Integrating ion mobility into comprehensive multidimensional metabolomics workflows: critical considerations

Characterization of Electrospray Ionization Complexity in Untargeted Metabolomic Studies

The annotation of metabolites detected in LC-MS-based untargeted metabolomics studies routinely applies accurate m/z of the intact metabolite (MS1) as well as chromatographic retention time and MS/MS data. Electrospray ionization and transfer of ions through the mass spectrometer can result in the generation of multiple "features" derived from the same metabolite with different m/z values but the same retention time. The complexity of the different charged and neutral adducts, in-source fragments, and charge states has not been previously and deeply characterized. In this paper, we report the first large-scale characterization using publicly available data sets derived from different research groups, instrument manufacturers, LC assays, sample types, and ion modes. 271 m/z differences relating to different metabolite feature pairs were reported, and 209 were annotated. The results show a wide range of different features being observed with only a core 32 m/z differences reported in >50% of the data sets investigated. There were no patterns reporting specific m/z differences that were observed in relation to ion mode, instrument manufacturer, LC assay type, and mammalian sample type, although some m/z differences were related to study group (mammal, microbe, plant) and mobile phase composition. The results provide the metabolomics community with recommendations of adducts, in-source fragments, and charge states to apply in metabolite annotation workflows.

Evaluation of a Reference-Free Collision Cross Section Calibration Strategy for Proteomics Using SLIM-Based High-Resolution Ion Mobility Spectrometry-Mass Spectrometry

Ion mobility spectrometry (IMS) is a gas-phase analytical technique that separates ions with different sizes and shapes and is compatible with mass spectrometry (MS) to provide an additional separation dimension. The rapid nature of the IMS separation combined with the high sensitivity of MS-based detection and the ability to derive structural information on analytes in the form of the property collision cross section (CCS) makes IMS particularly well-suited for characterizing complex samples in -omics applications. In such applications, the quality of CCS from IMS measurements is critical to confident annotation of the detected components in the complex -omics samples. However, most IMS instrumentation in mainstream use requires calibration to calculate CCS from measured arrival times, with the most notable exception being drift tube IMS measurements using multifield methods. The strategy for calibrating CCS values, particularly selection of appropriate calibrants, has important implications for CCS accuracy, reproducibility, and transferability between laboratories. The conventional approach to CCS calibration involves explicitly defining calibrants ahead of data acquisition and crucially relies upon availability of reference CCS values. In this work, we present a novel reference-free approach to CCS calibration which leverages trends among putatively identified features and computational CCS prediction to conduct calibrations post-data acquisition and without relying on explicitly defined calibrants. We demonstrated the utility of this reference-free CCS calibration strategy for proteomics application using high-resolution structures for lossless ion manipulations (SLIM)-based IMS-MS. We first validated the accuracy of CCS values using a set of synthetic peptides and then demonstrated using a complex peptide sample from cell lysate.

Solvent Composition Can Have a Measurable Influence on the Ion Mobility-Derived Collision Cross Section of Small Molecules

Ion mobility (IM) is an important analytical technique for increasing identification coverage of metabolites in untargeted studies, especially when integrated into traditional liquid chromatography-mass spectrometry workflows. While there has been extensive work surrounding best practices to obtain and standardize collision cross section (CCS) measurements necessary for comparing across different IM techniques and laboratories, there has been little investigation into experimental factors beyond the mobility separation region that could potentially influence CCS measurements. The first-principles derived CCS of 15 chemical standards were evaluated across 27 aqueous:organic solvent compositions using a high-precision drift tube instrument. A small but measurable dependency of the CCS on the solvent composition was observed, with the larger analytes from this study (m/z > 400) exhibiting a characteristic increase in CCS at the intermediate (40-60%) solvent compositions. Parallels to the behavior of solvent viscosity and protonation site tautomers (protomers) were noted, although the origin of these solvent-dependent CCS trends is as yet unclear. Taken together, these findings document a solvent dependency on CCS, which, while minor (<0.5%), identifies an important need for reporting the solvent system when utilizing CCS in comparative ion mobility studies.

Resolving D-Amino Acid Containing Peptides Using Ion Mobility-Mass Spectrometry: Challenges and Recent Developments

Peptides and proteins having D-amino acids in their sequences are now believed to be widespread among different living organisms. Their significance is attributed to the diverse functions of these molecules, such as having a certain pathological implication or enhancing biological activity. Indeed, some peptide molecules with D-amino acids in their structure have already found their way to clinical use such as the antibacterial gramicidin and the antidiabetic nateglinide. Ion mobility mass spectrometry (IM-MS) added an additional dimension of separation as it depends on ions mobility in the space, which is dependent on their shapes, and the shape depends on the orientation of atoms. Thus, D-amino acids containing peptides (DAACPs) will have different mobility and collision cross-section values than those with L-amino acids. Eventually, this will lead to baseline separation of the two peptides. Additionally, ion mobility can precisely locate the position of D-amino acids by analyzing the difference in the arrival times of the fragment ions. The importance of DAACPs, as well as the difficulties in discovering them, were addressed in this review. Similarly, we emphasized how recent developments in IM-MS have improved their detection and analysis. Consequently, the LC-IM-MS/MS platform appears to be promising in isomeric mixture analysis.

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.

Noncovalent Host-Guest Complexes of Artemisinin with α-, β-, and γ- Cyclodextrin Examined by Structural Mass Spectrometry Strategies

Cyclodextrins (CDs) are a family of macrocyclic oligosaccharides with amphiphilic properties, which can improve the stability, solubility, and bioavailability of therapeutic compounds. There has been growing interest in the advancement of efficient and reliable analytical methods that assist with elucidating CD host-guest drug complexation. In this study, we investigate the noncovalent ion complexes formed between naturally occurring dextrins (αCD, βCD, γCD, and maltohexaose) with the poorly water-soluble antimalarial drug, artemisinin, using a combination of ion mobility-mass spectrometry (IM-MS), tandem MS/MS, and theoretical modeling approaches. This study aims to determine if the drug can complex within the core dextrin cavity forming an inclusion complex or nonspecifically bind to the periphery of the dextrins. We explore the use of group I alkali earth metal additives to promote the formation of various noncovalent gas-phase ion complexes with different drug/dextrin stoichiometries (1:1, 1:2, 1:3, 1:4, and 2:1). Broad IM-MS collision cross section (CCS) mapping (n > 300) and power-law regression analysis were used to confirm the stoichiometric assignments. The 1:1 drug:αCD and drug:βCD complexes exhibited strong preferences for Li+ and Na+ charge carriers, whereas drug:γCD complexes preferred forming adducts with the larger alkali metals, K⁺, Rb⁺, and Cs⁺. Although the ion-measured CCS increased with cation size for the unbound artemisinin and CDs, the 1:1 drug:dextrin complexes exhibit near-identical CCS values regardless of the cation, suggesting these are inclusion complexes. Tandem MS/MS survival yield curves of the [artemisinin:βCD + X]⁺ ion (X = H, Li, Na, K) showed a decreased stability of the ion complex with increasing cation size. Empirical CCS measurements of the [artemisinin:βCD + Li]⁺ ion correlated with predicted CCS values from the low-energy theoretical structures of the drug incorporated within the βCD cavity, providing further evidence that gas-phase inclusion complexes are formed in these experiments. Taken together, this work demonstrates the utility of combining analytical information from IM-MS, MS/MS, and computational approaches in interpreting the presence of gas-phase inclusion phenomena.

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.

Collision cross-section as a universal molecular descriptor in the analysis of PFAS and use of ion mobility spectrum filtering for improved analytical sensitivities

The massive usage of per- and polyfluoroalkyl substances (PFAS), as well as their high chemical stability, have led to their ubiquitous presence in environmental matrices and direct human exposure through contaminated food, particularly fish. In the analysis of this large group of substances, the use of ion mobility coupled to mass spectrometry is of particular relevance because it uses an additional descriptor, the collision cross-section (CCS), which results in increased selectivity. In the present work, the ^TWCCS_N2 of 24 priority PFAS were experimentally obtained, and the reproducibility of these measurements was evaluated over seven weeks. The average values were employed to critically assess previously reported data and theoretical calculations. This gain in selectivity made it possible to increase the sensitivity of the detection on complex matrices (biota, food and human serum) by using the drift time associated to each analyte as a filter, thus reducing the interferences and background noise and allowing their detection at trace levels.

Liquid chromatography and differential mobility spectrometry-data-independent mass spectrometry for comprehensive multidimensional separations in metabolomics

The benefits of combining drift time ion mobility (DTIMS) with liquid chromatography-high-resolution mass spectrometry (HRMS) have been reported for metabolomics but the use of differential time mobility spectrometry (DMS) is less obvious due to the need for rapid scanning of the DMS cell. Drift DTIMS provides additional precursor ion selectivity and collisional cross-section information but the separation resolution between analytes remains cell- and component-dependent. With DMS, the addition of 2-propanol modifier can improve the selectivity but on cost of analyte MS response. In the present work, we investigate the liquid chromatography-mass spectrometry (LC-MS) analysis of a mix of 50 analytes, representative for urine and plasma metabolites, using scanning DMS with the single modifiers cyclohexane (Ch), toluene (Tol), acetonitrile (ACN), ethanol (EtOH), and 2-propanol (IPA), and a binary modifier mixture (cyclohexane/2-propanol) with emphasis on selectivity and signal sensitivity. 1.5% IPA in the N₂ stream was found to suppress the signal of 50% of the analytes which could be partially recovered with the use of IPA to 0.05% as a Ch/IPA mixture. The potential to use the separation voltage/compensation voltage/modifier (SV/CoV/Mod) feature as an additional analyte identifier for qualitative analysis is also presented and applied to a data-independent LCxDMS-SWATH-MS workflow for the analysis of endogenous metabolites and drugs of abuse in human urine samples from traffic control.