Special Issue "Mass Spectrometry-Based Lipidomics Volume 2"

A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Frontiers in Metabolomics".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 5172

Special Issue Editors

Dr. Amaury Cazenave Gassiot
E-Mail Website
Guest Editor
Department of Biochemistry, Yong Loo Lin School of Medicine, and Singapore Lipidomics Incubator (SLING), Life Sciences Institute, National University of Singapore, Singapore
Interests: lipidomics; chromatography; mass spectrometry; lipid biochemistry; harmonisation
Special Issues, Collections and Topics in MDPI journals
Dr. Federico Tesio Torta
E-Mail Website
Guest Editor
Department of Biochemistry, Yong Loo Lin School of Medicine, and Singapore Lipidomics Incubator (SLING), Life Sciences Institute, National University of Singapore, Singapore 119077, Singapore
Interests: lipidomics; protein and lipid biochemistry; metabolic diseases; harmonisation
Special Issues, Collections and Topics in MDPI journals
Dr. Bo Johannes Burla
E-Mail Website
Guest Editor
Singapore Lipidomics Incubator (SLING), Life Sciences Institute, National University of Singapore, Singapore
Interests: lipidomics; MS data processing; glycosphingolipids; platelets; reproducible data analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the last few years, it has become increasingly apparent that explaining complex biochemical pathways cannot rely solely on the study of genetic background and genetic variation. A better understanding of proteins, peptides, metabolites, and lipids at the molecular level is required. However, these components and their natural variations are still poorly characterized, be it in model organisms or human populations.

Of particular interest, the implication of lipids in many biological processes, such as plant growth, viral infection mechanisms, neuronal pathologies, autoimmune diseases, diabetes, obesity, or cancer, has only recently emerged. These developments have been made possible by advances in mass-spectrometry-based lipidomics and associated techniques, which have made it easier to grasp the lipidome’s complexity.

Mass-spectrometry-based lipidomic workflows have considerably improved over the years, yielding ever more comprehensive coverage, structural resolution, and better quantification of the lipidome and its variations. The field has now reached a point where translation to clinical applications is within reach. In this context, the lipidomics community must work towards developing ever-better analytics and establishing widely accepted guidelines for validation and reproducibility.

In this Special Issue, we would like to invite manuscripts on all aspects of mass-spectrometry-based lipidomic workflows: sample preparation, chromatographic separation, MS and MS/MS, quality control, data processing and statistical analysis, harmonization efforts, and clinical translation. Both review articles and original studies are welcome.

Dr. Amaury Cazenave Gassiot
Dr. Federico Tesio Torta
Dr. Bo Johannes Burla
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metabolites is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Lipidomics
  • Mass spectrometry
  • Targeted and untargeted lipidomics
  • Workflows

Published Papers (7 papers)

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Research

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Article
Shotgun Lipidomics for Differential Diagnosis of HPV-Associated Cervix Transformation
Metabolites 2022, 12(6), 503; https://0-doi-org.brum.beds.ac.uk/10.3390/metabo12060503 - 31 May 2022
Viewed by 396
Abstract
A dramatic increase in cervical diseases associated with human papillomaviruses (HPV) in women of reproductive age has been observed over the past decades. An accurate differential diagnosis of the severity of cervical intraepithelial neoplasia and the choice of the optimal treatment requires the [...] Read more.
A dramatic increase in cervical diseases associated with human papillomaviruses (HPV) in women of reproductive age has been observed over the past decades. An accurate differential diagnosis of the severity of cervical intraepithelial neoplasia and the choice of the optimal treatment requires the search for effective biomarkers with high diagnostic and prognostic value. The objective of this study was to introduce a method for rapid shotgun lipidomics to differentiate stages of HPV-associated cervix epithelium transformation. Tissue samples from 110 HPV-positive women with cervicitis (n = 30), low-grade squamous intraepithelial lesions (LSIL) (n = 30), high-grade squamous intraepithelial lesions (HSIL) (n = 30), and cervical cancers (n = 20) were obtained. The cervical epithelial tissue lipidome at different stages of cervix neoplastic transformation was studied by a shotgun label-free approach. It is based on electrospray ionization mass spectrometry (ESI-MS) data of a tissue extract. Lipidomic data were processed by the orthogonal projections to latent structures discriminant analysis (OPLS-DA) to build statistical models, differentiating stages of cervix transformation. Significant differences in the lipid profile between the lesion and surrounding tissues were revealed in chronic cervicitis, LSIL, HSIL, and cervical cancer. The lipids specific for HPV-induced cervical transformation mainly belong to glycerophospholipids: phosphatidylcholines, and phosphatidylethanolamines. The developed diagnostic OPLS-DA models were based on 23 marker lipids. More than 90% of these marker lipids positively correlated with the degree of cervix transformation. The algorithm was developed for the management of patients with HPV-associated diseases of the cervix, based on the panel of 23 lipids as a result. ESI-MS analysis of a lipid extract by direct injection through a loop, takes about 25 min (including preparation of the lipid extract), which is significantly less than the time required for the HPV test (several hours for hybrid capture and about an hour for PCR). This makes lipid mass spectrometric analysis a promising method for express diagnostics of HPV-associated neoplastic diseases of the cervix. Full article
(This article belongs to the Special Issue Mass Spectrometry-Based Lipidomics Volume 2)
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Article
Serum Lipidome Signatures of Dogs with Different Endocrinopathies Associated with Hyperlipidemia
Metabolites 2022, 12(4), 306; https://0-doi-org.brum.beds.ac.uk/10.3390/metabo12040306 - 30 Mar 2022
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Abstract
Hyperlipidemia (hypertriglyceridemia, hypercholesterolemia) is a common finding in human and veterinary patients with endocrinopathies (e.g., hypothyroidism and hypercortisolism (Cushing’s syndrome; CS)). Despite emerging use of lipidomics technology in medicine, the lipid profiles of these endocrinopathies have not been evaluated and characterized in dogs. [...] Read more.
Hyperlipidemia (hypertriglyceridemia, hypercholesterolemia) is a common finding in human and veterinary patients with endocrinopathies (e.g., hypothyroidism and hypercortisolism (Cushing’s syndrome; CS)). Despite emerging use of lipidomics technology in medicine, the lipid profiles of these endocrinopathies have not been evaluated and characterized in dogs. The aim of this study was to compare the serum lipidomes of dogs with naturally occurring CS or hypothyroidism with those of healthy dogs. Serum samples from 39 dogs with CS, 45 dogs with hypothyroidism, and 10 healthy beagle dogs were analyzed using a targeted lipidomics approach with liquid chromatography-mass spectrometry. There were significant differences between the lipidomes of dogs with CS, hypothyroidism, and the healthy dogs. The most significant changes were found in the lysophosphatidylcholines, lysophosphatidylethanolamines, lysophosphatidylinositols, phosphatidylcholines, phosphatidylethanolamines, phosphatidylglycerols, ceramides, and sphingosine 1-phosphates. Lipid alterations were especially pronounced in dogs with hypothyroidism. Several changes suggested a more atherogenic lipid profile in dogs with HT than in dogs with CS. In this study, we found so far unknown effects of naturally occurring hypothyroidism and CS on lipid metabolism in dogs. Our findings provide starting points to further examine differences in occurrence of atherosclerotic lesion formation between the two diseases. Full article
(This article belongs to the Special Issue Mass Spectrometry-Based Lipidomics Volume 2)
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Article
A Comprehensive Evaluation of Metabolomics Data Preprocessing Methods for Deep Learning
Metabolites 2022, 12(3), 202; https://0-doi-org.brum.beds.ac.uk/10.3390/metabo12030202 - 24 Feb 2022
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Abstract
Machine learning has greatly advanced over the past decade, owing to advances in algorithmic innovations, hardware acceleration, and benchmark datasets to train on domains such as computer vision, natural-language processing, and more recently the life sciences. In particular, the subfield of machine learning [...] Read more.
Machine learning has greatly advanced over the past decade, owing to advances in algorithmic innovations, hardware acceleration, and benchmark datasets to train on domains such as computer vision, natural-language processing, and more recently the life sciences. In particular, the subfield of machine learning known as deep learning has found applications in genomics, proteomics, and metabolomics. However, a thorough assessment of how the data preprocessing methods required for the analysis of life science data affect the performance of deep learning is lacking. This work contributes to filling that gap by assessing the impact of commonly used as well as newly developed methods employed in data preprocessing workflows for metabolomics that span from raw data to processed data. The results from these analyses are summarized into a set of best practices that can be used by researchers as a starting point for downstream classification and reconstruction tasks using deep learning. Full article
(This article belongs to the Special Issue Mass Spectrometry-Based Lipidomics Volume 2)
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Article
Multi-Omic Analysis to Characterize Metabolic Adaptation of the E. coli Lipidome in Response to Environmental Stress
Metabolites 2022, 12(2), 171; https://0-doi-org.brum.beds.ac.uk/10.3390/metabo12020171 - 11 Feb 2022
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Abstract
As an adaptive survival response to exogenous stress, bacteria undergo dynamic remodelling of their lipid metabolism pathways to alter the composition of their cellular membranes. Here, using Escherichia coli as a well characterised model system, we report the development and application of a [...] Read more.
As an adaptive survival response to exogenous stress, bacteria undergo dynamic remodelling of their lipid metabolism pathways to alter the composition of their cellular membranes. Here, using Escherichia coli as a well characterised model system, we report the development and application of a ‘multi-omics’ strategy for comprehensive quantitative analysis of the temporal changes in the lipidome and proteome profiles that occur under exponential growth phase versus stationary growth phase conditions i.e., nutrient depletion stress. Lipidome analysis performed using ‘shotgun’ direct infusion-based ultra-high resolution accurate mass spectrometry revealed a quantitative decrease in total lipid content under stationary growth phase conditions, along with a significant increase in the mol% composition of total cardiolipin, and an increase in ‘odd-numbered’ acyl-chain length containing glycerophospholipids. The inclusion of field asymmetry ion mobility spectrometry was shown to enable the enrichment and improved depth of coverage of low-abundance cardiolipins, while ultraviolet photodissociation-tandem mass spectrometry facilitated more complete lipid structural characterisation compared with conventional collision-induced dissociation, including unambiguous assignment of the odd-numbered acyl-chains as containing cyclopropyl modifications. Proteome analysis using data-dependent acquisition nano-liquid chromatography mass spectrometry and tandem mass spectrometry analysis identified 83% of the predicted E. coli lipid metabolism enzymes, which enabled the temporal dependence associated with the expression of key enzymes responsible for the observed adaptive lipid metabolism to be determined, including those involved in phospholipid metabolism (e.g., ClsB and Cfa), fatty acid synthesis (e.g., FabH) and degradation (e.g., FadA/B,D,E,I,J and M), and proteins involved in the oxidative stress response resulting from the generation of reactive oxygen species during β-oxidation or lipid degradation. Full article
(This article belongs to the Special Issue Mass Spectrometry-Based Lipidomics Volume 2)
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Article
Opti-nQL: An Optimized, Versatile and Sensitive Nano-LC Method for MS-Based Lipidomics Analysis
Metabolites 2021, 11(11), 720; https://0-doi-org.brum.beds.ac.uk/10.3390/metabo11110720 - 21 Oct 2021
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Abstract
Lipidomics is the comprehensive analysis of lipids in a given biological system. This investigation is often limited by the low amount and high complexity of biological samples, therefore highly sensitive lipidomics methods are required. Nanoflow-LC/MS offers extremely high sensitivity; however, it is challenging [...] Read more.
Lipidomics is the comprehensive analysis of lipids in a given biological system. This investigation is often limited by the low amount and high complexity of biological samples, therefore highly sensitive lipidomics methods are required. Nanoflow-LC/MS offers extremely high sensitivity; however, it is challenging as a more demanding maintenance is often needed compared to conventional microflow-LC approaches. Here, we developed a sensitive and reproducible lipidomics LC method, termed Opti-nQL, which can be applied to any biological system. Opti-nQL has been validated with cellular lipid extracts of human and mouse origin and with different lipid extraction methods. Among the resulting 4000 detected features, 700 and even more unique lipid molecular species have been identified covering 16 lipid sub-classes, while 400 lipids were uniquely structure defined by MS/MS. These results were obtained by analyzing an amount of lipids extract equivalent to 40 ng of proteins, being highly suitable for low abundant samples. MS analysis showed that theOpti-nQL method increases the number of identified lipids, which is evidenced by injecting 20 times less material than in microflow based chromatography, being more reproducible and accurate thus enhancing robustness of lipidomics analysis. Full article
(This article belongs to the Special Issue Mass Spectrometry-Based Lipidomics Volume 2)
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Article
Comparison of Kit-Based Metabolomics with Other Methodologies in a Large Cohort, towards Establishing Reference Values
Metabolites 2021, 11(10), 652; https://0-doi-org.brum.beds.ac.uk/10.3390/metabo11100652 - 24 Sep 2021
Cited by 1 | Viewed by 1156
Abstract
Metabolic profiling is an omics approach that can be used to observe phenotypic changes, making it particularly attractive for biomarker discovery. Although several candidate metabolites biomarkers for disease expression have been identified in recent clinical studies, the reference values of healthy subjects have [...] Read more.
Metabolic profiling is an omics approach that can be used to observe phenotypic changes, making it particularly attractive for biomarker discovery. Although several candidate metabolites biomarkers for disease expression have been identified in recent clinical studies, the reference values of healthy subjects have not been established. In particular, the accuracy of concentrations measured by mass spectrometry (MS) is unclear. Therefore, comprehensive metabolic profiling in large-scale cohorts by MS to create a database with reference ranges is essential for evaluating the quality of the discovered biomarkers. In this study, we tested 8700 plasma samples by commercial kit-based metabolomics and separated them into two groups of 6159 and 2541 analyses based on the different ultra-high-performance tandem mass spectrometry (UHPLC-MS/MS) systems. We evaluated the quality of the quantified values of the detected metabolites from the reference materials in the group of 2541 compared with the quantified values from other platforms, such as nuclear magnetic resonance (NMR), supercritical fluid chromatography tandem mass spectrometry (SFC-MS/MS) and UHPLC-Fourier transform mass spectrometry (FTMS). The values of the amino acids were highly correlated with the NMR results, and lipid species such as phosphatidylcholines and ceramides showed good correlation, while the values of triglycerides and cholesterol esters correlated less to the lipidomics analyses performed using SFC-MS/MS and UHPLC-FTMS. The evaluation of the quantified values by MS-based techniques is essential for metabolic profiling in a large-scale cohort. Full article
(This article belongs to the Special Issue Mass Spectrometry-Based Lipidomics Volume 2)
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Review

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Review
A Current Encyclopedia of Bioinformatics Tools, Data Formats and Resources for Mass Spectrometry Lipidomics
Metabolites 2022, 12(7), 584; https://0-doi-org.brum.beds.ac.uk/10.3390/metabo12070584 - 23 Jun 2022
Viewed by 398
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Mass Spectrometry-Based Lipidomics Volume 2)
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