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

Exploring the complementarity of fast multipulse and multidimensional NMR methods for metabolomics: a chemical ecology case study

This study investigates the potential and complementarity of high-throughput multipulse and multidimensional NMR methods for metabolomics. Through a chemical ecology case study, three methods are investigated, offering a continuum of methods with complementary features in terms of resolution, sensitivity and experiment time. Ultrafast 2D COSY, adiabatic INEPT and SYMAPS HSQC are shown to provide a very good classification ability, comparable to the reference 1D 1H NMR method. Moreover, a detailed analysis of discriminant buckets upon supervised statistical analysis shows that all methods are highly complementary, since they are able to highlight discriminant signals that could not be detected by 1D 1H NMR. In particular, fast 2D methods appear very efficient to discriminate signals located in highly crowded regions of the 1H spectrum. Overall, the combination of these recent methods within a single NMR metabolomics workflow allows to maximize the accessible metabolic information, and also raises exciting challenges in terms of NMR data analysis for chemical ecology.

New analytical methods focusing on polar metabolite analysis in mass spectrometry and NMR-based metabolomics

Following in the footsteps of genomics and proteomics, metabolomics has revolutionised the way we investigate and understand biological systems. Rapid development in the last 25 years has been driven largely by technical innovations in mass spectrometry and nuclear magnetic resonance spectroscopy. However, despite the modest size of metabolomes relative to proteomes and genomes, methodological capabilities for robust, comprehensive metabolite analysis remain a major challenge. Therefore, development of new methods and techniques remains vital for progress in the field. Here, we review developments in LC-MS, GC-MS and NMR methods in the last few years that have enhanced quantitative and comprehensive metabolome coverage, highlighting the techniques involved, their technical capabilities, relative performance, and potential impact.

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.

The exposome paradigm to predict environmental health in terms of systemic homeostasis and resource balance based on NMR data science

The environment, from microbial ecosystems to recycled resources, fluctuates dynamically due to many physical, chemical and biological factors, the profile of which reflects changes in overall state, such as environmental illness caused by a collapse of homeostasis. To evaluate and predict environmental health in terms of systemic homeostasis and resource balance, a comprehensive understanding of these factors requires an approach based on the "exposome paradigm", namely the totality of exposure to all substances. Furthermore, in considering sustainable development to meet global population growth, it is important to gain an understanding of both the circulation of biological resources and waste recycling in human society. From this perspective, natural environment, agriculture, aquaculture, wastewater treatment in industry, biomass degradation and biodegradable materials design are at the forefront of current research. In this respect, nuclear magnetic resonance (NMR) offers tremendous advantages in the analysis of samples of molecular complexity, such as crude bio-extracts, intact cells and tissues, fibres, foods, feeds, fertilizers and environmental samples. Here we outline examples to promote an understanding of recent applications of solution-state, solid-state, time-domain NMR and magnetic resonance imaging (MRI) to the complex evaluation of organisms, materials and the environment. We also describe useful databases and informatics tools, as well as machine learning techniques for NMR analysis, demonstrating that NMR data science can be used to evaluate the exposome in both the natural environment and human society towards a sustainable future.

Rapid nuclear magnetic resonance data acquisition with improved resolution and sensitivity for high-throughput metabolomic analysis

Nuclear magnetic resonance (NMR)-based metabolomics has witnessed rapid advancements in recent years with the continuous development of new methods to enhance the sensitivity, resolution, and speed of data acquisition. Some of the approaches were earlier used for peptide and protein resonance assignments and have now been adapted to metabolomics. At the same time, new NMR methods involving novel data acquisition techniques, suited particularly for high-throughput analysis in metabolomics, have been developed. In this review, we focus on the different sampling strategies or data acquisition methods that have been developed in our laboratory and other groups to acquire NMR spectra rapidly with high sensitivity and resolution for metabolomics. In particular, we focus on the use of multiple receivers, phase modulation NMR spectroscopy, and fast-pulsing methods for identification and assignments of metabolites.