A Mature ROMANCE: A Matter of Quantity and How Two Can Be Better than One

<div><div><div><p>Capillary electrophoresis coupled to mass spectrometry (CE-MS) is increasingly gaining momentum as an analytical tool in metabolomics, thanks to its ability to ob- tain information about the most polar elements in biological samples. This has been helped by improvements in peak robustness by means of mobility-scale representations of the electropherograms (mobilograms). As a necessary step towards the use of CE- MS for untargeted metabolomics data, the authors previously developed and introduced the ROMANCE software, with the purpose of automating mobilogram generation for large untargeted datasets while offering a simple and self-contained user interface. In natural continuation ROMANCE has been upgraded to its v2 to read other types of data (targeted MS data and 2D UV-like electropherograms), offer more flexibility in the transformation parameters (including field ramping delays and the use of sec- ondary markers), more measurement conditions (depending on detection and ionization modes), and most importantly tackle the issue of quantitative CE-MS. To prepare the ground for such an upgrade, we present a review of the current theoretical framework with regards to peak reproducibility and quantification, and we develop new formulas for multiple marker peak area corrections, for anticipating peak position precision, and for assessing peak shape distortion. We then present the new version of the software, and validate it experimentally. We contrast the multiple marker mobility transfor- mations with previous results, finding increased precision, and finally we showcase an application to actual untargeted metabolomics data.</p></div></div></div>

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Capillary Electrophoresis/Inductively-Coupled Plasma-Mass Spectrometry: Development and Optimization of a High Resolution Analytical Tool for the Size-Based Characterization of Nanomaterials in Dietary Supplements

Analytical Chemistry ◽

10.1021/ac5025655 ◽

2014 ◽

Vol 86 (23) ◽

pp. 11620-11627 ◽

Cited By ~ 54

Author(s):

Haiou Qu ◽

Thilak K. Mudalige ◽

Sean W. Linder

Keyword(s):

Mass Spectrometry ◽

Capillary Electrophoresis ◽

High Resolution ◽

Dietary Supplements ◽

Inductively Coupled Plasma ◽

Analytical Tool ◽

Inductively Coupled ◽

Plasma Mass Spectrometry

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Capillary Electrophoresis Mass Spectrometry as a Tool for Untargeted Metabolomics

High-Throughput Metabolomics - Methods in Molecular Biology ◽

10.1007/978-1-4939-9236-2_5 ◽

2019 ◽

pp. 55-77 ◽

Cited By ~ 2

Author(s):

Ángeles López-Gonzálvez ◽

Joanna Godzien ◽

Antonia García ◽

Coral Barbas

Keyword(s):

Mass Spectrometry ◽

Capillary Electrophoresis ◽

Untargeted Metabolomics ◽

Capillary Electrophoresis Mass Spectrometry

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Enhancing confidence of metabolite annotation in Capillary Electrophoresis-Mass Spectrometry untargeted metabolomics with relative migration time and in-source fragmentation

Journal of Chromatography A ◽

10.1016/j.chroma.2020.461758 ◽

2021 ◽

Vol 1635 ◽

pp. 461758

Author(s):

Maricruz Mamani-Huanca ◽

Alberto Gil de la Fuente ◽

Abraham Otero ◽

Ana Gradillas ◽

Joanna Godzien ◽

...

Keyword(s):

Mass Spectrometry ◽

Capillary Electrophoresis ◽

Migration Time ◽

Untargeted Metabolomics ◽

Metabolite Annotation ◽

Relative Migration ◽

Capillary Electrophoresis Mass Spectrometry

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Towards Predicting Gut Microbial Metabolism: Integration of Flux Balance Analysis and Untargeted Metabolomics

Metabolites ◽

10.3390/metabo10040156 ◽

2020 ◽

Vol 10 (4) ◽

pp. 156 ◽

Cited By ~ 1

Author(s):

Ellen Kuang ◽

Matthew Marney ◽

Daniel Cuevas ◽

Robert A. Edwards ◽

Erica M. Forsberg

Keyword(s):

Mass Spectrometry ◽

High Resolution Mass Spectrometry ◽

Metabolic Model ◽

Mass Spectrometry Data ◽

Untargeted Metabolomics ◽

Specific Gene ◽

Exact Mass ◽

Metabolomics Data ◽

Flux Balance ◽

Balance Analysis

Genomics-based metabolic models of microorganisms currently have no easy way of corroborating predicted biomass with the actual metabolites being produced. This study uses untargeted mass spectrometry-based metabolomics data to generate a list of accurate metabolite masses produced from the human commensal bacteria Citrobacter sedlakii grown in the presence of a simple glucose carbon source. A genomics-based flux balance metabolic model of this bacterium was previously generated using the bioinformatics tool PyFBA and phenotypic growth curve data. The high-resolution mass spectrometry data obtained through timed metabolic extractions were integrated with the predicted metabolic model through a program called MS_FBA. This program correlated untargeted metabolomics features from C. sedlakii with 218 of the 699 metabolites in the model using an exact mass match, with 51 metabolites further confirmed using predicted isotope ratios. Over 1400 metabolites were matched with additional metabolites in the ModelSEED database, indicating the need to incorporate more specific gene annotations into the predictive model through metabolomics-guided gap filling.

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MetaboQC: A tool for correcting untargeted metabolomics data with mass spectrometry detection using quality controls

Talanta ◽

10.1016/j.talanta.2017.05.076 ◽

2017 ◽

Vol 174 ◽

pp. 29-37 ◽

Cited By ~ 10

Author(s):

Mónica Calderón-Santiago ◽

María A. López-Bascón ◽

Ángela Peralbo-Molina ◽

Feliciano Priego-Capote

Keyword(s):

Mass Spectrometry ◽

Untargeted Metabolomics ◽

Mass Spectrometry Detection ◽

Metabolomics Data ◽

Quality Controls

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Pre-analytic Considerations for Mass Spectrometry-Based Untargeted Metabolomics Data

High-Throughput Metabolomics - Methods in Molecular Biology ◽

10.1007/978-1-4939-9236-2_20 ◽

2019 ◽

pp. 323-340

Author(s):

Dominik Reinhold ◽

Harrison Pielke-Lombardo ◽

Sean Jacobson ◽

Debashis Ghosh ◽

Katerina Kechris

Keyword(s):

Mass Spectrometry ◽

Untargeted Metabolomics ◽

Metabolomics Data

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Analysis of Mucopolysaccharidosis Type VI through Integrative Functional Metabolomics

International Journal of Molecular Sciences ◽

10.3390/ijms20020446 ◽

2019 ◽

Vol 20 (2) ◽

pp. 446 ◽

Cited By ~ 3

Author(s):

Abdellah Tebani ◽

Lenaig Abily-Donval ◽

Isabelle Schmitz-Afonso ◽

Monique Piraud ◽

Jérôme Ausseil ◽

...

Keyword(s):

Mass Spectrometry ◽

Liquid Chromatography ◽

Pathway Analysis ◽

Multivariate Data ◽

Data Modeling ◽

Integrative Analysis ◽

Untargeted Metabolomics ◽

Metabolomics Data ◽

Metabolic Phenotyping ◽

Mps Vi

Metabolic phenotyping is poised as a powerful and promising tool for biomarker discovery in inherited metabolic diseases. However, few studies applied this approach to mcopolysaccharidoses (MPS). Thus, this innovative functional approach may unveil comprehensive impairments in MPS biology. This study explores mcopolysaccharidosis VI (MPS VI) or Maroteaux–Lamy syndrome (OMIM #253200) which is an autosomal recessive lysosomal storage disease caused by the deficiency of arylsulfatase B enzyme. Urine samples were collected from 16 MPS VI patients and 66 healthy control individuals. Untargeted metabolomics analysis was applied using ultra-high-performance liquid chromatography combined with ion mobility and high-resolution mass spectrometry. Furthermore, dermatan sulfate, amino acids, carnitine, and acylcarnitine profiles were quantified using liquid chromatography coupled to tandem mass spectrometry. Univariate analysis and multivariate data modeling were used for integrative analysis and discriminant metabolites selection. Pathway analysis was done to unveil impaired metabolism. The study revealed significant differential biochemical patterns using multivariate data modeling. Pathway analysis revealed that several major amino acid pathways were dysregulated in MPS VI. Integrative analysis of targeted and untargeted metabolomics data with in silico results yielded arginine-proline, histidine, and glutathione metabolism being the most affected. This study is one of the first metabolic phenotyping studies of MPS VI. The findings might shed light on molecular understanding of MPS pathophysiology to develop further MPS studies to enhance diagnosis and treatments of this rare condition.

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IP4M: an integrated platform for mass spectrometry-based metabolomics data mining

BMC Bioinformatics ◽

10.1186/s12859-020-03786-x ◽

2020 ◽

Vol 21 (1) ◽

Cited By ~ 2

Author(s):

Dandan Liang ◽

Quan Liu ◽

Kejun Zhou ◽

Wei Jia ◽

Guoxiang Xie ◽

...

Keyword(s):

Mass Spectrometry ◽

Data Mining ◽

Correlation Analysis ◽

Data Processing ◽

Good Choice ◽

Untargeted Metabolomics ◽

Data Sets ◽

Metabolic Reaction ◽

Serum Samples ◽

Metabolomics Data

Abstract Background Metabolomics data analyses rely on the use of bioinformatics tools. Many integrated multi-functional tools have been developed for untargeted metabolomics data processing and have been widely used. More alternative platforms are expected for both basic and advanced users. Results Integrated mass spectrometry-based untargeted metabolomics data mining (IP4M) software was designed and developed. The IP4M, has 62 functions categorized into 8 modules, covering all the steps of metabolomics data mining, including raw data preprocessing (alignment, peak de-convolution, peak picking, and isotope filtering), peak annotation, peak table preprocessing, basic statistical description, classification and biomarker detection, correlation analysis, cluster and sub-cluster analysis, regression analysis, ROC analysis, pathway and enrichment analysis, and sample size and power analysis. Additionally, a KEGG-derived metabolic reaction database was embedded and a series of ratio variables (product/substrate) can be generated with enlarged information on enzyme activity. A new method, GRaMM, for correlation analysis between metabolome and microbiome data was also provided. IP4M provides both a number of parameters for customized and refined analysis (for expert users), as well as 4 simplified workflows with few key parameters (for beginners who are unfamiliar with computational metabolomics). The performance of IP4M was evaluated and compared with existing computational platforms using 2 data sets derived from standards mixture and 2 data sets derived from serum samples, from GC–MS and LC–MS respectively. Conclusion IP4M is powerful, modularized, customizable and easy-to-use. It is a good choice for metabolomics data processing and analysis. Free versions for Windows, MAC OS, and Linux systems are provided.

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