Direct Enantiomer Differentiation of Drugs and Drug-Like Compounds via Noncovalent Copper-Amino Acid Complexation and Ion Mobility-Mass Spectrometry.

Drug enantiomers can possess vastly different pharmacological properties, yet they are identical in their chemical composition and structural connectivity. Thus, resolving enantiomers poses a great challenge in the field of separation science. Enantiomer separations necessitate interaction of the analyte with a chiral environment─in mass spectrometry-based analysis, a common approach is through a three-point interaction with a chiral selector commonly introduced during sample preparation. In select cases, the structural difference imparted through noncovalent complexation results in enantiomer-specific structural differences, facilitating measurement using a structurally selective analytical technique such as ion mobility-mass spectrometry (IM-MS). In this work, we investigate the direct IM-MS differentiation of chiral drug compounds using mononuclear copper complexes incorporating an amino acid chiral selector. A panel of 20 chiral drugs and drug-like compounds were investigated for separation, and four l-amino acids (l-histidine, l-tryptophan, l-proline, and l-tyrosine) were evaluated as chiral selectors (CS) to provide the chiral environment necessary for differentiation. Enantiomer differentiation was achieved for four chiral molecule pairs (R/S-thalidomide, R/S-baclofen, R/S-metoprolol, and d/l-panthenol) with two-peak resolution (Rp-p) values ranging from 0.7 (>10% valley) to 1.5 (baseline separation). Calibration curves relating IM peak areas to enantiomeric concentrations enabled enantiomeric excess quantitation of racemic thalidomide and metoprolol with residuals of 5.7 and 2.5%, respectively. Theoretical models suggest that CuII and l-histidine complexation around the analyte chiral center is important for gas-phase stereoselectivity. This study demonstrates the potential of combining enantioselective noncovalent copper complexation with structurally selective IM-MS for differentiating chiral drugs and drug-like compounds.

Ion Mobility-Mass Spectrometry Strategies to Elucidate the Anhydrous Structure of Noncovalent Guest/Host Complexes.

Structural mass spectrometry (MS) techniques are fast and sensitive analytical methods to identify noncovalent guest/host complexation phenomena for desirable solution-phase properties. Current MS-based studies on guest/host complexes of drug and drug-like molecules are sparse, and there is limited guidance on how to interpret MS information in the context of host nanoencapsulation and inclusion. Here, we use structural MS strategies, combining energy-resolved MS (ERMS), ion mobility-MS (IM-MS), and computational modeling, to characterize 14 chemically distinct drug and drug-like compounds for their propensity to form guest/host complexes with the widely used excipient, beta-cyclodextrin (ßCD). The majority (11/14) yielded a 1:1 guest/host complex, and ion mobility collision cross section (CCS) analysis provided subtle evidence of gas-phase compaction of complexes in both polarities. The three distinct dissociation channels observed in ERMS (i.e., charged ßCD, charged guest, and partial guest loss) were used to direct charge-site assignments for computational modeling, and structural candidates were prioritized using helium-derived CCS measurements combined with root-mean-square distance analysis. The combined analytical information from ERMS, IM-MS, and computational modeling suggested that the majority of anhydrous complexes are inclusion complexes with ßCD. Taken together, this work demonstrates a roadmap for how multiple MS-based analytical measurements can be combined to interpret the structures that guest/host complexes adopt in the absence of water.

High-resolution ion mobility based on traveling wave structures for lossless ion manipulation resolves hidden lipid features.

High-resolution ion mobility (resolving power > 200) coupled with mass spectrometry (MS) is a powerful analytical tool for resolving isobars and isomers in complex samples. High-resolution ion mobility is capable of discerning additional structurally distinct features, which are not observed with conventional resolving power ion mobility (IM, resolving power ~ 50) techniques such as traveling wave IM and drift tube ion mobility (DTIM). DTIM in particular is considered to be the "gold standard" IM technique since collision cross section (CCS) values are directly obtained through a first-principles relationship, whereas traveling wave IM techniques require an additional calibration strategy to determine accurate CCS values. In this study, we aim to evaluate the separation capabilities of a traveling wave ion mobility structures for lossless ion manipulation platform integrated with mass spectrometry analysis (SLIM IM-MS) for both lipid isomer standards and complex lipid samples. A cross-platform investigation of seven subclass-specific lipid extracts examined by both DTIM-MS and SLIM IM-MS showed additional features were observed for all lipid extracts when examined under high resolving power IM conditions, with the number of CCS-aligned features that resolve into additional peaks from DTIM-MS to SLIM IM-MS analysis varying between 5 and 50%, depending on the specific lipid sub-class investigated. Lipid CCS values are obtained from SLIM IM (TW(SLIM)CCS) through a two-step calibration procedure to align these measurements to within 2% average bias to reference values obtained via DTIM (DTCCS). A total of 225 lipid features from seven lipid extracts are subsequently identified in the high resolving power IM analysis by a combination of accurate mass-to-charge, CCS, retention time, and linear mobility-mass correlations to curate a high-resolution IM lipid structural atlas. These results emphasize the high isomeric complexity present in lipidomic samples and underscore the need for multiple analytical stages of separation operated at high resolution.

Accelerating strain phenotyping with desorption electrospray ionization-imaging mass spectrometry and untargeted analysis of intact microbial colonies.

Reading and writing DNA were once the rate-limiting step in synthetic biology workflows. This has been replaced by the search for the optimal target sequences to produce systems with desired properties. Directed evolution and screening mutant libraries are proven technologies for isolating strains with enhanced performance whenever specialized assays are available for rapidly detecting a phenotype of interest. Armed with technologies such as CRISPR-Cas9, these experiments are capable of generating libraries of up to 1010 genetic variants. At a rate of 102 samples per day, standard analytical methods for assessing metabolic phenotypes represent a major bottleneck to modern synthetic biology workflows. To address this issue, we have developed a desorption electrospray ionization-imaging mass spectrometry screening assay that directly samples microorganisms. This technology increases the throughput of metabolic measurements by reducing sample preparation and analyzing organisms in a multiplexed fashion. To further accelerate synthetic biology workflows, we utilized untargeted acquisitions and unsupervised analytics to assess multiple targets for future engineering strategies within a single acquisition. We demonstrate the utility of the developed method using Escherichia coli strains engineered to overproduce free fatty acids. We determined discrete metabolic phenotypes associated with each strain, which include the primary fatty acid product, secondary products, and additional metabolites outside the engineered product pathway. Furthermore, we measured changes in amino acid levels and membrane lipid composition, which affect cell viability. In sum, we present an analytical method to accelerate synthetic biology workflows through rapid, untargeted, and multiplexed metabolomic analyses.

Noncovalent Host-Guest Complexes of Artemisinin with α-, ß-, 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, ßCD, 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:ßCD 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:ßCD + 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:ßCD + Li]+ ion correlated with predicted CCS values from the low-energy theoretical structures of the drug incorporated within the ßCD 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.

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.

Performance evaluation of in-source ion activation hardware for collision-induced unfolding of proteins and protein complexes on a drift tube ion mobility-mass spectrometer.

Native ion mobility-mass spectrometry (IM-MS) has emerged as an information-rich technique for gas phase protein structure characterization; however, IM resolution is currently insufficient for the detection of subtle structural differences in large biomolecules. This challenge has spurred the development of collision-induced unfolding (CIU) which utilizes incremental gas phase activation to unfold a protein in order to expand the number of measurable descriptors available for native protein ions. Although CIU is now routinely used in native mass spectrometry studies, the interlaboratory reproducibility of CIU has not been established. Here we evaluate the reproducibility of the CIU data produced across three laboratories (University of Michigan, Texas A&M University, and Vanderbilt University). CIU data were collected for a variety of protein ions ranging from 8.6-66 kDa. Within the same laboratory, the CIU fingerprints were found to be repeatable with root mean square deviation (RMSD) values of less than 5%. Collision cross section (CCS) values of the CIU intermediates were consistent across the laboratories, with most features exhibiting an interlaboratory reproducibility of better than 1%. In contrast, the activation potentials required to induce protein CIU transitions varied between the three laboratories. To address these differences, three source assemblies were constructed with an updated ion activation hardware design utilizing higher mechanical tolerance specifications. The production-grade assemblies were found to produce highly consistent CIU data for intact antibodies, exhibiting high precision ion CCS and CIU transition values, thus opening the door to establishing databases of CIU fingerprints to support future biomolecular classification efforts.

Insights and prospects for ion mobility-mass spectrometry in clinical chemistry.

INTRODUCTION: Ion mobility-mass spectrometry is an emerging technology in the clinical setting for high throughput and high confidence molecular characterization from complex biological samples. Ion mobility spectrometry can provide isomer separations on the basis of molecular structure, the ability of which is increasing through technological developments that afford enhanced resolving power. Integrating multiple separation dimensions, such as liquid chromatography-ion mobility-mass spectrometry (LC-IM-MS) provide dramatic enhancements in the mitigation of molecular interferences for high accuracy clinical measurements. AREAS COVERED: Multidimensional separations with LC-IM-MS provide better selectivity and sensitivity in molecular analysis. Mass spectrometry imaging of tissues to inform spatial molecular distribution is improved by complementary ion mobility analyses. Biomarker identification in surgical environments is enhanced by intraoperative biochemical analysis with mass spectrometry and holds promise for integration with ion mobility spectrometry. New prospects in high resolving power ion mobility are enhancing analysis capabilities, such as distinguishing isomeric compounds. EXPERT OPINION: Ion mobility-mass spectrometry holds many prospects for the field of isomer identification, molecular imaging, and intraoperative tumor margin delineation in clinical settings. These advantages are afforded while maintaining fast analysis times and subsequently high throughput. High resolving power ion mobility will enhance these advantages further, in particular for analyses requiring high confidence isobaric selectivity and detection.

Integrating ion mobility into comprehensive multidimensional metabolomics workflows: critical considerations.

BACKGROUND: Ion mobility (IM) separation capabilities are now widely available to researchers through several commercial vendors and are now being adopted into many metabolomics workflows. The added peak capacity that ion mobility offers with minimal compromise to other analytical figures-of-merit has provided real benefits to sensitivity and structural selectivity and have allowed more specific metabolite annotations to be assigned in untargeted workflows. One of the greatest promises of contemporary IM-enabled instrumentation is the capability of operating multiple analytical dimensions inline with minimal sample volumes, which has the potential to address many grand challenges currently faced in the omics fields. However, comprehensive operation of multidimensional mass spectrometry comes with its own inherent challenges that, beyond operational complexity, may not be immediately obvious to practitioners of these techniques. AIM OF REVIEW: In this review, we outline the strengths and considerations for incorporating IM analysis in metabolomics workflows and provide a critical but forward-looking perspective on the contemporary challenges and prospects associated with interpreting IM data into chemical knowledge. KEY SCIENTIFIC CONCEPTS OF REVIEW: We outline a strategy for unifying IM-derived collision cross section (CCS) measurements obtained from different IM techniques and discuss the emerging field of high resolution ion mobility (HRIM) that is poised to address many of the contemporary challenges associated with ion mobility metabolomics. Whereas the LC step limits the throughput of comprehensive LC-IM-MS, the higher peak capacity of HRIM can allow fast LC gradients or rapid sample cleanup via solid-phase extraction (SPE) to be utilized, significantly improving the sample throughput.

Multidimensional Separations of Intact Phase II Steroid Metabolites Utilizing LC-Ion Mobility-HRMS.

The detection and unambiguous identification of anabolic-androgenic steroid metabolites are essential in clinical, forensic, and antidoping analyses. Recently, sulfate phase II steroid metabolites have received increased attention in steroid metabolism and drug testing. In large part, this is because phase II steroid metabolites are excreted for an extended time, making them a potential long-term chemical marker of choice for tracking steroid misuse in sports. Comprehensive analytical methods, such as liquid chromatography-tandem mass spectrometry (LC-MS/MS), have been used to detect and identify glucuronide and sulfate steroids in human urine with high sensitivity and reliability. However, LC-MS/MS identification strategies can be hindered by the fact that phase II steroid metabolites generate nonselective ion fragments across the different metabolite markers, limiting the confidence in metabolite identifications that rely on exact mass measurement and MS/MS information. Additionally, liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is sometimes insufficient at fully resolving the analyte peaks from the sample matrix (commonly urine) chemical noise, further complicating accurate identification efforts. Therefore, we developed a liquid chromatography-ion mobility-high resolution mass spectrometry (LC-IM-HRMS) method to increase the peak capacity and utilize the IM-derived collision cross section (CCS) values as an additional molecular descriptor for increased selectivity and to improve identifications of intact steroid analyses at low concentrations.

Chemical Class Prediction of Unknown Biomolecules Using Ion Mobility-Mass Spectrometry and Machine Learning: Supervised Inference of Feature Taxonomy from Ensemble Randomization.

This work presents a machine learning algorithm referred to as the supervised inference of feature taxonomy from ensemble randomization (SIFTER), which supports the identification of features derived from untargeted ion mobility-mass spectrometry (IM-MS) experiments. SIFTER utilizes random forest machine learning on three analytical measurements derived from IM-MS (collision cross section, CCS), mass-to-charge (m/z), and mass defect (Δm) to classify unknown features into a taxonomy of chemical kingdom, super class, class, and subclass. Each of these classifications is assigned a calculated probability as well as alternate classifications with associated probabilities. After optimization, SIFTER was tested against a set of molecules not used in the training set. The average success rate in classifying all four taxonomy categories correctly was found to be >99%. Analysis of molecular features detected from a complex biological matrix and not used in the training set yielded a lower success rate where all four categories were correctly predicted for ∼80% of the compounds. This decline in performance is in part due to incompleteness of the training set across all potential taxonomic categories, but also resulting from a nearest-neighbor bias in the random forest algorithm. Ongoing efforts are focused on improving the class prediction accuracy of SIFTER through expansion of empirical data sets used for training as well as improvements to the core algorithm.

Resolution of Isomeric Mixtures in Ion Mobility Using a Combined Demultiplexing and Peak Deconvolution Technique.

A combined data acquisition and data processing strategy for improving the sensitivity and resolution of ion mobility measurements is described. This strategy is implemented on a commercially available drift tube ion mobility-mass spectrometry (IM-MS) instrument and utilizes both an existing ion multiplexing strategy to achieve up to an 8-fold gain in ion signal and a new postacquisition data reconstruction technique, termed "high resolution demultiplexing" (HRdm), to improve resolution in the ion mobility dimension. A series of isomeric mixtures were qualitatively investigated with HRdm, including biologically relevant lipids and carbohydrates, which were successfully resolved by HRdm, including two monosaccharide regioisomers which differed in drift time by only 0.8%. For a complex trisaccharide isomer mixture, HRdm was able to resolve 5 out of 6 components. An analysis of two-peak resolution (Rpp) and peak-to-peak separation (ΔP) indicated that HRdm performs with an effective resolving power (Rp) of between 180 to 250 for the highest deconvolution settings. Overall analysis times and drift time measurement precision were found to be unaffected between standard and HRdm processed data sets, which allowed statistically identical collision cross section values to be directly determined from all ion mobility spectra.

Recommendations for reporting ion mobility Mass Spectrometry measurements.

Here we present a guide to ion mobility mass spectrometry experiments, which covers both linear and nonlinear methods: what is measured, how the measurements are done, and how to report the results, including the uncertainties of mobility and collision cross section values. The guide aims to clarify some possibly confusing concepts, and the reporting recommendations should help researchers, authors and reviewers to contribute comprehensive reports, so that the ion mobility data can be reused more confidently. Starting from the concept of the definition of the measurand, we emphasize that (i) mobility values (K0 ) depend intrinsically on ion structure, the nature of the bath gas, temperature, and E/N; (ii) ion mobility does not measure molecular surfaces directly, but collision cross section (CCS) values are derived from mobility values using a physical model; (iii) methods relying on calibration are empirical (and thus may provide method-dependent results) only if the gas nature, temperature or E/N cannot match those of the primary method. Our analysis highlights the urgency of a community effort toward establishing primary standards and reference materials for ion mobility, and provides recommendations to do so. © 2019 The Authors. Mass Spectrometry Reviews Published by Wiley Periodicals, Inc.

Mass spectrometry and ion mobility study of poly(ethylene glycol)-based polyurethane oligomers.

RATIONALE: Commercial-grade polymer synthesis is performed via melt polymerization, which leads to polydispersion. The work reported herein provides a synthetic strategy to produce mono-dispersive polyurethane oligomers and an analytical strategy to distinguish these oligomers, providing chemists with the tools necessary to synthesize and identify specific polymer structures that exhibit a desired property. METHODS: Three isomeric poly(ethylene glycol)-polyurethane (PEG-PUR) oligomers were synthesized and analyzed via flow-injection ion mobility mass spectrometry (IM-MS). Each polymer oligomer was injected and run independently via flow injection at 100 µL•min-1 and analyzed in positive ion mode on a drift tube quadrupole time-of-flight (QTOF) instrument. Mobility measurements were determined using a single-field approach. For tandem mass spectrometry (MS/MS) experiments, the sodium-adducted singly charged precursor ion was isolated in the quadrupole and subjected to a range of collision energies. RESULTS: In MS experiments, both +1 and +2 sodium-adducted species were observed for each oligomer at m/z 837.4 and 430.2, respectively. When isolated and fragmented via MS/MS, the +1 precursor yielded distinct product ions for each of the three isomeric oligomers. Fragmentation generally occurred at urethane linkages via 1,3- and 1,5-H shift mechanisms. IM was also used to distinguish the three isomers, with greater IM separation observed for the +2 versus the +1 species. CONCLUSIONS: Mono-disperse PEG-PUR oligomers were synthesized and analyzed. Although the polymeric oligomers analyzed in this study are quite small and structurally simple, this work serves as a model system for the synthesis and structural characterization of larger, more complex block copolymers.

Algal Toxin Goniodomin A Binds Potassium Ion Selectively to Yield a Conformationally Altered Complex with Potential Biological Consequences.

The marine toxin goniodomin A (GDA) is a polycyclic macrolide containing a spiroacetal and three cyclic ethers as part of the macrocycle backbone. GDA is produced by three species of the Alexandrium genus of dinoflagellates, blooms of which are associated with "red tides", which are widely dispersed and can cause significant harm to marine life. The toxicity of GDA has been attributed to stabilization of the filamentous form of the actin group of structural proteins, but the structural basis for its binding is not known. Japanese workers, capitalizing on the assumed rigidity of the heavily substituted macrolide ring, assigned the relative configuration and conformation by relying on NMR coupling constants and NOEs; the absolute configuration was assigned by degradation to a fragment that was compared with synthetic material. We have confirmed the absolute structure and broad features of the conformation by X-ray crystallography but have found GDA to complex with alkali metal ions in spite of two of the heterocyclic rings facing outward. Such an arrangement would have been expected to impair the ability of GDA to form a crown-ether-type multidentate complex. GDA shows preference for K+, Rb+, and Cs+ over Li+ and Na+ in determinations of relative affinities by TLC on metal-ion-impregnated silica gel plates and by electrospray mass spectrometry. NMR studies employing the K+ complex of GDA, formed from potassium tetrakis[pentafluorophenyl]borate (KBArF20), reveal a major alteration of the conformation of the macrolide ring. These observations argue against the prior assumption of rigidity of the ring. Alterations in chemical shifts, coupling constants, and NOEs indicate the involvement of most of the molecule other than ring F. Molecular mechanics simulations suggest K+ forms a heptacoordinate complex involving OA, OB, OC, OD, OE, and the C-26 and C-27 hydroxy groups. We speculate that complexation of K+ with GDA electrostatically stabilizes the complex of GDA with filamentous actin in marine animals due to the protein being negatively charged at physiological pH. GDA may also cause potassium leakage through cell membranes. This study provides insight into the structural features and chemistry of GDA that may be responsible for significant ecological damage associated with the GDA-producing algal blooms.

Predicting Ion Mobility Collision Cross-Sections Using a Deep Neural Network: DeepCCS.

Untargeted metabolomic measurements using mass spectrometry are a powerful tool for uncovering new small molecules with environmental and biological importance. The small molecule identification step, however, still remains an enormous challenge due to fragmentation difficulties or unspecific fragment ion information. Current methods to address this challenge are often dependent on databases or require the use of nuclear magnetic resonance (NMR), which have their own difficulties. The use of the gas-phase collision cross section (CCS) values obtained from ion mobility spectrometry (IMS) measurements were recently demonstrated to reduce the number of false positive metabolite identifications. While promising, the amount of empirical CCS information currently available is limited, thus predictive CCS methods need to be developed. In this article, we expand upon current experimental IMS capabilities by predicting the CCS values using a deep learning algorithm. We successfully developed and trained a prediction model for CCS values requiring only information about a compound's SMILES notation and ion type. The use of data from five different laboratories using different instruments allowed the algorithm to be trained and tested on more than 2400 molecules. The resulting CCS predictions were found to achieve a coefficient of determination of 0.97 and median relative error of 2.7% for a wide range of molecules. Furthermore, the method requires only a small amount of processing power to predict CCS values. Considering the performance, time, and resources necessary, as well as its applicability to a variety of molecules, this model was able to outperform all currently available CCS prediction algorithms.

New Frontiers in Lipidomics Analyses using Structurally Selective Ion Mobility-Mass Spectrometry.

The growth of lipidomics and the high isomeric complexity of the lipidome has revealed a need for analytical techniques capable of structurally characterizing lipids with a high degree of specificity. Lipids are morphologically diverse molecules that can exist as any one of a large number of isomeric species, and as such are often indistinguishable by mass spectrometry without a complementary separation method. Recent developments in the field of lipidomics aim to address these challenges by utilizing a combination of multiple analytical techniques which are selective to lipid primary structure. This review summarizes two emerging strategies for lipidomic analysis, namely, ion mobility-mass spectrometry and ion fragmentation via ozonolysis.

Mass Spectrometry of Polyurethanes.

This review covers the applications of mass spectrometry (MS) and its hyphenated techniques to characterize polyurethane (PU) synthetic polymers and their respective hard and soft segments. PUs are commonly composed of hard segments including methylene bisphenyl diisocyanate (MDI) and toluene diisocyanate (TDI), and soft segments including polyester and polyether polyols. This literature review highlights MS techniques such as electrospray ionization (ESI), matrix assisted laser/desorption ionization (MALDI), ion mobility-mass spectrometry (IM-MS), and computational methods that have been used for the characterization of this polymer system. Here we review specific case studies where MS techniques have elucidated unique features pertaining to the makeup and structural integrity of complex PU materials and PU precursors.

Alkali Metal Cation Adduct Effect on Polybutylene Adipate Oligomers: Ion Mobility-Mass Spectrometry.

Polyurethane (PU) di-block copolymers are one of the most versatile polymeric materials, comprised of hard and soft segments that contribute to PU's broad range of applications. Polybutylene adipate (PBA) is a commonly used soft segment in PU systems. Characterizing the structure of PBA polymers is essential to understanding complex heterogeneity within a PU sample. In this study, ion mobility-mass spectrometry (IM-MS) and tandem mass spectrometry (MS/MS) are used to structurally characterize a PBA standard (Mn = 2250) adducted with a combination of monovalent alkali cations (Li, Na, K, Rb, and Cs). IM-MS profiles show unique trends associated with each cation-adducted PBA sample. Charge state trends: +1, +2, and +3 were extracted for cation-adducted PBA oligomers, and investigated to study gas-phase transitional folding. To quantitatively assess the gas-phase structural similarities and differences, a statistical test (ANOVA) was used to compare PBA oligomer-cation collisional cross sections (CCS). Fragmentation studies (MS/MS) identified the unique behavior of Li and Na for promoting 1,5 H-shift and 1,3 H-shift fragmentation, whereas the PBA precursor preferentially loses the larger K, Rb, and Cs cations as the ion activation energy is increased. The combination of adducted alkali cations, IM-MS, and MS/MS allow for unique structural characterization of this important PBA system.

Determining Double Bond Position in Lipids Using Online Ozonolysis Coupled to Liquid Chromatography and Ion Mobility-Mass Spectrometry.

The increasing focus on lipid metabolism has revealed a need for analytical techniques capable of structurally characterizing lipids with a high degree of specificity. Lipids can exist as any one of a large number of double bond positional isomers, which are indistinguishable by single-stage mass spectrometry alone. Ozonolysis reactions coupled to mass spectrometry have previously been demonstrated as a means for localizing double bonds in unsaturated lipids. Here we describe an online, solution-phase reactor using ozone produced via a low-pressure mercury lamp, which generates aldehyde products diagnostic of cleavage at a particular double bond position. This flow-cell device is utilized in conjunction with structurally selective ion mobility-mass spectrometry. The lamp-mediated reaction was found to be effective for multiple lipid species in both positive and negative ionization modes, and the conversion efficiency from precursor to product ions was tunable across a wide range (20-95%) by varying the flow rate through the ozonolysis device. Ion mobility separation of the ozonolysis products generated additional structural information and revealed the presence of saturated species in a complex mixture. The method presented here is simple, robust, and readily coupled to existing instrument platforms with minimal modifications necessary. For these reasons, application to standard lipidomic workflows is possible and aids in more comprehensive structural characterization of a myriad of lipid species.