Recent Advances in Mass Spectrometry-Based Structural Elucidation Techniques

Molecular networking: An efficient tool for discovering and identifying natural products

Natural products (NPs), play a crucial role in drug development. However, the discovery of NPs is accidental, and conventional identification methods lack accuracy. To overcome these challenges, an increasing number of researchers are directing their attention to Molecular networking (MN). MN based on secondary mass spectrometry has become an important tool for the separation, purification and structural identification of NPs. However, most new tools are not well known. This review started with the most basic MN tool and explains it from the principle, workflow, and application. Then introduce the principles and workflows of the remaining eight new types of MN tools. The reliability of various MNs is mainly verified based on the application of phytochemistry and metabolomics. Subsequently, the principles and applications of 12 structural annotation tools are introduced. For the first time, the scope of 9 kinds of MN tools is compared horizontally, and 12 kinds of structured annotation tools are classified from the type of compound structure suitable for identification. The advantages and disadvantages of various tools are summarized, and make suggestions for future application directions and the development of computing tools in this review. MN tools are expected to enhance the efficiency of the discovery and identification in NPs.

Analysis of carbohydrates and glycoconjugates by matrix-assisted laser desorption/ionization mass spectrometry: An update for 2021-2022

The use of matrix-assisted laser desorption/ionization (MALDI) mass spectrometry for the analysis of carbohydrates and glycoconjugates is a well-established technique and this review is the 12th update of the original article published in 1999 and brings coverage of the literature to the end of 2022. As with previous review, this review also includes a few papers that describe methods appropriate to analysis by MALDI, such as sample preparation, even though the ionization method is not MALDI. The review follows the same format as previous reviews. It is divided into three sections: (1) general aspects such as theory of the MALDI process, matrices, derivatization, MALDI imaging, fragmentation, quantification and the use of computer software for structural identification. (2) Applications to various structural types such as oligo- and polysaccharides, glycoproteins, glycolipids, glycosides and biopharmaceuticals, and (3) other general areas such as medicine, industrial processes, natural products and glycan synthesis where MALDI is extensively used. Much of the material relating to applications is presented in tabular form. MALDI is still an ideal technique for carbohydrate analysis, particularly in its ability to produce single ions from each analyte and advancements in the technique and range of applications show little sign of diminishing.

Advances in AI-based strategies and tools to facilitate natural product and drug development

Natural products and their derivatives have been important for treating diseases in humans, animals, and plants. However, discovering new structures from natural sources is still challenging. In recent years, artificial intelligence (AI) has greatly aided the discovery and development of natural products and drugs. AI facilitates to: connect genetic data to chemical structures or vice-versa, repurpose known natural products, predict metabolic pathways, and design and optimize metabolites biosynthesis. More recently, the emergence and improvement in neural networks such as deep learning and ensemble automated web based bioinformatics platforms have sped up the discovery process. Meanwhile, AI also improves the identification and structure elucidation of unknown compounds from raw data like mass spectrometry and nuclear magnetic resonance. This article reviews these AI-driven methods and tools, highlighting their practical applications and guide for efficient natural product discovery and drug development.

An Investigation of the Structures of [(Glycine)(1-Methyluracil)]M+ Complexes (M=H, Li, Na, K) in the Gas Phase by IRMPD Spectroscopy and Theoretical Methods

The presence of ions in the complexation of molecules can profoundly affect the structure, resulting in changes to functionality and stability. These non-covalent interactions drive many biological processes both necessary and inimical and require extensive research to understand and predict their effects. Protonated and alkali metalated complexes of glycine (Gly) and 1-methyluracil (1-mUra) were studied using infrared multiphoton dissociation (IRMPD) spectroscopy and density functional theory (DFT) calculations. The experimental and simulated vibrational spectra were compared to help elucidate the structure of each complex. The lowest energy structure for [(Gly)(1-mUra)]H+ consists of amine protonated Gly bound to O4 of canonical 1-mUra through a single ionic hydrogen bond with another, intraglycine ionic hydrogen bond between the protonated amine group and the carbonyl oxygen. For [(Gly)(1-mUra)]Li+, [(Gly)(1-mUra)]Na+ and [(Gly)(1-mUra)]K+, the experimental spectra are most consistent with the metal cations binding in a trigonal planar geometry with 1-mUra bound to the metal cation via the O4 carbonyl. In [(Gly)(1-mUra)]Li+ and [(Gly)(1-mUra)]Na+, the metal cation is bound to canonical Gly via the carbonyl oxygen and amine nitrogen, but in [(Gly)(1-mUra)]K+, Gly is bound through both oxygens and contains an intraglycine hydrogen bond from the hydroxyl to the amine nitrogen. It was found that the B3LYP/6-31+G(d,p) vibrational spectra are most consistent with the experimental spectra, but M062X was better than B3LYP at determining the lowest-energy structures.

Recent advances in the analytical methods for quantitative determination of antioxidants in food matrices

Antioxidants are crucial in reducing oxidative stress and enhancing health, necessitating precise quantification in food matrices. Advanced techniques such as biosensors and nanosensors offer high sensitivity and specificity, enabling real-time monitoring and accurate antioxidant quantification in complex food systems. These technologies herald a new era in food analysis, improving food quality and safety through sophisticated detection methods. Their application facilitates comprehensive antioxidant profiling, driving innovation in food technology to meet the rising demand for nutritional optimization and food integrity. These are complemented by electrochemical techniques, spectroscopy, and chromatography. Electrochemical methods provide rapid response times, spectroscopy offers versatile chemical composition analysis, and chromatography excels in precise separation and quantification. Collectively, these methodologies establish a comprehensive framework for food analysis, essential for improving food quality, safety, and nutritional value. Future research should aim to refine these analytical methods, promising significant advancements in food and nutritional science.

Review of polysaccharides from Citrus medica L. var. sarcodactylis. (Fingered citron): Their extraction, purification, structural characteristics, bioactivity and potential applications

Citrus medica L. var. sarcodactylis. (Fingered citron), commonly known as Buddha's hand, is a revered member of the Citrus genus belonging to the Rutaceae family with a long history in China. Fingered citron is known for its multifaceted utility as traditional herbal medicine, functional food and ornamental plant. Fingered citron polysaccharides (FCPs) are the key bioactive components of Citrus medica L. var. sarcodactylis, garnering global attention for their potential medicinal and culinary benefits. Various extraction methods like solvent extraction, ultrasound or microwave-assisted extraction have been used to obtain FCPs. Nonetheless, the structural characteristics of FCPs remain incompletely understood, necessitating further research and elucidation of the potential structure-activity relationship via the combined use of various advanced analytical techniques. Furthermore, FCPs exhibit diverse bioactivities, such as antioxidant activity, immunomodulatory effect, anti-inflammatory effect and gut microbiota regulatory activity, among others, positioning them as viable candidates for the therapeutic and health-promoting applications. Consequently, this review seeks to offer a comprehensive overview of FCPs, covering their extraction, purification, structural features, biological activities, and potential applications, underscoring the significant promise of FCPs as valuable natural compounds with multiple bioactive properties, advocating for their expanded utilization and integration into industry and therapeutics.

Characterizing the Dynamics of Solvated Disaccharides with In-Electrospray Ionization Hydrogen/Deuterium Exchange-Mass Spectrometry

Carbohydrates have various biological functions that are based on their structures. However, the composition and the glycosidic-bond linkage and configuration of carbohydrates present challenges for their characterization. Furthermore, isomeric features contribute to the formation of intramolecular hydrogen bonds, which influence the flexibility and dynamics of carbohydrates. Hydrogen/deuterium exchange-mass spectrometry (HDX-MS) enables the analysis of protein dynamics by monitoring deuterium labeling after HDX for different lengths of time. In-electrospray ionization (in-ESI) HDX-MS has been used to rapidly label solvated carbohydrates with labeling occurring during desolvation of ESI droplets. Therefore, HDX-labeling times can be altered by changing the spray-solvent conductivity, which changes the initial size of ESI droplets and their resulting lifetimes. Here, we utilize in-ESI HDX-MS to characterize nine isomeric disaccharides with different monosaccharide compositions and glycosidic-bond linkages and configurations. We compared both the relative D-uptake of isomers at individual conductivities, or HDX-labeling times, and the trends associated with labeling at multiple conductivities. Interestingly, the relative D-uptake trends were correlated to isomeric features that affect disaccharide flexibility, including formation of intramolecular hydrogen bonds. Among the isomeric features studied, linkage was observed to have a significant influence on relative D-uptake with (1-3)-linked disaccharides having more change in relative D-uptake with changing conductivity compared to other linkages. Overall, this research illustrates how in-ESI HDX-MS can be applied to structurally characterize disaccharides with distinct isomeric features. Furthermore, this work shows that in-ESI HDX-MS can be used to monitor the dynamics of solvated molecules with rapidly exchanging functional groups.

Enantiomeric Analysis of Chiral Drugs Using Mass Spectrometric Methods: A Comprehensive Review

Chirality plays a crucial role in the drug development process, influencing fundamental chemical and biochemical processes and significantly affecting our daily lives. This review provides a comprehensive examination of mass spectrometric (MS) methods for the enantiomeric analysis of chiral drugs. It thoroughly investigates MS-hyphenated techniques, emphasizing their critical role in achieving enantioselective analysis. Furthermore, it delves into the intricate chiral recognition mechanisms inherent in MS, elucidating the fundamental principles that govern successful chiral separations. By critically assessing the obstacles and potential benefits associated with each MS-based method, this review offers valuable insights for researchers navigating the complexities of chiral analysis. Both qualitative and quantitative approaches are explored, presenting a comparative analysis of their strengths and limitations. This review is aimed at significantly enhancing the understanding of chiral MS methods, serving as a crucial resource for researchers and practitioners engaged in enantioselective studies.

Serum N-Glycan Profiling of Patients with Narcolepsy Type 1 Using LC-MS/MS

The neurological condition known as narcolepsy type 1 (NT1) is an uncommon condition marked by extreme daytime sleepiness, cataplexy, sleep paralysis, hallucinations, disrupted nocturnal sleep, and low or undetectable levels of orexin in the CSF fluid. NT1 has been hypothesized to be an immunological disorder; its treatment is currently only symptomatic, and misdiagnosis is not uncommon. This study compares the N-glycome of NT1 patients with healthy controls in search of potential glycan biomarkers using LC-MS/MS. A total of 121 candidate N-glycans were identified, 55 of which were isomeric N-glycan structures and 65 were not. Seventeen N-glycan biomarker candidates showed significant differences between the NT1 and control cohorts. All of the candidate glycan biomarkers were isomeric except HexNAc6Hex7Fuc0NeuAc1 (6701) and HexNAc6Hex7Fuc1NeuAc2 (6712). Therefore, with isomeric and nonisomeric structures, a total of 20 candidate N-glycan biomarkers are reported in this study, and interestingly, all are either sialylated or sialylated-fucosylated and upregulated in NT1 relative to the control. The distribution levels of all the identified N-glycans show that the sialylated glycan type is the most abundant in NT1 and is majorly disialylated, although the trisialylated subtype is three-fold higher in NT1 compared to the healthy control. The first isomers of HexNAc5Hex6Fuc0NeuAc3 (5603), HexNAc6Hex7Fuc0NeuAc2 (6702), and HexNAc6Hex7Fuc1NeuAc4 (6714) expressed a high level of fold changes (FC) of 1.62, 2.19, and 2.98, respectively. These results suggest a different N-glycome profile of NT1 and a relationship between sialylated glycan isomers in NT1 disease development or progression. The revelation of N-glycan expression alterations in this study may improve NT1 diagnostic methods, understanding of NT1 pathology, and the development of new targeted therapeutics.

Two-Dimensional Tandem Mass Spectrometry for Biopolymer Structural Analysis

Biopolymer analysis, including proteomics and glycomics, relies heavily on the use of mass spectrometry for structural elucidation, including sequence determination. Novel methods to improve sample workup, instrument performance, and data analysis continue to be developed to address shortcomings associated with sample preparation, analysis time, data quality, and data interpretation. Here, we present a new method that couples in-source collision-induced dissociation (IS-CID) with two-dimensional tandem mass spectrometry (2D MS/MS) as a way to simplify proteomics and glycomics workflows while also providing additional insight into analyte structures over traditional MS/MS experiments. Specifically, IS-CID is employed as a gas-phase digestion method, i.e., to break down intact full-length polysaccharide or peptide ions prior to mass analysis. The resulting mixtures of oligomeric ions are analyzed by 2D-MS/MS, a technique that allows association of product ions with their precursor ions without isolation of the latter. A novel data analysis strategy is introduced to leverage the second dimension of 2D MS/MS spectra, in which stairstep patterns, representing outputs of a molecule's MSn scans, are extracted for structural interconnectivity information on the oligomer. The results demonstrate the potential applicability of 2D MS/MS strategies to the modern omics workflow and structural analysis of various classes of biopolymers.

Formation and Detection of Gizzerosine in Animal Feed Matrices: Progress and Perspectives

Gizzerosine is responsible for gizzard erosion and black vomit, owing to excessive gastric acid secretion in poultry. It is a biogenic amine that forms during feed processing. Gizzerosine, a derivative of histamine, is a serious threat to animal feed safety and poultry production because it is more potent after ingestion and more harmful to poultry than histamine. The difficulty of obtaining gizzerosine and the lack of simple, rapid, and sensitive in vitro detection techniques have hindered studies on the effects of gizzerosine on gizzard health and poultry production. In this review, we evaluated the natural formation and the chemical synthesis methods of gizzerosine and introduced seven detection methods and their principles for analyzing gizzerosine. This review summarizes the issues of gizzerosine research and suggests methods for the future development of gizzerosine detection methods.

Structural Diversity and Biological Activity of Cyanopeptolins Produced by Nostoc edaphicum CCNP1411

Cyanopeptolins (CPs) are one of the most commonly occurring class of cyanobacterial nonribosomal peptides. For the majority of these compounds, protease inhibition has been reported. In the current work, the structural diversity of cyanopeptolins produced by Nostoc edaphicum CCNP1411 was explored. As a result, 93 CPs, including 79 new variants, were detected and structurally characterized based on their mass fragmentation spectra. CPs isolated in higher amounts were additionally characterized by NMR. To the best of our knowledge, this is the highest number of cyanopeptides found in one strain. The biological assays performed with the 34 isolated CPs confirmed the significance of the amino acid located between Thr and the unique 3-amino-6-hydroxy-2-piperidone (Ahp) on the activity of the compounds against serine protease and HeLa cancer cells.

Multimodal Image Fusion Offers Better Spatial Resolution for Mass Spectrometry Imaging

High-resolution reconstruction has attracted increasing research interest in mass spectrometry imaging (MSI), but it remains a challenging ill-posed problem. In the present study, we proposed a deep learning model to fuse multimodal images to enhance the spatial resolution of MSI data, namely, DeepFERE. Hematoxylin and eosin (H&E) stain microscopy imaging was used to pose constraints in the process of high-resolution reconstruction to alleviate the ill-posedness. A novel model architecture was designed to achieve multi-task optimization by incorporating multi-modal image registration and fusion in a mutually reinforced framework. Experimental results demonstrated that the proposed DeepFERE model is able to produce high-resolution reconstruction images with rich chemical information and a detailed structure on both visual inspection and quantitative evaluation. In addition, our method was found to be able to improve the delimitation of the boundary between cancerous and para-cancerous regions in the MSI image. Furthermore, the reconstruction of low-resolution spatial transcriptomics data demonstrated that the developed DeepFERE model may find wider applications in biomedical fields.

Prediction of a Large-Scale Database of Collision Cross-Section and Retention Time Using Machine Learning to Reduce False Positive Annotations in Untargeted Metabolomics

Metabolite identification in untargeted metabolomics is complex, with the risk of false positive annotations. This work aims to use machine learning to successively predict the retention time (Rt) and the collision cross-section (CCS) of an open-access database to accelerate the interpretation of metabolomic results. Standards of metabolites were tested using liquid chromatography coupled with high-resolution mass spectrometry. In CCSBase and QSRR predictor machine learning models, experimental results were used to generate predicted CCS and Rt of the Human Metabolome Database. From 542 standards, 266 and 301 compounds were detected in positive and negative electrospray ionization mode, respectively, corresponding to 380 different metabolites. CCS and Rt were then predicted using machine learning tools for almost 114,000 metabolites. R² score of the linear regression between predicted and measured data achieved 0.938 and 0.898 for CCS and Rt, respectively, demonstrating the models' reliability. A CCS and Rt index filter of mean error ± 2 standard deviations could remove most misidentifications. Its application to data generated from a toxicology study on tobacco cigarettes reduced hits by 76%. Regarding the volume of data produced by metabolomics, the practical workflow provided allows for the implementation of valuable large-scale databases to improve the biological interpretation of metabolomics data.