Ion mobility-derived collision cross section as an additional measure for lipid fingerprinting and identification

Discrimination of Common E. coli Strains in Urine by Liquid Chromatography-Ion Mobility-Tandem Mass Spectrometry and Machine Learning

Accurate identification of bacterial strains in clinical samples is essential to provide an appropriate antibiotherapy to the patient and reduce the prescription of broad-spectrum antimicrobials, leading to antibiotic resistance. In this study, we utilized the combination of a multidimensional analytical technique, liquid chromatography-ion mobility-tandem mass spectrometry (LC-IM-MS/MS), and machine learning to accurately identify and distinguish 11 Escherichia coli (E. coli) strains in artificially contaminated urine samples. Machine learning was utilized on the LC-IM-MS/MS data of the inoculated urine samples to reveal lipid, metabolite, and peptide isomeric biomarkers for the identification of the bacteria strains. Tandem MS and LC separation proved effective in discriminating diagnostic isomers in the negative ion mode, while IM separation was more effective in resolving conformational biomarkers in the positive ion mode. Using hierarchical clustering, the strains are clustered accurately according to their group highlighting the uniqueness of the discriminating biomarkers to the class of each E. coli strain. These results show the great potential of using LC-IM-MS/MS and machine learning for targeted omics applications to diagnose infectious diseases in various environmental and clinical samples accurately.

A strategy to comprehensively and quickly identify the chemical constituents in Wuteng tablets by ultra-high-performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry

Population growth and improved industrialization have led to a sharp rise in the demand for plant medicine. In recent years, there has been a general concern about developing new medicinal resources, cutting down on pharmaceutical waste, and discovering new, effective components of traditional Chinese medicine. A novel medication called Wuteng tablets is made from Schisandra chinensis stems and shows promise as a treatment for Alzheimer's disease. This work is the first development of an overall identification technique based on ultra-high-performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry (UHPLC-Q/TOF-MS). Using the MS-DIAL integrated informatics platform and UNIFI software, the chemical components of Wuteng tablets were identified, and the amount of lignin in the tablets was ascertained. This study will identify the chemical components of such medications, aid in the development and utilization of medicinal plant resources, and serve as a foundation for the analysis of the components of their biopharmaceutical origin.

Advancing Protein Analysis: A Low-Pressure Drift Tube Orbitrap Mass Spectrometer for Ultraviolet Photodissociation-Based Structural Characterization

Owing to its ability to generate extensive fragmentation of proteins, ultraviolet photodissociation (UVPD) mass spectrometry (MS) has emerged as a versatile ion activation technique for the structural characterization of native proteins and protein complexes. Interpreting these fragmentation patterns provides insight into the secondary and tertiary structures of protein ions. However, the inherent complexity and diversity of proteins often pose challenges in resolving their numerous conformations. To address this limitation, we combined UVPD-MS with drift tube ion mobility, offering potential to acquire conformationally selective MS/MS information. A low-pressure drift tube (LPDT) Orbitrap mass spectrometer equipped with 193 nm UVPD capabilities enables the analysis of protein conformers through the analysis of arrival time distributions (ATDs) of individual fragment ions. ATDs of fragment ions are compared for different backbone cleavage sites of the protein or different precursor charge states to give information about regions of potential folding or elongation. This integrated platform offers promise for advancing our understanding of protein structures in the gas phase.

Advances of Ion Mobility Platform for Plant Metabolomics

Metabolomics aims to profile the extensive array of metabolites that exists in different types of matrices using modern analytical techniques. These techniques help to separate, identify, and quantify the plethora of chemical compounds at various analytical platforms. Hence, ion mobility spectrometry (IMS) has emerged as an advanced analytical approach, exclusively owing to the 3D separation of metabolites and their isomers. Furthermore, separated metabolites are identified based on their mass fragmentation pattern and CCS (collision cross-section) values. The IMS provides an advanced alternative dimension to separate the isomeric metabolites with enhanced throughput with lesser chemical noise. Thus, the present review highlights the types, factors affecting the resolution, and applications of IMMS (Ion mobility mass spectrometry) for isomeric separations, and ionic contaminants in the plant samples. Furthermore, an overview of IMS-based applications for the identification of plant metabolites (volatile and non-volatile) over the last few decades has been discussed, followed by future assumptions for creating IM-based databases. Such approaches could be significant to accelerate and improve our knowledge of the vast chemical diversity found in plants.

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.

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%.

Rapid Characterization of Phospholipids from Biological Matrix Enabled by Indium Tin Oxide (ITO) Coated Slide Assisted Enrichment MALDI Mass Spectrometry

Lipidomic analysis of human serum is essential to monitor the individual's health status. Herein, we develop a facile strategy for rapid characterization of phospholipids in human serum via indium tin oxide (ITO) coated glass slide solid phase extraction MALDI mass spectrometry (ITO-SPE-MALDI-MS). Phospholipid species are retained on ITO slide via solid phase extraction owing to the unique property of the ITO material; the measurement of phospholipid species from 1 μl human serum within 2 min is achievable. A comparison of ITO-SPE strategy with conventional extraction methods was further carried out using liquid chromatography-mass spectrometry (LC-MS) and ion-mobility mass spectrometry (IM-MS), resulting in a comparable enrichment performance for the phospholipid analysis. Furthermore, rapid lipidomic profiling of serum samples from human colorectal cancer patients and cell lines was demonstrated. Our results indicate that ITO-SPE-MALDI-MS provides a higher throughput strategy for the analysis of phospholipid species in complex biological mixtures, showcasing its potential for applications in the analysis of clinical biofluids.

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.

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.

Sodium doping and trapped ion mobility spectrometry improve lipid detection for novel MALDI-MSI analysis of oats

Oat (Avena sativa L.) is an important cereal grain with a unique nutritional profile including a high proportion of lipids. Understanding lipid composition and distribution in oats is valuable for plant, food and nutritional research, and can be achieved using MALDI mass spectrometry imaging (MALDI-MSI). However, this approach presents several challenges for sample preparation (hardness of grains) and analysis (isobaric and isomeric properties of lipids). Here, oat sections were successfully mounted onto gelatin-coated indium tin oxide slides with minimal tearing. Poor detection of triacylglycerols was resolved by applying sodium chloride during mounting, increasing signal intensity. In combination with trapped ion mobility spectrometry (TIMS), lipid identification significantly improved, and we report the separation of several isobaric and isomeric lipids with visualisation of their "true" spatial distributions. This study describes a novel MALDI-TIMS-MSI analytical technique for oat lipids, which may be used to improve the discovery of biomarkers for grain quality.

Integration of non-targeted multicomponent profiling, targeted characteristic chromatograms and quantitative to accomplish systematic quality evaluation strategy of Huo-Xiang-Zheng-Qi oral liquid

Huo-Xiang-Zheng-Qi oral liquid (HXZQOL) is a well-known traditional Chinese medicine formula for the treatment of gastrointestinal diseases, with the pharmacologic effects of antiinflammatory, immune protection and gastrointestinal motility regulation. More significantly, HXZQOL is recommended for the treatment of COVID-19 patients with gastrointestinal symptoms, and it has been clinically proven to reduce the inflammatory response in patients with COVID-19. However, the effective and overall quality control of HXZQOL is currently limited due to its complex composition, especially the large amount of volatile and non-volatile active components involved. In this study, aimed to fully develop a comprehensive strategy based on non-targeted multicomponent identification, targeted authentication and quantitative analysis for quality evaluation of HXZQOL from different batches. Firstly, the non-targeted high-definition MSE (HDMSE) approach is established based on UHPLC/IM-QTOF-MS, utilized for multicomponent comprehensive characterization of HXZQOL. Combined with in house library-driven automated peak annotation and comparison of 47 reference compounds, 195 components were initially identified. In addition, HS-SPME-GC-MS was employed to analyze the volatile organic compounds (VOCs) in HXZQOL, and a total of 61 components were identified by comparison to the NIST database, reference compounds as well as retention indices. Secondly, based on the selective ion monitoring (SIM) of 24 "identity markers" (involving each herbal medicine), characteristic chromatograms (CCs) were established on LC-MS and GC-MS respectively, to authenticate 15 batches of HXZQOL samples. The targeted-SIM CCs showed that all marker compounds in 15 batches of samples could be accurately monitored, which could indicate preparations authenticity. Finally, a parallel reaction monitoring (PRM) method was established and validated to quantify the nine compounds in 15 batches of HXZQOL. Conclusively, this study first reports chemical-material basis, SIM CCs and quality evaluation of HXZQOL, which is of great implication to quality control and ensuring the authenticity of the preparation.

Investigating Performance of the SLIM-Based High Resolution Ion Mobility Platform for Separation of Isomeric Phosphatidylcholine Species

Lipids are structurally diverse molecules that play a pivotal role in a plethora of biological processes. However, deciphering the biological roles of the specific lipids is challenging due to the existence of numerous isomers. This high chemical complexity of the lipidome is one of the major challenges in lipidomics research, as the traditional liquid chromatography-mass spectrometry (LC-MS) based approaches are often not powerful enough to resolve these isomeric and isobaric nuances within complex samples. Thus, lipids are uniquely suited to the benefits provided by multidimensional liquid chromatography-ion mobility-mass spectrometry (LC-IM-MS) analysis. However, many forms of lipid isomerism, including double-bond positional isomers and regioisomers, are structurally similar such that their collision cross section (CCS) differences are unresolvable via conventional IM approaches. Here we evaluate the performance of a high resolution ion mobility (HRIM) system based on structures for lossless ion manipulation (SLIM) technology interfaced to a high resolution quadrupole time-of-flight (QTOF) analyzer to address the noted lipidomic isomerism challenge. SLIM implements the traveling wave ion mobility technique along an ∼13 m ion path, providing longer path lengths to enable improved separation of isomeric features. We demonstrate the power of HRIM-MS to dissect isomeric PC standards differing only in double bond (DB) and stereospecific number (SN) positions. The partial separation of protonated DB isomers is significantly enhanced when they are analyzed as metal adducts. For sodium adducts, we achieve close to baseline separation of three different PC 18:1/18:1 isomers with different cis-double bond locations. Similarly, PC 18:1/18:1 (cis-9) can be resolved from the corresponding PC 18:1/18:1 (trans-9) form. The separation capacity is further enhanced when using silver ion doping, enabling the baseline separation of regioisomers that cannot be resolved when measured as sodium adducts. The sensitivity and reproducibility of the approach were assessed, and the performance for more complex mixtures was benchmarked by identifying PC isomers in total brain and liver lipid extracts.

A practical strategy enabling more reliable identification of ginsenosides from Panax quinquefolius flower by dimension-enhanced liquid chromatography/mass spectrometry and quantitative structure-retention relationship-based retention behavior prediction

To accurately identify the metabolites is crucial in a number of research fields, and discovery of new compounds from the natural products can benefit the development of new drugs. However, the preferable phytochemistry or liquid chromatography/mass spectrometry approach is time-/labor-extensive or receives unconvincing identifications. Herein, we presented a strategy, by integrating offline two-dimensional liquid chromatography/ion mobility-quadrupole time-of-flight mass spectrometry (2D-LC/IM-QTOF-MS), exclusion list-containing high-definition data-dependent acquisition (HDDDA-EL), and quantitative structure-retention relationship (QSRR) prediction of the retention time (tR), to facilitate the in-depth and more reliable identification of herbal components and thus to discover new compounds more efficiently. Using the saponins in Panax quinquefolius flower (PQF) as a case, high orthogonality (0.79) in separating ginsenosides was enabled by configuring the XBridge Amide and CSH C18 columns. HDDDA-EL could improve the coverage in MS2 acquisition by 2.26 folds compared with HDDDA (2933 VS 1298). Utilizing 106 reference compounds, an accurate QSRR prediction model (R2 = 0.9985 for the training set and R2 = 0.88 for the validation set) was developed based on Gradient Boosting Machine (GBM), by which the predicted tR matching could significantly reduce the isomeric candidates identification for unknown ginsenosides. Isolation and establishment of the structures of two malonylginsenosides by NMR partially verified the practicability of the integral strategy. By these efforts, 421 ginsenosides were identified or tentatively characterized, and 284 thereof were not ever reported from the Panax species. The current strategy is thus powerful in the comprehensive metabolites characterization and rapid discovery of new compounds from the natural products.

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.

Combining Experimental with Computational Infrared and Mass Spectra for High-Throughput Nontargeted Chemical Structure Identification

The inability to identify the structures of most metabolites detected in environmental or biological samples limits the utility of nontargeted metabolomics. The most widely used analytical approaches combine mass spectrometry and machine learning methods to rank candidate structures contained in large chemical databases. Given the large chemical space typically searched, the use of additional orthogonal data may improve the identification rates and reliability. Here, we present results of combining experimental and computational mass and IR spectral data for high-throughput nontargeted chemical structure identification. Experimental MS/MS and gas-phase IR data for 148 test compounds were obtained from NIST. Candidate structures for each of the test compounds were obtained from PubChem (mean = 4444 candidate structures per test compound). Our workflow used CSI:FingerID to initially score and rank the candidate structures. The top 1000 ranked candidates were subsequently used for IR spectra prediction, scoring, and ranking using density functional theory (DFT-IR). Final ranking of the candidates was based on a composite score calculated as the average of the CSI:FingerID and DFT-IR rankings. This approach resulted in the correct identification of 88 of the 148 test compounds (59%). 129 of the 148 test compounds (87%) were ranked within the top 20 candidates. These identification rates are the highest yet reported when candidate structures are used from PubChem. Combining experimental and computational MS/MS and IR spectral data is a potentially powerful option for prioritizing candidates for final structure verification.

Utilization of LC-MS/MS and Drift Tube Ion Mobility for Characterizing Intact Oxidized Arachidonate-Containing Glycerophosphatidylethanolamine

Lipid peroxidation is a key component in the pathogenesis of numerous disease states, where the oxidative damage of lipids frequently leads to membrane dysfunction and subsequent cellular death. Glycerophosphoethanolamine (PE) is the second most abundant phospholipid found in cellular membranes and, when oxidized, has been identified as an executor of ferroptotic cell death. PE commonly exists in the plasmalogen form, where the presence of the vinyl ether bond and its enrichment in polyunsaturated fatty acids make it especially susceptible to oxidative degradation. This results in a multitude of oxidized products complicating identification and often requiring several analytical techniques for interpretation. In the present study, we outline an analytical approach for the structural characterization of intact oxidized products of arachidonate-containing diacyl and plasmalogen PE. Intact oxidized PE structures, including structural and positional isomers, were identified using complementary liquid chromatography techniques, drift tube ion mobility, and high-resolution tandem mass spectrometry. This work establishes a comprehensive method for the analysis of intact lipid peroxidation products and provides an important pathway to investigate how lipid peroxidation initially impacts glycerophospholipids and their role in redox biology.

Ion mobility mass spectrometry for the study of mycobacterial mycolic acids

Lipids are highly structurally diverse molecules involved in a wide variety of biological processes. The involvement of lipids is even more pronounced in mycobacteria, including the human pathogen Mycobacterium tuberculosis, which produces a highly complex and diverse set of lipids in the cell envelope. These lipids include mycolic acids, which are among the longest fatty acids in nature and can contain up to 90 carbon atoms. Mycolic acids are ubiquitously found in mycobacteria and are alpha branched and beta hydroxylated lipids. Discrete modifications, such as alpha, alpha', epoxy, methoxy, keto, and carboxy, characterize mycolic acids at the species level. Here, we used high precision ion mobility-mass spectrometry to build a database including 206 mass-resolved collision cross sections (CCSs) of mycolic acids originating from the strict human pathogen M. tuberculosis, the opportunistic strains M. abscessus, M. marinum and M. avium, and the nonpathogenic strain M. smegmatis. Primary differences between the mycolic acid profiles could be observed between mycobacterial species. Acyl tail length and modifications were the primary structural descriptors determining CCS magnitude. As a resource for researchers, this work provides a detailed catalogue of the mass-resolved collision cross sections for mycolic acids along with a workflow to generate and analyse the dataset generated.

Comparison of Data-Dependent Acquisition, Data-Independent Acquisition, and Parallel Reaction Monitoring in Trapped Ion Mobility Spectrometry-Time-of-Flight Tandem Mass Spectrometry-Based Lipidomics

The parallel accumulation-serial fragmentation (PASEF) approach based on trapped ion mobility spectrometry (TIMS) enables mobility-resolved fragmentation and a higher number of fragments in the same time period compared to conventional MS/MS experiments. Furthermore, the ion mobility dimension offers novel approaches for fragmentation. Using parallel reaction monitoring (prm), the ion mobility dimension allows a more accurate selection of precursor windows, while using data-independent aquisition (dia) spectral quality is improved through ion-mobility filtering. Owing to favorable implementation in proteomics, the transferability of these PASEF modes to lipidomics is of great interest, especially as a result of the high complexity of analytes with similar fragments. However, these novel PASEF modes have not yet been thoroughly evaluated for lipidomics applications. Therefore, data-dependent acquisition (dda)-, dia-, and prm-PASEF were compared using hydrophilic interaction liquid chromatography (HILIC) for phospholipid class separation in human plasma samples. Results show that all three PASEF modes are generally suitable for usage in lipidomics. Although dia-PASEF achieves a high sensitivity in generating MS/MS spectra, the fragment-to-precursor assignment for lipids with both, similar retention time as well as ion mobility, was difficult in HILIC-MS/MS. Therefore, dda-PASEF is the method of choice to investigate unknown samples. However, the best data quality was achieved by prm-PASEF, owing to the focus on fragmentation of specified targets. The high selectivity and sensitivity in generating MS/MS spectra of prm-PASEF could be a potential alternative for targeted lipidomics, e.g., in clinical applications.

Strain-Level Discrimination of Bacteria by Liquid Chromatography and Paper Spray Ion Mobility Mass Spectrometry

Determining bacterial identity at the strain level is critical for public health to enable proper medical treatments and reduce antibiotic resistance. Herein, we used liquid chromatography, ion mobility, and tandem MS (LC-IM-MS/MS) to distinguish Escherichia coli (E. coli) strains. Numerical multivariate statistics (principal component analysis, followed by linear discriminant analysis) showed the capability of this method to perform strain-level discrimination with prediction rates of 96.1% and 100% utilizing the negative and positive ion information, respectively. The tandem MS and LC separation proved effective in discriminating diagnostic lipid isomers in the negative mode, while IM separation was more effective in resolving lipid conformational biomarkers in the positive ion mode. Because of the clinical importance of early detection for rapid medical intervention, a faster technique, paper spray (PS)-IM-MS/MS, was used to discriminate the E. coli strains. The achieved prediction rates of the analysis of E. coli strains by PS-IM-MS/MS were 62.5% and 73.5% in the negative and positive ion modes, respectively. The strategy of numerical data fusion of negative and positive ion data increased the classification rates of PS-IM-MS/MS to 80.5%. Lipid isomers and conformers were detected, which served as strain-indicating biomarkers. The two complementary multidimensional techniques revealed biochemical differences between the E. coli strains confirming the results obtained from comparative genomic analysis. Moreover, the results suggest that PS-IM-MS/MS is a rapid, highly selective, and sensitive method for discriminating bacterial strains in environmental and food samples.

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.

Lipidomics in food quality and authentication: A comprehensive review of novel trends and applications using chromatographic and spectroscopic techniques

Lipid analysis is an integral part of food authentication and quality control which provides consumers with the necessary information to make an informed decision about their lipid intake. Recent advancement in lipid analysis and lipidome scope represents great opportunities for food science. In this review we provide a comprehensive overview of available tools for extraction, analysis and interpretation of data related to dietary fats analyses. Different analytical platforms are discussed including GC, MS, NMR, IR and UV with emphasis on their merits and limitations alongside complementary tools such as chemometric models and lipid-targeted online databases. Applications presented here include quality control, authentication of organic and delicacy food, tracing dietary fat source and investigating the effect of heat/storage on lipids. A multitude of analytical methods with different sensitivity, affordability, reproducibility and ease of operation are now available to comprehensively analyze dietary fats. Application of these methods range from studies which favor the use of large data generating platforms such as MS-based methods, to routine quality control which demands easy to use affordable equipment as TLC and IR. Hence, this review provides a navigation tool for food scientists to help develop an optimal protocol for their future lipid analysis quest.

Development and application of lipidomics for food research

Lipidomics is an emerging and promising omics derived from metabolomics to comprehensively analyze all of lipid molecules in biological matrices. The purpose of this chapter is to introduce the development and application of lipidomics for food research. First, three aspects of sample preparation are introduced: food sampling, lipid extraction, and transportation and storage. Second, five types of instruments for data acquisition are summarized: direct infusion-mass spectrometry (MS), chromatographic separation-MS, ion mobility-MS, MS imaging, and nuclear magnetic resonance spectroscopy. Third, data acquisition and analysis software are described for the lipidomics software development. Fourth, the application of lipidomics for food research is discussed such as food origin and adulteration analysis, food processing research, food preservation research, and food nutrition and health research. All the contents suggest that lipidomics is a powerful tool for food research based on its ability of lipid component profile analysis.

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.

SMART: A data reporting standard for mass spectrometry imaging

Mass spectrometry imaging (MSI) is an important analytical technique that simultaneously reports the spatial location and abundance of detected ions in biological, chemical, clinical, and pharmaceutical studies. As MSI grows in popularity, it has become evident that data reporting varies among different research groups and between techniques. The lack of consistency in data reporting inherently creates additional challenges in comparing intra- and inter-laboratory MSI data. In this tutorial, we propose a unified data reporting system, SMART, based on the common features shared between techniques. While there are limitations to any reporting system, SMART was decided upon after significant discussion to more easily understand and benchmark MSI data. SMART is not intended to be comprehensive but rather capture essential baseline information for a given MSI study; this could be within a study (e.g., effect of spot size on the measured ion signals) or between two studies (e.g., different MSI platform technologies applied to the same tissue type). This tutorial does not attempt to address the confidence with which annotations are made nor does it deny the importance of other parameters that are not included in the current SMART format. Ultimately, the goal of this tutorial is to discuss the necessity of establishing a uniform reporting system to communicate MSI data in publications and presentations in a simple format to readily interpret the parameters and baseline outcomes of the data.

A highly sensitive method (supercritical fluid chromatography coupled with ion mobility mass spectrometry) for determination of multiple compounds in radix curcumae

A highly sensitive method was developed for simultaneously separating and identifying multiple compounds in radix curcumae. The determination of these compounds was achieved by combining supercritical fluid chromatography with drift tube ion mobility quadrupole time-of-flight MS. Related parameters were optimized: the RX-SIL column was used as the stationary phase, methanol was selected as the organic modifier, back pressure was 120 bar, back temperature was 60°C, the mobile phase flow rate was 1.75 mL/min, the makeup solvent was 0.2% formic acid/methanol with a flow rate of 0.7 mL/min. Under optimal conditions, multipolar compounds were separated. Furthermore, these compounds were identified by the values of collision sectional areas. The established method was verified by related parameters and exhibited good linearity, sensitivity, precision and accuracy. It could be extended to analyze other curcuminoids and sesquiterpenoids in natural products.

CCS Predictor 2.0: An Open-Source Jupyter Notebook Tool for Filtering Out False Positives in Metabolomics

Metabolite annotation continues to be the widely accepted bottleneck in nontargeted metabolomics workflows. Annotation of metabolites typically relies on a combination of high-resolution mass spectrometry (MS) with parent and tandem measurements, isotope cluster evaluations, and Kendrick mass defect (KMD) analysis. Chromatographic retention time matching with standards is often used at the later stages of the process, which can also be followed by metabolite isolation and structure confirmation utilizing nuclear magnetic resonance (NMR) spectroscopy. The measurement of gas-phase collision cross-section (CCS) values by ion mobility (IM) spectrometry also adds an important dimension to this workflow by generating an additional molecular parameter that can be used for filtering unlikely structures. The millisecond timescale of IM spectrometry allows the rapid measurement of CCS values and allows easy pairing with existing MS workflows. Here, we report on a highly accurate machine learning algorithm (CCSP 2.0) in an open-source Jupyter Notebook format to predict CCS values based on linear support vector regression models. This tool allows customization of the training set to the needs of the user, enabling the production of models for new adducts or previously unexplored molecular classes. CCSP produces predictions with accuracy equal to or greater than existing machine learning approaches such as CCSbase, DeepCCS, and AllCCS, while being better aligned with FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. Another unique aspect of CCSP 2.0 is its inclusion of a large library of 1613 molecular descriptors via the Mordred Python package, further encoding the fine aspects of isomeric molecular structures. CCS prediction accuracy was tested using CCS values in the McLean CCS Compendium with median relative errors of 1.25, 1.73, and 1.87% for the 170 [M - H]^-, 155 [M + H]⁺, and 138 [M + Na]⁺ adducts tested. For superclass-matched data sets, CCS predictions via CCSP allowed filtering of 36.1% of incorrect structures while retaining a total of 100% of the correct annotations using a Δ_CCS threshold of 2.8% and a mass error of 10 ppm.

Multicomponent Characterization of the Flower Bud of Panax notoginseng and Its Metabolites in Rat Plasma by Ultra-High Performance Liquid Chromatography/Ion Mobility Quadrupole Time-of-Flight Mass Spectrometry

The flower bud of Panax notoginseng (PNF) consumed as a tonic shows potential in the prevention and treatment of cardiovascular diseases. To identify the contained multi-components and, in particular, to clarify which components can be absorbed and what metabolites are transformed, unveiling the effective substances of PNF is of vital significance. A unique ultrahigh-performance liquid chromatography/ion mobility quadrupole time-of-flight mass spectrometry (UHPLC/IM-QTOF-MS) profiling approach and efficient data processing by the UNIFI^TM bioinformatics platform were employed to comprehensively identify the multi-components of PNF and the related metabolites in the plasma of rats after oral administration (at a dose of 3.6 g/kg). Two MS² data acquisition modes operating in the negative electrospray ionization mode, involving high-definition MS^E (HDMS^E) and data-dependent acquisition (DDA), were utilized aimed to extend the coverage and simultaneously ensure the quality of the MS² spectra. As a result, 219 components from PNF were identified or tentatively characterized, and 40 thereof could be absorbed. Moreover, 11 metabolites were characterized from the rat plasma. The metabolic pathways mainly included the phase I (deglycosylation and oxidation). To the best of our knowledge, this is the first report that systematically studies the in vivo metabolites of PNF, which can assist in better understanding its tonifying effects and benefit its further development.

Differential Kendrick's Plots as an Innovative Tool for Lipidomics in Complex Samples: Comparison of Liquid Chromatography and Infusion-Based Methods to Sample Differential Study

Lipidomics has developed rapidly over the past decade. Nontargeted lipidomics from biological samples remains a challenge due to the high structural diversity, the concentration range of lipids, and the complexity of biological samples. We introduce here the use of differential Kendrick's plots as a rapid visualization tool for a qualitative nontargeted analysis of lipids categories and classes from data generated by either liquid chromatography-mass spectrometry (LC-MS) or direct infusion (nESI-MS). Each lipid class is easily identified by comparison with the theoretical Kendrick plot pattern constructed from exact mass measurements and by using MSKendrickFilter, an in-house Python software. The lipids are identified with the LIPID MAPS database. In addition, in LC-MS, the software based on the Kendrick plots returns the retention time from all the lipids belonging to the same series. Lipid extracts from a yeast (Saccharomyces cerevisiae) are used as a model. An on/off case comparing Kendrick plots from two cell lines (prostate cancer cell lines treated or not with a DGAT2 inhibition) clearly shows the effect of the inhibition. Our study demonstrates the good performance of direct infusion as a fast qualitative screening method as well as for the analysis of chromatograms. A fast screening semiquantitative approach is also possible, while the targeted mode remains the golden standard for precise quantitative analysis.

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.

Traveling Wave Ion Mobility-Derived Collision Cross Section Database for Plant Specialized Metabolites: An Application to Ventilago harmandiana Pierre

The combination of ion mobility mass spectrometry (IM-MS) and chromatography is a valuable tool for identifying compounds in natural products. In this study, using an ultra-performance liquid chromatography system coupled to a high-resolution quadrupole/traveling wave ion mobility spectrometry/time-of-flight MS (UPLC-TWIMS-QTOF), we have established and validated a comprehensive TWCCSN2 and MS database for 112 plant specialized metabolites. The database included 15 compounds that were isolated and purified in-house and are not commercially available. We obtained accurate m/z, retention times, fragment ions, and TWIMS-derived CCS (TWCCSN2) values for 207 adducts (ESI+ and ESI-). The database included novel 158 TWCCSN2 values from 79 specialized metabolites. In the presence of plant matrix, the CCS measurement was reproducible and robust. Finally, we demonstrated the application of the database to extend the metabolite coverage of Ventilago harmandiana Pierre. In addition to pyranonaphthoquinones, a group of known specialized metabolites in V. harmandiana, we identified flavonoids, xanthone, naphthofuran, and protocatechuic acid for the first time through targeted analysis. Interestingly, further investigation using IM-MS of unknown features suggested the presence of organonitrogen compounds and lipid and lipid-like molecules, which is also reported for the first time. Data are available on the MassIVE (https://massive.ucsd.edu, data set identifier MSV000090213).

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.

Integrating Enhanced Profiling and Chemometrics to Unveil the Potential Markers for Differentiating among the Leaves of Panax ginseng, P. quinquefolius, and P. notoginseng by Ultra-High Performance Liquid Chromatography/Ion Mobility-Quadrupole Time-of-Flight Mass Spectrometry

The leaves of Panax species (e.g., Panax ginseng-PGL, P. quinquefolius-PQL, and P. notoginseng-PNL) can serve as a source for healthcare products. Comprehensive characterization and unveiling of the metabolomic difference among PGL, PQL, and PNL are critical to ensure their correct use. For this purpose, enhanced profiling and chemometrics were integrated to probe into the ginsenoside markers for PGL/PQL/PNL by ultra-high performance liquid chromatography/ion mobility-quadrupole time-of-flight mass spectrometry (UHPLC/IM-QTOF-MS). A hybrid scan approach (HDMSE-HDDDA) was established achieving the dimension-enhanced metabolic profiling, with 342 saponins identified or tentatively characterized from PGL/PQL/PNL. Multivariate statistical analysis (33 batches of leaf samples) could unveil 42 marker saponins, and the characteristic ginsenosides diagnostic for differentiating among PGL/PQL/PNL were primarily established. Compared with the single DDA or DIA, the HDMSE-HDDDA hybrid scan approach could balance between the metabolome coverage and spectral reliability, leading to high-definition MS spectra and the additional collision-cross section (CCS) useful to differentiate isomers.

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.

Recent developments in data acquisition, treatment and analysis with ion mobility-mass spectrometry for lipidomics

Lipids are involved in many biological processes and their study is constantly increasing. To identify a lipid among thousand requires of reliable methods and techniques. Ion Mobility (IM) can be coupled with Mass Spectrometry (MS) to increase analytical selectivity in lipid analysis of lipids. IM-MS has experienced an enormous development in several aspects, including instrumentation, sensitivity, amount of information collected and lipid identification capabilities. This review summarizes the latest developments in IM-MS analyses for lipidomics and focusses on the current acquisition modes in IM-MS, the approaches for the pre-treatment of the acquired data and the subsequent data analysis. Methods and tools for the calculation of Collision Cross Section (CCS) values of analytes are also reviewed. CCS values are commonly studied to support the identification of lipids, providing a quasi-orthogonal property that increases the confidence level in the annotation of compounds and can be matched in CCS databases. The information contained in this review might be of help to new users of IM-MS to decide the adequate instrumentation and software to perform IM-MS experiments for lipid analyses, but also for other experienced researchers that can reconsider their routines and protocols. This article is protected by copyright. All rights reserved.

Improving confidence in lipidomic annotations by incorporating empirical ion mobility regression analysis and chemical class prediction

MOTIVATION

Mass spectrometry-based untargeted lipidomics aims to globally characterize the lipids and lipid-like molecules in biological systems. Ion mobility increases coverage and confidence by offering an additional dimension of separation and a highly reproducible metric for feature annotation, the collision cross-section (CCS).

RESULTS

We present a data processing workflow to increase confidence in molecular class annotations based on CCS values. This approach uses class-specific regression models built from a standardized CCS repository (the Unified CCS Compendium) in a parallel scheme that combines a new annotation filtering approach with a machine learning class prediction strategy. In a proof-of-concept study using murine brain lipid extracts, 883 lipids were assigned higher confidence identifications using the filtering approach, which reduced the tentative candidate lists by over 50% on average. An additional 192 unannotated compounds were assigned a predicted chemical class.

AVAILABILITY AND IMPLEMENTATION

All relevant source code is available at https://github.com/McLeanResearchGroup/CCS-filter.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Mass Spectrometry-Based Techniques to Elucidate the Sugar Code

Cells encode information in the sequence of biopolymers, such as nucleic acids, proteins, and glycans. Although glycans are essential to all living organisms, surprisingly little is known about the "sugar code" and the biological roles of these molecules. The reason glycobiology lags behind its counterparts dealing with nucleic acids and proteins lies in the complexity of carbohydrate structures, which renders their analysis extremely challenging. Building blocks that may differ only in the configuration of a single stereocenter, combined with the vast possibilities to connect monosaccharide units, lead to an immense variety of isomers, which poses a formidable challenge to conventional mass spectrometry. In recent years, however, a combination of innovative ion activation methods, commercialization of ion mobility-mass spectrometry, progress in gas-phase ion spectroscopy, and advances in computational chemistry have led to a revolution in mass spectrometry-based glycan analysis. The present review focuses on the above techniques that expanded the traditional glycomics toolkit and provided spectacular insight into the structure of these fascinating biomolecules. To emphasize the specific challenges associated with them, major classes of mammalian glycans are discussed in separate sections. By doing so, we aim to put the spotlight on the most important element of glycobiology: the glycans themselves.

Combining Micropunch Histology and Multidimensional Lipidomic Measurements for In-Depth Tissue Mapping

While decades of technical and analytical advancements have been utilized to discover novel lipid species, increase speciation, and evaluate localized lipid dysregulation at subtissue, cellular, and subcellular levels, many challenges still exist. One limitation is that the acquisition of both in-depth spatial information and comprehensive lipid speciation is extremely difficult, especially when biological material is limited or lipids are at low abundance. In neuroscience, for example, it is often desired to focus on only one brain region or subregion, which can be well under a square millimeter for rodents. Herein, we evaluate a micropunch histology method where cortical brain tissue at 2.0, 1.25, 1.0, 0.75, 0.5, and 0.25 mm diameter sizes and 1 mm depth was collected and analyzed with multidimensional liquid chromatography, ion mobility spectrometry, collision induced dissociation, and mass spectrometry (LC-IMS-CID-MS) measurements. Lipid extraction was optimized for the small sample sizes, and assessment of lipidome coverage for the 2.0 to 0.25 mm diameter sizes showed a decline from 304 to 198 lipid identifications as validated by all 4 analysis dimensions (~35% loss in coverage for ~88% less tissue). While losses were observed, the ~200 lipids and estimated 4630 neurons contained within the 0.25 punch still provided in-depth characterization of the small tissue region. Furthermore, while localization routinely achieved by mass spectrometry imaging (MSI) and single cell analyses is greater, this diameter is sufficiently small to isolate subcortical, hypothalamic, and other brain regions in adult rats, thereby increasing the coverage of lipids within relevant spatial windows without sacrificing speciation. Therefore, micropunch histology enables in-depth, region-specific lipid evaluations, an approach that will prove beneficial to a variety of lipidomic applications.

A novel hybrid scan approach enabling the ion-mobility separation and the alternate data-dependent and data-independent acquisitions (HDDIDDA): Its combination with off-line two-dimensional liquid chromatography for comprehensively characterizing the multicomponents from Compound Danshen Dripping Pill

Data-dependent acquisition (DDA) and data-independent acquisition (DIA)-based MSⁿ strategies are extensively applied in metabolites characterization. DDA gives accurate MSⁿ information, but receives low coverage, while DIA covers the entire mass range, but the precursor-product ions matching often yields false positives. Currently available MS scan approaches rarely integrate DIA and DDA within a duty circle. Utilizing a Vion™ IM-QTOF (ion mobility-quadrupole time-of-flight) mass spectrometer, we report a novel hybrid scan approach, namely HDDIDDA, which involves three scan events: 1) IM-enabled full scan (MS¹), 2) high-definition MS^E (HDMS^E) of all precursor ions (MS²); and 3) high-definition DDA (HDDDA) of top N precursors (MS²). As a proof-of-concept, the HDDIDDA approach combined with off-line two-dimensional liquid chromatography (2D-LC) was applied to characterize the multiple ingredients from a reputable Chinese patent medicine, Compound Danshen Dripping Pill (CDDP) used for treating the cardiovascular diseases. An off-line 2D-LC system by configuring an XBridge Amide column and an HSS T3 column showed a measurable orthogonality of 0.92 and enhanced the separation of co-eluting components. A fit-for-purpose HDDIDDA methodology was developed in the negative mode to characterize saponins and salvianolic acids, while tanshinones in the positive mode. Computational workflows to efficiently process the acquired HDMS^E and HDDDA data were established, and the searching of an in-house CDDP library (recording 712 compounds) eventually characterized 403 components from CDDP, indicating approximate 12-fold improvement compared with the previous report. The HDDIDDA approach can measure collision cross section of each component, and merges the merits of DIA and DDA in MS² data acquisition.

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.

Least absolute shrinkage and selection operator-based prediction of collision cross section values for ion mobility mass spectrometric analysis of lipids

A least absolute shrinkage and selection operator (LASSO)-based prediction method was developed for the prediction of lipids’ CCS values.