significance. For this purpose, untargeted plasma lipidomic analysis was carried out in early AD patients (n = 31) diagnosed with cerebrospinal fluid (CSF) biomarkers. Cluster analysis was carried out to define early AD subgroups according to the lipid levels. Then, the clinical significance of each lipid profile subgroup was studied, analyzing differences for other variables (cognitive status, CSF biomarkers, medication, comorbidities, age, and gender). The cluster analysis revealed two different groups of AD patients. Cluster 1 showed higher levels of plasma lipids and better cognitive status than Cluster 2. However, no differences were found for the other variables (age, gender, medication, comorbidities, cholesterol, and triglycerides levels) between both groups. Plasma lipid levels could differentiate two early AD subgroups, which showed different cognitive statuses. However, further research with a large cohort and longitudinal study evaluating the clinical evolution of these patients is required. In general, it would involve a relevant advance in the knowledge of AD pathological mechanisms, potential treatments, and precision medicine.

Addressing the untargeted lipidomics challenge in urine samples: Comparative study of extraction methods by UHPLC-ESI-QTOF-MS

Urine analysis has remained a fundamental and widely used method in clinical diagnostics for over a century. With its minimal invasive nature and comprehensive range of analytes, urine has established itself as a clinical diagnostic tool for various disorders, including renal, urological, metabolic, and endocrine diseases. Furthermore, urine's unique attributes make it an attractive matrix for biomarker discovery, as well as in assessing the metabolic and physiological states of patients and healthy individuals alike. However, limitations in our knowledge of average values and sources of urinary lipids decrease the wider clinical application of urinary lipidomics. In this context, untargeted lipidomics analysis relies heavily on the extraction and analysis of lipids in biological samples. Nevertheless, this type of analysis presents challenges in lipid identification due to the diverse nature of lipids. Therefore, proper sample treatment before analysis is crucial to obtain robust and reproducible lipidomic profiles. To address this gap, we conducted a comparative study of a urine pool sample collected from twenty healthy volunteers using four different lipid extraction methods: one biphasic and three monophasic protocols. The extracted lipids were then analyzed using UHPLC-MS and MS/MS, and the semi-quantification of all the accurately annotated lipid species was performed for each extraction method.

Integrative processing of untargeted metabolomic and lipidomic data using MultiABLER

Mass spectrometry (MS)-based untargeted metabolomic and lipidomic approaches are being used increasingly in biomedical research. The adoption and integration of these data are critical to the overall multi-omic toolkit. Recently, a sample extraction method called Multi-ABLE has been developed, which enables concurrent generation of proteomic and untargeted metabolomic and lipidomic data from a small amount of tissue. The proteomics field has a well-established set of software for processing of acquired data; however, there is a lack of a unified, off-the-shelf, ready-to-use bioinformatics pipeline that can take advantage of and prepare concurrently generated metabolomic and lipidomic data for joint downstream analyses. Here we present an R pipeline called MultiABLER as a unified and simple upstream processing and analysis pipeline for both metabolomics and lipidomics datasets acquired using liquid chromatography-tandem mass spectrometry. The code is available via an open-source license at https://github.com/holab-hku/MultiABLER.

Development and application of lipidomics for food research

Lipidomics is an emerging and promising omics derived from metabolomics to comprehensively analyze all of lipid molecules in biological matrices. The purpose of this chapter is to introduce the development and application of lipidomics for food research. First, three aspects of sample preparation are introduced: food sampling, lipid extraction, and transportation and storage. Second, five types of instruments for data acquisition are summarized: direct infusion-mass spectrometry (MS), chromatographic separation-MS, ion mobility-MS, MS imaging, and nuclear magnetic resonance spectroscopy. Third, data acquisition and analysis software are described for the lipidomics software development. Fourth, the application of lipidomics for food research is discussed such as food origin and adulteration analysis, food processing research, food preservation research, and food nutrition and health research. All the contents suggest that lipidomics is a powerful tool for food research based on its ability of lipid component profile analysis.

Accurate Analysis of Lipid Building Blocks Using the Tool LipidOne

LC/MS-based analysis techniques combined with specialized lipid platforms allow the qualitative and quantitative determination of thousands of lipid molecules. Each individual molecule can be considered as an assembly of smaller parts, often called building blocks that are the result of a myriad of biochemical synthesis and transformation processes. LipidOne is a new lipidomic tool that automatically highlights all qualitative and quantitative changes in lipid building blocks both among all detected lipid classes and between experimental groups. Thanks to LipidOne, the discovered differences among lipid building blocks can be easily linked to the activity of specific enzymes.

ADViSELipidomics: a workflow for analyzing lipidomics data

SUMMARY

ADViSELipidomics is a novel Shiny app for preprocessing, analyzing and visualizing lipidomics data. It handles the outputs from LipidSearch and LIQUID for lipid identification and quantification and the data from the Metabolomics Workbench. ADViSELipidomics extracts information by parsing lipid species (using LIPID MAPS classification) and, together with information available on the samples, performs several exploratory and statistical analyses. When the experiment includes internal lipid standards, ADViSELipidomics can normalize the data matrix, providing normalized concentration values per lipids and samples. Moreover, it identifies differentially abundant lipids in simple and complex experimental designs, dealing with batch effect correction. Finally, ADViSELipidomics has a user-friendly graphical user interface and supports an extensive series of interactive graphics.

AVAILABILITY AND IMPLEMENTATION

ADViSELipidomics is freely available at https://github.com/ShinyFabio/ADViSELipidomics.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Lipidic profiles of patients starting peritoneal dialysis suggest an increased cardiovascular risk beyond classical dyslipidemia biomarkers

Patients on peritoneal dialysis (PD) have an increased risk of cardiovascular disease (CVD) and an atherogenic lipid profile generated by exposure to high glucose dialysis solutions. In the general population, the reduction of classic lipids biomarkers is associated with improved clinical outcomes; however, the same results have not been seen in PD population, a lack of data this study aims to fulfill. Single-center prospective observational study of a cohort of CKD patients who started renal replacement therapy with continuous ambulatory peritoneal dialysis. The differences in the lipid profile and analytical variables before and 6 months after the start of peritoneal dialysis were analyzed. Samples were analyzed on an Ultra-Performance Liquid Chromatography system. Thirty-nine patients were enrolled in this study. Their mean age was 57.9 ± 16.3 years. A total of 157 endogenous lipid species of 11 lipid subclasses were identified. There were significant increases in total free fatty acids (p < 0.05), diacylglycerides (p < 0.01), triacylglycerides, (p < 0.01), phosphatidylcholines (p < 0.01), phosphatidylethanolamines (p < 0.01), ceramides (p < 0.01), sphingomyelins (p < 0.01), and cholesterol esters (p < 0.01) from baseline to 6 months. However, there were no differences in the classical lipid markers, neither lysophosphatidylcholines, monoacylglycerides, and sphingosine levels. 6 months after the start of the technique, PD patients present changes in the lipidomic profile beyond the classic markers of dyslipidemia.

Plasma Lipidomics Approach in Early and Specific Alzheimer’s Disease Diagnosis

Background: The brain is rich in lipid content, so a physiopathological pathway in Alzheimer’s disease (AD) could be related to lipid metabolism impairment. The study of lipid profiles in plasma samples could help in the identification of early AD changes and new potential biomarkers. Methods: An untargeted lipidomic analysis was carried out in plasma samples from preclinical AD (n = 11), mild cognitive impairment-AD (MCI-AD) (n = 31), and healthy (n = 20) participants. Variables were identified by means of two complementary methods, and lipid families’ profiles were studied. Then, a targeted analysis was carried out for some identified lipids. Results: Statistically significant differences were obtained for the diglycerol (DG), lysophosphatidylethanolamine (LPE), lysophosphatidylcholine (LPC), monoglyceride (MG), and sphingomyelin (SM) families as well as for monounsaturated (MUFAs) lipids, among the participant groups. In addition, statistically significant differences in the levels of lipid families (ceramides (Cer), LPE, LPC, MG, and SM) were observed between the preclinical AD and healthy groups, while statistically significant differences in the levels of DG, MG, and PE were observed between the MCI-AD and healthy groups. In addition, 18:1 LPE showed statistically significant differences in the targeted analysis between early AD (preclinical and MCI) and healthy participants. Conclusion: The different plasma lipid profiles could be useful in the early and minimally invasive detection of AD. Among the lipid families, relevant results were obtained from DGs, LPEs, LPCs, MGs, and SMs. Specifically, MGs could be potentially useful in AD detection; while LPEs, LPCs, and SM seem to be more related to the preclinical stage, while DGs are more related to the MCI stage. Specifically, 18:1 LPE showed a potential utility as an AD biomarker.

LipidMS 3.0: an R-package and a web-based tool for LC-MS/MS data processing and lipid annotation

MOTIVATION

LipidMS was initially envisioned to use fragmentation rules and data-independent acquisition (DIA) for lipid annotation. However, data-dependent acquisition (DDA) remains the most widespread acquisition mode for untargeted LC-MS/MS-based lipidomics. Here we present LipidMS 3.0, an R package that not only adds DDA and new lipid classes to its pipeline, but also the required functionalities to cover the whole data analysis workflow from pre-processing (i.e., peak-peaking, alignment and grouping) to lipid annotation.

RESULTS

We applied the new workflow in the data analysis of a commercial human serum pool spiked with 68 representative lipid standards acquired in full scan, DDA and DIA modes. When focusing on the detected lipid standard features and total identified lipids, LipidMS 3.0 data pre-processing performance is similar to XCMS, whereas it complements the annotations returned by MS-DIAL, providing a higher level of structural information and a lower number of incorrect annotations. To extend and facilitate LipidMS 3.0 usage among less experienced R-programming users, the workflow was also implemented as a web-based application.

AVAILABILITY

The LipidMS R-package is freely available at https://CRAN.R-project.org/package=LipidMS and as a website at http://www.lipidms.com.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Caenorhabditis elegans deep lipidome profiling by using integrative mass spectrometry acquisitions reveals significantly altered lipid networks

Lipidomics coverage improvement is essential for functional lipid and pathway construction. A powerful approach to discovering organism lipidome is to combine various data acquisitions, such as full scan mass spectrometry (full MS), data-dependent acquisition (DDA), and data-independent acquisition (DIA). Caenorhabditis elegans (C. elegans) is a useful model for discovering toxic-induced metabolism, high-throughput drug screening, and a variety of human disease pathways. To determine the lipidome of C. elegans and investigate lipid disruption from the molecular level to the system biology level, we used integrative data acquisition. The methyl-tert-butyl ether method was used to extract L4 stage C. elegans after exposure to triclosan (TCS), perfluorooctanoic acid, and nanopolystyrene (nPS). Full MS, DDA, and DIA integrations were performed to comprehensively profile the C. elegans lipidome by Q-Exactive Plus MS. All annotated lipids were then analyzed using lipid ontology and pathway analysis. We annotated up to 940 lipids from 20 lipid classes involved in various functions and pathways. The biological investigations revealed that when C. elegans were exposed to nPS, lipid droplets were disrupted, whereas plasma membrane-functionalized lipids were likely to be changed in the TCS treatment group. The nPS treatment caused a significant disruption in lipid storage. Triacylglycerol, glycerophospholipid, and ether class lipids were those primarily hindered by toxicants. Finally, toxicant exposure frequently involved numerous lipid-related pathways, including the phosphoinositide 3-kinase/protein kinase B pathway. In conclusion, an integrative data acquisition strategy was used to characterize the C. elegans lipidome, providing valuable biological insights into hypothesis generation and validation.

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Multiple data acquisitions were used to profile the lipidome of C. elegans.

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940 detected lipids of 20 main classes involved in various pathways.

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Relevant hypotheses were generated using high-coverable lipidomics and pathways analysis.

LipidOne: user-friendly lipidomic data analysis tool for a deeper interpretation in a systems biology scenario

SUMMARY

LC/MS-based analysis techniques combined with specialized lipid tool allow for the qualitative and quantitative determination of thousands of lipid molecules. Some recent bioinformatics tools have been developed to study changes in the lipid profile in case-control experiments and correlate these changes to different enzyme activity or gene expression. However, the existing tools have the limitation to treat only the assembled lipid molecules. In reality, each individual molecule can be considered as an assembly of smaller parts, often called building blocks. These are the result of a myriad of biochemical synthesis and transformation processes that, from a systems biology perspective, should not be ignored. Here, we present LipidOne, a new lipidomic tool which highlights all qualitative and quantitative changes in lipid building blocks both among all detected lipid classes and among experimental groups. Thanks to LipidOne, even differences in lipid building blocks can now be linked to the activity of specific classes of enzymes, transcripts and genes.

AVAILABILITY AND IMPLEMENTATION

LipidOne software is freely available at www.dcbb.unipg.it/LipidOne and https://github.com/matteogiulietti/LipidOne.

CONTACT

roberto.pellegrino@unipg.it.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A Comprehensive Review of Lipidomics and Its Application to Assess Food Obtained from Farm Animals

Lipids are one of the major macronutrients essential for adequate growth and maintenance of human health. Their structure is not only complex but also diverse, which makes systematic and holistic analyses challenging; consequently, little is known regarding the relationship between phenotype and mechanism of action. In recent years, rapid advancements have been made in the fields of lipidomics and bioinformatics. In comparison with traditional approaches, mass spectrometry-based lipidomics can rapidly identify as well as quantify >1,000 lipid species at the same time, facilitating comprehensive, robust analyses of lipids in tissues, cells, and body fluids. Accordingly, lipidomics is now being widely applied in various fields, particularly food and nutrition science. In this review, we discuss lipid classification, extraction techniques, and detection and analysis using lipidomics. We also cover how lipidomics is being used to assess food obtained from livestock and poultry. The information included herein should serve as a reference to determine how to characterize lipids in animal food samples, enhancing our understanding of the application of lipidomics in the field in animal husbandry.

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.

Mild Muscle Mitochondrial Fusion Distress Extends Drosophila Lifespan through an Early and Systemic Metabolome Reorganization

In a global aging population, it is important to understand the factors affecting systemic aging and lifespan. Mitohormesis, an adaptive response caused by different insults affecting the mitochondrial network, triggers a response from the nuclear genome inducing several pathways that promote longevity and metabolic health. Understanding the role of mitochondrial function during the aging process could help biomarker identification and the development of novel strategies for healthy aging. Herein, we interfered the muscle expression of the Drosophila genes Marf and Opa1, two genes that encode for proteins promoting mitochondrial fusion, orthologues of human MFN2 and OPA1. Silencing of Marf and Opa1 in muscle increases lifespan, improves locomotor capacities in the long term, and maintains muscular integrity. A metabolomic analysis revealed that muscle down-regulation of Marf and Opa1 promotes a non-autonomous systemic metabolome reorganization, mainly affecting metabolites involved in the energetic homeostasis: carbohydrates, lipids and aminoacids. Interestingly, the differences are consistently more evident in younger flies, implying that there may exist an anticipative adaptation mediating the protective changes at the older age. We demonstrate that mild mitochondrial muscle disturbance plays an important role in Drosophila fitness and reveals metabolic connections between tissues. This study opens new avenues to explore the link of mitochondrial dynamics and inter-organ communication, as well as their relationship with muscle-related pathologies, or in which muscle aging is a risk factor for their appearance. Our results suggest that early intervention in muscle may prevent sarcopenia and promote healthy aging.

The Hitchhiker's Guide to Untargeted Lipidomics Analysis: Practical Guidelines

Lipidomics is a newly emerged discipline involving the identification and quantification of thousands of lipids. As a part of the omics field, lipidomics has shown rapid growth both in the number of studies and in the size of lipidome datasets, thus, requiring specific and efficient data analysis approaches. This paper aims to provide guidelines for analyzing and interpreting lipidome data obtained using untargeted methods that rely on liquid chromatography coupled with mass spectrometry (LC-MS) to detect and measure the intensities of lipid compounds. We present a state-of-the-art untargeted LC-MS workflow for lipidomics, from study design to annotation of lipid features, focusing on practical, rather than theoretical, approaches for data analysis, and we outline possible applications of untargeted lipidomics for biological studies. We provide a detailed R notebook designed specifically for untargeted lipidome LC-MS data analysis, which is based on xcms software.

c-MYC Triggers Lipid Remodelling During Early Somatic Cell Reprogramming to Pluripotency

Metabolic rewiring and mitochondrial dynamics remodelling are hallmarks of cell reprogramming, but the roles of the reprogramming factors in these changes are not fully understood. Here we show that c-MYC induces biosynthesis of fatty acids and increases the rate of pentose phosphate pathway. Time-course profiling of fatty acids and complex lipids during cell reprogramming using lipidomics revealed a profound remodelling of the lipid content, as well as the saturation and length of their acyl chains, in a c-MYC-dependent manner. Pluripotent cells displayed abundant cardiolipins and scarce phosphatidylcholines, with a prevalence of monounsaturated acyl chains. Cells undergoing cell reprogramming showed an increase in mitochondrial membrane potential that paralleled that of mitochondrial-specific cardiolipins. We conclude that c-MYC controls the rewiring of somatic cell metabolism early in cell reprogramming by orchestrating cell proliferation, synthesis of macromolecular components and lipid remodelling, all necessary processes for a successful phenotypic transition to pluripotency.

Reviewing the metabolome coverage provided by LC-MS: Focus on sample preparation and chromatography-A tutorial

Metabolomics has become an invaluable tool for both studying metabolism and biomarker discovery. The great technical advances in analytical chemistry and bioinformatics have considerably increased the number of measurable metabolites, yet an important part of the human metabolome remains uncovered. Among the various MS hyphenated techniques available, LC-MS stands out as the most used. Here, we aimed to show the capabilities of LC-MS to uncover part of the metabolome and how to best proceed with sample preparation and LC to maximise metabolite detection. The analyses of various open metabolite databases served us to estimate the size of the already detected human metabolome, the expected metabolite composition of most used human biospecimens and which part of the metabolome can be detected when LC-MS is used. Based on an extensive review and on our experience, we have outlined standard procedures for LC-MS analysis of urine, cells, serum/plasma, tissues and faeces, to guide in the selection of the sample preparation method that best matches with one or more LC techniques in order to get the widest metabolome coverage. These standard procedures may be a useful tool to explore, at a glance, the wide spectrum of possibilities available, which can be a good starting point for most of the LC-MS metabolomic studies.

Multi-dimensional characterization and identification of sterols in untargeted LC-MS analysis using all ion fragmentation technology

Sterols are an important type of lipids, and play many important roles in physiological and pathological processes. However, comprehensive analysis of sterols especially identification of unknown sterols is challenging. In this work, LC-MS with all ion fragmentation (AIF) technology was developed for untargeted analysis of sterols in biological samples. AIF technology provided holistic and multi-dimensional characterization for both knowns and unknowns sterols, including accurate m/z, isotope pattern, retention time (RT), and co-eluted peak profiles between MS1 and MS2 ions in one analysis. We further developed an analysis strategy by integrating the multi-dimensional properties to support unambiguous identification of sterols, including distinguishing sterol isomers. The developed strategy enabled to identify a total of 23 sterols in mouse samples, and quantified 19 sterols in mouse liver tissues. More importantly, we demonstrated that AIF based multi-dimensional analysis provided a possibility to identify sterols without chemical standards and facilitated to discover novel compounds with sterol-like structures in biological samples. In summary, we employed the LC-MS based AIF technology to develop multi-dimensional characterization and identification of both known and unknown sterols in complex biological samples. The comprehensive analysis of sterols facilitates to provide molecular insights to many physiological and pathological activities in biology.

Mass spectrometry-based lipidomics in food science and nutritional health: A comprehensive review

With the advance in science and technology as well as the improvement of living standards, the function of food is no longer just to meet the needs of survival. Food science and its associated nutritional health issues have been increasingly debated. Lipids, as complex metabolites, play a key role both in food and human health. Taking advantages of mass spectrometry (MS) by combining its high sensitivity and accuracy with extensive selective determination of all lipid classes, MS-based lipidomics has been employed to resolve the conundrum of addressing both qualitative and quantitative aspects of high-abundance and low-abundance lipids in complex food matrices. In this review, we systematically summarize current applications of MS-based lipidomics in food field. First, common MS-based lipidomics procedures are described. Second, the applications of MS-based lipidomics in food science, including lipid composition characterization, adulteration, traceability, and other issues, are discussed. Third, the application of MS-based lipidomics for nutritional health covering the influence of food on health and disease is introduced. Finally, future research trends and challenges are proposed. MS-based lipidomics plays an important role in the field of food science, promoting continuous development of food science and integration of food knowledge with other disciplines. New methods of MS-based lipidomics have been developed to improve accuracy and sensitivity of lipid analysis in food samples. These developments offer the possibility to fully characterize lipids in food samples, identify novel functional lipids, and better understand the role of food in promoting healt.

Rapid qualitative profiling of metabolites present in Fusarium solani, a rhizospheric fungus derived from Senna spectabilis, using GC/MS and UPLC-QTOF/MSE techniques assisted by UNIFI information system

Fungi are an important source of natural products found in a variety of plant species. A wide range of methods for the detection of metabolites present in fungi have been reported in the literature. The search for methodologies that allow the rapid detection of compounds present in crude extracts is crucial to enable the metabolite annotation doing a qualitative analysis of the complex matrix. Mass spectrometry is an important ally when it comes to in silico detection of previously reported metabolites. In this work, the ethyl acetate extract of Fusarium solani was analyzed by gas chromatography coupled to mass spectrometry (GC/MS) after derivatization process. The ethyl acetate extract was also investigated by liquid chromatography coupled with high-resolution tandem mass spectrometry assisted by the UNIFI software system. A library containing previously reported metabolites from the Fusarium genus was added to the UNIFI platform. Simultaneously, the extract was analyzed through anticholinesterase and antifungal assays. The analysis of the derivatized extract by GC/MS led to the putative identification of five metabolites, and the investigation using Ultra-High Performance Liquid Chromatography - Quadrupole Time-of-Flight Mass Spectrometry (UPLC-QTOF) analysis in data-independent acquisition mode (mass spectrometry) led to the annotation of 15 compounds present in the built-in Fusarium library added to the UNIFI system. The Fusarium solani extract showed potential anticholinesterase and in vitro antifungal activity supported by the detection of bioactive metabolites.

A lipidome atlas in MS-DIAL 4

We present Mass Spectrometry-Data Independent Analysis software version 4 (MS-DIAL 4), a comprehensive lipidome atlas with retention time, collision cross-section and tandem mass spectrometry information. We formulated mass spectral fragmentations of lipids across 117 lipid subclasses and included ion mobility tandem mass spectrometry. Using human, murine, algal and plant biological samples, we annotated and semiquantified 8,051 lipids using MS-DIAL 4 with a 1-2% estimated false discovery rate. MS-DIAL 4 helps standardize lipidomics data and discover lipid pathways.

Extensive Profiling of Polyphenols from two Trollius Species Using a Combination of Untargeted and Targeted Approaches

Various species of globeflowers, belonging to the genus Trollius, have been extensively used in traditional Chinese medicine due to their anti-inflammatory, antimicrobial, and antiviral properties, which are mainly attributed to their high polyphenol content. Differences in polyphenol composition, and abundances, will lead to varying treatment efficacies of globeflowers. Herein, we employ a combination of targeted and untargeted mass spectrometry (MS) approaches to characterize and quantify a comprehensive array of polyphenols, mainly including flavonoids and phenolic acids in two globeflower species commonly used in Chinese medicine, Trollius chinensis Bunge and Trollius ledebouri Reichb. In addition, free radical scavenging activity was investigated to evaluate the association between polyphenol composition and antioxidation capacity. Liquid chromatography (LC)-based separation and multiple-reaction-monitoring (MRM) transitions were optimized using a library of 78 polyphenol reference compounds to achieve absolute quantification on triple quadrupoles MS (QqQ). The analytical method was further expanded via high-resolution MS to provide relative quantitation of an additional 104 endogenous polyphenols in globeflowers not included in our reference library. Our results revealed stark differences in polyphenol content between T. chinensis and T. ledebouri, emphasizing the need for systematic characterization of polyphenol composition to ensure treatment efficacy and consistency in standardizing the use of globeflowers in Chinese medicine.

A new approach for characterization of phosphatidylcholines and lysophosphatidylcholine in human plasma

Aim: Characterization of phosphatidylcholines (PCs) and lysophosphatidylcholine in human plasma using LC-IT-MSⁿ. The characterization approach was based on trapping the eluted positive ions and applying low voltage for fragmentation to MS² and further fragmentation of the most abundant two peaks to obtain MS³. This approach allowed linking the MS³ data to MS² and precursor ion. Methodology: The fatty acid part, at sn-1 and sn-2 of the glycerol backbone, could be identified based on the favored cleavage pathway. Conclusion: The dysregulated PCs and lysophosphatidylcholines in human plasma obtained from acute coronary syndrome cases, and Type 2 diabetes patients suffering no coronary syndromes were estimated and matched versus healthy volunteers. An epoxide form of 16:0-18:2 PC was confirmed, m/z 774.6.

Lipidomics from sample preparation to data analysis: a primer

Lipids are amongst the most important organic compounds in living organisms, where they serve as building blocks for cellular membranes as well as energy storage and signaling molecules. Lipidomics is the science of the large-scale determination of individual lipid species, and the underlying analytical technology that is used to identify and quantify the lipidome is generally mass spectrometry (MS). This review article provides an overview of the crucial steps in MS-based lipidomics workflows, including sample preparation, either liquid–liquid or solid-phase extraction, derivatization, chromatography, ion-mobility spectrometry, MS, and data processing by various software packages. The associated concepts are discussed from a technical perspective as well as in terms of their application. Furthermore, this article sheds light on recent advances in the technology used in this field and its current limitations. Particular emphasis is placed on data quality assurance and adequate data reporting; some of the most common pitfalls in lipidomics are discussed, along with how to circumvent them.

The metaRbolomics Toolbox in Bioconductor and beyond

Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub.

A microscale solid-phase microextraction probe for the in situ analysis of perfluoroalkyl substances and lipids in biological tissues using mass spectrometry

An ambient mass spectrometry method for rapid, in situ, and microscale analysis of PFASs and lipids simultaneously in biological tissues for investigation of their biological correlation.