Two data pre-processing workflows to facilitate the discovery of biomarkers by 2D NMR metabolomics

Comparison of extraction methods in vitro Plasmodium falciparum: A 1H NMR and LC-MS joined approach

Malaria is a parasitic disease that remains a global concern and the subject of many studies. Metabolomics has emerged as an approach to better comprehend complex pathogens and discover possible drug targets, thus giving new insights that can aid in the development of antimalarial therapies. However, there is no standardized method to extract metabolites from in vitro Plasmodium falciparum intraerythrocytic parasites, the stage that causes malaria. Additionally, most methods are developed with either LC-MS or NMR analysis in mind, and have rarely been evaluated with both tools. In this work, three extraction methods frequently found in the literature were reproduced and samples were analyzed through both LC-MS and 1H NMR, and evaluated in order to reveal which is the most repeatable and consistent through an array of different tools, including chemometrics, peak detection and annotation. The most reliable method in this study proved to be a double extraction with methanol and methanol/water (80:20, v/v). Metabolomic studies in the field should move towards standardization of methodologies and the use of both LC-MS and 1H NMR in order to make data more comparable between studies and facilitate the achievement of biologically interpretable information.

Nuclear Magnetic Resonance Spectroscopy in Clinical Metabolomics and Personalized Medicine: Current Challenges and Perspectives

Personalized medicine is probably the most promising area being developed in modern medicine. This approach attempts to optimize the therapies and the patient care based on the individual patient characteristics. Its success highly depends on the way the characterization of the disease and its evolution, the patient’s classification, its follow-up and the treatment could be optimized. Thus, personalized medicine must combine innovative tools to measure, integrate and model data. Towards this goal, clinical metabolomics appears as ideally suited to obtain relevant information. Indeed, the metabolomics signature brings crucial insight to stratify patients according to their responses to a pathology and/or a treatment, to provide prognostic and diagnostic biomarkers, and to improve therapeutic outcomes. However, the translation of metabolomics from laboratory studies to clinical practice remains a subsequent challenge. Nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) are the two key platforms for the measurement of the metabolome. NMR has several advantages and features that are essential in clinical metabolomics. Indeed, NMR spectroscopy is inherently very robust, reproducible, unbiased, quantitative, informative at the structural molecular level, requires little sample preparation and reduced data processing. NMR is also well adapted to the measurement of large cohorts, to multi-sites and to longitudinal studies. This review focus on the potential of NMR in the context of clinical metabolomics and personalized medicine. Starting with the current status of NMR-based metabolomics at the clinical level and highlighting its strengths, weaknesses and challenges, this article also explores how, far from the initial “opposition” or “competition”, NMR and MS have been integrated and have demonstrated a great complementarity, in terms of sample classification and biomarker identification. Finally, a perspective discussion provides insight into the current methodological developments that could significantly raise NMR as a more resolutive, sensitive and accessible tool for clinical applications and point-of-care diagnosis. Thanks to these advances, NMR has a strong potential to join the other analytical tools currently used in clinical settings.

Combining rapid 2D NMR experiments with novel pre-processing workflows and MIC quality measures for metabolomics

INTRODUCTION

The use of 2D NMR data sources (COSY in this paper) allows to reach general metabolomics results which are at least as good as the results obtained with 1D NMR data, and this with a less advanced and less complex level of pre-processing. But a major issue still exists and can largely slow down a generalized use of 2D data sources in metabolomics: the experiment duration.

OBJECTIVE

The goal of this paper is to overcome the experiment duration issue in our recently published MIC strategy by considering faster 2D COSY acquisition techniques: a conventional COSY with a reduced number of transients and the use of the Non-Uniform Sampling (NUS) method. These faster alternatives are all submitted to novel 2D pre-processing workflows and to Metabolomic Informative Content analyses. Eventually, results are compared to those obtained with conventional COSY spectra.

METHODS

To pre-process the 2D data sources, the Global Peak List (GPL) workflow and the Vectorization workflow are used. To compare this data sources and to detect the more informative one(s), MIC (Metabolomic Informative Content) indexes are used, based on clustering and inertia measures of quality.

RESULTS

Results are discussed according to a multi-factor experimental design (which is unsupervised and based on human urine samples). Descriptive PCA results and MIC indexes are shown, leading to the direct and objective comparison of the different data sets.

CONCLUSION

In conclusion, it is demonstrated that conventional COSY spectra recorded with only one transient per increment and COSY spectra recorded with 50% of non-uniform sampling provide very similar MIC results as the initial COSY recorded with four transients, but in a much shorter time. Consequently, using techniques like the reduction of the number of transients or NUS can really open the door to a potential high-throughput use of 2D COSY spectra in metabolomics.

Introduction of a new method for two-dimensional NMR quantitative analysis in metabolomics studies

NMR is one of the most important platforms for metabolomic studies. Though 2D NMR has been applied in metabolomics, most applications have mainly focused on metabolite identification whilst limitations causing a bottle-neck for applying high-throughput 2D NMR data for quantity related statistical analysis lies on the data interpretation methods. In this study, instead of using the traditional methods of calculating the 2D NMR data to search for the important features, a new procedure, which applies the high-resolution 1D NMR metabolites chemical shift range to filter the 2D NMR data, was developed. This new method was demonstrated using both a mixture of standard metabolites and a case study on plant extracts using 2D non-uniform sampling (NUS) total correlation spectroscopy (TOCSY) data. As a result, our method successfully filtered out the important features with a high success rate, and the extracted peaks showed high linearity between the calculated intensities and the concentrations of metabolites from a range of 0.05 mM-2 mM. The method was successfully applied to a metabolomics case study which included 18 Begonia samples that showed excellent peak extractions. In summary, our study has provided a practical new 2D NMR data extraction method for use in future metabolomics studies.