Current Experimental, Bioinformatic and Statistical Methods used in NMR Based Metabolomics

Zacharias, Helena U.; Hochrein, Jochen; Klein, Matthias S.; Samol, Claudia; Oefner, Peter J.; Gronwald, Wolfram

doi:10.2174/2213235x113019990001

Cited by 18 publications

(26 citation statements)

References 81 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Specific methods employed in the study of metabolomics include nuclear magnetic resonance (NMR) [ 15 ], gas chromatography mass spectrometry (GC-MS) [ 16 ], liquid chromatography mass spectrometry (LC-MS) [ 16 ], capillary-electrophoresis mass spectrometry (CE-MS) [ 17 ], and high performance liquid chromatography (HPLC) [ 18 ]. NMR measures differences in the magnetic properties of atomic nuclei, mass spectrometry measures differences in the mass and electrical charge of the metabolites, and chromatography distinguishes metabolites by differences in adhesion properties.…”

Section: Introductionmentioning

confidence: 99%

Metabolomics and Type 2 Diabetes: Translating Basic Research into Clinical Application

Klein

Shearer

2016

Journal of Diabetes Research

122

View full text Add to dashboard Cite

Type 2 diabetes (T2D) and its comorbidities have reached epidemic proportions, with more than half a billion cases expected by 2030. Metabolomics is a fairly new approach for studying metabolic changes connected to disease development and progression and for finding predictive biomarkers to enable early interventions, which are most effective against T2D and its comorbidities. In metabolomics, the abundance of a comprehensive set of small biomolecules (metabolites) is measured, thus giving insight into disease-related metabolic alterations. This review shall give an overview of basic metabolomics methods and will highlight current metabolomics research successes in the prediction and diagnosis of T2D. We summarized key metabolites changing in response to T2D. Despite large variations in predictive biomarkers, many studies have replicated elevated plasma levels of branched-chain amino acids and their derivatives, aromatic amino acids and α-hydroxybutyrate ahead of T2D manifestation. In contrast, glycine levels and lysophosphatidylcholine C18:2 are depressed in both predictive studies and with overt disease. The use of metabolomics for predicting T2D comorbidities is gaining momentum, as are our approaches for translating basic metabolomics research into clinical applications. As a result, metabolomics has the potential to enable informed decision-making in the realm of personalized medicine.

show abstract

Section: Introductionmentioning

confidence: 99%

Metabolomics and Type 2 Diabetes: Translating Basic Research into Clinical Application

Klein

Shearer

2016

Journal of Diabetes Research

122

View full text Add to dashboard Cite

show abstract

“…Ultrafiltration was undertaken with Amicon centrifugal filter units (cut-off 10 kDa) (Merck KGaA, Darmstadt, Germany) as described by ( Sundekilde et al., 2013a ). Filters were prewashed as described by ( Zacharias et al., 2013 ). A double wash was necessary to remove contaminants and protective agents released by the filter.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of sample preparation methods for NMR-based metabolomics of cow milk

Yanibada

Boudra

Debrauwer

et al. 2018

Heliyon

View full text Add to dashboard Cite

The quality of milk metabolome analyzed by nuclear magnetic resonance (NMR) is greatly influenced by the way samples are prepared. Although this analytical method is increasingly used to study milk metabolites, a thorough examination of available sample preparation protocols for milk has not been reported yet. We evaluated the performance of eight milk preparation methods namely (1) raw milk without any processing; (2) skimmed milk; (3) ultrafiltered milk; (4) skimming followed by ultrafiltration; (5) ultracentrifuged milk; (6) methanol; (7) dichloromethane; and (8) methanol/dichloromethane, in terms of spectra quality, repeatability, signal-to-noise ratio, extraction efficiency and yield criteria. A pooled sample of milk was used for all protocols. Skimming, ultracentrifugation and unprocessed milk protocols showed poor NMR spectra quality. Protocols involving multiple steps, namely methanol/dichloromethane extraction, and skimming followed by ultrafiltration produced inadequate results for signal-to-noise ratio parameter. Methanol and skimming associated to ultrafiltration provided good repeatability results compared to the other protocols. Chemical-based sample preparation protocols, particularly methanol, showed more efficient metabolite extraction compared to physical preparation methods. When considering all evaluation parameters, the methanol extraction protocol proved to be the best method. As a proof of utility, methanol protocol was then applied to milk samples from dairy cows fed a diet with or without a feed additive, showing a clear separation between the two groups of cows.

show abstract

“…Self-organizing maps are a widely used method for two-dimensional data visualization [ 37 ]. For more details about unsupervised machine learning in the context of NMR-based metabolomics, we refer the interested reader for example to Zacharias et al (2013) [ 38 ].…”

Section: Statistical Data Analysis Strategiesmentioning

confidence: 99%

Statistical Analysis of NMR Metabolic Fingerprints: Established Methods and Recent Advances

2018

Self Cite

View full text Add to dashboard Cite

In this review, we summarize established and recent bioinformatic and statistical methods for the analysis of NMR-based metabolomics. Data analysis of NMR metabolic fingerprints exhibits several challenges, including unwanted biases, high dimensionality, and typically low sample numbers. Common analysis tasks comprise the identification of differential metabolites and the classification of specimens. However, analysis results strongly depend on the preprocessing of the data, and there is no consensus yet on how to remove unwanted biases and experimental variance prior to statistical analysis. Here, we first review established and new preprocessing protocols and illustrate their pros and cons, including different data normalizations and transformations. Second, we give a brief overview of state-of-the-art statistical analysis in NMR-based metabolomics. Finally, we discuss a recent development in statistical data analysis, where data normalization becomes obsolete. This method, called zero-sum regression, builds metabolite signatures whose estimation as well as predictions are independent of prior normalization.

show abstract

Current Experimental, Bioinformatic and Statistical Methods used in NMR Based Metabolomics

Cited by 18 publications

References 81 publications

Metabolomics and Type 2 Diabetes: Translating Basic Research into Clinical Application

Metabolomics and Type 2 Diabetes: Translating Basic Research into Clinical Application

Evaluation of sample preparation methods for NMR-based metabolomics of cow milk

Statistical Analysis of NMR Metabolic Fingerprints: Established Methods and Recent Advances

Contact Info

Product

Resources

About