Ion Mobility-Derived Collision Cross-Sections Add Extra Capability in Distinguishing Isomers and Compounds with Similar Retention Times: The Case of Aphidicolanes

Integration of feature-based molecular networking and high-definition data-dependent acquisition for the comprehensive multicomponent characterization of Honghua Xiaoyao Tablet

Systematically identifying the chemical constituents in complex matrices is a challenge due to the inherent characteristics of compounds. The combination of liquid chromatography-tandem mass spectrometry (LC-MS) and classical molecular networking (CLMN) is a powerful technology for annotating small molecules. However, the low coverage from inappropriate acquisition modes and the inseparability of isomeric compound nodes still hinders the comprehensive metabolite characterization. A novel strategy that integrated high-definition data-dependent acquisition (HDDDA) from traveling-wave ion mobility mass spectrometry (TWIMS) and feature-based molecular networking (FBMN) was developed to improve chemical component characterization and enhance isomeric component discernment. The data-dependent acquisition (DDA) and HDDDA, were effectively and visually evaluated by CLMN and FBMN via the number of nodes, clustered nodes and clusters. Moreover, the efficiency of the three strategies was validated. The results strongly demonstrated that the HDDDA-FBMN strategy improves MS coverage and offers significant advantages for isomer identification. With the assistance of the UNIFI platform, the developed strategy was successfully applied to systematically investigate the chemical profile of Honghua Xiaoyao Tablet (HHXYT), a traditional folk empirical prescription for treating various gynecological diseases. 184 compounds were unambiguously identified or tentatively characterized, including 12 pairs of isomers, and two unreported compounds. In conclusion, this hybrid approach achieves dimensionally enhanced MS data acquisition and visual recognition of isomeric compounds, accelerating the structural characterization in complex systems. We anticipate that HDDDA-FBMN strategies will be a flexible and versatile tool for the chemical components in a complex system of TCMs.

Matrix free laser desorption ionization coupled to trapped ion mobility mass spectrometry: an innovative approach for isomer differentiation and molecular network visualization

The chemical profiling of complex mixtures of natural products (NPs) is a major challenge in analytical chemistry and generally addressed by liquid chromatography coupled to mass spectrometry (LC-MS). In recent years also matrix free laser desorption ionization-mass spectrometry (LDI-MS) has become a versatile and time efficient complement to LC-MS. However, the absence of chromatographic separation in LDI-MS does not permit the differentiation of isomers. Providing a potential solution to this problem, the current work presents a combined LDI-Ion mobility spectrometry-tandem mass spectrometry (LDI-IMS-MS2) approach, which facilitated the successful differentiation of four constitutional xanthone isomers namely butyraxanthone D, cratoxylone, garcinone D and parvixanthone G. In addition, the experimental collision cross section (CCS) distribution values of nine unreported xanthones are described. Based on these results, a proof of concept for the so far unexplored concept of a LDI-IMS-MS2 based molecular network is being presented.

Marine natural products

Covering: January to the end of December 2022This review covers the literature published in 2022 for marine natural products (MNPs), with 645 citations (633 for the period January to December 2022) referring to compounds isolated from marine microorganisms and phytoplankton, green, brown and red algae, sponges, cnidarians, bryozoans, molluscs, tunicates, echinoderms, the submerged parts of mangroves and other intertidal plants. The emphasis is on new compounds (1417 in 384 papers for 2022), together with the relevant biological activities, source organisms and country of origin. Pertinent reviews, biosynthetic studies, first syntheses, and syntheses that led to the revision of structures or stereochemistries, have been included. An analysis of NP structure class diversity in relation to biota source and biome is discussed.

Collision cross section measurement and prediction methods in omics

Omics studies such as metabolomics, lipidomics, and proteomics have become important for understanding the mechanisms in living organisms. However, the compounds detected are structurally different and contain isomers, with each structure or isomer leading to a different result in terms of the role they play in the cell or tissue in the organism. Therefore, it is important to detect, characterize, and elucidate the structures of these compounds. Liquid chromatography and mass spectrometry have been utilized for decades in the structure elucidation of key compounds. While prediction models of parameters (such as retention time and fragmentation pattern) have also been developed for these separation techniques, they have some limitations. Moreover, ion mobility has become one of the most promising techniques to give a fingerprint to these compounds by determining their collision cross section (CCS) values, which reflect their shape and size. Obtaining accurate CCS enables its use as a filter for potential analyte structures. These CCS values can be measured experimentally using calibrant-independent and calibrant-dependent approaches. Identification of compounds based on experimental CCS values in untargeted analysis typically requires CCS references from standards, which are currently limited and, if available, would require a large amount of time for experimental measurements. Therefore, researchers use theoretical tools to predict CCS values for untargeted and targeted analysis. In this review, an overview of the different methods for the experimental and theoretical estimation of CCS values is given where theoretical prediction tools include computational and machine modeling type approaches. Moreover, the limitations of the current experimental and theoretical approaches and their potential mitigation methods were discussed.

Integrating ion mobility into comprehensive multidimensional metabolomics workflows: critical considerations

BACKGROUND

Ion mobility (IM) separation capabilities are now widely available to researchers through several commercial vendors and are now being adopted into many metabolomics workflows. The added peak capacity that ion mobility offers with minimal compromise to other analytical figures-of-merit has provided real benefits to sensitivity and structural selectivity and have allowed more specific metabolite annotations to be assigned in untargeted workflows. One of the greatest promises of contemporary IM-enabled instrumentation is the capability of operating multiple analytical dimensions inline with minimal sample volumes, which has the potential to address many grand challenges currently faced in the omics fields. However, comprehensive operation of multidimensional mass spectrometry comes with its own inherent challenges that, beyond operational complexity, may not be immediately obvious to practitioners of these techniques.

AIM OF REVIEW

In this review, we outline the strengths and considerations for incorporating IM analysis in metabolomics workflows and provide a critical but forward-looking perspective on the contemporary challenges and prospects associated with interpreting IM data into chemical knowledge.

KEY SCIENTIFIC CONCEPTS OF REVIEW

We outline a strategy for unifying IM-derived collision cross section (CCS) measurements obtained from different IM techniques and discuss the emerging field of high resolution ion mobility (HRIM) that is poised to address many of the contemporary challenges associated with ion mobility metabolomics. Whereas the LC step limits the throughput of comprehensive LC-IM-MS, the higher peak capacity of HRIM can allow fast LC gradients or rapid sample cleanup via solid-phase extraction (SPE) to be utilized, significantly improving the sample throughput.