3D TOCSY-HSQC NMR for Metabolic Flux Analysis Using Non-Uniform Sampling

A simple 1H (12C/13C) filtered experiment to quantify and trace isotope enrichment in complex environmental and biological samples

Nuclear magnetic resonance (NMR) based 13C tracing has broad applications across medical and environmental research. As many biological and environmental samples are heterogeneous, they experience considerable spectral overlap and relatively low signal. Here a 1D 1H-12C/13C is introduced that uses "in-phase/opposite-phase" encoding to simultaneously detect and discriminate both protons attached to 12C and 13C at full 1H sensitivity in every scan. Unlike traditional approaches that focus on the 12C/13C satellite ratios in a 1H spectrum, this approach creates separate sub-spectra for the 12C and 13C bound protons. These spectra can be used for both quantitative and qualitative analysis of complex samples with significant spectral overlap. Due to the presence of the 13C dipole, faster relaxation of the 1H-13C pairs results in slight underestimation compared to the 1H-12C pairs. However, this is easily compensated for, by collecting an additional reference spectrum, from which the absolute percentage of 13C can be calculated by difference. When combined with the result, 12C and 13C percent enrichment in both 1H-12C and 1H-13C fractions are obtained. As the approach uses isotope filtered 1H NMR for detection, it retains nearly the same sensitivity as a standard 1H spectrum. Here, a proof-of-concept is performed using simple mixtures of 12C and 13C glucose, followed by suspended algal cells with varying 12C /13C ratios representing a complex mixture. The results consistently return 12C/13C ratios that deviate less than 1 % on average from the expected. Finally, the sequence was used to monitor and quantify 13C% enrichment in Daphnia magna neonates which were fed a 13C diet over 1 week. The approach helped reveal how the organisms utilized the 12C lipids they are born with vs. the 13C lipids they assimilate from their diet during growth. Given the experiments simplicity, versatility, and sensitivity, we anticipate it should find broad application in a wide range of tracer studies, such as fluxomics, with applications spanning various disciplines.

NMR methods for the analysis of mixtures

NMR spectroscopy is a powerful approach for the analysis of mixtures. Its usefulness arises in large part from the vast landscape of methods, and corresponding pulse sequences, that have been and are being designed to tackle the specific properties of mixtures of small molecules. This feature article describes a selection of methods that aim to address the complexity, the low concentrations, and the changing nature that mixtures can display. These notably include pure-shift and diffusion NMR methods, hyperpolarisation methods, and fast 2D NMR methods such as ultrafast 2D NMR and non-uniform sampling. Examples or applications are also described, in fields such as reaction monitoring and metabolomics, to illustrate the relevance and limitations of different methods.

Metabolomics and modelling approaches for systems metabolic engineering

Metabolic engineering involves the manipulation of microbes to produce desirable compounds through genetic engineering or synthetic biology approaches. Metabolomics involves the quantitation of intracellular and extracellular metabolites, where mass spectrometry and nuclear magnetic resonance based analytical instrumentation are often used. Here, the experimental designs, sample preparations, metabolite quenching and extraction are essential to the quantitative metabolomics workflow. The resultant metabolomics data can then be used with computational modelling approaches, such as kinetic and constraint-based modelling, to better understand underlying mechanisms and bottlenecks in the synthesis of desired compounds, thereby accelerating research through systems metabolic engineering. Constraint-based models, such as genome scale models, have been used successfully to enhance the yield of desired compounds from engineered microbes, however, unlike kinetic or dynamic models, constraint-based models do not incorporate regulatory effects. Nevertheless, the lack of time-series metabolomic data generation has hindered the usefulness of dynamic models till today. In this review, we show that improvements in automation, dynamic real-time analysis and high throughput workflows can drive the generation of more quality data for dynamic models through time-series metabolomics data generation. Spatial metabolomics also has the potential to be used as a complementary approach to conventional metabolomics, as it provides information on the localization of metabolites. However, more effort must be undertaken to identify metabolites from spatial metabolomics data derived through imaging mass spectrometry, where machine learning approaches could prove useful. On the other hand, single-cell metabolomics has also seen rapid growth, where understanding cell-cell heterogeneity can provide more insights into efficient metabolic engineering of microbes. Moving forward, with potential improvements in automation, dynamic real-time analysis, high throughput workflows, and spatial metabolomics, more data can be produced and studied using machine learning algorithms, in conjunction with dynamic models, to generate qualitative and quantitative predictions to advance metabolic engineering efforts.

13C metabolic flux analysis: Classification and characterization from the perspective of mathematical modeling and application in physiological research of neural cell

¹³C metabolic flux analysis (¹³C-MFA) has emerged as a forceful tool for quantifying in vivo metabolic pathway activity of different biological systems. This technology plays an important role in understanding intracellular metabolism and revealing patho-physiology mechanism. Recently, it has evolved into a method family with great diversity in experiments, analytics, and mathematics. In this review, we classify and characterize the various branch of ¹³C-MFA from a unified perspective of mathematical modeling. By linking different parts in the model to each step of its workflow, the specific technologies of ¹³C-MFA are put into discussion, including the isotope labeling model (ILM), isotope pattern measuring technique, optimization algorithm and statistical method. Its application in physiological research in neural cell has also been reviewed.

Metabolic flux analysis: a comprehensive review on sample preparation, analytical techniques, data analysis, computational modelling, and main application areas

Metabolic flux analysis (MFA) quantitatively describes cellular fluxes to understand metabolic phenotypes and functional behaviour after environmental and/or genetic perturbations. In the last decade, the application of stable isotopes became extremely important to determine and integrate in vivo measurements of metabolic reactions in systems biology. 13C-MFA is one of the most informative methods used to study central metabolism of biological systems. This review aims to outline the current experimental procedure adopted in 13C-MFA, starting from the preparation of cell cultures and labelled tracers to the quenching and extraction of metabolites and their subsequent analysis performed with very powerful software. Here, the limitations and advantages of nuclear magnetic resonance spectroscopy and mass spectrometry techniques used in carbon labelled experiments are elucidated by reviewing the most recent published papers. Furthermore, we summarise the most successful approaches used for computational modelling in flux analysis and the main application areas with a particular focus in metabolic engineering.

Optimised collection of non-uniformly sampled 2D-HSQC NMR spectra for use in metabolic flux analysis

Nuclear magnetic resonance (NMR) spectroscopy is integral to metabolic studies; yet, it can suffer from the long acquisition times required to collect data of sufficient signal strength and resolution. The use of non-uniform sampling (NUS) allows faster collection of NMR spectra without loss of spectral integrity. When planning experimental methodologies to perform metabolic flux analysis (MFA) of cell metabolism, a variety of options are available for the acquisition of NUS NMR data. Before beginning data collection, decisions have to be made regarding selection of pulse sequence, number of transients and NUS specific parameters such as the sampling level and sampling schedule. Poor choices will impact data quality, which may have a negative effect on the subsequent analysis and biological interpretation. Herein, we describe factors that should be considered when setting up non-uniformly sampled 2D-¹ H,¹³ C HSQC NMR experiments for MFA and provide a standard protocol for users to follow.

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.

Fast quantitative 2D NMR for metabolomics and lipidomics: A tutorial

Nuclear magnetic resonance (NMR) is a well-known analytical technique for the analysis of complex mixtures. Its quantitative capability makes it ideally suited to metabolomics or lipidomics studies involving large sample collections of complex biological samples. To overcome the ubiquitous limitation of spectral overcrowding when recording 1D NMR spectra on such samples, the acquisition of 2D NMR spectra allows a better separation between overlapped resonances while yielding accurate quantitative data when appropriate analytical protocols are implemented. Moreover, the experiment duration can be considerably reduced by applying fast acquisition methods. Here, we describe the general workflow to acquire fast quantitative 2D NMR spectra in the "omics" context. It is illustrated on three representative and complementary experiments: UF COSY, ZF-TOCSY with nonuniform sampling, and HSQC with nonuniform sampling. After giving some details and recommendations on how to apply this protocol, its implementation in the case of targeted and untargeted metabolomics/lipidomics studies is described.

Selective Amino Acid-Only in Vivo NMR: A Powerful Tool To Follow Stress Processes

In vivo NMR of small 13C-enriched aquatic organisms is developing as a powerful tool to detect and explain toxic stress at the biochemical level. Amino acids are a very important category of metabolites for stress detection as they are involved in the vast majority of stress response pathways. As such, they are a useful proxy for stress detection in general, which could then be a trigger for more in-depth analysis of the metabolome. 1H-13C heteronuclear single quantum coherence (HSQC) is commonly used to provide additional spectral dispersion in vivo and permit metabolite assignment. While some amino acids can be assigned from HSQC, spectral overlap makes monitoring them in vivo challenging. Here, an experiment typically used to study protein structures is adapted for the selective detection of amino acids inside living Daphnia magna (water fleas). All 20 common amino acids can be selectively detected in both extracts and in vivo. By monitoring bisphenol-A exposure, the in vivo amino acid-only approach identified larger fluxes in a greater number of amino acids when compared to published works using extracts from whole organism homogenates. This suggests that amino acid-only NMR of living organisms may be a very sensitive tool in the detection of stress in vivo and is highly complementary to more traditional metabolomics-based methods. The ability of selective NMR experiments to help researchers to "look inside" living organisms and only detect specific molecules of interest is quite profound and paves the way for the future development of additional targeted experiments for in vivo research and monitoring.

Identification of Unknown Metabolomics Mixture Compounds by Combining NMR, MS, and Cheminformatics

Metabolomics aims at the comprehensive identification of metabolites in complex mixtures to characterize the state of a biological system and elucidate their roles in biochemical pathways. For many biological samples, a large number of spectral features observed by NMR spectroscopy and mass spectrometry (MS) belong to unknowns, i.e., these features do not belong to metabolites that have been previously identified, and their spectral information is not available in databases. By combining NMR, MS, and combinatorial cheminformatics, the analysis of unknowns can be pursued in complex mixtures requiring minimal purification. This chapter describes the SUMMIT MS/NMR approach covering sample preparation, NMR and MS data collection and processing, and the identification of likely unknowns with the use of cheminformatics tools and the prediction of NMR spectral properties.

Focusing on “the important” through targeted NMR experiments: an example of selective13C–12C bond detection in complex mixtures

Here, a targeted NMR experiment is introduced which selectively detects the formation of¹³C–¹²C bonds in mixtures.

NMR and MS-based Stable Isotope-Resolved Metabolomics and Applications in Cancer Metabolism

There is considerable interest in defining metabolic reprogramming in human diseases, which is recognized as a hallmark of human cancer. Although radiotracers have a long history in specific metabolic studies, stable isotope-enriched precursors coupled with modern high resolution mass spectrometry and NMR spectroscopy have enabled systematic mapping of metabolic networks and fluxes in cells, tissues and living organisms including humans. These analytical platforms are high in information content, are complementary and cross-validating in terms of compound identification, quantification, and isotope labeling pattern analysis of a large number of metabolites simultaneously. Furthermore, new developments in chemoselective derivatization and in vivo spectroscopy enable tracking of labile/low abundance metabolites and metabolic kinetics in real-time. Here we review developments in Stable Isotope Resolved Metabolomics (SIRM) and recent applications in cancer metabolism using a wide variety of stable isotope tracers that probe both broad and specific aspects of cancer metabolism required for proliferation and survival.

Environmental metabolomics with data science for investigating ecosystem homeostasis

A natural ecosystem can be viewed as the interconnections between complex metabolic reactions and environments. Humans, a part of these ecosystems, and their activities strongly affect the environments. To account for human effects within ecosystems, understanding what benefits humans receive by facilitating the maintenance of environmental homeostasis is important. This review describes recent applications of several NMR approaches to the evaluation of environmental homeostasis by metabolic profiling and data science. The basic NMR strategy used to evaluate homeostasis using big data collection is similar to that used in human health studies. Sophisticated metabolomic approaches (metabolic profiling) are widely reported in the literature. Further challenges include the analysis of complex macromolecular structures, and of the compositions and interactions of plant biomass, soil humic substances, and aqueous particulate organic matter. To support the study of these topics, we also discuss sample preparation techniques and solid-state NMR approaches. Because NMR approaches can produce a number of data with high reproducibility and inter-institution compatibility, further analysis of such data using machine learning approaches is often worthwhile. We also describe methods for data pretreatment in solid-state NMR and for environmental feature extraction from heterogeneously-measured spectroscopic data by machine learning approaches.

Plant metabolism as studied by NMR spectroscopy

The study of plant metabolism impacts a broad range of domains such as plant cultural practices, plant breeding, human or animal nutrition, phytochemistry and green biotechnologies. Plant metabolites are extremely diverse in terms of structure or compound families as well as concentrations. This review attempts to illustrate how NMR spectroscopy, with its broad variety of experimental approaches, has contributed widely to the study of plant primary or specialized metabolism in very diverse ways. The review presents recent developments of one-dimensional and multi-dimensional NMR methods to study various aspects of plant metabolism. Through recent examples, it highlights how NMR has proved to be an invaluable tool for the global characterization of sample composition within metabolomic studies, and shows some examples of use for targeted phytochemistry, with a special focus on compound identification and quantitation. In such cases, NMR approaches are often used to provide snapshots of the plant sample composition. The review also covers dynamic aspects of metabolism, with a description of NMR techniques to measure metabolic fluxes - in most cases after stable isotope labelling. It is mainly intended for NMR specialists who would be interested to learn more about the potential of their favourite technique in plant sciences and about specific details of NMR approaches in this field. Therefore, as a practical guide, a paragraph on the specific precautions that should be taken for sample preparation is also included. In addition, since the quality of NMR metabolic studies is highly dependent on approaches to data processing and data sharing, a specific part is dedicated to these aspects. The review concludes with perspectives on the emerging methods that could change significantly the role of NMR in the field of plant metabolism by boosting its sensitivity. The review is illustrated throughout with examples of studies selected to represent diverse applications of liquid-state or HR-MAS NMR.

SOFAST-HMQC-an efficient tool for metabolomics

Nuclear magnetic resonance (NMR)-based metabolomics relies mostly on 1D NMR; however, the technique is limited by overlap of the signals from the metabolites. In order to circumvent this problem, 2D ¹H-¹³C correlation spectroscopy techniques are often used. However owing to poorer natural abundance and gyromagnetic ratio of ¹³C, the acquisition time for 2D ¹H-¹³C heteronuclear single quantum coherence spectroscopy (HSQC) is long. This makes it almost impossible to be used in high throughput study. We have reported the application of selective optimized flip angle short transient (SOFAST) technique coupled to heteronuclear multiple quantum correlation (HMQC) along with nonlinear sampling (NUS) in urine and serum samples. This technique takes sevenfold less experimental time than the conventional ¹H-¹³C HSQC experiment with retention of almost all molecular information. Hence, this can be used for high throughput study. Graphical abstract SOFAST-HMQC is a two-dimensional NMR technique that significantly decreases experimental time without loss of information. This technique is applied in complex biofluid samples that are used for high throughput metabolomics studies and shows promise of better information recovery than conventional two-dimensional NMR technique in shorter time.

Accurate Identification of Unknown and Known Metabolic Mixture Components by Combining 3D NMR with Fourier Transform Ion Cyclotron Resonance Tandem Mass Spectrometry

Metabolite identification in metabolomics samples is a key step that critically impacts downstream analysis. We recently introduced the SUMMIT NMR/mass spectrometry (MS) hybrid approach for the identification of the molecular structure of unknown metabolites based on the combination of NMR, MS, and combinatorial cheminformatics. Here, we demonstrate the feasibility of the approach for an untargeted analysis of both a model mixture and E. coli cell lysate based on 2D/3D NMR experiments in combination with Fourier transform ion cyclotron resonance MS and MS/MS data. For 19 of the 25 model metabolites, SUMMIT yielded complete structures that matched those in the mixture independent of database information. Of those, seven top-ranked structures matched those in the mixture, and four of those were further validated by positive ion MS/MS. For five metabolites, not part of the 19 metabolites, correct molecular structural motifs could be identified. For E. coli, SUMMIT MS/NMR identified 20 previously known metabolites with three or more ¹H spins independent of database information. Moreover, for 15 unknown metabolites, molecular structural fragments were determined consistent with their spin systems and chemical shifts. By providing structural information for entire metabolites or molecular fragments, SUMMIT MS/NMR greatly assists the targeted or untargeted analysis of complex mixtures of unknown compounds.

Time is ripe: maturation of metabolomics in chronobiology

Sleep and circadian rhythms studies have recently benefited from metabolomics analyses, uncovering new connections between chronobiology and metabolism. From untargeted mass spectrometry to quantitative nuclear magnetic resonance spectroscopy, a diversity of analytical approaches has been applied for biomarker discovery in the field. In this review we consider advances in the application of metabolomics technologies which have uncovered significant effects of sleep and circadian cycles on several metabolites, namely phosphatidylcholine species, medium-chain carnitines, and aromatic amino acids. Study design and data processing measures essential for detecting rhythmicity in metabolomics data are also discussed. Future developments in these technologies are anticipated vis-à-vis validating early findings, given metabolomics has only recently entered the ring with other systems biology assessments in chronometabolism studies.