A comparison of collision cross section values obtained via travelling wave ion mobility-mass spectrometry and ultra high performance liquid chromatography-ion mobility-mass spectrometry: Application to the characterisation of metabolites in rat urine

Progress, applications, and challenges in high-throughput effect-directed analysis for toxicity driver identification - is it time for HT-EDA?

The rapid increase in the production and global use of chemicals and their mixtures has raised concerns about their potential impact on human and environmental health. With advances in analytical techniques, in particular, high-resolution mass spectrometry (HRMS), thousands of compounds and transformation products with potential adverse effects can now be detected in environmental samples. However, identifying and prioritizing the toxicity drivers among these compounds remain a significant challenge. Effect-directed analysis (EDA) emerged as an important tool to address this challenge, combining biotesting, sample fractionation, and chemical analysis to unravel toxicity drivers in complex mixtures. Traditional EDA workflows are labor-intensive and time-consuming, hindering large-scale applications. The concept of high-throughput (HT) EDA has recently gained traction as a means of accelerating these workflows. Key features of HT-EDA include the combination of microfractionation and downscaled bioassays, automation of sample preparation and biotesting, and efficient data processing workflows supported by novel computational tools. In addition to microplate-based fractionation, high-performance thin-layer chromatography (HPTLC) offers an interesting alternative to HPLC in HT-EDA. This review provides an updated perspective on the state-of-the-art in HT-EDA, and novel methods/tools that can be incorporated into HT-EDA workflows. It also discusses recent studies on HT-EDA, HT bioassays, and computational prioritization tools, along with considerations regarding HPTLC. By identifying current gaps in HT-EDA and proposing new approaches to overcome them, this review aims to bring HT-EDA a step closer to monitoring applications.

Predicting ion mobility collision cross sections and assessing prediction variation by combining conventional and data driven modeling

The use of collision cross section (CCS) values derived from ion mobility studies is proving to be an increasingly important tool in the characterization and identification of molecules detected in complex mixtures. Here, a novel machine learning (ML) based method for predicting CCS integrating both molecular modeling (MM) and ML methodologies has been devised and shown to be able to accurately predict CCS values for singly charged small molecular weight molecules from a broad range of chemical classes. The model performed favorably compared to existing models, improving compound identifications for isobaric analytes in terms of ranking and assigning identification probability values to the annotation. Furthermore, charge localization was seen to be correlated with CCS prediction accuracy and with gas-phase proton affinity demonstrating the potential to provide a proxy for prediction error based on chemical structural properties. The presented approach and findings represent a further step towards accurate prediction and application of computationally generated CCS values.

Interplatform comparison between three ion mobility techniques for human plasma lipid collision cross sections

The implementation of ion mobility spectrometry (IMS) in liquid chromatography-high-resolution mass spectrometry (LC-HRMS) workflows has become a valuable tool for improving compound annotation in metabolomics analyses by increasing peak capacity and by adding a new molecular descriptor, the collision cross section (CCS). Although some studies reported high repeatability and reproducibility of CCS determination and only few studies reported good interplatform agreement for small molecules, standardized protocols are still missing due to the lack of reference CCS values and reference materials. We present a comparison of CCS values of approximatively one hundred lipid species either commercially available or extracted from human plasma. We used three different commercial ion mobility technologies from different laboratories, drift tube IMS (DTIMS), travelling wave IMS (TWIMS) and trapped IMS (TIMS), to evaluate both instrument repeatability and interlaboratory reproducibility. We showed that CCS discrepancies of 0.3% (average) could occur depending on the data processing software tools. Moreover, eleven CCS calibrants were evaluated yielding mean RSD below 2% for eight calibrants, ESI Low concentration tuning mix (Tune Mix) showing the lowest RSD (< 0.5%) in both ion modes. Tune Mix calibrated CCS from the three different IMS instruments proved to be well correlated and highly reproducible (R2 > 0.995 and mean RSD ≤ 1%). More than 90% of the lipid CCS had deviations of less than 1%, demonstrating high comparability between techniques, and the possibility to use the CCS as molecular descriptor. We highlighted the need of standardized procedures for calibration, data acquisition, and data processing. This work demonstrates that using harmonized analytical conditions are required for interplatform reproducibility for CCS determination of human plasma lipids.

MobiLipid: A Tool for Enhancing CCS Quality Control of Ion Mobility-Mass Spectrometry Lipidomics by Internal Standardization

Ion mobility-mass spectrometry (IM-MS) offers benefits for lipidomics by obtaining IM-derived collision cross sections (CCS), a conditional property of an ion that can enhance lipid identification. While drift tube (DT) IM-MS retains a direct link to the primary experimental method to derive CCS values, other IM technologies rely solely on external CCS calibration, posing challenges due to dissimilar chemical properties between lipids and calibrants. To address this, we introduce MobiLipid, a novel tool facilitating the CCS quality control of IM-MS lipidomics workflows by internal standardization. MobiLipid utilizes a newly established DTCCSN2 library for uniformly (U)13C-labeled lipids, derived from a U13C-labeled yeast extract, containing 377 DTCCSN2 values. This automated open-source R Markdown tool enables internal monitoring and straightforward compensation for CCSN2 biases. It supports lipid class- and adduct-specific CCS corrections, requiring only three U13C-labeled lipids per lipid class-adduct combination across 10 lipid classes without requiring additional external measurements. The applicability of MobiLipid is demonstrated for trapped IM (TIM)-MS measurements of an unlabeled yeast extract spiked with U13C-labeled lipids. Monitoring the CCSN2 biases of TIMCCSN2 values compared to DTCCSN2 library entries utilizing MobiLipid resulted in mean absolute biases of 0.78% and 0.33% in positive and negative ionization mode, respectively. By applying the CCS correction integrated into the tool for the exemplary data set, the mean absolute CCSN2 biases of 10 lipid classes could be reduced to approximately 0%.

Toward Part-per-Million Precision in the Determination of an Ion's Collision Cross Section Using Multipass Cyclic Ion Mobility

In cyclic ion mobility (cIMS), ions are permitted to travel multiple passes around the drift cell, increasing the distance traveled and the relative separation between ions. This study tests the hypothesis that multiple passes around the cell can also result in improved precision when measuring an ion's mobility and the collision cross section (TWCCS) derived therefrom. Experiments were performed with a diverse set of compounds, including 16 polycyclic aromatic hydrocarbons using gas chromatographic atmospheric pressure chemical ionization and a set of drug molecules by direct infusion electrospray ionization. The average periodic drift time, viz., the average time required for the ion to travel around the cIMS cell once, shifts dramatically, approaching part-per-million (ppm) precision as the number of passes increases to ∼100. Extrapolation of the precision of the CCS values with respect to the number of passes led to the prediction that the precision will reach 1000 ppm after 50 passes, 100 ppm after 100 passes, and <10 ppm after 150 passes. Experiments wherein the number of passes exceeded 100 produced TWCCS values having within-run precisions ranging between 15 and 117 ppm. The improved precision with an increasing number of passes may be a consequence of mitigating space-charge effects by allowing the ions to occupy a larger region of the cIMS cell. A method is proposed to enable practical measurements of TWCCS with ppm precision and is demonstrated to characterize an unknown drug mixture.

Impact of Source Conditions on Collision Cross Section Determination by Trapped Ion Mobility Spectrometry

Collision cross section (CCS) values determined in ion mobility-mass spectrometry (IM-MS) are increasingly employed as additional descriptors in metabolomics studies. CCS values must therefore be reproducible and the causes of deviations must be carefully known and controlled. Here, we analyzed lipid standards by trapped ion mobility spectrometry-mass spectrometry (TIMS-MS) to evaluate the effects of solvent and flow rate in flow injection analysis (FIA), as well as electrospray source parameters including nebulizer gas pressure, drying gas flow rate, and temperature, on the ion mobility and CCS values. The stability of ion mobility experiments was studied over 10 h, which established the need for a delay-time of 20 min to stabilize source parameters (mostly pressure and temperature). Modifications of electrospray source parameters induced shifts of ion mobility peaks and even the occurrence of an additional peak in the ion mobility spectra. This behavior could be essentially explained by ion-solvent cluster formation. Changes in source parameters were also found to impact CCS value measurements, resulting in deviations up to 0.8%. However, internal calibration with the Tune Mix calibrant reduced the CCS deviations to 0.1%. Thus, optimization of source parameters is essential to achieve a good desolvation of lipid ions and avoid misinterpretation of peaks in ion mobility spectra due to solvent effects. This work highlights the importance of internal calibration to ensure interoperable CCS values, usable in metabolomics annotation.

Identification and quantification of bipyridyl dicarboxylic acid isomers by ion mobility spectrometry

The identification of positional isomers is of interest because different isomers have different chemical or biological functions and applications. The analysis of positional isomers is sometimes challenging since they have similar chemical structures and properties. For example, the analysis of mass cannot identify different positional isomers because they have identical mass-to-charge ratios and show a single mass peak in mass spectrometry. In this study, an efficient and simple qualitative and quantitative analytical method for differentiating 2,2'-bipyridine-3,3'-dicarboxylic acid (3,3'-BDA), 2,2'-bipyridine-4,4'-dicarboxylic acid (4,4'-BDA), and 2,2'-bipyridine-5,5'-dicarboxylic acid (5,5'-BDA) was developed by using ion mobility spectrometry (IMS). The results revealed that the three BDA isomers formed non-covalent complexes with cyclodextrins (CDs) and Mg2+ ions in the gas phase: [β-CD+3,3'/4,4'/5,5'-BDA+Mg]2+ and [γ-CD+3,3'/4,4'/5,5'-BDA+Mg]2+, which were distinguished by measuring the mobility of the complexes because of their spatial conformational differences. The peak-to-peak resolution (Rp-p) values of the three isomers of [γ-CD+3,3'/4,4'/5,5'-BDA+Mg]2+ reached 2.983 and 2.892, respectively. The conformations of the ternary complexes simulated by the theoretical calculations revealed the different interactions and shapes of the stereoisomers, and the predicted results agreed with the experimental results. Simultaneously, further studies on the collisional dissociation of the ternary complexes revealed that the dissociation energies of the different complex ions varied were different owing to the diverse different conformations. Finally, the relative quantitative analysis of the different isomers in mixed samples was performed and satisfactory linearity results (R2 > 0.99) were obtained. Thus, an effective analytical method was proposed for the identification and quantification of BDA isomers without chemical derivatization, offering a promising approach for the identification of similar derivatives or positional isomers that could be applied in various fields including chemicals and pharmaceuticals.

Increasing coverage of the urinary polar metabolome using ultra high-performance hydrophobic interaction liquid chromatography combined with linear and cyclic travelling wave ion mobility and mass spectrometry

The use of HILIC-based separations for the analysis of polar metabolites in metabolic phenotyping studies is well established. Here, we demonstrate the increased coverage of the polar metabolome obtained by travelling wave (TW) ion mobility (IM) instruments combined with HILIC and mass spectrometry (MS) for metabotyping rat and mouse urine samples. Profiling was performed using either a linear TW IM-MS based instrument with a path length of 40 cm or an instrument with a cyclic travelling wave analyser (cIM) with a path length of 95 cm. Due to the added resolution afforded by using both the linear and cyclic IM geometries with MS detection (IM-MS) significant increases in feature count (m/z-tR pairs) were generally obtained compared to HILIC-MS alone. In addition, the use of both linear and cyclic IM-MS improved the quality of the mass spectra obtained as a result of the separation of co-eluting analytes. As would be expected from the increased path length of the cyclic IM-MS instrument compared to the linear device, the largest gains in feature detection were obtained for the HILIC-cIM-MS combination. By increasing the resolution of coeluting components, the cyclic IM-MS instrumentation also provided the largest improvement in the quality of the mass spectral data obtained. When applied to mouse urines obtained from both control and gefitinib-dosed mice, time-related changes were detected in those obtained from the treated animals that were not seen in the controls. Polar metabolites affected by drug administration included, but were not limited to, hypoxanthine, 1,3-dimethyluracil and acetylcarnitine. The changes seen in the relative concentrations of these endogenous metabolites appeared to be related to drug concentrations in the plasma and urine suggesting a pharmacometabodynamic link.

(Re-)use and (re-)analysis of publicly available metabolomics data

Metabolomics, the systematic measurement of small molecules (<1000 Da) in a given biological sample, is a fast-growing field with many different applications. In contrast to transcriptomics and proteomics, sharing of data is not as widespread in metabolomics, though more scientists are sharing their data nowadays. However, to improve data analysis tools and develop new data analytical approaches and to improve metabolite annotation and identification, sharing of reference data is crucial. Here, different possibilities to share (metabolomics) data are reviewed and some recent approaches and applications regarding the (re-)use and (re-)analysis are highlighted.

Enzyme activity- and chemometrics-assisted comprehensive two-dimensional liquid chromatography coupled with ion mobility quadrupole time-of-flight mass spectrometry for the analysis of honeysuckle

A unique and effective comprehensive two-dimensional liquid chromatography system was established and applied for the analysis of bioactive components in honeysuckle. Under the optimal conditions, Eclipse Plus C₁₈ (2.1 × 100 mm, 3.5 μm, Agilent) and SB-C₁₈ (4.6 × 50 mm, 1.8 μm, Agilent) columns were chosen for the first dimension (¹D) and the second dimension (²D) separation. The optimal flow rates of ¹D and ²D were 0.12 mL/min and 2.0 mL/min, respectively. Additionally, the proportion of organic solution was optimized to enhance orthogonality and integrated shift, and full gradient elution mode was adopted to improve chromatographic resolution. Furthermore, a total of 57 compounds were identified by molecular weight, retention time and collision cross-section value obtained from ion mobility mass spectrometry. Based on the data obtained from the principal component analysis, partial least squares discriminant analysis, and hierarchical cluster analysis, the categories of honeysuckle in different regions were significantly different. Moreover, the half maximal inhibitory concentration values of most samples were between 0.37 and 1.55 mg/mL, and most samples were potent α-glucosidase inhibitors, which is better for the evaluation of the quality of drugs from two aspects of substance content and activity.

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.

Ion Mobility Mass Spectrometry Development and Applications

Although as an analytical method with high specificity and high sensitivity, mass spectrometry (MS) has a wide range of applications in many fields, it still needs other technologies as the assist and supplement to enhance the scope and capability of analysis. Coupling with ion mobility (IM) can make an enhancement effect in the field of pharmaceutical analysis as a supplementary method. The two-dimensional mass technology improves the confidence of compounds annotations while increasing peak capacity, with the gradual deepening of theoretical research on IM-MS, it has shown unique advantages in the complex analysis conditions. IM-MS owns great potential for improving the depth, range, dimension of in-depth drug research. In this review, the principle, instruments and methods, applications, advantages and limitations of IM-MS are described. Here, we also elaborate on the prospects in structural evaluation, separation, and identification of complex compounds for the drug discovery and development phase and the great advantages of macromolecules and omics.

Paper Spray Ionization Ion Mobility Mass Spectrometry of Sebum Classifies Biomarker Classes for the Diagnosis of Parkinson's Disease

Parkinson's disease (PD) is the second most common neurodegenerative disorder, and identification of robust biomarkers to complement clinical diagnosis will accelerate treatment options. Here, we demonstrate the use of direct infusion of sebum from skin swabs using paper spray ionization coupled with ion mobility mass spectrometry (PS-IM-MS) to determine the regulation of molecular classes of lipids in sebum that are diagnostic of PD. A PS-IM-MS method for sebum samples that takes 3 min per swab was developed and optimized. The method was applied to skin swabs collected from 150 people and elucidates ∼4200 features from each subject, which were independently analyzed. The data included high molecular weight lipids (>600 Da) that differ significantly in the sebum of people with PD. Putative metabolite annotations of several lipid classes, predominantly triglycerides and larger acyl glycerides, were obtained using accurate mass, tandem mass spectrometry, and collision cross section measurements.

Applications of ion mobility-mass spectrometry in the chemical analysis in traditional Chinese medicines

离子淌度质谱(IM-MS)是一种将离子淌度分离与质谱分析相结合的新型分析技术。IM-MS的主要优势不仅是在质谱检测前提供了基于气相离子形状、大小、电荷数等因素的多一维分离,而且能够提供碰撞截面积、漂移时间等质谱信息进而辅助化合物鉴定。近年来,随着IM-MS技术的不断发展,该技术在中药化学成分分析中受到越来越多的关注。首先,IM-MS已成功应用于改善中药复杂成分尤其是同分异构体或等量异位素等成分的分离;其次,IM-MS可通过多重碎裂模式辅助高质量中药小分子质谱信息的获取;此外,IM-MS提供的高维质谱数据信息还可促进中药复杂体系多成分的整合分析。该文在对IM-MS分类和基本原理进行概述的基础上,从分离能力及分离策略、多重碎裂模式、多维质谱数据处理策略3个方面,重点综述了IM-MS在中药化学成分分析中的应用,以期为IM-MS在中药化学成分研究提供参考。

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.

Direct infusion electrospray ionization-ion mobility-mass spectrometry for rapid metabolite marker discovery of medicinal Phellodendron Bark

Ion-mobility mass spectrometry (IM-MS) currently serves as a powerful tool for the structural identification of numerous biological compounds and small molecules. In this work, rapid metabolomic analysis of closely-related herbal medicines by direct injection ion mobility-quadrupole time-of-flight mass spectrometry (DI-IM-QTOF MS) was established. Phellodendron chinense Bark (PC) and Phellodendron amurense Bark (PA) were studied as a case. Thirty-three batches of PC and twenty-two batches of PA have been directly injected in electrospray ionization-IM-QTOF MS in positive mode. Without chromatographic separation, each run was completed within 3 min. After data alignment and statistical analysis, a total of seven chemical markers were found (p-value < 0.05, VIP > 1.00). Among them, the ion m/z 342.17 and m/z 356.18 present a single peak in the drift spectrum, respectively, but their drift time has a certain deviation compared with the pure substance of known compounds. In addition, the MS/MS spectra also confirmed that the single peak includes two chemical isomers. To investigate the composition ratio of individual isomers, the calibration curves of relative drift time (rDT) based on the standard superposition method were established, which were found to fit the least square regression. The ion [M]⁺m/z 342.17 was recognized consisting of magnoflorine (MAG) and phellodendrine (PHE), and their composition ratio in PA and PC samples was calculated. The results were compared with those obtained by the HPLC quantitative method, which produced equivalent quantification results. Our DI-IM-QTOF MS methodology provides an additional methodology for the relative quantification of unresolved isomers in drift tube IM-MS and offers DI-IM-QTOF MS based metabolomics.

In Silico Collision Cross Section Calculations to Aid Metabolite Annotation

The interpretation of ion mobility coupled to mass spectrometry (IM-MS) data to predict unknown structures is challenging and depends on accurate theoretical estimates of the molecular ion collision cross section (CCS) against a buffer gas in a low or atmospheric pressure drift chamber. The sensitivity and reliability of computational prediction of CCS values depend on accurately modeling the molecular state over accessible conformations. In this work, we developed an efficient CCS computational workflow using a machine learning model in conjunction with standard DFT methods and CCS calculations. Furthermore, we have performed Traveling Wave IM-MS (TWIMS) experiments to validate the extant experimental values and assess uncertainties in experimentally measured CCS values. The developed workflow yielded accurate structural predictions and provides unique insights into the likely preferred conformation analyzed using IM-MS experiments. The complete workflow makes the computation of CCS values tractable for a large number of conformationally flexible metabolites with complex molecular structures.

Prediction of Collision Cross Section Values: Application to Non-Intentionally Added Substance Identification in Food Contact Materials

The synthetic chemicals in food contact materials can migrate into food and endanger human health. In this study, the traveling wave collision cross section in nitrogen values of more than 400 chemicals in food contact materials were experimentally derived by traveling wave ion mobility spectrometry. A support vector machine-based collision cross section (CCS) prediction model was developed based on CCS values of food contact chemicals and a series of molecular descriptors. More than 92% of protonated and 81% of sodiated adducts showed a relative deviation below 5%. Median relative errors for protonated and sodiated molecules were 1.50 and 1.82%, respectively. The model was then applied to the structural annotation of oligomers migrating from polyamide adhesives. The identification confidence of 11 oligomers was improved by the direct comparison of the experimental data with the predicted CCS values. Finally, the challenges and opportunities of current machine-learning models on CCS prediction were also discussed.

Metabolomic Profiling of Human Urine Samples Using LC-TIMS-QTOF Mass Spectrometry

The identification of metabolites in biological samples is challenging due to their chemical and structural diversity. Ion mobility spectrometry (IMS) separates ionized molecules based on their mobility in a carrier buffer gas giving information about the ionic shape by measuring the rotationally averaged collision cross-section (CCS) value. This orthogonal descriptor, in combination with the m/z, isotopic pattern distribution, and MS/MS spectrum, has the potential to improve the identification of molecular molecules in complex mixtures. Urine metabolomics can reveal metabolic differences, which arise as a result of a specific disease or in response to therapeutic intervention. It is, however, complicated by the presence of metabolic breakdown products derived from a wide range of lifestyle and diet-related byproducts, many of which are poorly characterized. In this study, we explore the use of trapped ion mobility spectrometry (TIMS) via LC parallel accumulation with serial fragmentation (PASEF) for urine metabolomics. A total of 362 urine metabolites were characterized from 80 urine samples collected from healthy volunteers using untargeted metabolomics employing HILIC and RP chromatography. Additionally, three analytes (Trp, Phe, and Tyr) were selected for targeted quantification. Both the untargeted and targeted data was highly reproducible and reported CCS measurements for identified metabolites were robust in the presence of the urine matrix. A comparison of CCS values among different laboratories was also conducted, showing less than 1.3% ΔCCS values across different platforms. This is the first report of a human urine metabolite database compiled with CCS values experimentally acquired using an LC-PASEF TIMS-qTOF platform.

Investigation into Small Molecule Isomeric Glucuronide Metabolite Differentiation Using In Silico and Experimental Collision Cross-Section Values

Identifying isomeric metabolites remains a challenging and time-consuming process with both sensitivity and unambiguous structural assignment typically only achieved through the combined use of LC-MS and NMR. Ion mobility mass spectrometry (IMMS) has the potential to produce timely and accurate data using a single technique to identify drug metabolites, including isomers, without the requirement for in-depth interpretation (cf. MS/MS data) using an automated computational pipeline by comparison of experimental collision cross-section (CCS) values with predicted CCS values. An ion mobility enabled Q-Tof mass spectrometer was used to determine the CCS values of 28 (14 isomeric pairs of) small molecule glucuronide metabolites, which were then compared to two different in silico models; a quantum mechanics (QM) and a machine learning (ML) approach to test these approaches. The difference between CCS values within isomer pairs was also assessed to evaluate if the difference was large enough for unambiguous structural identification through in silico prediction. A good correlation was found between both the QM- and ML-based models and experimentally determined CCS values. The predicted CCS values were found to be similar between ML and QM in silico methods, with the QM model more accurately describing the difference in CCS values between isomer pairs. Of the 14 isomeric pairs, only one (naringenin glucuronides) gave a sufficient difference in CCS values for the QM model to distinguish between the isomers with some level of confidence, with the ML model unable to confidently distinguish the studied isomer pairs. An evaluation of analyte structures was also undertaken to explore any trends or anomalies within the data set.

The Pharmacometabodynamics of Gefitinib after Intravenous Administration to Mice: A Preliminary UPLC-IM-MS Study

The effects of intravenous gefitinib (10 mg/kg), an anilinoquinazoline thymidylate kinase inhibitor (TKI), selective for the epidermal growth factor receptor (EGFR), on the urinary metabotypes of mice were studied. We hypothesized that, in response to the administration of gefitinib, there might be significant changes in the excretion of many endogenous metabolites in the urine, which could be correlated with the plasma pharmacokinetics (PK) of the drug. In order to investigate this conjecture, urine from male C57 BL6 mice was collected before IV dosing (10 mg/kg) and at 0–3, 3–8, and 8–24 h post-dose. The samples were profiled by UPLC/IM/MS and compared with the profiles obtained from undosed control mice with the data analyzed using multivariate statistical analysis (MVA). This process identified changes in endogenous metabolites over time and these were compared with drug and drug metabolite PK and excretion. While the MVA of these UPLC/IM/MS data did indeed reveal time-related changes for endogenous metabolites that appeared to be linked to drug administration, this analysis did not highlight the presence of either the drug or its metabolites in urine. Endogenous metabolites affected by gefitinib administration were identified by comparison of mass spectral, retention time and ion mobility-derived collision cross section data (compared to authentic standards wherever possible). The changes in endogenous metabolites resulting from gefitinib administration showed both increases (e.g., tryptophan, taurocholic acid, and the dipeptide lysyl-arginine) and decreases (e.g., deoxyguanosine, 8-hydroxydeoxyguanosine, and asparaginyl-histidine) relative to the control animals. By 8–24 h, the post-dose concentrations of most metabolites had returned to near control values. From these studies, we conclude that changes in the amounts of endogenous metabolites excreted in the urine mirrored, to some extent, the plasma pharmacokinetics of the drug. This phenomenon is similar to pharmacodynamics, where the pharmacological effects are related to the drug concentrations, and by analogy, we have termed this effect “pharmacometabodynamics”.

Ion Mobility–Mass Spectrometry for Bioanalysis

This paper aims to cover the main strategies based on ion mobility spectrometry (IMS) for the analysis of biological samples. The determination of endogenous and exogenous compounds in such samples is important for the understanding of the health status of individuals. For this reason, the development of new approaches that can be complementary to the ones already established (mainly based on liquid chromatography coupled to mass spectrometry) is welcomed. In this regard, ion mobility spectrometry has appeared in the analytical scenario as a powerful technique for the separation and characterization of compounds based on their mobility. IMS has been used in several areas taking advantage of its orthogonality with other analytical separation techniques, such as liquid chromatography, gas chromatography, capillary electrophoresis, or supercritical fluid chromatography. Bioanalysis is not one of the areas where IMS has been more extensively applied. However, over the last years, the interest in using this approach for the analysis of biological samples has clearly increased. This paper introduces the reader to the principles controlling the separation in IMS and reviews recent applications using this technique in the field of bioanalysis.

Rapid determination of the pharmacokinetics and metabolic fate of gefitinib in the mouse using a combination of UPLC/MS/MS, UPLC/QToF/MS, and ion mobility (IM)-enabled UPLC/QToF/MS

The metabolism and pharmacokinetics of gefitinib (Iressa^®, N-(3-chloro-4-fluorophenyl)-7-methoxy-6-(3-morpholino-propoxy)quinazolin-4-amine), a selective thymidylate kinase inhibitor for the epidermal growth factor receptor (EGFR), was studied after IV and PO administration to male C57BL6 mice at 10 and 50 mg/kg respectively.The pharmacokinetics and metabolism of gefitinib were investigated using a range of rapid UHPLC-MS and UHPLC-IM-HRMS methods, using both reversed-phase (RP) and hydrophilic interaction liquid chromatography (HILIC), to rapidly determine the drugs pharmacokinetics and metabolic fate.Rapid oral absorption resulted in peak plasma concentrations at 1 h of ca. 7 µg/mL, that declined with a half-life of 3.8 h (2.6 h for the IV route), and providing an estimated oral bioavailability of 53%. Gefitinib itself was the major circulating drug-related compound in plasma extracts, with a total of 11 metabolites identified.The urinary profiles determined using both HILIC and RP-UPLC-IM-MS detected gefitinib and 10 metabolites or 15 metabolites respectively including the detection of a number of novel glucuronide conjugates.Despite rapid, sub 5 min, LC profiling methods being employed metabolite coverage was shown to be high and compared well with that of previous studies.

Travelling Wave Ion Mobility-Derived Collision Cross Section for Mycotoxins: Investigating Interlaboratory and Interplatform Reproducibility

Parent and modified mycotoxin analysis remains a challenge because of their chemical diversity, the presence of isomeric forms, and the lack of analytical standards. The creation and application of a collision cross section (CCS) database for mycotoxins may bring new opportunities to overcome these analytical challenges. However, it is still an open question whether common CCS databases can be used independently from the instrument type and ion mobility mass spectrometry (IM-MS) technologies, which utilize different methodologies for determining the gas-phase mobility. Here, we demonstrated the reproducibility of CCS measurements for mycotoxins in an interlaboratory study (average RSD 0.14% ± 0.079) and across different traveling wave IM-MS (TWIMS) systems commercially available (ΔCCS% < 2). The separation in the drift time dimension of critical pairs of isomers for modified mycotoxins was also achieved. In addition, the comparison of measured and predicted CCS values, including regulated and emerging mycotoxins, was addressed.

Profiling of the known-unknown Passiflora variant complement by liquid chromatography - Ion mobility - Mass spectrometry

Liquid Chromatography - Ion Mobility - Mass Spectrometry (LC-IM-MS) was utilized for non-targeted screening analysis to understand the variance in the composition of Passiflora species. Multivariate analysis was employed to explore a chemometric processing strategy for IM based Passiflora variant differentation. This approach was applied to the comparative analyses of extracts of the medicinal plants Passiflora alata, Passiflora edulis, Passiflora incarnata and Passiflora caerulea. In total, 255 occurrences of IM-MS resolved coeluting marker isomers and isobaric species were detected, providing increased coverage and specificity of species component markers compared to conventional LC-MS. A large proportion of medical plant phytochemical analysis information often remains redundant in that it is not phenotypic specific. Here, generation of Passiflora variant 'known-unknown' libraries has been used to compare Passiflora species to investigate unique variant features. Investigations of predicted collision cross section have enabled comparison of an element of the 'known-unknown' IM isomeric complement to be performed, facilitating a reduction in the number of possible variant unique isomeric identifications. In combination with spectral interpretation, it has been possible to resassign isomeric 'known-unknowns' as 'knowns'. The strategies employed illustrates the potential to facilitate identification of medicinal plant phytochemical components.

The Age of Data-Driven Proteomics: How Machine Learning Enables Novel Workflows

A lot of energy in the field of proteomics is dedicated to the application of challenging experimental workflows, which include metaproteomics, proteogenomics, data independent acquisition (DIA), non-specific proteolysis, immunopeptidomics, and open modification searches. These workflows are all challenging because of ambiguity in the identification stage; they either expand the search space and thus increase the ambiguity of identifications, or, in the case of DIA, they generate data that is inherently more ambiguous. In this context, machine learning-based predictive models are now generating considerable excitement in the field of proteomics because these predictive models hold great potential to drastically reduce the ambiguity in the identification process of the above-mentioned workflows. Indeed, the field has already produced classical machine learning and deep learning models to predict almost every aspect of a liquid chromatography-mass spectrometry (LC-MS) experiment. Yet despite all the excitement, thorough integration of predictive models in these challenging LC-MS workflows is still limited, and further improvements to the modeling and validation procedures can still be made. Therefore, highly promising recent machine learning developments in proteomics are pointed out in this viewpoint, alongside some of the remaining challenges.

Metabolic Phenotyping Using UPLC–MS and Rapid Microbore UPLC–IM–MS: Determination of the Effect of Different Dietary Regimes on the Urinary Metabolome of the Rat

A rapid reversed-phase gradient method employing a 50 mm × 1 mm i.d., C18 microbore column, combined with ion mobility and high-resolution mass spectrometry, was applied to the metabolic phenotyping of urine samples obtained from rats receiving different diets. This method was directly compared to a “conventional” method employing a 150 × 2.1 mm i.d. column packed with the same C18 bonded phase using the same samples. Multivariate statistical analysis of the resulting data showed similar class discrimination for both microbore and conventional methods, despite the detection of fewer mass/retention time features by the former. Multivariate statistical analysis highlighted a number of ions that represented diet-specific markers in the samples. Several of these were then identified using the combination of mass, ion-mobility-derived collision cross section and retention time including N-acetylglutamate, urocanic acid, and xanthurenic acid. Kynurenic acid was tentatively identified based on mass and ion mobility data.

Interlaboratory and Interplatform Study of Steroids Collision Cross Section by Traveling Wave Ion Mobility Spectrometry

Collision cross section (CCS) databases based on single-laboratory measurements must be cross-validated to extend their use in peak annotation. This work addresses the validation of the first comprehensive ^TWCCS_N2 database for steroids. First, its long-term robustness was evaluated (i.e., a year and a half after database generation; Synapt G2-S instrument; bias within ±1.0% for 157 ions, 95.7% of the total ions). It was further cross-validated by three external laboratories, including two different TWIMS platforms (i.e., Synapt G2-Si and two Vion IMS QToF; bias within the threshold of ±2.0% for 98.8, 79.9, and 94.0% of the total ions detected by each instrument, respectively). Finally, a cross-laboratory ^TWCCS_N2 database was built for 87 steroids (142 ions). The cross-laboratory database consists of average ^TWCCS_N2 values obtained by the four TWIMS instruments in triplicate measurements. In general, lower deviations were observed between ^TWCCS_N2 measurements and reference values when the cross-laboratory database was applied as a reference instead of the single-laboratory database. Relative standard deviations below 1.5% were observed for interlaboratory measurements (<1.0% for 85.2% of ions) and bias between average values and ^TWCCS_N2 measurements was within the range of ±1.5% for 96.8% of all cases. In the context of this interlaboratory study, this threshold was also suitable for ^TWCCS_N2 measurements of steroid metabolites in calf urine. Greater deviations were observed for steroid sulfates in complex urine samples of adult bovines, showing a slight matrix effect. The implementation of a scoring system for the application of the CCS descriptor in peak annotation is also discussed.

Identification and quantification of amino acids and related compounds based on Differential Mobility Spectrometry

A new metabolite descriptor allowing fast quantification for the diagnosis of metabolic diseases.