The Application of Ion Mobility-Mass Spectrometry in Untargeted Metabolomics: from Separation to Identification

Measurement of very low-molecular weight metabolites by traveling wave ion mobility and its use in human urine samples

The collision cross-sections (CCS) measurement using ion mobility spectrometry (IMS) in combination with mass spectrometry (MS) offers a great opportunity to increase confidence in metabolite identification. However, owing to the lack of sensitivity and resolution, IMS has an analytical challenge in studying the CCS values of very low-molecular-weight metabolites (VLMs ≤ 250 Da). Here, we describe an analytical method using ultrahigh-performance liquid chromatography (UPLC) coupled to a traveling wave ion mobility-quadrupole-time-of-flight mass spectrometer optimized for the measurement of VLMs in human urine samples. The experimental CCS values, along with mass spectral properties, were reported for the 174 metabolites. The experimental data included the mass-to-charge ratio (m/z), retention time (RT), tandem MS (MS/MS) spectra, and CCS values. Among the studied metabolites, 263 traveling wave ion mobility spectrometry (TWIMS)-derived CCS values (TWCCSN2) were reported for the first time, and more than 70% of these were CCS values of VLMs. The TWCCSN2 values were highly repeatable, with inter-day variations of <1% relative standard deviation (RSD). The developed method revealed excellent TWCCSN2 accuracy with a CCS difference (ΔCCS) within ±2% of the reported drift tube IMS (DTIMS) and TWIMS CCS values. The complexity of the urine matrix did not affect the precision of the method, as evidenced by ΔCCS within ±1.92%. According to the Metabolomics Standards Initiative, 55 urinary metabolites were identified with a confidence level of 1. Among these 55 metabolites, 53 (96%) were VLMs. The larger number of confirmed compounds found in this study was a result of the addition of TWCCSN2 values, which clearly increased metabolite identification confidence.

Application of Ion Mobility Spectrometry and the Derived Collision Cross Section in the Analysis of Environmental Organic Micropollutants

Ion mobility spectrometry (IMS) is a rapid gas-phase separation technique, which can distinguish ions on the basis of their size, shape, and charge. The IMS-derived collision cross section (CCS) can serve as additional identification evidence for the screening of environmental organic micropollutants (OMPs). In this work, we summarize the published experimental CCS values of environmental OMPs, introduce the current CCS prediction tools, summarize the use of IMS and CCS in the analysis of environmental OMPs, and finally discussed the benefits of IMS and CCS in environmental analysis. An up-to-date CCS compendium for environmental contaminants was produced by combining CCS databases and data sets of particular types of environmental OMPs, including pesticides, drugs, mycotoxins, steroids, plastic additives, per- and polyfluoroalkyl substances (PFAS), polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), and polybrominated diphenyl ethers (PBDEs), as well as their well-known transformation products. A total of 9407 experimental CCS values from 4170 OMPs were retrieved from 23 publications, which contain both drift tube CCS in nitrogen (DTCCSN2) and traveling wave CCS in nitrogen (TWCCSN2). A selection of publicly accessible and in-house CCS prediction tools were also investigated; the chemical space covered by the training set and the quality of CCS measurements seem to be vital factors affecting the CCS prediction accuracy. Then, the applications of IMS and the derived CCS in the screening of various OMPs were summarized, and the benefits of IMS and CCS, including increased peak capacity, the elimination of interfering ions, the separation of isomers, and the reduction of false positives and false negatives, were discussed in detail. With the improvement of the resolving power of IMS and enhancements of experimental CCS databases, the practicability of IMS in the analysis of environmental OMPs will continue to improve.

Opportunities and challenges for sample preparation and enrichment in mass spectrometry for single-cell metabolomics

Single-cell heterogeneity in metabolism, drug resistance and disease type poses the need for analytical techniques for single-cell analysis. As the metabolome provides the closest view of the status quo in the cell, studying the metabolome at single-cell resolution may unravel said heterogeneity. A challenge in single-cell metabolome analysis is that metabolites cannot be amplified, so one needs to deal with picolitre volumes and a wide range of analyte concentrations. Due to high sensitivity and resolution, MS is preferred in single-cell metabolomics. Large numbers of cells need to be analysed for proper statistics; this requires high-throughput analysis, and hence automation of the analytical workflow. Significant advances in (micro)sampling methods, CE and ion mobility spectrometry have been made, some of which have been applied in high-throughput analyses. Microfluidics has enabled an automation of cell picking and metabolite extraction; image recognition has enabled automated cell identification. Many techniques have been used for data analysis, varying from conventional techniques to novel combinations of advanced chemometric approaches. Steps have been set in making data more findable, accessible, interoperable and reusable, but significant opportunities for improvement remain. Herein, advances in single-cell analysis workflows and data analysis are discussed, and recommendations are made based on the experimental goal.

AllCCS2: Curation of Ion Mobility Collision Cross-Section Atlas for Small Molecules Using Comprehensive Molecular Representations

The development of ion mobility-mass spectrometry (IM-MS) has revolutionized the analysis of small molecules, such as metabolomics, lipidomics, and exposome studies. The curation of comprehensive reference collision cross-section (CCS) databases plays a pivotal role in the successful application of IM-MS for small-molecule analysis. In this study, we presented AllCCS2, an enhanced version of AllCCS, designed for the universal prediction of the ion mobility CCS values of small molecules. AllCCS2 incorporated newly available experimental CCS data, including 10,384 records and 7713 unified values, as training data. By leveraging a neural network trained on diverse molecular representations encompassing mass spectrometry features, molecular descriptors, and graph features extracted using a graph convolutional network, AllCCS2 achieved exceptional prediction accuracy. AllCCS2 achieved median relative error (MedRE) values of 0.31, 0.72, and 1.64% in the training, validation, and testing sets, respectively, surpassing existing CCS prediction tools in terms of accuracy and coverage. Furthermore, AllCCS2 exhibited excellent compatibility with different instrument platforms (DTIMS, TWIMS, and TIMS). The prediction uncertainties in AllCCS2 from the training data and the prediction model were comprehensively investigated by using representative structure similarity and model prediction variation. Notably, small molecules with high structural similarities to the training set and lower model prediction variation exhibited improved accuracy and lower relative errors. In summary, AllCCS2 serves as a valuable resource to support applications of IM-MS technologies. The AllCCS2 database and tools are freely accessible at http://allccs.zhulab.cn/.

Gas-phase structure of polymer ions: Tying together theoretical approaches and ion mobility spectrometry

An increasing number of studies take advantage of ion mobility spectrometry (IMS) coupled to mass spectrometry (IMS-MS) to investigate the spatial structure of gaseous ions. Synthetic polymers occupy a unique place in the field of IMS-MS. Indeed, due to their intrinsic dispersity, they offer a broad range of homologous ions with different lengths. To help rationalize experimental data, various theoretical approaches have been described. First, the study of trend lines is proposed to derive physicochemical and structural parameters. However, the evaluation of data fitting reflects the overall behavior of the ions without reflecting specific information on their conformation. Atomistic simulations constitute another approach that provide accurate information about the ion shape. The overall scope of this review is dedicated to the synergy between IMS-MS and theoretical approaches, including computational chemistry, demonstrating the essential role they play to fully understand/interpret IMS-MS data.

An In Silico Infrared Spectral Library of Molecular Ions for Metabolite Identification

Infrared ion spectroscopy (IRIS) continues to see increasing use as an analytical tool for small-molecule identification in conjunction with mass spectrometry (MS). The IR spectrum of an m/z selected population of ions constitutes a unique fingerprint that is specific to the molecular structure. However, direct translation of an IR spectrum to a molecular structure remains challenging, as reference libraries of IR spectra of molecular ions largely do not exist. Quantum-chemically computed spectra can reliably be used as reference, but the challenge of selecting the candidate structures remains. Here, we introduce an in silico library of vibrational spectra of common MS adducts of over 4500 compounds found in the human metabolome database. In total, the library currently contains more than 75,000 spectra computed at the DFT level that can be queried with an experimental IR spectrum. Moreover, we introduce a database of 189 experimental IRIS spectra, which is employed to validate the automated spectral matching routines. This demonstrates that 75% of the metabolites in the experimental data set are correctly identified, based solely on their exact m/z and IRIS spectrum. Additionally, we demonstrate an approach for specifically identifying substructures by performing a search without m/z constraints to find structural analogues. Such an unsupervised search paves the way toward the de novo identification of unknowns that are absent in spectral libraries. We apply the in silico spectral library to identify an unknown in a plasma sample as 3-hydroxyhexanoic acid, highlighting the potential of the method.

Collision Cross Section Prediction Based on Machine Learning

Ion mobility-mass spectrometry (IM-MS) is a powerful separation technique providing an additional dimension of separation to support the enhanced separation and characterization of complex components from the tissue metabolome and medicinal herbs. The integration of machine learning (ML) with IM-MS can overcome the barrier to the lack of reference standards, promoting the creation of a large number of proprietary collision cross section (CCS) databases, which help to achieve the rapid, comprehensive, and accurate characterization of the contained chemical components. In this review, advances in CCS prediction using ML in the past 2 decades are summarized. The advantages of ion mobility-mass spectrometers and the commercially available ion mobility technologies with different principles (e.g., time dispersive, confinement and selective release, and space dispersive) are introduced and compared. The general procedures involved in CCS prediction based on ML (acquisition and optimization of the independent and dependent variables, model construction and evaluation, etc.) are highlighted. In addition, quantum chemistry, molecular dynamics, and CCS theoretical calculations are also described. Finally, the applications of CCS prediction in metabolomics, natural products, foods, and the other research fields are reflected.

Out of the cycle: Impact of cell cycle aberrations on cancer metabolism and metastasis

The use of cell cycle inhibitors has necessitated a better understanding of the cell cycle in tumor biology to optimize the therapeutic approach. Cell cycle aberrations are common in cancers, and it is increasingly acknowledged that these aberrations exert oncogenic effects beyond the cell cycle. Multiple facets such as cancer metabolism, immunity and metastasis are also affected, all of which are beyond the effect of cell proliferation alone. This review comprehensively summarized the important recent findings and advances in these interrelated processes. In cancer metabolism, cell cycle regulators can modulate various pathways in aerobic glycolysis, glucose uptake and gluconeogenesis, mainly through transcriptional regulation and kinase activities. Amino acid metabolism is also regulated through cell cycle progression. On cancer metastasis, metabolic plasticity, immune evasion, tumor microenvironment adaptation and metastatic site colonization are intricately related to the cell cycle, with distinct regulatory mechanisms at each step of invasion and dissemination. Throughout the synthesis of current understanding, knowledge gaps and limitations in the literature are also highlighted, as are new therapeutic approaches such as combinational therapy and challenges in tackling emerging targeted therapy resistance. A greater understanding of how the cell cycle modulates diverse aspects of cancer biology can hopefully shed light on identifying new molecular targets by harnessing the vast potential of the cell cycle.

Integrating the potential of ion mobility spectrometry-mass spectrometry in the separation and structural characterisation of lipid isomers

It is increasingly evident that a more detailed molecular structure analysis of isomeric lipids is critical to better understand their roles in biological processes. The occurrence of isomeric interference complicates conventional tandem mass spectrometry (MS/MS)-based determination, necessitating the development of more specialised methodologies to separate lipid isomers. The present review examines and discusses recent lipidomic studies based on ion mobility spectrometry combined with mass spectrometry (IMS-MS). Selected examples of the separation and elucidation of structural and stereoisomers of lipids are described based on their ion mobility behaviour. These include fatty acyls, glycerolipids, glycerophospholipids, sphingolipids, and sterol lipids. Recent approaches for specific applications to improve isomeric lipid structural information using direct infusion, coupling imaging, or liquid chromatographic separation workflows prior to IMS-MS are also discussed, including: 1) strategies to improve ion mobility shifts; 2) advanced tandem MS methods based on activation of lipid ions with electrons or photons, or gas-phase ion-molecule reactions; and 3) the use of chemical derivatisation techniques for lipid characterisation.

Untargeted metabolomics approach using UHPLC-IMS-QTOF MS for surface body samples to identify low-volatility chemosignals related to maternal care in mice

The present study is focused on the determination of low-volatile chemosignals excreted or secreted by mouse pups in their early days of life involved in maternal care induction in mice adult females. Untargeted metabolomics was employed to differentiate between samples collected with swabs from facial and anogenital area from neonatal mouse pups receiving maternal care (first two weeks of life) and the elder mouse pups in the weaning period (4th week old). The sample extracts were analysed by ultra-high pressure liquid chromatography (UHPLC) coupled to ion mobility separation (IMS) in combination with high resolution mass spectrometry (HRMS). After data processing with Progenesis QI and multivariate statistical analysis, five markers present in the first two weeks of mouse pups life and putatively involved in materno-filial chemical communication were tentatively identified: arginine, urocanic acid, erythro-sphingosine (d17:1), sphingosine (d18:1) and sphinganine. The four-dimensional data and the tools associated to the additional structural descriptor obtained by IMS separation were of great help in the compound identification. The results demonstrated the great potential of UHPLC-IMS-HRMS based untargeted metabolomics to identity putative pheromones in mammals.

Metabolomics-centered mining of plant metabolic diversity and function: Past decade and future perspectives

Plants are natural experts in organic synthesis, being able to generate large numbers of specific metabolites with widely varying structures that help them adapt to variable survival challenges. Metabolomics is a research discipline that integrates the capabilities of several types of research including analytical chemistry, statistics, and biochemistry. Its ongoing development provides strategies for gaining a systematic understanding of quantitative changes in the levels of metabolites. Metabolomics is usually performed by targeting either a specific cell, a specific tissue, or the entire organism. Considerable advances in science and technology over the last three decades have propelled us into the era of multi-omics, in which metabolomics, despite at an earlier developmental stage than genomics, transcriptomics, and proteomics, offers the distinct advantage of studying the cellular entities that have the greatest influence on end phenotype. Here, we summarize the state of the art of metabolite detection and identification, and illustrate these techniques with four case study applications: (i) comparing metabolite composition within and between species, (ii) assessing spatio-temporal metabolic changes during plant development, (iii) mining characteristic metabolites of plants in different ecological environments and upon exposure to various stresses, and (iv) assessing the performance of metabolomics as a means of functional gene identification , metabolic pathway elucidation, and metabolomics-assisted breeding through analyzing plant populations with diverse genetic variations. In addition, we highlight the prominent contributions of joint analyses of plant metabolomics and other omics datasets, including those from genomics, transcriptomics, proteomics, epigenomics, phenomics, microbiomes, and ion-omics studies. Finally, we discuss future directions and challenges exploiting metabolomics-centered approaches in understanding plant metabolic diversity.

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.

Ion mobility mass spectrometry in the omics era: Challenges and opportunities for metabolomics and lipidomics

Researchers worldwide are taking advantage of novel, commercially available, technologies, such as ion mobility mass spectrometry (IM-MS), for metabolomics and lipidomics applications in a variety of fields including life, biomedical, and food sciences. IM-MS provides three main technical advantages over traditional LC-MS workflows. Firstly, in addition to mass, IM-MS allows collision cross-section values to be measured for metabolites and lipids, a physicochemical identifier related to the chemical shape of an analyte that increases the confidence of identification. Second, IM-MS increases peak capacity and the signal-to-noise, improving fingerprinting as well as quantification, and better defining the spatial localization of metabolites and lipids in biological and food samples. Third, IM-MS can be coupled with various fragmentation modes, adding new tools to improve structural characterization and molecular annotation. Here, we review the state-of-the-art in IM-MS technologies and approaches utilized to support metabolomics and lipidomics applications and we assess the challenges and opportunities in this growing field.

Different ion mobility-mass spectrometry coupling techniques to promote metabolomics

Metabolomics has become increasingly popular in recent years for many applications ranging from clinical diagnosis, human health to biotechnological questioning. Despite technological advances, metabolomic studies are still currently limited by the difficulty of identifying all metabolites, a class of compounds with great chemical diversity. Although lengthy chromatographic analyses are often used to obtain comprehensive data, many isobar and isomer metabolites still remain unresolved, which is a critical point for the compound identification. Currently, ion mobility spectrometry is being explored in metabolomics as a way to improve metabolome coverage, analysis throughput and isomer separation. In this review, all the steps of a typical workflow for untargeted metabolomics are discussed considering the use of an ion mobility instrument. An overview of metabolomics is first presented followed by a brief description of ion mobility instrumentation. The ion mobility potential for complex mixture analysis is discussed regarding its coupling with a mass spectrometer alone, providing gas-phase separation before mass analysis as well as its combination with different separation platforms (conventional hyphenation but also multidimensional ion mobility couplings), offering multidimensional separation. Various instrumental and analytical conditions for improving the ion mobility separation are also described. Finally, data mining, including software packages and visualization approaches, as well as the construction of ion mobility databases for the metabolite identification are examined.

Collision Cross-Section Calibration Strategy for Lipid Measurements in SLIM-Based High-Resolution Ion Mobility

Structures for lossless ion manipulation-based high-resolution ion mobility (HRIM) interfaced with mass spectrometry has emerged as a powerful tool for the separation and analysis of many isomeric systems. IM-derived collision cross section (CCS) is increasingly used as a molecular descriptor for structural analysis and feature annotation, but there are few studies on the calibration of CCS from HRIM measurements. Here, we examine the accuracy, reproducibility, and practical applicability of CCS calibration strategies for a broad range of lipid subclasses and develop a straightforward and generalizable framework for obtaining high-resolution CCS values. We explore the utility of using structurally similar custom calibrant sets as well as lipid subclass-specific empirically derived correction factors. While the lipid calibrant sets lowered overall bias of reference CCS values from ∼2-3% to ∼0.5%, application of the subclass-specific correction to values calibrated with a broadly available general calibrant set resulted in biases <0.4%. Using this method, we generated a high-resolution CCS database containing over 90 lipid values with HRIM. To test the applicability of this method to a broader class range typical of lipidomics experiments, a standard lipid mix was analyzed. The results highlight the importance of both class and arrival time range when correcting or scaling CCS values and provide guidance for implementation of the method for more general applications.

The study of human serum metabolome on the health effects of glyphosate and early warning of potential damage

Glyphosate is one of the most widely used herbicide with high efficiency, low toxicity and broad-spectrum. In recent decades, increasing evidence suggests that glyphosate may cause adverse health effects on human beings. However, until now, there is little data on the human metabolic changes. Since occupational workers are under greater health risks than ordinary people, the understanding regarding the health effects of glyphosate on occupational workers is very important for the early warning of potential damage. In this study, serum metabolic alterations in workers from three chemical factories were analyzed by gas chromatography-mass spectrometry (GC-MS) to assess the potential health risks caused by glyphosate at the molecular level. It was found that the levels of 27 metabolites changed significantly in the exposed group compared to the controls. The altered metabolic pathways, including amino acid metabolism, energy metabolism (glycolysis and TCA cycle) and glutathione metabolism (oxidative stress), etc., indicated a series of changes occur in health profile of the human body after glyphosate exposure, and the suboptimal health status of human may further evolve into various diseases, such as Parkinson's disease, renal and liver dysfunction, hepatocellular carcinoma, and colorectal cancer. Subsequently, 4 biomarkers (i.e., benzoic acid, 2-ketoisocaproic acid, alpha-ketoglutarate, and monoolein) were identified as potential biomarkers related to glyphosate exposure based on the partial correlation analyses, linear regression analyses, and FDR correction. Receiver-operating curve (ROC) analyses manifested that these potential biomarkers and their combinational pattern had good performance and potential clinical value to assess the potential health risk associated with glyphosate exposure while retaining high accuracy. Our findings provided new insights on mechanisms of health effects probably induced by glyphosate, and may be valuable for the health risk assessment of glyphosate exposure.

Chemical and biological assessments of environmental mixtures: A review of current trends, advances, and future perspectives

Considering the chemical complexity and toxicity data gaps of environmental mixtures, most studies evaluate the chemical risk individually. However, humans are usually exposed to a cocktail of chemicals in real life. Mixture health assessment remains to be a research area having significant knowledge gaps. Characterization of chemical composition and bioactivity/toxicity are the two critical aspects of mixture health assessments. This review seeks to introduce the recent progress and tools for the chemical and biological characterization of environmental mixtures. The state-of-the-art techniques include the sampling, extraction, rapid detection methods, and the in vitro, in vivo, and in silico approaches to generate the toxicity data of an environmental mixture. Application of these novel methods, or new approach methodologies (NAMs), has increased the throughput of generating chemical and toxicity data for mixtures and thus refined the mixture health assessment. Combined with computational methods, the chemical and biological information would shed light on identifying the bioactive/toxic components in an environmental mixture.

Molecular networking and collision cross section prediction for structural isomer and unknown compound identification in plant metabolomics: a case study applied to Zhanthoxylum heitzii extracts

Mass spectrometry-based plant metabolomics allow large-scale analysis of a wide range of compounds and the discovery of potential new active metabolites with minimal sample preparation. Despite recent tools for molecular networking, many metabolites remain unknown. Our objective is to show the complementarity of collision cross section (CCS) measurements and calculations for metabolite annotation in a real case study. Thus, a systematic and high-throughput investigation of root, bark, branch, and leaf of the Gabonese plant Zhanthoxylum heitzii was performed through ultra-high performance liquid chromatography high-resolution tandem mass spectrometry (UHPLC-QTOF/MS). A feature-based molecular network (FBMN) was employed to study the distribution of metabolites in the organs of the plants and discover potential new components. In total, 143 metabolites belonging to the family of alkaloids, lignans, polyphenols, fatty acids, and amino acids were detected and a semi-quantitative analysis in the different organs was performed. A large proportion of medical plant phytochemicals is often characterized by isomerism and, in the absence of reference compounds, an additional dimension of gas phase separation can result in improvements to both quantitation and compound annotation. The inclusion of ion mobility in the ultra-high performance liquid chromatography mass spectrometry workflow (UHPLC-IMS-MS) has been used to collect experimental CCS values in nitrogen and helium (CCS_N2 and CCS_He) of Zhanthoxylum heitzii features. Due to a lack of reference data, the investigation of predicted collision cross section has enabled comparison with the experimental values, helping in dereplication and isomer identification. Moreover, in combination with mass spectra interpretation, the comparison of experimental and theoretical CCS values allowed annotation of unknown features. The study represents a practical example of the potential of modern mass spectrometry strategies in the identification of medicinal plant phytochemical components.

Potential hypoglycemic, hypolipidemic, and anti-inflammatory bioactive components in Nelumbo nucifera leaves explored by bioaffinity ultrafiltration with multiple targets

Leaf of Nelumbo nucifera Gaertn. (N. nucifera) has been widely used as the main ingredient in lipid-lowering herbal teas and some prescriptions in China due to their excellent hypoglycemic and hypolipidemic effects. However, the active components responsible for these beneficial properties and their mechanisms remain unexplored. In this work, the N. nucifera leaf extracts significantly promoted the glucose consumption of HepG2 cells, and also exhibited remarkable inhibitory activities against α-glucosidase, pancreatic lipase, and COX-2. Furthermore, the top four potential active compounds (N-nornuciferine, Nuciferine, 2-Hydroxy-1-methoxyaporphine, and Isorhamnetin 3-O-glucoside) targeting the above three enzymes were screened out by bioaffinity ultrafiltration with multiple targets coupled with HPLC-MS/MS. The enzyme inhibitory activities of candidate compounds were verified by enzyme inhibition assay and molecular docking. In addition, molecular docking revealed the binding information between the candidate molecules and enzymes. The current study provided valuable information in discovering functional active ingredients from complex medicinal plant extracts.

Plants Metabolome Study: Emerging Tools and Techniques

Metabolomics is now considered a wide-ranging, sensitive and practical approach to acquire useful information on the composition of a metabolite pool present in any organism, including plants. Investigating metabolomic regulation in plants is essential to understand their adaptation, acclimation and defense responses to environmental stresses through the production of numerous metabolites. Moreover, metabolomics can be easily applied for the phenotyping of plants; and thus, it has great potential to be used in genome editing programs to develop superior next-generation crops. This review describes the recent analytical tools and techniques available to study plants metabolome, along with their significance of sample preparation using targeted and non-targeted methods. Advanced analytical tools, like gas chromatography-mass spectrometry (GC-MS), liquid chromatography mass-spectroscopy (LC-MS), capillary electrophoresis-mass spectrometry (CE-MS), fourier transform ion cyclotron resonance-mass spectrometry (FTICR-MS) matrix-assisted laser desorption/ionization (MALDI), ion mobility spectrometry (IMS) and nuclear magnetic resonance (NMR) have speed up precise metabolic profiling in plants. Further, we provide a complete overview of bioinformatics tools and plant metabolome database that can be utilized to advance our knowledge to plant biology.

AlphaTims: Indexing Trapped Ion Mobility Spectrometry-TOF Data for Fast and Easy Accession and Visualization

High-resolution MS-based proteomics generates large amounts of data, even in the standard LC-tandem MS configuration. Adding an ion mobility dimension vastly increases the acquired data volume, challenging both analytical processing pipelines and especially data exploration by scientists. This has necessitated data aggregation, effectively discarding much of the information present in these rich datasets. Taking trapped ion mobility spectrometry (TIMS) on a quadrupole TOF (Q-TOF) platform as an example, we developed an efficient indexing scheme that represents all data points as detector arrival times on scales of minutes (LC), milliseconds (TIMS), and microseconds (TOF). In our open-source AlphaTims package, data are indexed, accessed, and visualized by a combination of tools of the scientific Python ecosystem. We interpret unprocessed data as a sparse four-dimensional matrix and use just-in-time compilation to machine code with Numba, accelerating our computational procedures by several orders of magnitude while keeping to familiar indexing and slicing notations. For samples with more than six billion detector events, a modern laptop can load and index raw data in about a minute. Loading is even faster when AlphaTims has already saved indexed data in an HDF5 file, a portable scientific standard used in extremely large-scale data acquisition. Subsequently, data accession along any dimension and interactive visualization happens in milliseconds. We have found AlphaTims to be a key enabling tool to explore high-dimensional LC-TIMS-Q-TOF data and have made it freely available as an open-source Python package with a stand-alone graphical user interface at https://github.com/MannLabs/alphatims or as part of the AlphaPept 'ecosystem'.

Ion mobility-based sterolomics reveals spatially and temporally distinctive sterol lipids in the mouse brain

Aberrant sterol lipid metabolism is associated with physiological dysfunctions in the aging brain and aging-dependent disorders such as neurodegenerative diseases. There is an unmet demand to comprehensively profile sterol lipids spatially and temporally in different brain regions during aging. Here, we develop an ion mobility-mass spectrometry based four-dimensional sterolomics technology leveraged by a machine learning-empowered high-coverage library (>2000 sterol lipids) for accurate identification. We apply this four-dimensional technology to profile the spatially resolved landscapes of sterol lipids in ten functional regions of the mouse brain, and quantitatively uncover ~200 sterol lipids uniquely distributed in specific regions with concentrations spanning up to 8 orders of magnitude. Further spatial analysis pinpoints age-associated differences in region-specific sterol lipid metabolism, revealing changes in the numbers of altered sterol lipids, concentration variations, and age-dependent coregulation networks. These findings will contribute to our understanding of abnormal sterol lipid metabolism and its role in brain diseases.

Metabolomics-Guided Elucidation of Plant Abiotic Stress Responses in the 4IR Era: An Overview

Plants are constantly challenged by changing environmental conditions that include abiotic stresses. These are limiting their development and productivity and are subsequently threatening our food security, especially when considering the pressure of the increasing global population. Thus, there is an urgent need for the next generation of crops with high productivity and resilience to climate change. The dawn of a new era characterized by the emergence of fourth industrial revolution (4IR) technologies has redefined the ideological boundaries of research and applications in plant sciences. Recent technological advances and machine learning (ML)-based computational tools and omics data analysis approaches are allowing scientists to derive comprehensive metabolic descriptions and models for the target plant species under specific conditions. Such accurate metabolic descriptions are imperatively essential for devising a roadmap for the next generation of crops that are resilient to environmental deterioration. By synthesizing the recent literature and collating data on metabolomics studies on plant responses to abiotic stresses, in the context of the 4IR era, we point out the opportunities and challenges offered by omics science, analytical intelligence, computational tools and big data analytics. Specifically, we highlight technological advancements in (plant) metabolomics workflows and the use of machine learning and computational tools to decipher the dynamics in the chemical space that define plant responses to abiotic stress conditions.

Ion Mobility-High-Resolution Mass Spectrometry (IM-HRMS) for the Analysis of Contaminants of Emerging Concern (CECs): Database Compilation and Application to Urine Samples

Ion mobility mass spectrometry (IM-MS)-derived collision cross section (CCS) values can serve as a valuable additional identification parameter within the analysis of compounds of emerging concern (CEC) in human matrices. This study introduces the first comprehensive database of ^DTCCS_N2 values of 148 CECs and their metabolites including bisphenols, alternative plasticizers (AP), organophosphate flame retardants (OP), perfluoroalkyl chemicals (PFAS), and others. A total of 311 ions were included in the database, whereby the ^DTCCS_N2 values for 113 compounds are reported for the first time. For 105 compounds, more than one ion is reported. Moreover, the ^DTCCS_N2 values of several isomeric CECs and their metabolites are reported to allow a distinction between isomers. Comprehensive quality assurance guidelines were implemented in the workflow of acquiring ^DTCCS_N2 values to ensure reproducible experimental conditions. The reliability and reproducibility of the complied database were investigated by analyzing pooled human urine spiked with 30 AP and OP metabolites at two concentration levels. For all investigated metabolites, the ^DTCCS_N2 values measured in urine showed a percent error of <1% in comparison to database values. ^DTCCS_N2 values of OP metabolites showed an average percent error of 0.12% (50 ng/mL in urine) and 0.15% (20 ng/mL in urine). For AP metabolites, these values were 0.10 and 0.09%, respectively. These results show that the provided database can be of great value for enhanced identification of CECs in environmental and human matrices, which can advance future suspect screening studies on CECs.

Recent applications of ion mobility spectrometry in natural product research

Ion mobility spectrometry (IMS) is a rapid separation technique capable of extracting complementary structural information to chromatography and mass spectrometry (MS). IMS, especially in combination with MS, has experienced inordinate growth in recent years as an analytical technique, and elicited intense interest in many research fields. In natural product analysis, IMS shows promise as an additional tool to enhance the performance of analytical methods used to identify promising drug candidates. Potential benefits of the incorporation of IMS into analytical workflows currently used in natural product analysis include the discrimination of structurally similar secondary metabolites, improving the quality of mass spectral data, and the use of mobility-derived collision cross-section (CCS) values as an additional identification criterion in targeted and untargeted analyses. This review aims to provide an overview of the application of IMS to natural product analysis over the last six years. Instrumental aspects and the fundamental background of IMS will be briefly covered, and recent applications of the technique for natural product analysis will be discussed to demonstrate the utility of the technique in this field.