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Selectivity in NMR and LC-MS Metabolomics: The Importance of Sample Preparation and Separation, and how to Measure Selectivity in LC-MS Metabolomics.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Analytical Pharmaceutical Chemistry.
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 [en]
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: urn:nbn:se:uu:diva-318296ISBN: 978-91-554-9879-5 (print)OAI: oai:DiVA.org:uu-318296DiVA, id: diva2:1085784
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
List of papers
1. The cyanobacterial amino acid beta-N-methylamino-L-alanine perturbs the intermediary metabolism in neonatal rats
Open this publication in new window or tab >>The cyanobacterial amino acid beta-N-methylamino-L-alanine perturbs the intermediary metabolism in neonatal rats
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2017 (English)In: Amino Acids, ISSN 0939-4451, E-ISSN 1438-2199, Vol. 49, no 5, p. 905-919, article id 10.1007/s00726-017-2391-8Article in journal (Refereed) Published
Abstract [en]

The neurotoxic amino acid β-N-methylamino-l-alanine (BMAA) is produced by most cyanobacteria. BMAA is considered as a potential health threat because of its putative role in neurodegenerative diseases. We have previously observed cognitive disturbances and morphological brain changes in adult rodents exposed to BMAA during the development. The aim of this study was to characterize changes of major intermediary metabolites in serum following neonatal exposure to BMAA using a non-targeted metabolomic approach. NMR spectroscopy was used to obtain serum metabolic profiles from neonatal rats exposed to BMAA (40, 150, 460mg/kg) or vehicle on postnatal days 9-10. Multivariate data analysis of binned NMR data indicated metabolic pattern differences between the different treatment groups. In particular five metabolites, d-glucose, lactate, 3-hydroxybutyrate, creatine and acetate, were changed in serum of BMAA-treated neonatal rats. These metabolites are associated with changes in energy metabolism and amino acid metabolism. Further statistical analysis disclosed that all the identified serum metabolites in the lowest dose group were significantly (p<0.05) decreased. The neonatal rat model used in this study is so far the only animal model that displays significant biochemical and behavioral effects after a low short-term dose of BMAA. The demonstrated perturbation of intermediary metabolism may contribute to BMAA-induced developmental changes that result in long-term effects on adult brain function.

Keywords
β-N-methylamino-L-alanine, cyanobacteria, energy metabolism, neurotoxin, metabolomics, NMR
National Category
Analytical Chemistry Pharmaceutical Sciences
Research subject
Analytical Pharmaceutical Chemistry; Pharmaceutical Science
Identifiers
urn:nbn:se:uu:diva-205735 (URN)10.1016/j.tox.2013.07.010 (DOI)000327005300002 ()23886855 (PubMedID)
Funder
Swedish Research Council Formas
Available from: 2013-08-22 Created: 2013-08-22 Last updated: 2018-01-11
2. NMR-based metabolic profiling in healthy individuals overfed different types of fat: links to changes in liver fat accumulation and lean tissue mass.
Open this publication in new window or tab >>NMR-based metabolic profiling in healthy individuals overfed different types of fat: links to changes in liver fat accumulation and lean tissue mass.
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2015 (English)In: Nutrition & Diabetes, ISSN 2044-4052, E-ISSN 2044-4052, Vol. 5, no 19, p. e182-Article in journal (Refereed) Published
Abstract [en]

BACKGROUND: Overeating different dietary fatty acids influence the amount of liver fat stored during weight gain, however, the mechanisms responsible are unclear. We aimed to identify non-lipid metabolites that may differentiate between saturated (SFA) and polyunsaturated fatty acid (PUFA) overfeeding using a non-targeted metabolomic approach. We also investigated the possible relationships between plasma metabolites and body fat accumulation.

METHODS: In a randomized study (LIPOGAIN study), n=39 healthy individuals were overfed with muffins containing SFA or PUFA. Plasma samples were precipitated with cold acetonitrile and analyzed by nuclear magnetic resonance (NMR) spectroscopy. Pattern recognition techniques were used to overview the data, identify variables contributing to group classification and to correlate metabolites with fat accumulation.

RESULTS: We previously reported that SFA causes a greater accumulation of liver fat, visceral fat and total body fat, whereas lean tissue levels increases less compared with PUFA, despite comparable weight gain. In this study, lactate and acetate were identified as important contributors to group classification between SFA and PUFA (P<0.05). Furthermore, the fat depots (total body fat, visceral adipose tissue and liver fat) and lean tissue correlated (P(corr)>0.5) all with two or more metabolites (for example, branched amino acids, alanine, acetate and lactate). The metabolite composition differed in a manner that may indicate higher insulin sensitivity after a diet with PUFA compared with SFA, but this needs to be confirmed in future studies.

CONCLUSION: A non-lipid metabolic profiling approach only identified a few metabolites that differentiated between SFA and PUFA overfeeding. Whether these metabolite changes are involved in depot-specific fat storage and increased lean tissue mass during overeating needs further investigation.

National Category
Medical and Health Sciences Nutrition and Dietetics
Identifiers
urn:nbn:se:uu:diva-267034 (URN)10.1038/nutd.2015.31 (DOI)000368899900002 ()26479316 (PubMedID)
Funder
Swedish Research Council, K2015-54X-22081-04-3Swedish Diabetes Association
Note

Rosqvist, Engskog, Haglöf, Riséus and Pettersson contributed equally to this work.

Available from: 2015-11-17 Created: 2015-11-17 Last updated: 2017-12-01Bibliographically approved
3. The co-feature ratio, a novel method for the measurement of chromatographic and signal selectivity in LC-MS-based metabolomics.
Open this publication in new window or tab >>The co-feature ratio, a novel method for the measurement of chromatographic and signal selectivity in LC-MS-based metabolomics.
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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.

National Category
Analytical Chemistry Pharmaceutical Sciences
Research subject
Analytical Pharmaceutical Chemistry
Identifiers
urn:nbn:se:uu:diva-314239 (URN)10.1016/j.aca.2016.12.022 (DOI)000393252000005 ()28093124 (PubMedID)
Available from: 2017-01-31 Created: 2017-01-31 Last updated: 2018-01-13Bibliographically approved
4. Selectivity evaluation using the co-feature ratio in LC/MS metabolomics: comparison of HILIC stationary phases’ performance for the analysis of plasma, urine and cell extracts.
Open this publication in new window or tab >>Selectivity evaluation using the co-feature ratio in LC/MS metabolomics: comparison of HILIC stationary phases’ performance for the analysis of plasma, urine and cell extracts.
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(English)Manuscript (preprint) (Other academic)
National Category
Medicinal Chemistry
Identifiers
urn:nbn:se:uu:diva-318089 (URN)
Available from: 2017-03-24 Created: 2017-03-24 Last updated: 2018-01-13

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