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Metabolites
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Mass Spectrometry-Based Metabolomics: Challenges and Applications
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Mass Spectrometry-Based Metabolomics: Challenges and Applications

A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Metabolomic Profiling Technology".

Deadline for manuscript submissions: closed (31 March 2020) | Viewed by 36760

Special Issue Editors

Dr. María Eugenia Monge

E-Mail Website
Guest Editor
Centro de Investigaciones en Bionanociencias, Godoy Cruz 2390, C1425FQD, Ciudad de Buenos Aires, Argentina
Interests: metabolomics; mass spectrometry; liquid chromatography; lipidomics; biomarker discovery for disease diagnosis; analytical chemistry; atmospheric chemistry; ambient mass spectrometry; chemometrics
Dr. Christina M. Jones

E-Mail Website
Guest Editor
National Institute of Standards and Technology, Gaithersburg, MD, USA
Interests: metabolomics; precision medicine; foodomics; lipidomics; mass spectrometry; analytical chemistry; metrology; liquid chromatography; ambient mass spectrometry; chemometrics

Special Issue Information

Dear Colleagues,

This Special Issue of Metabolites entitled “Mass Spectrometry-Based Metabolomics: Challenges and Applications” encourages authors to submit new scientific applications and challenges associated with mass spectrometry-based metabolomics in the format of research and review articles.

Metabolomics combines the expertise of analytical chemists, biochemists, statisticians, biologists, computational scientists, and medical doctors, among others, in a joint effort to holistically understand biochemical processes in complex biological systems through the analysis and characterization of small molecules (MW < 1500). Mass spectrometry is one of the primary analytical platforms used to explore the metabolome, as it is highly sensitive and versatile for chemical analyses. It is typically used in combination with additional separation techniques, such as gas or liquid chromatography, and/or ion mobility for enhancing peak capacity and, thereby, improving analysis of complex samples. Moreover, advancements in new, soft ambient ion generation techniques for surface sampling, and in situ analysis in real time, with little to no sample preparation, have broadened mass spectrometry-based metabolomics applications, tackling high-throughput analytical challenges.

Multidisciplinary efforts enrich the metabolomics field by addressing metabolite-related scientific questions through hypothesis-testing (targeted) and hypothesis-generating (untargeted) studies. These are designed to interrogate complex samples, such as biological fluids and tissues from human subjects, animal models, and plants, as well as samples from microorganisms, in vitro models, food, and marine environments, based on the original scientific question.

Many challenges have been identified thus far in the field, including metabolite annotation in discovery-based studies, the validation of proposed biomarkers, the development of user-friendly visualization methods for understanding multivariate analysis outputs, the standardization of large data processing workflows, the achievement of comparable results in inter-laboratory comparisons, and the translation of findings from health-related investigations into clinical settings.

Based on this brief overview, we are pleased to receive contributions from scientific groups around the world working to move this exciting, comprehensive, and versatile field forward.

Dr. María Eugenia Monge
Dr. Christina M. Jones
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 2700 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

  • Metabolomics
  • Lipidomics
  • Mass spectrometry
  • Multivariate analysis
  • Chemometrics
  • Biochemical processes

Published Papers (7 papers)

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Research

Jump to: Review, Other

11 pages, 1829 KiB  
Article
Novel Biomarkers to Distinguish between Type 3c and Type 2 Diabetes Mellitus by Untargeted Metabolomics
by Cristina Jimenez-Luna, Ariadna Martin-Blazquez, Carmelo Dieguez-Castillo, Caridad Diaz, Jose Luis Martin-Ruiz, Olga Genilloud, Francisca Vicente, Jose Perez del Palacio, Jose Prados and Octavio Caba
Metabolites 2020, 10(11), 423; https://doi.org/10.3390/metabo10110423 - 22 Oct 2020
Cited by 12 | Viewed by 3030
Abstract
Pancreatogenic diabetes mellitus (T3cDM) is a highly frequent complication of pancreatic disease, especially chronic pancreatitis, and it is often misdiagnosed as type 2 diabetes mellitus (T2DM). A correct diagnosis allows the appropriate treatment of these patients, improving their quality of life, and various [...] Read more.
Pancreatogenic diabetes mellitus (T3cDM) is a highly frequent complication of pancreatic disease, especially chronic pancreatitis, and it is often misdiagnosed as type 2 diabetes mellitus (T2DM). A correct diagnosis allows the appropriate treatment of these patients, improving their quality of life, and various technologies have been employed over recent years to search for specific biomarkers of each disease. The main aim of this metabolomic project was to find differential metabolites between T3cDM and T2DM. Reverse-phase liquid chromatography coupled to high-resolution mass spectrometry was performed in serum samples from patients with T3cDM and T2DM. Multivariate Principal Component and Partial Least Squares-Discriminant analyses were employed to evaluate between-group variations. Univariate and multivariate analyses were used to identify potential candidates and the area under the receiver-operating characteristic (ROC) curve was calculated to evaluate their diagnostic value. A panel of five differential metabolites obtained an area under the ROC curve of 0.946. In this study, we demonstrate the usefulness of untargeted metabolomics for the differential diagnosis between T3cDM and T2DM and propose a panel of five metabolites that appear altered in the comparison between patients with these diseases. Full article
(This article belongs to the Special Issue Mass Spectrometry-Based Metabolomics: Challenges and Applications)
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32 pages, 968 KiB  
Article
Circulating Metabolites as Potential Biomarkers for Neurological Disorders—Metabolites in Neurological Disorders
by Amanda Donatti, Amanda M. Canto, Alexandre B. Godoi, Douglas C. da Rosa and Iscia Lopes-Cendes
Metabolites 2020, 10(10), 389; https://doi.org/10.3390/metabo10100389 - 29 Sep 2020
Cited by 17 | Viewed by 5000
Abstract
There are, still, limitations to predicting the occurrence and prognosis of neurological disorders. Biomarkers are molecules that can change in different conditions, a feature that makes them potential tools to improve the diagnosis of disease, establish a prognosis, and monitor treatments. Metabolites can [...] Read more.
There are, still, limitations to predicting the occurrence and prognosis of neurological disorders. Biomarkers are molecules that can change in different conditions, a feature that makes them potential tools to improve the diagnosis of disease, establish a prognosis, and monitor treatments. Metabolites can be used as biomarkers, and are small molecules derived from the metabolic process found in different biological media, such as tissue samples, cells, or biofluids. They can be identified using various strategies, targeted or untargeted experiments, and by different techniques, such as high-performance liquid chromatography, mass spectrometry, or nuclear magnetic resonance. In this review, we aim to discuss the current knowledge about metabolites as biomarkers for neurological disorders. We will present recent developments that show the need and the feasibility of identifying such biomarkers in different neurological disorders, as well as discuss relevant research findings in the field of metabolomics that are helping to unravel the mechanisms underlying neurological disorders. Although several relevant results have been reported in metabolomic studies in patients with neurological diseases, there is still a long way to go for the clinical use of metabolites as potential biomarkers in these disorders, and more research in the field is needed. Full article
(This article belongs to the Special Issue Mass Spectrometry-Based Metabolomics: Challenges and Applications)
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14 pages, 2382 KiB  
Article
Discovering Temporal Patterns in Longitudinal Nontargeted Metabolomics Data via Group and Nuclear Norm Regularized Multivariate Regression
by Zhaozhou Lin, Qiao Zhang, Shengyun Dai and Xiaoyan Gao
Metabolites 2020, 10(1), 33; https://doi.org/10.3390/metabo10010033 - 13 Jan 2020
Cited by 3 | Viewed by 2366
Abstract
Temporal associations in longitudinal nontargeted metabolomics data are generally ignored by common pattern recognition methods such as partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA). To discover temporal patterns in longitudinal metabolomics, a multitask learning (MTL) method [...] Read more.
Temporal associations in longitudinal nontargeted metabolomics data are generally ignored by common pattern recognition methods such as partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA). To discover temporal patterns in longitudinal metabolomics, a multitask learning (MTL) method employing structural regularization was proposed. The group regularization term of the proposed MTL method enables the selection of a small number of tentative biomarkers while maintaining high prediction accuracy. Meanwhile, the nuclear norm imposed into the regression coefficient accounts for the interrelationship of the metabolomics data obtained on consecutive time points. The effectiveness of the proposed method was demonstrated by comparison study performed on a metabolomics dataset and a simulating dataset. The results showed that a compact set of tentative biomarkers charactering the whole antipyretic process of Qingkailing injection were selected with the proposed method. In addition, the nuclear norm introduced in the new method could help the group norm to improve the method’s recovery ability. Full article
(This article belongs to the Special Issue Mass Spectrometry-Based Metabolomics: Challenges and Applications)
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20 pages, 6377 KiB  
Article
Polar Lipids in Starch-Rich Commodities to be Analyzed with LC-MS-Based Metabolomics—Optimization of Ionization Parameters and High-Throughput Extraction Protocols
by Christin Claassen, Jürgen Kuballa and Sascha Rohn
Metabolites 2019, 9(8), 167; https://doi.org/10.3390/metabo9080167 - 12 Aug 2019
Cited by 5 | Viewed by 3605
Abstract
Metabolomics-based approaches are still receiving growing attention with regard to food authenticity testing. Such studies require enormous sample numbers with negligible experimental or analytical variations to obtain statistically reliable results. In this context, an extraction protocol in line with optimized ionization parameters was [...] Read more.
Metabolomics-based approaches are still receiving growing attention with regard to food authenticity testing. Such studies require enormous sample numbers with negligible experimental or analytical variations to obtain statistically reliable results. In this context, an extraction protocol in line with optimized ionization parameters was developed in consideration of potential starch-derived matrix effects focusing on the polar lipids of potatoes. Therefore, well-known extractions (Bligh and Dyer, Folch, Matyash, and a n-hexane-based procedure) were compared in a non-targeted and a targeted approach regarding the extractability of their lipids such as phosphatidylcholines, phosphatidylethanolamines, galacto- and glucocerebrosides, di- and triglycerides, and acylated steryl glucosides. The selected Folch method was also scrutinized in view of its ability to remove the matrix’s starch and consequently improved by substituting trichlormethane with ethyl acetate as a “greener” Folch approach. Moreover, the challenge of starch-derived contamination and imminent ion suppression in the electrospray ionization source (ESI) was addressed by an optimization of ionization parameters varying desolvation settings, removing injection peaks, and increasing the angles and distances of the ESI-device. Long-term stability tests over five days were performed successfully with a combination of appropriate extraction and decreased desolvation settings during ionization. In conclusion, the present methodology provided the basis for on-going large-scale metabolomic studies with respect to the botanical origin of potatoes using UPLC-IMS-QToF (ultra-high performance liquid chromatography ion mobility spectroscopy quadrupole-time of flight mass spectrometer). Full article
(This article belongs to the Special Issue Mass Spectrometry-Based Metabolomics: Challenges and Applications)
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15 pages, 4038 KiB  
Article
Microbial Transformations of Organically Fermented Foods
by Ruma Raghuvanshi, Allyssa G. Grayson, Isabella Schena, Onyebuchi Amanze, Kezia Suwintono and Robert A. Quinn
Metabolites 2019, 9(8), 165; https://doi.org/10.3390/metabo9080165 - 10 Aug 2019
Cited by 21 | Viewed by 7237
Abstract
Fermenting food is an ancient form of preservation ingrained many in human societies around the world. Westernized diets have moved away from such practices, but even in these cultures, fermented foods are seeing a resurgent interested due to their believed health benefits. Here, [...] Read more.
Fermenting food is an ancient form of preservation ingrained many in human societies around the world. Westernized diets have moved away from such practices, but even in these cultures, fermented foods are seeing a resurgent interested due to their believed health benefits. Here, we analyze the microbiome and metabolome of organically fermented vegetables, using a salt brine, which is a common ‘at-home’ method of food fermentation. We found that the natural microbial fermentation had a strong effect on the food metabolites, where all four foods (beet, carrot, peppers and radishes) changed through time, with a peak in molecular diversity after 2–3 days and a decrease in diversity during the final stages of the 4-day process. The microbiome of all foods showed a stark transition from one that resembled a soil community to one dominated by Enterobacteriaceae, such as Erwinia spp., within a single day of fermentation and increasing amounts of Lactobacillales through the fermentation process. With particular attention to plant natural products, we observed significant transformations of polyphenols, triterpenoids and anthocyanins, but the degree of this metabolism depended on the food type. Beets, radishes and peppers saw an increase in the abundance of these compounds as the fermentation proceeded, but carrots saw a decrease through time. This study showed that organically fermenting vegetables markedly changed their chemistry and microbiology but resulted in high abundance of Enterobacteriaceae which are not normally considered as probiotics. The release of beneficial plant specialized metabolites was observed, but this depended on the fermented vegetable. Full article
(This article belongs to the Special Issue Mass Spectrometry-Based Metabolomics: Challenges and Applications)
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Review

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32 pages, 743 KiB  
Review
Exploring the Lipidome: Current Lipid Extraction Techniques for Mass Spectrometry Analysis
by Julian Aldana, Adriana Romero-Otero and Mónica P. Cala
Metabolites 2020, 10(6), 231; https://doi.org/10.3390/metabo10060231 - 3 Jun 2020
Cited by 49 | Viewed by 9517
Abstract
In recent years, high-throughput lipid profiling has contributed to understand the biological, physiological and pathological roles of lipids in living organisms. Across all kingdoms of life, important cell and systemic processes are mediated by lipids including compartmentalization, signaling and energy homeostasis. Despite important [...] Read more.
In recent years, high-throughput lipid profiling has contributed to understand the biological, physiological and pathological roles of lipids in living organisms. Across all kingdoms of life, important cell and systemic processes are mediated by lipids including compartmentalization, signaling and energy homeostasis. Despite important advances in liquid chromatography and mass spectrometry, sample extraction procedures remain a bottleneck in lipidomic studies, since the wide structural diversity of lipids imposes a constrain in the type and amount of lipids extracted. Differences in extraction yield across lipid classes can induce a bias on down-stream analysis and outcomes. This review aims to summarize current lipid extraction techniques used for untargeted and targeted studies based on mass spectrometry. Considerations, applications, and limitations of these techniques are discussed when used to extract lipids in complex biological matrices, such as tissues, biofluids, foods, and microorganisms. Full article
(This article belongs to the Special Issue Mass Spectrometry-Based Metabolomics: Challenges and Applications)
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Other

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18 pages, 3818 KiB  
Concept Paper
Multi-Omics Analyses Detail Metabolic Reprogramming in Lipids, Carnitines, and Use of Glycolytic Intermediates between Prostate Small Cell Neuroendocrine Carcinoma and Prostate Adenocarcinoma
by Bei Gao, Hui-Wen Lue, Jennifer Podolak, Sili Fan, Ying Zhang, Archana Serawat, Joshi J. Alumkal, Oliver Fiehn and George V. Thomas
Metabolites 2019, 9(5), 82; https://doi.org/10.3390/metabo9050082 - 26 Apr 2019
Cited by 28 | Viewed by 5189
Abstract
As the most common cancer in men, prostate cancer is molecularly heterogeneous. Contributing to this heterogeneity are the poorly understood metabolic adaptations of the two main types of prostate cancer, i.e., adenocarcinoma and small cell neuroendocrine carcinoma (SCNC), the latter being more aggressive [...] Read more.
As the most common cancer in men, prostate cancer is molecularly heterogeneous. Contributing to this heterogeneity are the poorly understood metabolic adaptations of the two main types of prostate cancer, i.e., adenocarcinoma and small cell neuroendocrine carcinoma (SCNC), the latter being more aggressive and lethal. Using transcriptomics, untargeted metabolomics and lipidomics profiling on LASCPC-01 (prostate SCNC) and LNCAP (prostate adenocarcinoma) cell lines, we found significant differences in the cellular phenotypes of the two cell lines. Gene set enrichment analysis on the transcriptomics data showed 62 gene sets were upregulated in LASCPC-01, while 112 gene sets were upregulated in LNCAP. ChemRICH analysis on metabolomics and lipidomics data revealed a total of 25 metabolite clusters were significantly different. LASCPC-01 exhibited a higher glycolytic activity and lower levels of triglycerides, while the LNCAP cell line showed increases in one-carbon metabolism as an exit route of glycolytic intermediates and a decrease in carnitine, a mitochondrial lipid transporter. Our findings pinpoint differences in prostate neuroendocrine carcinoma versus prostate adenocarcinoma that could lead to new therapeutic targets in each type. Full article
(This article belongs to the Special Issue Mass Spectrometry-Based Metabolomics: Challenges and Applications)
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