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The co-feature ratio, a novel method for the measurement of chromatographic and signal selectivity in LC-MS-based metabolomics.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Analytical Pharmaceutical Chemistry.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Analytical Pharmaceutical Chemistry.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Analytical Pharmaceutical Chemistry.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Medical Radiation Science.
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2017 (English)In: Analytica Chimica Acta, ISSN 0003-2670, E-ISSN 1873-4324, Vol. 956, p. 40-47Article in journal (Refereed) Published
Abstract [en]

Evaluation of analytical procedures, especially in regards to measuring chromatographic and signal selectivity, is highly challenging in untargeted metabolomics. The aim of this study was to suggest a new straightforward approach for a systematic examination of chromatographic and signal selectivity in LC-MS-based metabolomics. By calculating the ratio between each feature and its co-eluting features (the co-features), a measurement of the chromatographic selectivity (i.e. extent of co-elution) as well as the signal selectivity (e.g. amount of adduct formation) of each feature could be acquired, the co-feature ratio. This approach was used to examine possible differences in chromatographic and signal selectivity present in samples exposed to three different sample preparation procedures. The capability of the co-feature ratio was evaluated both in a classical targeted setting using isotope labelled standards as well as without standards in an untargeted setting. For the targeted analysis, several metabolites showed a skewed quantitative signal due to poor chromatographic selectivity and/or poor signal selectivity. Moreover, evaluation of the untargeted approach through multivariate analysis of the co-feature ratios demonstrated the possibility to screen for metabolites displaying poor chromatographic and/or signal selectivity characteristics. We conclude that the co-feature ratio can be a useful tool in the development and evaluation of analytical procedures in LC-MS-based metabolomics investigations. Increased selectivity through proper choice of analytical procedures may decrease the false positive and false negative discovery rate and thereby increase the validity of any metabolomic investigation.

Place, publisher, year, edition, pages
2017. Vol. 956, p. 40-47
National Category
Analytical Chemistry Pharmaceutical Sciences
Research subject
Analytical Pharmaceutical Chemistry
Identifiers
URN: urn:nbn:se:uu:diva-314239DOI: 10.1016/j.aca.2016.12.022ISI: 000393252000005PubMedID: 28093124OAI: oai:DiVA.org:uu-314239DiVA, id: diva2:1070196
Available from: 2017-01-31 Created: 2017-01-31 Last updated: 2018-01-13Bibliographically approved
In thesis
1. Selectivity in NMR and LC-MS Metabolomics: The Importance of Sample Preparation and Separation, and how to Measure Selectivity in LC-MS Metabolomics.
Open this publication in new window or tab >>Selectivity in NMR and LC-MS Metabolomics: The Importance of Sample Preparation and Separation, and how to Measure Selectivity in LC-MS Metabolomics.
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Until now, most metabolomics protocols have been optimized towards high sample throughput and high metabolite coverage, parameters considered to be highly important for identifying influenced biological pathways and to generate as many potential biomarkers as possible. From an analytical point of view this can be troubling, as neither sample throughput nor the number of signals relates to actual quality of the detected signals/metabolites. However, a method’s selectivity for a specific signal/metabolite is often closely associated to the quality of that signal, yet this is a parameter often neglected in metabolomics.

This thesis demonstrates the importance of considering selectivity when developing NMR and LC-MS metabolomics methods, and introduces a novel approach for measuring chromatographic and signal selectivity in LC-MS metabolomics.

Selectivity for various sample preparations and HILIC stationary phases was compared. The choice of sample preparation affected the selectivity in both NMR and LC-MS. For the stationary phases, selectivity differences related primarily to retention differences of unwanted matrix components, e.g. inorganic salts or glycerophospholipids. Metabolites co-eluting with these matrix components often showed an incorrect quantitative signal, due to an influenced ionization efficiency and/or adduct formation.

A novel approach for measuring selectivity in LC-MS metabolomics has been introduced. By dividing the intensity of each feature (a unique mass at a specific retention time) with the total intensity of the co-eluting features, a ratio representing the combined chromatographic (amount of co-elution) and signal (e.g. in-source fragmentation) selectivity is acquired. The calculated co-feature ratios have successfully been used to compare the selectivity of sample preparations and HILIC stationary phases.

In conclusion, standard approaches in metabolomics research might be unwise, as each metabolomics investigation is often unique.  The methods used should be adapted for the research question at hand, primarily based on any key metabolites, as well as the type of sample to be analyzed. Increased selectivity, through proper choice of analytical methods, may reduce the risks of matrix-associated effects and thereby reduce the false positive and false negative discovery rate of any metabolomics investigation.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2017. p. 40
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 232
Keywords
Metabolomics, NMR, LC-MS, HILIC, UHPLC, Q-ToF, selectivity, co-feature ratio, method evaluation, data evaluation
National Category
Pharmaceutical Sciences Analytical Chemistry
Research subject
Analytical Pharmaceutical Chemistry
Identifiers
urn:nbn:se:uu:diva-318296 (URN)978-91-554-9879-5 (ISBN)
Public defence
2017-05-19, B41, BMC, Husargatan 3, Uppsala, 10:15 (Swedish)
Opponent
Supervisors
Available from: 2017-04-26 Created: 2017-03-30 Last updated: 2018-01-13

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Elmsjö, AlbertHaglöf, JakobEngskog, Mikael K. R.Nestor, MarikaArvidsson, TorbjörnPettersson, Curt

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