Improved NMR Detection of Phospho-Metabolites in a Complex Mixture

Advances in Chromatographic and Mass Spectrometric Techniques for Analyzing Reducing Monosaccharides and Their Phosphates in Biological Samples

Reducing monosaccharides and their phosphates are critical metabolites in the central carbon metabolism pathway of living organisms. Variations in their content can indicate abnormalities in metabolic pathways and the onset of certain diseases, necessitating their analysis and detection. Reducing monosaccharides and their phosphates exhibit significant variations in content within biological samples and are present in many isomers, which makes the accurate quantification of reducing monosaccharides and their phosphates in biological samples a challenging task. Various analytical methods such as spectroscopy, fluorescence detection, colorimetry, nuclear magnetic resonance spectroscopy, sensor-based techniques, chromatography, and mass spectrometry are employed to detect monosaccharides and phosphates. In comparison, chromatography and mass spectrometry are highly favored for their ability to simultaneously analyze multiple components and their high sensitivity and selectivity. This review thoroughly evaluates the current chromatographic and mass spectrometric methods used for detecting reducing monosaccharides and their phosphates from 2013 to 2023, highlighting their efficacy and the advancements in these analytical technologies.

Quantitative Analysis of the Human Semen Phosphorometabolome by 31P-NMR

Phosphorus-containing metabolites occupy a prominent position in cell pathways. The phosphorometabolomic approach in human sperm samples will deliver valuable information as new male fertility biomarkers could emerge. This study analyzed, by 31P-NMR, seminal plasma and whole semen from asthenozoospermic and normozoospermic samples (71% vs. 27% and 45% vs. 17%, total and progressive sperm motility, respectively), and also ejaculates from healthy donors. At least 16 phosphorus-containing metabolites involved in central energy metabolism and phospholipid, nucleotide, and nicotinamide metabolic pathways were assigned and different abundances between the samples with distinct sperm quality was detected. Specifically, higher levels of phosphocholine, glucose-1-phosphate, and to a lesser degree, acetyl phosphate were found in the asthenozoospermic seminal plasma. Notably, the phosphorometabolites implicated in lipid metabolism were highlighted in the seminal plasma, while those associated with carbohydrate metabolism were more abundant in the spermatozoa. Higher levels of phosphocholine, glucose-1-phosphate, and acetyl phosphate in the seminal plasma with poor quality suggest their crucial role in supporting sperm motility through energy metabolic pathways. In the seminal plasma, phosphorometabolites related to lipid metabolism were prominent; however, spermatozoa metabolism is more dependent on carbohydrate-related energy pathways. Understanding the presence and function of sperm phosphorylated metabolites will enhance our knowledge of the metabolic profile of healthy human sperm, improving assessment and differential diagnosis.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

Labile Metabolite Profiling in Human Blood Using Phosphorus NMR Spectroscopy

Phosphorus metabolites occupy a unique place in cellular function as critical intermediates and products of cellular metabolism. Human blood is the most widely used biospecimen in the clinic and in the metabolomics field, and hence an ability to profile phosphorus metabolites in blood, quantitatively, would benefit a wide variety of investigations of cellular functions in health and diseases. Mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy are the two premier analytical platforms used in the metabolomics field. However, detection and quantitation of phosphorus metabolites by MS can be challenging due to their lability, high polarity, structural isomerism, and interaction with chromatographic columns. The conventionally used 1H NMR, on the other hand, suffers from poor resolution of these compounds. As a remedy, 31P NMR promises an important alternative to both MS and 1H NMR. However, numerous challenges including the instability of phosphorus metabolites, their chemical shift sensitivity to solvent composition, pH, salt, and temperature, and the lack of identified metabolites have so far restricted the scope of 31P NMR. In the current study, we describe a method to analyze nearly 25 phosphorus metabolites in blood using a simple one-dimensional (1D) NMR spectrum. Establishment of the identity of unknown metabolites involved a combination of (a) comprehensively analyzing an array of 1D and two-dimensional (2D) 1H/31P homonuclear and heteronuclear NMR spectra of blood; (b) mapping the central carbon metabolic pathway; (c) developing and using 1H and 31P spectral and chemical shift databases; and finally (d) confirming the putative metabolite peaks with spiking using authentic compounds. The resulting simple 1D 31P NMR-based method offers an ability to visualize and quantify the levels of intermediates and products of multiple metabolic pathways, including central carbon metabolism, in one step. Overall, the findings represent a new dimension for blood metabolite analysis and are anticipated to greatly impact the blood metabolomics field.

Advances in the Synthesis and Analysis of Biologically Active Phosphometabolites

Phosphorus-containing metabolites cover a large molecular diversity and represent an important domain of small molecules which are highly relevant for life and represent essential interfaces between biology and chemistry, between the biological and abiotic world. The large but not unlimited amount of phosphate minerals on our planet is a key resource for living organisms on our planet, while the accumulation of phosphorus-containing waste is associated with negative effects on ecosystems. Therefore, resource-efficient and circular processes receive increasing attention from different perspectives, from local and regional levels to national and global levels. The molecular and sustainability aspects of a global phosphorus cycle have become of much interest for addressing the phosphorus biochemical flow as a high-risk planetary boundary. Knowledge of balancing the natural phosphorus cycle and the further elucidation of metabolic pathways involving phosphorus is crucial. This requires not only the development of effective new methods for practical discovery, identification, and high-information content analysis, but also for practical synthesis of phosphorus-containing metabolites, for example as standards, as substrates or products of enzymatic reactions, or for discovering novel biological functions. The purpose of this article is to review the advances which have been achieved in the synthesis and analysis of phosphorus-containing metabolites which are biologically active.

Metabolomics in Cell Biology

Metabolomics has long been used in a biomedical context. The most typical samples are body fluids in which small molecules can be detected and quantified using technologies such as Nuclear Magnetic Resonance (NMR) and Mass Spectrometry (MS). Many studies, in particular in the wider field of cancer research, are based on cellular models. Different cancer cells can have vastly different ways of regulating metabolism and responses to drug treatments depend on specific metabolic mechanisms which are often cell type specific. This has led to a series of publications using metabolomics to study metabolic mechanisms. Cell-based metabolomics has specific requirements and allows for interesting approaches where metabolism is followed in real-time. Here applications of metabolomics in cell biology have been reviewed, providing insight into specific technologies used and showing exemplary case studies with an emphasis towards applications which help to understand drug mechanisms.

Virtual decoupling to break the simplification versus resolution trade-off in nuclear magnetic resonance of complex metabolic mixtures

The heteronuclear single quantum correlation (HSQC) experiment developed by Bodenhausen and Ruben (1980) in the early days of modern nuclear magnetic resonance (NMR) is without a doubt one of the most widely used experiments, with applications in almost every aspect of NMR including metabolomics. Acquiring this experiment, however, always implies a trade-off: simplification versus resolution. Here, we present a method that artificially lifts this barrier and demonstrate its application towards metabolite identification in a complex mixture. Based on the measurement of clean in-phase and clean anti-phase (CLIP/CLAP) HSQC spectra (Enthart et al., 2008), we construct a virtually decoupled HSQC (vd-HSQC) spectrum that maintains the highest possible resolution in the proton dimension. Combining this vd-HSQC spectrum with a J -resolved spectrum (Pell and Keeler, 2007) provides useful information for the one-dimensional proton spectrum assignment and for the identification of metabolites in Dreissena polymorpha (Prud'homme et al., 2020).