Spatial molecular-dynamically ordered NMR spectroscopy of intact bodies and heterogeneous systems

High-resolution magic-angle spinning NMR metabolic profiling with spatially localized spectroscopy under slow sample spinning

Owing to its feasibility and versatility, High-Resolution Magic-Angle Spinning (HRMAS) NMR spectroscopy is considered an essential analytical technique in metabolomics for assessing the biochemical composition of tissue samples. Localized profiling with HRMAS has recently emerged and shown promise for spatial resolution of metabolic profiles within the sampling tissues. However, the requisite sample spinning in a few kHz can perturb the tissues spatially and morphologically. This study explored a simple approach to slow sample spinning experiments at 500 Hz without needing pulse-assist sideband suppression experiments to acquire localized spectral data. Slow-spinning localized one-and two-dimensional spectroscopy, including Total Correlation Spectroscopy (TOCSY), were explored on soft tissues for metabolic profiling. We also examined inhomogeneous radiofrequency B1 field distribution across the sampling volume, which can affect the quantification analysis.

A Pure Shift-Based Nuclear Magnetic Resonance Method for In-Phase Three-Dimensional Diffusion-Ordered Spectroscopy

Diffusion-ordered nuclear magnetic resonance spectroscopy (DOSY) plays a vital role in mixture studies. However, its applications to complex mixture samples are generally limited by spectral congestion along the chemical shift domain caused by extensive J coupling networks and abundant compounds. Herein, we develop the in-phase multidimensional DOSY strategy for complex mixture analyses by simultaneously revealing molecular self-diffusion behaviors and multiplet structures with optimal spectral resolution. As a proof of concept, two pure shift-based three-dimensional (3D) DOSY protocols are proposed to record high-resolution 3D spectroscopic view with separated mixture components and their resolved multiplet coupling structures, thus suitable for analyzing complex mixtures that contain abundant compounds and complicated molecular structures, even under adverse magnetic field conditions. Therefore, this study shows a promising tool for component analyses and multiplet structure studies on practical mixture samples.

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.