Nuclear Magnetic Resonance (NMR), as an advanced technology, has widespread applications in various fields like chemistry, biology, and medicine. However, issues such as long acquisition times for multidimensional spectra and low sensitivity limit the broader application of NMR. Traditional algorithms aim to address these issues but have limitations in speed and accuracy. Deep Learning (DL), a branch of Artificial Intelligence (AI) technology, has shown remarkable success in many fields including NMR. This paper presents an overview of the basics of DL and current applications of DL in NMR, highlights existing challenges, and suggests potential directions for improvement.

Glycans are ubiquitous in nature, decorating our cells and serving as the initial points of contact with any visiting entities. These glycan interactions are fundamental to host-pathogen recognition and are related to various diseases, including inflammation and cancer. Therefore, understanding the conformations and dynamics of glycans, as well as the key features that regulate their interactions with proteins, is crucial for designing new therapeutics. Due to the intrinsic flexibility of glycans, NMR is an essential tool for unravelling these properties. In this review, we describe the key NMR parameters that can be extracted from the different experiments, and which allow us to deduce the necessary geometry and molecular motion information, with a special emphasis on assessing the internal motions of the glycosidic linkages. We specifically address the NMR peculiarities of various natural glycans, from histo-blood group antigens to glycosaminoglycans, and also consider the special characteristics of their synthetic analogues (glycomimetics). Finally, we discuss the application of NMR protocols to study glycan-related molecular recognition events, both from the carbohydrate and receptor perspectives, including the use of stable isotopes and paramagnetic NMR methods to overcome the inherent degeneracy of glycan chemical shifts.

The nonlinear effects associated with intermolecular multiple-quantum coherences (iMQCs) that are present in magnetic resonance imaging (MRI), localized spectroscopy (MRS), and spatially resolved thermometry of biological tissues are reviewed. These nonlinear effects occur especially for samples with a high concentration of resonant nuclei, at ultra-high magnetic fields or under hyperpolarization conditions. The classical Bloch equations and approaches based on quantum mechanical density operator evolution were employed for description of nonlinear effects on the spin system response in the presence of distant (long-range) dipolar field in samples containing high molecular mobility like liquids. The multiple spin echoes that appear in the presence of dipolar demagnetization fields in the presence of homogenous and heterogenous spin interactions and their applications are also discussed. One emphasis of the review is on the excitation, evolution, and detection of intermolecular zero-quantum coherences (iZQCs) and intermolecular double-quantum coherences (iDQCs) in the presence of correlated field gradients that represent the basis for CRAZED pulse sequences (Warren et al. Science 262 (1993) 2005-2009). The physics behind these methods employed for magnetically equivalent and non-equivalent spins, J-coupled spin, in homonuclear and heteronuclear systems is discussed. The principles of magnetic resonance localized spectroscopy and imaging applications for brain investigations to reduce the effect of inhomogeneous magnetic fields and to increase the image resolution is reviewed. The physics related to the used of CRAZED methods to produce fundamentally different contrast than does conventional imaging is also addressed. Collective effects in the presence of strong nuclear magnetization that can affect MRI and MRS results such as spectral clustering and spin turbulence are summarized.

Magnetic resonance imaging (MRI) is an indispensable tool used in both the lab and the clinic. Part of the strength of MRI comes from its ability to deliver anatomical information highlighted with different types of contrasts, including functional and diffusion-oriented acquisitions that are often incompatible with normal, multi-shot scans. For these problems, Nobel-award-winning techniques such as Echo Planar Imaging (EPI) have been essential in opening a manifold of new applications. EPI, however, has challenges when dealing with sharp changes in magnetic susceptibility, including those arising in the presence of air/tissue or air/fat interfaces, from non-ferromagnetic metal implants, as well when the main magnetic field cannot be shimmed to achieve the desired degree of homogeneity, as often is the case in systems built using permanent magnets. Among the techniques being proposed to deal with this kind of problem is spatiotemporally-encoded (SPEN) MRI. The present review focuses on the principles of this technique, with an emphasis on: i) explaining SPEN's resilience to field inhomogeneities, on the basis of expanded bandwidth considerations vis-à-vis EPI; ii) "the good, the bad and the ugly" associated with the undersampling that SPEN usually has to carry out when employing expanded bandwidths; iii) recent developments in data processing algorithms seeking to alleviate the "bad and the ugly" part of these experiments by formulating SPEN image reconstruction as an optimization problem, and then relying on compressed sensing and parallel imaging concepts to achieve improved image quality; and iv) the incorporation of experimental improvements including scan interleaving, simultaneous multi-banding and multi-echo elements, to keep in line with advancements in other areas of fast MRI. The strengths and weaknesses of these data sampling and processing strategies are assessed, and examples of their leverage in functional, but foremost diffusion-weighted, imaging applications, are presented.

Magnetic resonance elastography (MRE) is an emerging clinical imaging modality for characterizing the viscoelastic properties of soft biological tissues. MRE shows great promise in the noninvasive diagnosis of various diseases, especially those associated with soft tissue changes involving the extracellular matrix, cell density, or fluid turnover including altered blood perfusion - all hallmarks of inflammation from early events to cancer development. This review covers the fundamental principles of measuring tissue viscoelasticity by MRE, which are based on the stimulation and encoding of shear waves and their conversion into parameter maps of mechanical properties by inverse problem solutions of the wave equation. Technical challenges posed by real-world biological tissue properties such as viscosity, heterogeneity, anisotropy, and nonlinear elastic behavior of tissues are discussed. Applications of MRE measurement in both humans and animal models are presented, with emphasis on the detection, characterization, and staging of diseases related to the cascade of biomechanical property changes from early to chronic inflammation in the liver and brain. Overall, MRE provides valuable insights into the biophysics of soft tissues for imaging-based detection and staging of inflammation-associated tissue changes.

Nuclear magnetic resonance instruments are becoming available to the do-it-yourself community, and there is increasing interest in the practical aspects of building a magnetic resonance imaging instrument from scratch. This review is focused on the different steps involved in such an endeavour, the challenges encountered and their solutions; it is based on experience gained at a four-day "hackathon" (named "ezyMRI") at Singapore University of Technology and Design in spring 2024. One day of this event was devoted to educational lectures and three days to system construction and testing; seventy young researchers from all parts of the world formed six teams focusing respectively on magnet, gradient coil, RF coil, console, system integration, and design, which together produced a working MRI instrument in three days.

NMR spectroscopy is a versatile technique for studies of molecular structures, dynamic processes, and intermolecular interactions across a broad range of systems, including small molecules, macromolecules, biomolecular assemblies, and materials in both solution and solid-state environments. As the complexity of NMR studies continues to pose challenges for practitioners, the integration of machine learning is recognized as a promising research direction for improving data acquisition, processing, and analysis. Here, we summarize recent findings in this area, highlighting common applications such as signal detection, chemical shift assignment, structure determination, chemical shift prediction, non-uniform sampling reconstruction, and denoising. For each of these applications, we discuss machine learning methods, design choices, and key publicly available data repositories. We conclude by identifying major trends and emerging directions at the intersection of machine learning and NMR spectroscopy that could help advance research in the field.

Benchtop NMR spectrometers, with moderate magnetic field strengths (B=1-2.4T) and sub-ppm chemical shift resolution, are an affordable and portable alternative to standard laboratory NMR (B≥7T). However, in moving to lower magnetic field instruments, sensitivity and chemical shift resolution are significantly reduced. The sensitivity limitation can be overcome by using hyperpolarisation to boost benchtop NMR signals by orders of magnitude. Of the wide range of hyperpolarisation methods currently available, dynamic nuclear polarisation (DNP), parahydrogen-induced polarisation (PHIP) and photochemically-induced dynamic nuclear polarisation (photo-CIDNP) have, to date, shown the most promise for integration with benchtop NMR for analytical applications. In this review we provide a summary of the theory of each of these techniques and discuss examples of how they have been integrated with benchtop NMR detection. Progress towards the use of hyperpolarised benchtop NMR for analytical applications, ranging from reaction monitoring to probing biomolecular interactions, is discussed, along with perspectives for the future.

Food metabolomics has emerged as a powerful tool for characterizing complex food systems, offering a non-targeted highly discriminative approach for detecting authenticity, assessing quality, and ensuring safety across an array of food matrices. By capturing the complete spectral signature of a sample and reducing it to manageable variables, this technique provides an extensive metabolite snapshot that encompasses everything from minor compounds to major constituents. A key advantage lies in the reproducibility and robustness of NMR spectroscopy, allowing the comparison of spectra even across different instruments and laboratories. Such comparability fosters collaborative efforts and facilitates the establishment of large, community-built datasets, which are critical for advancing reliable classification models and enabling wide-scale deployment of non-targeted protocols. Rigor in each step, ranging from selecting representative authentic samples to optimizing acquisition parameters, data processing, and classification algorithms, proves essential for achieving consistent, high-quality metabolomics data. As validation and standardization practices become more widely accepted, NMR-based non-targeted approaches will accelerate innovations in food product monitoring and labeling, reduce analytical uncertainties, and address emerging challenges in food fraud detection. Ultimately, by combining best-in-class protocols, collaborative networks, and open-access data repositories, non-targeted NMR metabolomics has the potential to revolutionize traceability and foster global consumer confidence in the authenticity and quality of the food supply chain.

Cardiovascular magnetic resonance (CMR) imaging is an established non-invasive tool for the assessment of cardiovascular diseases, which are the leading cause of death globally. CMR provides dynamic and static multi-contrast and multi-parametric images, including cine for functional evaluation, contrast-enhanced imaging and parametric mapping for tissue characterization, and MR angiography for the assessment of the aortic, coronary and pulmonary circulation. However, clinical CMR imaging sequences still have some limitations such as the requirement for multiple breath-holds, incomplete spatial coverage, complex planning and acquisition, low scan efficiency and long scan times. To address these challenges, novel techniques have been developed during the last two decades, focusing on automated planning and acquisition timing, improved respiratory and cardiac motion handling strategies, image acceleration algorithms employing undersampled reconstruction, all-in-one imaging techniques that can acquire multiple contrast/parameters in a single scan, deep learning based methods applied along the entire CMR imaging pipeline, as well as imaging at high- and low-field strengths. In this article, we aim to provide a comprehensive review of CMR imaging, covering both established and emerging techniques, to give an overview of the present and future applications of CMR.

Three-dimensional (3D) printing has emerged as a transformative technology for nuclear magnetic resonance (NMR) instrumentation, offering flexibility in the design and fabrication of custom tools that enhance experimental capabilities. Additive manufacturing has made it possible for many NMR labs to build their own magic angle spinning assemblies, sample handling devices, and other critical components. We summarize common 3D printing techniques, such as fused deposition modeling (FDM) and stereolithography (SLA) for polymers, along with metal printing methods like selective laser melting. By facilitating rapid prototyping, 3D printing accelerates the development and optimization of NMR systems, as well as bypassing traditional manufacturing constraints. This review also discusses perspectives on the future of 3D printing in NMR and related methods, providing cost-effective, in-house solutions that increase participation, allow for sharing and remixing of innovations, and broaden applications across chemical, biological, and materials research.

The molecules in nematic liquid crystal phases move rapidly but not randomly producing partial molecular orientation described by sets of order parameters. The molecules of pure liquid crystals are flexible by virtue of bond rotational motion, which has a profound effect on the properties of the liquid crystal phase. NMR spectroscopy can study these phenomena by H, H and C resonances in the nematic and paranematic phases.

This review provides an up-to-date account of the development of two solid-state (SS)NMR methods for enhancing resolution and sensitivity, fast magic angle spinning (MAS) and dynamic nuclear polarization (DNP), and the resulting progress in surface science. We demonstrate the high resolution and efficiency that can be achieved by using two-dimensional homo- and heteronuclear correlation experiments with small rotors capable of MAS at rates exceeding 100 kHz. DNP has offered significant enhancements in signal sensitivity and allowed access to nuclei and experiments that are beyond the limits of conventional SSNMR. The continuing progress in fast MAS and DNP methodologies in recent years generated an unprecedented shift in SSNMR's capabilities in the studies of surface and interface regions of solids, especially mesoporous supports and catalysts. We give numerous examples of recent applications and discuss the prospects for further improvements of both methods.

Amyloid fibrils are insoluble, fibrous nanostructures that accumulate extracellularly in biological tissue during the progression of several human disorders, including Alzheimer's disease (AD) and type 2 diabetes. Fibrils are assembled from protein monomers via the transient formation of soluble, cytotoxic oligomers, and have a common molecular architecture consisting of a spinal core of hydrogen-bonded protein β-strands. For the past 25 years, NMR spectroscopy has been at the forefront of research into the structure and assembly mechanisms of amyloid aggregates. Until the recent boom in fibril structure analysis by cryo-electron microscopy, solid-state NMR was unrivalled in its ability to provide atomic-level models of amyloid fibril architecture. Solution-state NMR has also provided complementary information on the early stages in the amyloid assembly mechanism. Now, both NMR modalities are proving to be valuable in unravelling the complex interactions between amyloid species and a diverse range of physiological metal ions, molecules and surfaces that influence the assembly pathway, kinetics, morphology and clearance in vivo. Here, an overview is presented of the main applications of solid-state and solution-state NMR for studying the interactions between amyloid proteins and biomembranes, glycosaminoglycan polysaccharides, metal ions, polyphenols, synthetic therapeutics and diagnostics. Key NMR methodology is reviewed along with examples of how to overcome the challenges of detecting interactions with aggregating proteins. The review heralds this new role for NMR in providing a comprehensive and pathologically-relevant view of the interactions between protein and non-protein components of amyloid. Coverage of both solid- and solution-state NMR methods and applications herein will be informative and valuable to the broad communities that are interested in amyloid proteins.

Studying multidomain proteins, especially those combining well-folded domains with intrinsically disordered regions (IDRs), requires specific Nuclear Magnetic Resonance (NMR) techniques to address their structural complexity. To illustrate this, we focus here on the nucleocapsid protein from SARS-CoV-2, which includes both structured and disordered regions. We applied a suite of NMR methods, combining ARTINA software for automatic assignment and structure modelling with multi-receiver experiments that simultaneously capture signals from different nuclear spins, increasing both data quality and acquisition efficiency. Studies of signal temperature-dependence, heteronuclear relaxation and secondary structure propensity (SSP) analysis, as well as experiments employing either H or C detection to achieve simultaneous snapshots of globular and disordered regions, were used to analyse both the isolated N-terminal domain (NTD) and a construct (NTR) comprising the NTD and two flanking highly disordered regions (IDR1, IDR2). This comprehensive approach allowed us to characterize the NTD's structure and to evaluate how the IDRs affect the overall conformation and dynamics, as well as the interaction with RNA. The findings underscore the importance of applying such a combination of tailored NMR techniques for effectively studying multidomain proteins with heterogeneous structural and dynamic properties.

We describe the utility of small nutation angle (acute; <90°) H radiofrequency pulses for efficient manipulation of magnetization in selectively [CH]-labeled methyl groups of otherwise deuterated proteins. Focusing primarily on NMR applications that target either fast (pico-to-nanosecond) motions of the methyl group three-fold rotation axis, or slow (micro-to-millisecond) processes associated with chemical exchange, we show that significant simplification of the CH spin-system and, as a consequence, of NMR pulse schemes, may be achieved in certain cases by the proper choice of the flip-angle of the H acute-angle pulse. In other instances, appropriate adjustment of acute-angle H pulses permits optimization of the sensitivity of NMR experiments. The results of acute-angle pulse based NMR experiments are validated by comparison with well-established NMR techniques for the characterization of fast dynamics of methyl-containing side-chains and chemical exchange processes.

In-cell NMR spectroscopy has recently emerged as a unique source of atomically resolved information on the structure, dynamics, and interactions of nucleic acids (NAs) within the intracellular space of living cells. Its recent applications have helped reveal fundamental differences in the behaviour of NAs in cells compared to the in vitro conditions commonly used for their study, as well as in physiologically distinct cellular states. This review covers the fundamental principles and practical aspects of acquiring in-cell NMR data in currently established eukaryotic cellular models, Xenopus laevis oocytes, and human cells. The primary purpose of this review is to present and discuss the technical and conceptual aspects of in-cell NMR sample preparations and their manipulations during in-cell NMR data acquisition, as understanding these aspects is vital for comprehending the physiological significance of in-cell NMR data and the information they provide. Considerations on the planning of in-cell NMR experiments and the presentation of in-cell NMR data on nucleic acids are discussed. We hope this will enable readers to navigate through the ever-growing pool of in-cell NMR literature and gain the knowledge needed to assess and comprehend published data independently. Additionally, we hope it will inspire some readers to actively participate in this rapidly expanding and fascinating field of cellular structural biology.

In recent years, there has been remarkable progress in the field of dissolution dynamic nuclear polarization (D-DNP). This method has shown significant potential for enhancing nuclear polarization by over 10,000 times, resulting in a substantial increase in sensitivity. The unprecedented signal enhancements achieved with D-DNP have opened new possibilities for in vitro analysis. This method enables the monitoring of structural and enzymatic kinetics with excellent time resolution at low concentrations. Furthermore, these advances can be straightforwardly translated to in vivo magnetic resonance imaging and magnetic resonance spectroscopy (MRI and MRS) experiments. D-DNP studies have used a range of C labeled molecules to gain deeper insights into the cellular metabolic pathways and disease hallmarks. Over the last 15 years, D-DNP has been used to analyze glutamine, a key player in the cellular metabolism, involved in many diseases including cancer. Glutamine is the most abundant amino acid in blood plasma and the major carrier of nitrogen, and it is converted to glutamate inside the cell, where the latter is the most abundant amino acid. It has been shown that increased glutamine consumption by cells is a hallmark of tumor cancer metabolism. In this review, we first highlight the significance of glutamine in metabolism, providing an in-depth description of its use at the cellular level as well as its specific roles in various organs. Next, we present a comprehensive overview of the principles of D-DNP. Finally, we review the state of the art in D-DNP glutamine analysis and its application in oncology, neurology, and perfusion marker studies.

Zero and ultralow-field nuclear magnetic resonance (ZULF NMR) is an NMR modality where experiments are performed in fields at which spin-spin interactions within molecules and materials are stronger than Zeeman interactions. This typically occurs at external fields of microtesla strength or below, considerably smaller than Earth's field. In ZULF NMR, the measurement of spin-spin couplings and spin relaxation rates provides a nondestructive means for identifying chemicals and chemical fragments, and for conducting sample or process analyses. The absence of the symmetry imposed by a strong external magnetic field enables experiments that exploit terms in the nuclear spin Hamiltonian that are suppressed in high-field NMR, which in turn opens up new capabilities in a broad range of fields, from the search for dark matter to the preparation of hyperpolarized contrast agents for clinical imaging. Furthermore, as in ZULF NMR the Larmor frequencies are typically in the audio band, the nuclear spins can be manipulated with d.c. magnetic field pulses, and highly sensitive magnetometers are used for detection. In contrast to high-field NMR, the low-frequency signals readily pass through conductive materials such as metals, and heterogeneous samples do not lead to resonance line broadening, meaning that high-resolution spectroscopy is possible. Notable practical advantages of ZULF NMR spectroscopy are the low cost and relative simplicity and portability of the spectrometer system. In recent years ZULF NMR has become more accessible, thanks to improvements in magnetometer sensitivity and commercial availability, and the development of hyperpolarization methods that provide a simple means to boost signal strengths by several orders of magnitude. These topics are reviewed and a perspective on potential future avenues of ZULF-NMR research is presented.

Dynamic nuclear polarization (DNP) is a method for achieving high levels of nuclear spin polarization by transferring spin polarization from electrons to nuclei by microwave irradiation, resulting in higher sensitivity in NMR/MRI. In particular, DNP using photoexcited triplet electron spins (triplet-DNP) can provide a hyperpolarized nuclear spin state at room temperature and in low magnetic field. In this review article, we highlight recent developments in materials and instrumentation for the application of triplet-DNP. First, a brief history and principles of triplet-DNP will be presented. Next, important advances in recent years will be outlined: new materials to hyperpolarize water and biomolecules; high-sensitivity solution NMR by dissolution triplet-DNP; and strategies for further improvement of the polarization. In view of these developments, future directions to widen the range of applications of triplet-DNP will be discussed.

The fast-growing field of metabolomics focuses on the analyses of complicated mixtures of small molecules present in biological samples. To date, metabolomics has provided a wealth of information on biological systems and impacted numerous areas of basic and life sciences. A major focus of metabolomics has been on biomedicine with the goal of biomarker discovery, drug discovery and improved mechanistic understanding of the pathogenesis of many human diseases. Analytical methods play a pivotal role in metabolomics, with the two most widely used platforms being nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS). Among their many complementary capabilities, NMR is generally more reproducible and quantitative, whereas MS is more sensitive. Recent technological advances in NMR have resulted in multifaceted developments, including improvements in sensitivity, resolution and speed, along with expanded metabolite identification and quantitation, which together provide exciting potential for future studies. In addition to NMR developments, the combination of NMR with MS provides numerous benefits that are becoming more evident over time. Hence, the metabolomics field has witnessed an increased number of studies and applications that combine NMR with MS in numerous areas, including new methods development for unknown identification, metabolite quantitation, disease biomarker discovery, mechanistic understanding of disease pathogenesis, and dietary risk factors of diseases among others. This report describes the current status of state-of-the-art methods in NMR-based metabolomics, along with recent advances and future prospects, with an emphasis on the benefits of combining NMR with MS.