Collision cross section prediction of deprotonated phenolics in a travelling-wave ion mobility spectrometer using molecular descriptors and chemometrics

Structural studies of supramolecular complexes and assemblies by ion mobility mass spectrometry

Recent advances in instrumentation and development of computational strategies for ion mobility mass spectrometry (IM-MS) studies have contributed to an extensive growth in the application of this analytical technique to comprehensive structural description of supramolecular systems. Apart from the benefits of IM-MS for interrogation of intrinsic properties of noncovalent aggregates in the experimental gas-phase environment, its merits for the description of native structural aspects, under the premises of having maintained the noncovalent interactions innate upon the ionization process, have attracted even more attention and gained increasing interest in the scientific community. Thus, various types of supramolecular complexes and assemblies relevant for biological, medical, material, and environmental sciences have been characterized so far by IM-MS supported by computational chemistry. This review covers the state-of-the-art in this field and discusses experimental methods and accompanying computational approaches for assessing the reliable three-dimensional structural elucidation of supramolecular complexes and assemblies by IM-MS.

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

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

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

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

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.

Highly accurate and large-scale collision cross sections prediction with graph neural networks

The collision cross section (CCS) values derived from ion mobility spectrometry can be used to improve the accuracy of compound identification. Here, we have developed the Structure included graph merging with adduct method for CCS prediction (SigmaCCS) based on graph neural networks using 3D conformers as inputs. A model was trained, evaluated, and tested with >5,000 experimental CCS values. It achieved a coefficient of determination of 0.9945 and a median relative error of 1.1751% on the test set. The model-agnostic interpretation method and the visualization of the learned representations were used to investigate the chemical rationality of SigmaCCS. An in-silico database with 282 million CCS values was generated for three different adduct types of 94 million compounds. Its source code is publicly available at https://github.com/zmzhang/SigmaCCS . Altogether, SigmaCCS is an accurate, rational, and off-the-shelf method to directly predict CCS values from molecular structures.

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.

Prediction of Retention Time and Collision Cross Section (CCSH+, CCSH-, and CCSNa+) of Emerging Contaminants Using Multiple Adaptive Regression Splines

Ultra-high performance liquid chromatography coupled to ion mobility separation and high-resolution mass spectrometry instruments have proven very valuable for screening of emerging contaminants in the aquatic environment. However, when applying suspect or nontarget approaches (i.e., when no reference standards are available), there is no information on retention time (RT) and collision cross-section (CCS) values to facilitate identification. In silico prediction tools of RT and CCS can therefore be of great utility to decrease the number of candidates to investigate. In this work, Multiple Adaptive Regression Splines (MARS) were evaluated for the prediction of both RT and CCS. MARS prediction models were developed and validated using a database of 477 protonated molecules, 169 deprotonated molecules, and 249 sodium adducts. Multivariate and univariate models were evaluated showing a better fit for univariate models to the experimental data. The RT model (R² = 0.855) showed a deviation between predicted and experimental data of ±2.32 min (95% confidence intervals). The deviation observed for CCS data of protonated molecules using the CCS_H model (R² = 0.966) was ±4.05% with 95% confidence intervals. The CCS_H model was also tested for the prediction of deprotonated molecules, resulting in deviations below ±5.86% for the 95% of the cases. Finally, a third model was developed for sodium adducts (CCS_Na, R² = 0.954) with deviation below ±5.25% for 95% of the cases. The developed models have been incorporated in an open-access and user-friendly online platform which represents a great advantage for third-party research laboratories for predicting both RT and CCS data.

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).

Prediction of Collision Cross-Section Values for Extractables and Leachables from Plastic Products

The use of ion mobility separation (IMS) in conjunction with high-resolution mass spectrometry has proved to be a reliable and useful technique for the characterization of small molecules from plastic products. Collision cross-section (CCS) values derived from IMS can be used as a structural descriptor to aid compound identification. One limitation of the application of IMS to the identification of chemicals from plastics is the lack of published empirical CCS values. As such, machine learning techniques can provide an alternative approach by generating predicted CCS values. Herein, experimental CCS values for over a thousand chemicals associated with plastics were collected from the literature and used to develop an accurate CCS prediction model for extractables and leachables from plastic products. The effect of different molecular descriptors and machine learning algorithms on the model performance were assessed. A support vector machine (SVM) model, based on Chemistry Development Kit (CDK) descriptors, provided the most accurate prediction with 93.3% of CCS values for [M + H]⁺ adducts and 95.0% of CCS values for [M + Na]⁺ adducts in testing sets predicted with <5% error. Median relative errors for the CCS values of the [M + H]⁺ and [M + Na]⁺ adducts were 1.42 and 1.76%, respectively. Subsequently, CCS values for the compounds in the Chemicals associated with Plastic Packaging Database and the Food Contact Chemicals Database were predicted using the SVM model developed herein. These values were integrated in our structural elucidation workflow and applied to the identification of plastic-related chemicals in river water. False positives were reduced, and the identification confidence level was improved by the incorporation of predicted CCS values in the suspect screening workflow.

A Collision Cross Section Database for Extractables and Leachables from Food Contact Materials

The chemicals in food contact materials (FCMs) can migrate into food and endanger human health. In this study, we developed a database of traveling wave collision cross section in nitrogen (^TWCCS_N2) values for extractables and leachables from FCMs. The database contains a total of 1038 ^TWCCS_N2 values from 675 standards including those commonly used additives and nonintentionally added substances in FCMs. The ^TWCCS_N2 values in the database were compared to previously published values, and 85.7, 87.7, and 64.9% [M + H]⁺, [M + Na]⁺, and [M - H]^- adducts showed deviations <2%, with the presence of protomers, post-ion mobility spectrometry dissociation of noncovalent clusters and inconsistent calibration are possible sources of CCS deviations. Our experimental ^TWCCS_N2 values were also compared to CCS values from three prediction tools. Of the three, CCSondemand gave the most accurate predictions. The ^TWCCS_N2 database developed will aid the identification and differentiation of chemicals from FCMs in targeted and untargeted analysis.

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.

The relevant role of ion mobility separation in LC-HRMS based screening strategies for contaminants of emerging concern in the aquatic environment

Ion mobility separation (IMS) coupled to high resolution mass spectrometry (IMS-HRMS) is a promising technique for (non-)target/suspect analysis of micropollutants in complex matrices. IMS separates ionized compounds based on their charge, shape and size facilitating the removal of co-eluting isomeric/isobaric species. Additionally, IMS data can be translated into collision cross-section (CCS) values, which can be used to increase the identification reliability. However, IMS-HRMS for the screening of contaminants of emerging concern (CECs) have been scarcely explored. In this study, the role of IMS-HRMS for the identification of CECs in complex matrices is highlighted, with emphasis on when and with which purpose is of use. The utilization of IMS can result in much cleaner mass spectra, which considerably facilitates data interpretation and the obtaining of reliable identifications. Furthermore, the robustness of IMS measurements across matrices permits the use of CCS as an additional relevant parameter during the identification step even when reference standards are not available. Moreover, an effect on the number of true and false identifications could be demonstrated by including IMS restrictions within the identification workflow. Data shown in this work is of special interest for environmental researchers dealing with the detection of CECs with state-of-the-art IMS-HRMS instruments.

Discovery and Characterization of Phenolic Compounds in Bearberry (Arctostaphylos uva-ursi) Leaves Using Liquid Chromatography-Ion Mobility-High-Resolution Mass Spectrometry

The characterization and quantification of phenolic compounds in bearberry leaves were performed using hyphenated ion mobility spectroscopy (IMS) and a quadrupole time-of-flight mass spectrometer. A higher identification confidence level was obtained by comparing the measured collision cross section (^TWCCS_N2) with predicted values using a machine learning algorithm. A total of 88 compounds were identified, including 14 arbutin derivatives, 33 hydrolyzable tannins, 6 flavanols, 26 flavonols, 9 saccharide derivatives, and glycosidic compounds. Those most reliably reproduced in all samples were quantified against respective standards. Arbutin (47-107 mg/g), 1,2,3,4,6-pentagalloylglucose (6.6-12.9 mg/g), and quercetin 3-galactoside/quercetin 3-glucoside (2.7-5.7 mg/g) were the most abundant phenolic components in the leaves. Quinic acid and ellagic acid were also detected at relatively high concentrations. The antioxidant activity of the most abundant compounds was evaluated. A critical view of the advantages and limitations of traveling wave IMS and CCS for the discovery of natural products is given.

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.

Plant growth and metabolic changes in 'Super Hot' chili fruit (Capsicum annuum) exposed to supplemental LED lights

Light-emitting diodes (LEDs) of different colors improve plant growth and increase levels of secondary metabolites. This study aimed to determine the effect of red, blue, and red + blue LEDs (1:1) on the secondary metabolites composition in chili, focusing on capsaicinoids, at the top and middle of the plant canopy in 'Super Hot' chili. The accumulated yield of the chili fruit was the highest for control, followed by blue, red and red + blue LEDs, with the top canopy giving twice more yield than the middle canopy. UPLC-MS/MS analysis of chili fruit's methanolic extracts was used to determine capsaicinoids levels. Blue LEDs significantly increased nordihydrocapsaicin, capsaicin, dihydrocapsaicin, homocapsaicin and homodihydrocapsaicin contents by 57 %, 43 %, 56 %, 28 %, and 54 %, respectively, compared to the control. Also, 24 tentatively annotated metabolites, including phenylalanine, cinnamate, and valine, which are involved in the biosynthesis of capsaicinoids, were semi-quantitatively evaluated to determine the impact of LED exposure on the biosynthetic pathway of capsaicinoids. Supplemental blue LED placed at the top and between the canopy may boost the levels of capsaicinoids in chili fruit grown in greenhouses.

An RP-LC-UV-TWIMS-HRMS and Chemometric Approach to Differentiate between Momordicabalsamina Chemotypes from Three Different Geographical Locations in Limpopo Province of South Africa

Momordica balsamina leaf extracts originating from three different geographical locations were analyzed using reversed-phase liquid chromatography (RP-LC) coupled to travelling wave ion mobility (TWIMS) and high-resolution mass spectrometry (HRMS) in conjunction with chemometric analysis to differentiate between potential chemotypes. Furthermore, the cytotoxicity of the three individual chemotypes was evaluated using HT-29 colon cancer cells. A total of 11 molecular species including three flavonol glycosides, five cucurbitane-type triterpenoid aglycones and three glycosidic cucurbitane-type triterpenoids were identified. The cucurbitane-type triterpenoid aglycones were detected in the positive ionization mode following dehydration [M + H − H₂O]⁺ of the parent compound, whereas the cucurbitane-type triterpenoid glycosides were primarily identified following adduct formation with ammonia [M + NH₄]⁺. The principle component analysis (PCA) loadings plot and a variable influence on projection (VIP) analysis revealed that the isomeric pair balsaminol E and/or karavilagen E was the key molecular species contributing to the distinction between geographical samples. Ultimately, based on statistical analysis, it is hypothesized that balsaminol E and/or karavilagen E are likely responsible for the cytotoxic effects in HT-29 cells.

Recent applications of ion mobility spectrometry in natural product research

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

Metabolite collision cross section prediction without energy-minimized structures

Matching experimental ion mobility-mass spectrometry data to computationally-generated collision cross section (CCS) values enables more confident metabolite identifications. Here, we show for the first time that accurately predicting CCS values with simple models for the largest library of metabolite cross sections is indeed possible, achieving a root mean square error of 7.0 Ų (median error of ∼2%) using linear methods accesible to most researchers. A comparison on the performance of 2D vs. 3D molecular descriptors for the purposes of CCS prediction is also presented for the first time, enabling CCS prediction without a priori knowledge of the metabolite's energy-minimized structure.

Detailed Phenolic Characterization of Protea Pure and Hybrid Cultivars by Liquid Chromatography-Ion Mobility-High Resolution Mass Spectrometry (LC-IM-HR-MS)

In this study we report a detailed investigation of the polyphenol composition of Protea pure (P. cynaroides and P. neriifolia) and hybrid cultivars (Black beauty and Limelight). Aqueous methanol extracts of leaf and bract tissues were analyzed by ultrahigh pressure liquid chromatography hyphenated to photodiode array and ion mobility-high resolution mass spectrometric (UHPLC-PDA-IM-HR-MS) detection. A total of 67 metabolites were characterized based on their relative reversed phase (RP) retention, UV-vis spectra, low and high collision energy HR-MS data, and collisional cross section (CCS) values. These metabolites included 41 phenolic acid esters and 25 flavonoid derivatives, including 5 anthocyanins. In addition, an undescribed hydroxycinnamic acid-polygalatol ester, caffeoyl-O-polygalatol (1,5-anhydro-[6-O-caffeoyl]-sorbitol(glucitol)) was isolated and characterized by 1D and 2D NMR for the first time. This compound and its isomer are shown to be potential chemo-taxonomic markers.

A parallelized molecular collision cross section package with optimized accuracy and efficiency

A new parallelized calculation package predicts collision cross sections with high accuracy and efficiency.

Comprehensive Three-Dimensional LC × LC × Ion Mobility Spectrometry Separation Combined with High-Resolution MS for the Analysis of Complex Samples

Comprehensive two-dimensional liquid chromatography (LC × LC) and ion mobility spectrometry-mass spectrometry (IMS-MS) are increasingly being used to address challenges associated with the analysis of highly complex samples. In this work, we evaluate the potential of the combination of these techniques in the form of a comprehensive three-dimensional LC × LC × IMS separation system. As application, hydrophilic interaction chromatography (HILIC) × reversed phase LC (RP-LC) × IMS-high-resolution MS (HR-MS) was used to analyze a range of phenolic compounds, including hydrolyzable and condensed tannins, flavonoids, and phenolic acids in several natural products. A protocol for the extraction and visualization of the four-dimensional data obtained using this approach was developed. We show that the combination of HILIC, RP-LC, and IMS offers excellent separation of complex phenolic samples in three dimensions. Benefits associated with the incorporation of IMS include improved MS sensitivity and mass-spectral data quality. IMS also provided separation of trimeric procyanidin isomeric species that could not be differentiated by HILIC × RP-LC or HR-MS. On the traveling wave IMS (TWIMS) system used here, both IMS separation performance and the extent of second dimension (²D) undersampling depend on the upper mass scan limit, which might present a limitation for the analysis of larger molecular ions. The performance of the LC × LC × IMS system was characterized in terms of practical peak capacity and separation power, using established theory and taking undersampling and orthogonality into account. An average increase in separation performance by a factor of 13 was found for the samples analyzed here when IMS was incorporated into the HILIC × RP-LC-MS workflow.

A novel integrated non-targeted metabolomic analysis reveals significant metabolite variations between different lettuce (Lactuca sativa. L) varieties

Lettuce is an important leafy vegetable that represents a significant dietary source of antioxidants and bioactive compounds. However, the levels of metabolites in different lettuce cultivars are poorly characterized. In this study, we used combined GC × GC-TOF/MS and UPLC-IMS-QTOF/MS to detect and relatively quantify metabolites in 30 lettuce cultivars representing large genetic diversity. Comparison with online databases, the published literature, standards as well using collision cross-section values enabled putative identification of 171 metabolites. Sixteen of these 171 metabolites (including phenolic acid derivatives, glycosylated flavonoids, and one iridoid) were present at significantly different levels in leaf and head type lettuces, which suggested the significant metabolomic variations between the leaf and head types of lettuce are related to secondary metabolism. A combination of the results and metabolic network analysis techniques suggested that leaf and head type lettuces contain not only different levels of metabolites but also have significant variations in the corresponding associated metabolic networks. The novel lettuce metabolite library and novel non-targeted metabolomics strategy devised in this study could be used to further characterize metabolic variations between lettuce cultivars or other plants. Moreover, the findings of this study provide important insight into metabolic adaptations due to natural and human selection, which could stimulate further research to potentially improve lettuce quality, yield, and nutritional value.

High resolution-ion mobility mass spectrometry as an additional powerful tool for structural characterization of mycotoxin metabolites

This work was designed as a proof of concept, to demonstrate the successful use of the comparison between theoretical and experimental collision cross section (CCS) values to support the identification of isomeric forms. To this purpose, thirteen mycotoxins were considered and analyzed using drift time ion mobility mass spectrometry. A good linear correlation (r² = 0.962) between theoretical and experimental CCS was found. The average ΔCCS was 3.2%, fully consistent with the acceptability threshold value commonly set at 5%. The agreement between theoretical and experimental CCS obtained for mycotoxin glucuronides suggested the potential of the CCS matching in supporting the annotation procedure.

Analysis of Phenolic Compounds in Rooibos Tea (Aspalathus linearis) with a Comparison of Flavonoid-Based Compounds in Natural Populations of Plants from Different Regions

Tea samples from 17 populations of "wild tea" ecotypes Aspalathus linearis (rooibos tea) and 2 populations of Aspalathus pendula were analyzed. Recent advances in column technology together with high-resolution mass spectrometry were applied to improve resolution, facilitating the identification of several new compounds as well as grouping of the wild tea ecotypes according to their chemical composition. The collisional cross-section data obtained from ion mobility-mass spectrometry is reported for the flavonoids in rooibos for the first time. Enzyme pathways for the synthesis of the unique flavonoids found in rooibos tea are also proposed. A. linearis and A. pendula produce similar combinations of main phenolic compounds, with no diagnostically different discontinuities between populations or species. Northern resprouters (Gifberg and Nieuwoudtville) contain higher phenylpropenoic acid glucoside levels while teas from Wupperthal and surrounding areas were found to contain unique dihydrochalcones (phloridzin and a sieboldin analog), which are reported here for the first time.

Advancing the large-scale CCS database for metabolomics and lipidomics at the machine-learning era

Metabolomics and lipidomics aim to comprehensively measure the dynamic changes of all metabolites and lipids that are present in biological systems. The use of ion mobility-mass spectrometry (IM-MS) for metabolomics and lipidomics has facilitated the separation and the identification of metabolites and lipids in complex biological samples. The collision cross-section (CCS) value derived from IM-MS is a valuable physiochemical property for the unambiguous identification of metabolites and lipids. However, CCS values obtained from experimental measurement and computational modeling are limited available, which significantly restricts the application of IM-MS. In this review, we will discuss the recently developed machine-learning based prediction approach, which could efficiently generate precise CCS databases in a large scale. We will also highlight the applications of CCS databases to support metabolomics and lipidomics.

A structural examination and collision cross section database for over 500 metabolites and xenobiotics using drift tube ion mobility spectrometry

The confident identification of metabolites and xenobiotics in biological and environmental studies is an analytical challenge due to their immense dynamic range, vast chemical space and structural diversity. Ion mobility spectrometry (IMS) is widely used for small molecule analyses since it can separate isomeric species and be easily coupled with front end separations and mass spectrometry for multidimensional characterizations. However, to date IMS metabolomic and exposomic studies have been limited by an inadequate number of accurate collision cross section (CCS) values for small molecules, causing features to be detected but not confidently identified. In this work, we utilized drift tube IMS (DTIMS) to directly measure CCS values for over 500 small molecules including primary metabolites, secondary metabolites and xenobiotics. Since DTIMS measurements do not need calibrant ions or calibration like some other IMS techniques, they avoid calibration errors which can cause problems in distinguishing structurally similar molecules. All measurements were performed in triplicate in both positive and negative polarities with nitrogen gas and seven different electric fields, so that relative standard deviations (RSD) could be assessed for each molecule and structural differences studied. The primary metabolites analyzed to date have come from key metabolism pathways such as glycolysis, the pentose phosphate pathway and the tricarboxylic acid cycle, while the secondary metabolites consisted of classes such as terpenes and flavonoids, and the xenobiotics represented a range of molecules from antibiotics to polycyclic aromatic hydrocarbons. Different CCS trends were observed for several of the diverse small molecule classes and when urine features were matched to the database, the addition of the IMS dimension greatly reduced the possible number of candidate molecules. This CCS database and structural information are freely available for download at http://panomics.pnnl.gov/metabolites/ with new molecules being added frequently.

Computational intelligence models to predict porosity of tablets using minimum features

The effects of different formulations and manufacturing process conditions on the physical properties of a solid dosage form are of importance to the pharmaceutical industry. It is vital to have in-depth understanding of the material properties and governing parameters of its processes in response to different formulations. Understanding the mentioned aspects will allow tighter control of the process, leading to implementation of quality-by-design (QbD) practices. Computational intelligence (CI) offers an opportunity to create empirical models that can be used to describe the system and predict future outcomes in silico. CI models can help explore the behavior of input parameters, unlocking deeper understanding of the system. This research endeavor presents CI models to predict the porosity of tablets created by roll-compacted binary mixtures, which were milled and compacted under systematically varying conditions. CI models were created using tree-based methods, artificial neural networks (ANNs), and symbolic regression trained on an experimental data set and screened using root-mean-square error (RMSE) scores. The experimental data were composed of proportion of microcrystalline cellulose (MCC) (in percentage), granule size fraction (in micrometers), and die compaction force (in kilonewtons) as inputs and porosity as an output. The resulting models show impressive generalization ability, with ANNs (normalized root-mean-square error [NRMSE] =1%) and symbolic regression (NRMSE =4%) as the best-performing methods, also exhibiting reliable predictive behavior when presented with a challenging external validation data set (best achieved symbolic regression: NRMSE =3%). Symbolic regression demonstrates the transition from the black box modeling paradigm to more transparent predictive models. Predictive performance and feature selection behavior of CI models hints at the most important variables within this factor space.

Investigation of the Complete Suite of the Leucine and Isoleucine Isomers: Toward Prediction of Ion Mobility Separation Capabilities

In this study we investigated 11 isomers with the molecular formula C₆H₁₃NO₂ (m/z 131) to ascertain the potential of utilizing drift tube ion mobility mass spectrometry to aid in the separation of isomeric mixtures. This study of small molecules provides a detailed examination of the application of uniform field ion mobility for a narrow scope of isomers with variations in both bond coordination and stereochemistry. For small molecules, it was observed that in general constitutional isomers are more readily separated by uniform field mobility in comparison to stereoisomers such as enantiomers or diastereomers. Diastereomers exhibited differences in their collision cross section (CCS), but were unresolvable in a mixture, whereas the enantiomers studied did not exhibit statistically different CCS values. A mathematical relationship relating the CCS to resolving power was developed in order to predict the required ion mobility resolving power needed to separate the various isomer classes. For the majority of isomers evaluated in this study, a uniform field-based resolving power of 100 was predicted to be sufficient to resolve over half (∼60%) of all hypothetical isomer pairs, including leucine and isoleucine, whereas their stereoisomers (d- and l-forms) are predicted to be significantly more challenging, if not impossible, to separate by conventional drift tube techniques.

Recent Advances and Future Challenges in Modified Mycotoxin Analysis: Why HRMS Has Become a Key Instrument in Food Contaminant Research

Mycotoxins are secondary metabolites produced by pathogenic fungi in crops worldwide. These compounds can undergo modification in plants, leading to the formation of a large number of possible modified forms, whose toxicological relevance and occurrence in food and feed is still largely unexplored. The analysis of modified mycotoxins by liquid chromatography-mass spectrometry remains a challenge because of their chemical diversity, the large number of isomeric forms, and the lack of analytical standards. Here, the potential benefits of high-resolution and ion mobility mass spectrometry as a tool for separation and structure confirmation of modified mycotoxins have been investigated/reviewed.

Ion-Mobility-Derived Collision Cross Section as an Additional Identification Point for Multiresidue Screening of Pesticides in Fish Feed

Ion mobility spectrometry allows for the measurement of the collision cross section (CCS), which provides information about the shape of an ionic molecule in the gas phase. Although the hyphenation of traveling-wave ion mobility spectrometry (TWIMS) with high-resolution quadrupole time-of-flight mass spectrometry (QTOFMS) has been mainly used for structural elucidation purposes, its potential for fast screening of small molecules in complex samples has not yet been thoroughly evaluated. The current work explores the capabilities of ultrahigh-performance liquid chromatography (UHPLC) coupled to a new design TWIMS-QTOFMS for the screening and identification of a large set of pesticides in complex salmon feed matrices. A database containing TWIMS-derived CCS values for more than 200 pesticides is hereby presented. CCS measurements showed high intra- and interday repeatability (RSD < 1%), and they were not affected by the complexity of the investigated matrices (ΔCCS ≤ 1.8%). The use of TWIMS in combination with QTOFMS was demonstrated to provide an extra-dimension, which resulted in increased peak capacity and selectivity in real samples. Thus, many false-positive detections could be straightforwardly discarded just by applying a maximum ΔCCS tolerance of ±2%. CCS was proposed as a valuable additional identification point in the pesticides screening workflow. Several commercial fish feed samples were finally analyzed to demonstrate the applicability of the proposed approach. Ethoxyquin and pirimiphos-methyl were identified in most of the analyzed samples, whereas tebuconazole and piperonil butoxide were identified for the first time in fish feed samples.