Ion Mobility Mass Spectrometry for the Separation and Characterization of Small Molecules

Computational tools and algorithms for ion mobility spectrometry-mass spectrometry

Ion mobility spectrometry-mass spectrometry (IMS-MS or IM-MS) is a powerful analytical technique that combines the gas-phase separation capabilities of IM with the identification and quantification capabilities of MS. IM-MS can differentiate molecules with indistinguishable masses but different structures (e.g., isomers, isobars, molecular classes, and contaminant ions). The importance of this analytical technique is reflected by a staged increase in the number of applications for molecular characterization across a variety of fields, from different MS-based omics (proteomics, metabolomics, lipidomics, etc.) to the structural characterization of glycans, organic matter, proteins, and macromolecular complexes. With the increasing application of IM-MS there is a pressing need for effective and accessible computational tools. This article presents an overview of the most recent free and open-source software tools specifically tailored for the analysis and interpretation of data derived from IM-MS instrumentation. This review enumerates these tools and outlines their main algorithmic approaches, while highlighting representative applications across different fields. Finally, a discussion of current limitations and expectable improvements is presented.

Benchtop volatilomics supercharged: How machine learning based design of experiment helps optimizing untargeted GC-IMS gas phase metabolomics

Gas chromatography-ion mobility spectrometry (GC-IMS) plays a significant role in both targeted and non-targeted analyses. However, the non-linear behavior of IMS and its complex ion chemistry pose challenges for finding optimal experimental conditions using existing methodologies. To address these issues, integrating machine learning (ML) strategies offers a promising approach. In this study, we propose a hybrid strategy, combining design of experiment (DOE) and machine learning (ML) for optimizing GC-IMS conditions in non-targeted volatilomic/flavoromic analysis, with saffron volatiles as a case study. To begin, a rotatable circumscribed central composite design (CCD) is used to define five influential GC-IMS factors of sample amount, headspace temperature, incubation time, injection volume, and split ratio. Subsequently, two ML models are utilized: multiple linear regression (MLR) as a linear model and Bayesian regularized-artificial neural network (BR-ANN) as a nonlinear model. These models are employed to predict the response variables of total peak areas (PAs) and the number of detected peaks (PNs) in GC-IMS. The findings show that there is a direct correlation between the factors in GC-IMS and the PNs, suggesting that MLR is a suitable approach for building a model in this scenario. However, the PAs exhibit nonlinear behavior, suggesting that BR-ANN is better suitable to capture this complexity. Notably, Derringer's desirability function is utilized to integrate the PAs and PNs, and in this scenario, MLR demonstrates satisfactory performance in modeling the GC-IMS factors.

Automated 2D peak detection in gas chromatography-ion mobility spectrometry through persistent homology

BACKGROUND

Gas chromatography-ion mobility spectrometry (GC-IMS) is a powerful analytical technique which has gained widespread use in a variety of fields. Detecting peaks in GC-IMS data is essential for chemical identification. Topological data analysis (TDA) has the ability to record alterations in topology throughout the entire spectrum of GC-IMS data and retain this data in diagrams known as persistence diagrams. To put it differently, TDA naturally identifies characteristics such as mountains, volcanoes, and their higher-dimensional equivalents within the original data and measures their significance.

RESULTS

In the present contribution, a novel approach based on persistent homology (a flagship technique of TDA) is suggested for automatic 2D peak detection in GC-IMS. For this purpose, two different GC-IMS data examples (urine and olive oil) are used to show the performance of the proposed method. The outputs of the algorithm are GC-IMS chromatogram with detected peaks, persistence plot showing the importance (intensity) of the detected peaks and a table with retention times (RT), drift times (DT), and persistence scores of detected peaks. The RT and DT can be used for identification of the peaks and persistence scores for quantitation. Additionally, watershed segmentation is applied to the GC-IMS images to index individual peaks and segment overlapping compounds allowing for a more accurate identification and quantification of individual peaks.

SIGNIFICANCE

Inspection of the results for GC-IMS datasets showed the accurate and reliable performance of the proposed strategy based on persistent homology for automatic 2D GC-IMS peak detection for qualitative and quantitative analysis. In addition, this approach can be easily extended to other types of hyphenated chromatographic and/or spectroscopic data.

Differentiation of steroid isomers by steroid analogues adducted trapped ion mobility spectrometry-mass spectrometry

Steroids are one of the important indicators of health and disease. However, due to the high similarity of steroid structures, there are several potential obstacles in the differentiation of steroids, especially for their isomers. Herein, we described a trapped ion mobility spectrometry-mass spectrometry (TIMS-MS) approach based on the steroid analogue adduction for isomer-specific identification of steroids. The application of dexamethasone (DEX) to form heterodimers with steroids enhanced the separation of their isomers in TIMS. Two isomer pairs including 17-hydroxyprogesterone/11-deoxycorticosterone and androsterone/epiandrosterone were successfully separated as the heterodimers with DEX by TIMS. The stability of DEX-adducted heterodimers is comparable with steroid dimers. Owing to the high separation efficiency and stability, the relative quantification of steroid isomers was demonstrated with the proposed method.

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.

Research progress on nanomaterial-based matrices for matrix-assisted laser desorption/ionization time-of-flight mass spectrometry analysis

Matrix assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is a novel soft ionization bio-mass spectrometry technology emerging in the 1980s, which can realize rapid detection of non-volatile, highly polar, and thermally unstable macromolecules. However, the analysis of small molecular compounds has been a major problem for MALDI-TOF MS all the time. In the MALDI analysis process based on traditional matrices, large numbers of interference peaks in the low molecular weight area and "sweet spots" phenomenon are produced, so the detection method needs to be further optimized. The promotion of matrix means the improvement of MALDI performance. In recent years, many new nanomaterial-based matrices have been successfully applied to the analysis of small molecular compounds, which makes MALDI applicable to a wider range of detection and useful in more fields such as pharmacy and environmental science. In this paper, the newly developed MALDI matrix categories in recent years are reviewed initially. Meanwhile, the potential applications, advantages and disadvantages of various matrices are analyzed. Finally, the future development prospects of nanomaterial-based matrices are also prospected.

Implementation of charged microdroplet-based derivatization of bile acids on a cyclic ion mobility spectrometry-mass spectrometry platform

Herein, we report the first implementation of charged microdroplet-based derivatization on a commercially-available cyclic ion mobility spectrometry-mass spectrometry platform. We have demonstrated the potential of our approach to improve separability of challenging isomers, but more importantly to rapidly screen derivatization reactions through droplet chemistry. Additionally, the use of cyclic ion mobility separations and tandem mass spectrometry reveals insights into product formation that would be lost with single stage mass spectrometry. Overall, we anticipate broad utility of our methodology owing to the simple design and setup for performing these droplet-based reactions and future work coupling these reactions online with liquid chromatography.

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.

High-Field Asymmetric Waveform Ion Mobility Spectrometry Analysis of Carcinogenic Aromatic Amines in Tobacco Smoke with an Orbitrap Tribrid Mass Spectrometer

Smoking is a risk factor for bladder cancer (BC), although the specific chemicals responsible for BC remain uncertain. Considerable research has focused on aromatic amines (AAs), including o-toluidine (o-tol), o-anisidine (o-anis), 2-naphthylamine (2-NA), and 4-aminobiphenyl (4-ABP), which are linked to human BC based on elevated BC incidence in occupationally exposed factory workers. These AAs arise at nanogram levels per combusted cigarette. The unambiguous identification of AAs, particularly low-molecular-weight monocyclic AAs in tobacco smoke extracts, by liquid chromatography-mass spectrometry (LC-MS) is challenging due to their poor performance on reversed-phase columns and co-elution with isobaric interferences from the complex tobacco smoke matrix. We employed a tandem liquid-liquid and solid-phase extraction method to isolate AAs from the basic fraction of tobacco smoke condensate (TSC) and utilized high-field asymmetric waveform ion mobility spectrometry (FAIMS) coupled to high-resolution accurate mass (HRAM) Orbitrap LC-MS2 to assay AAs in TSC. The employment of FAIMS greatly reduced sample complexity by removing precursor co-isolation interfering species at the MS1 scan stage, resulting in dramatically improved signal-to-noise of the precursor ions and cleaner, high-quality MS2 spectra for unambiguous identification and quantification of AAs in TSC. We demonstrate the power of LC/FAIMS/MS2 by characterizing and quantifying two low-molecular-weight carcinogenic AAs, o-tol and o-anis, in TSC, using stable isotopically labeled internal standards. These results demonstrate the power of FAIMS in trace-level analyses of AA carcinogens in the complex tobacco smoke matrix.

General Method to Obtain Collision Cross-Section Values in Multipass High-Resolution Cyclic Ion Mobility Separations

In recent years, ion mobility spectrometry-mass spectrometry (IMS-MS) has advanced the field of omics-based research, especially with the development of high-resolution platforms; however, these separations have generally been qualitative in nature. The rotationally averaged ion neutral collision cross section (CCS) is one of the only quantitative metrics available for aiding in characterizing biomolecules in IMS-MS. However, determining the CCS of an ion for multipass IMS systems, such as in cyclic ion mobility-mass spectrometry (cIMS-MS) and structures for lossless ion manipulations, has been challenging due to the lack of methods available for calculating CCS when more than a single pass is required for separation as well as the laborious nature of requiring calibrants and unknown compounds to be subjected to identical number of passes, which may not be possible in certain instances because of peak splitting, high levels of diffusion, etc. Herein, we present a general method that uses average ion velocities for calculating CCS values in cIMS-MS-based separations. Initially, we developed calibration curves using common CCS calibrants [i.e., tetra-alkylammonium salts, polyalanine, and hexakis(fluoroalkoxy)phosphazines] at different traveling wave (TW) conditions and the calculated cIMS CCS values were within ∼1% error or less compared to previously established drift tube IMS CCS measurements. Since it has been established that glycans can split into their α/β anomers, we utilized this method for two glycan species, 2α-mannobiose and melibiose. Both glycans were analyzed at the same TW conditions as the calibrants, and we observed anomer splitting at pathlengths of 20 m for 2α-mannobiose and 40 m for melibiose and thus assigned two unique CCS values for each glycan, which is the first time this has ever been done. We have demonstrated that the use of average ion velocities is a robust approach for obtaining CCS values with good agreement to CCS measurements from the previous literature and anticipate that this methodology can be applied to any IMS-MS platform that utilizes multipass separations. Our future work aims to incorporate this methodology for the development of a high-resolution CCS database to aid in the characterization of human milk oligosaccharides.