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Metabolites
Special Issues
High-Throughput Metabolomics
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High-Throughput Metabolomics

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

Deadline for manuscript submissions: closed (31 January 2020) | Viewed by 6095

Special Issue Editor

Prof. Dr. Seongho Kim

E-Mail Website
Guest Editor
Karmanos Cancer Institute, School of Medicine, Wayne State University, Detroit, MI, USA
Interests: clinical trial design; survival analysis; PK/PD; metabolomics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

High-throughput metabolomics is widely employed for the identification and quantification of biochemical metabolites. Multiple high-throughput analytical platforms—including liquid chromatography–mass spectrometry (LC-MS), gas chromatography–mass spectrometry (GC-MS), nuclear magnetic resonance spectroscopy (NMR), and two-dimensional MS (2D-MS)—have been used for the comprehensive characterization of metabolites in biological systems, including discovery applications, single cell methods, and imaging MS. This Special Issue is focused on the current use of high-throughput metabolomics in biological and clinical research. Specific areas include, but are not limited to, the identification of metabolomics markers, the application of MS imaging, single cell metabolomics, 2D-MS based metabolomics, data integration, and computational and statistical methods of high-throughput metabolomics.

Dr. Seongho Kim
Guest Editor

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
  • High-throughput analysis
  • Mass spectrometry
  • High-dimensional data analysis
  • Bioinformatics
  • Chemometrics
  • Computational metabolomics
  • Analytical chemistry
  • Data science

Published Papers (2 papers)

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Research

17 pages, 1691 KiB  
Article
Integrating Two-Dimensional Gas and Liquid Chromatography-Mass Spectrometry for Untargeted Colorectal Cancer Metabolomics: A Proof-of-Principle Study
by Fang Yuan, Seongho Kim, Xinmin Yin, Xiang Zhang and Ikuko Kato
Metabolites 2020, 10(9), 343; https://doi.org/10.3390/metabo10090343 - 25 Aug 2020
Cited by 9 | Viewed by 2758
Abstract
Untargeted metabolomics is expected to lead to a better mechanistic understanding of diseases and thus applications of precision medicine and personalized intervention. To further increase metabolite coverage and achieve high accuracy of metabolite quantification, the present proof-of-principle study was to explore the applicability [...] Read more.
Untargeted metabolomics is expected to lead to a better mechanistic understanding of diseases and thus applications of precision medicine and personalized intervention. To further increase metabolite coverage and achieve high accuracy of metabolite quantification, the present proof-of-principle study was to explore the applicability of integration of two-dimensional gas and liquid chromatography-mass spectrometry (GC × GC-MS and 2DLC-MS) platforms to characterizing circulating polar metabolome extracted from plasma collected from 29 individuals with colorectal cancer in comparison with 29 who remained cancer-free. After adjustment of multiple comparisons, 20 metabolites were found to be up-regulated and 8 metabolites were found to be down-regulated, which pointed to the dysregulation in energy metabolism and protein synthesis. While integrating the GC × GC-MS and 2DLC-MS data can dramatically increase the metabolite coverage, this study had a limitation in analyzing the non-polar metabolites. Given the small sample size, these results need to be validated with a larger sample size and with samples collected prior to diagnostic and treatment. Nevertheless, this proof-of-principle study demonstrates the potential applicability of integration of these advanced analytical platforms to improve discrimination between colorectal cancer cases and controls based on metabolite profiles in future studies. Full article
(This article belongs to the Special Issue High-Throughput Metabolomics)
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11 pages, 435 KiB  
Article
MetPC: Metabolite Pipeline Consisting of Metabolite Identification and Biomarker Discovery Under the Control of Two-Dimensional FDR
by Jaehwi Kim and Jaesik Jeong
Metabolites 2019, 9(5), 103; https://doi.org/10.3390/metabo9050103 - 25 May 2019
Cited by 1 | Viewed by 2799
Abstract
Due to the complex features of metabolomics data, the development of a unified platform, which covers preprocessing steps to data analysis, has been in high demand over the last few decades. Thus, we developed a new bioinformatics tool that includes a few of [...] Read more.
Due to the complex features of metabolomics data, the development of a unified platform, which covers preprocessing steps to data analysis, has been in high demand over the last few decades. Thus, we developed a new bioinformatics tool that includes a few of preprocessing steps and biomarker discovery procedure. For metabolite identification, we considered a hierarchical statistical model coupled with an Expectation–Maximization (EM) algorithm to take care of latent variables. For biomarker metabolite discovery, our procedure controls two-dimensional false discovery rate (fdr2d) when testing for multiple hypotheses simultaneously. Full article
(This article belongs to the Special Issue High-Throughput Metabolomics)
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