A High-Throughput Ion Mobility Spectrometry–Mass Spectrometry Screening Method for Opioid Profiling

In 2017, the United States Department of Health and Human Services declared the widespread misuse and abuse of prescription and illicit opioids an epidemic. However, this epidemic dates back to the 1990s when opioids were extensively prescribed for pain management. Currently, opioids are still recommended for pain management, and given their abuse potential, rapid screening is imperative for patient treatment. Of particular importance is assessing pain management patient compliance, where evaluating drug use is crucial for preventing opioid abuse and potential overdoses. In this work, we utilized drift tube ion mobility spectrometry coupled with mass spectrometry (DTIMS-MS) to develop a rapid screening method for 33 target opioids and opioid urinary metabolites. Collision cross section values were determined for all target molecules using a flow-injection DTIMS-MS method, and clear differentiation of 27 out of the 33 opioids without prior chromatographic separation was observed when utilizing a high resolution demultiplexing screening approach. An automated solid phase extraction (SPE) platform was then coupled to DTIMS-MS for 10 s sample-to-sample analyses. This SPE-IMS-MS approach enabled the rapid screening of urine samples for opioids and presents a major improvement in sample throughput compared to traditional chromatographic analyses coupled with MS, which routinely take several minutes per sample. Overall, this vast reduction in analysis time facilitates a faster turn-around for patient samples, providing great benefits to clinical applications

A High-Throughput Ion Mobility Spectrometry–Mass Spectrometry Screening Method for Opioid Profiling

In 2017, the United States Department of Health and Human Services declared the widespread misuse and abuse of prescription and illicit opioids an epidemic. However, this epidemic dates back to the 1990s when opioids were extensively prescribed for pain management. Currently, opioids are still recommended for pain management, and given their abuse potential, rapid screening is imperative for patient treatment. Of particular importance is assessing pain management patient compliance, where evaluating drug use is crucial for preventing opioid abuse and potential overdoses. In this work, we utilized drift tube ion mobility spectrometry coupled with mass spectrometry (DTIMS-MS) to develop a rapid screening method for 33 target opioids and opioid urinary metabolites. Collision cross section values were determined for all target molecules using a flow-injection DTIMS-MS method, and clear differentiation of 27 out of the 33 opioids without prior chromatographic separation was observed when utilizing a high resolution demultiplexing screening approach. An automated solid phase extraction (SPE) platform was then coupled to DTIMS-MS for 10 s sample-to-sample analyses. This SPE-IMS-MS approach enabled the rapid screening of urine samples for opioids and presents a major improvement in sample throughput compared to traditional chromatographic analyses coupled with MS, which routinely take several minutes per sample. Overall, this vast reduction in analysis time facilitates a faster turn-around for patient samples, providing great benefits to clinical applications

Improving Ion Mobility Measurement Sensitivity by Utilizing Helium in an Ion Funnel Trap

Ion mobility instruments that utilize nitrogen as buffer gas are often preceded by an ion trap and accumulation region that also uses nitrogen, and for different inert gases, no significant effects upon performance are expected for ion mobility spectrometry (IMS) of larger ions. However, we have observed significantly improved performance for an ion funnel trap upon adding helium; the signal intensities for higher <i>m</i>/<i>z</i> species were improved by more than an order of magnitude compared to using pure nitrogen. The effect of helium upon IMS resolving power was also studied by introducing a He/N₂ gas mixture into the drift cell, and in some cases, a slight improvement was observed compared to pure N₂. The improvement in signal can be largely attributed to faster and more efficient ion ejection into the drift tube from the ion funnel trap

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

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

Online Ozonolysis Combined with Ion Mobility-Mass Spectrometry Provides a New Platform for Lipid Isomer Analyses

One of the most significant challenges in contemporary lipidomics lies in the separation and identification of lipid isomers that differ only in site(s) of unsaturation or geometric configuration of the carbon–carbon double bonds. While analytical separation techniques including ion mobility spectrometry (IMS) and liquid chromatography (LC) can separate isomeric lipids under appropriate conditions, conventional tandem mass spectrometry cannot provide unequivocal identification. To address this challenge, we have implemented ozone-induced dissociation (OzID) in-line with LC, IMS, and high resolution mass spectrometry. Modification of an IMS-capable quadrupole time-of-flight mass spectrometer was undertaken to allow the introduction of ozone into the high-pressure trapping ion funnel region preceding the IMS cell. This enabled the novel LC-OzID-IMS-MS configuration where ozonolysis of ionized lipids occurred rapidly (10 ms) without prior mass-selection. LC-elution time alignment combined with accurate mass and arrival time extraction of ozonolysis products facilitated correlation of precursor and product ions without mass-selection (and associated reductions in duty cycle). Unsaturated lipids across 11 classes were examined using this workflow in both positive and negative ion modalities, and in all cases, the positions of carbon–carbon double bonds were unequivocally assigned based on predictable OzID transitions. Under these conditions, geometric isomers exhibited different IMS arrival time distributions and distinct OzID product ion ratios providing a means for discrimination of <i>cis/trans</i> double bonds in complex lipids. The combination of OzID with multidimensional separations shows significant promise for facile profiling of unsaturation patterns within complex lipidomes including human plasma

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

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

Mass spectrometry profiling of pentosan polysulfate sodium (PPS) (ASMS 2017)

Pentosan polysulfate (PPS) is a semisynthetic heterogenous sulfated polysaccharide derived from xylan, the β-1,4-linked polymer of xylose. PPS sold by the brand name Elmiron in United States is taken orally to alleviate pain associated with interstitial cystitis. PPS is a mixture of hundreds or more discrete molecules built from a range of oligoxylose lengths modified with different combinations of functional group modifications, including sulfation, 4-O-methyl-glucuronidylation, acetylation, and others. The overall goal of our research is to develop an approach using MS together with other methods such as NMR to profile PPS at the molecular level. Profiling PPS according to its molecular composition would be invaluable for understanding biological activity, bioavailability, and pharmacokinetics, as well as for quality control.One Elmiron (100 mg PPS) capsule was extracted with 1 ml of HPLC-grade water, and further dilutions were made with this stock solution. Diluted PPS at a concentration of 0.5mg/ml was treated with an ion exchange resin for few hours, centrifuged and the supernatant collected. To this supernatant butylamine (15mM) and hexafluoroisopropanol (60mM) were added as an ion-pair reagent (final pH ~8.5). The treated sample was fractionated on C18 SPE cartridge using acetonitrile (ACN) starting from concentration of 10% up to 100% ACN. Each fraction was individually analyzed by FTICR and IMS-MS both in positive and negative mode. Agilent drift tube-IMS-QTOF MS and home-built drift tube IMS-MS were used to characterize PPS from different lots and locations of production.The mass spectrum obtained from PPS directly dissolved in water is complex and difficult to interpret due in-source fragmentation of sulfated oligosaccharides and presence of multiple metal ion adducts [M+Na]. We have explored the potential of ion-pair reversed phase chromatography to extract and analyze PPS using C18-SPE followed by MS detection using FTICR and IMS. When each eluate was injected directly in FTICR without any chromatographic separation, most of the PPS eluted in fraction containing 10% and 20% ACN. Analysis of mass spectra revealed presence of multiply charged state species, mostly +2, +3 and +4 for data collected in positive mode. Analysis of deconvulated peaks in positive mode displayed abundant neutral loss of 171.03 across the entire MS1 spectrum. This neutral loss of 171.03 units is most likely coming from the group –OSO₃NH₂(CH₂)₃CH₃ from PPS backbone. IMS-MS is capable of separating molecules that have the same mass-to-charge (m/z) ratio but different sizes, shapes or conformations. Therefore it is appealing for separating PPS with different polymerized sizes and different charge states and for reducing the complexity of mass spectra. Low-molecular-weight heparin, another sulfated oligosaccharide, was used as a standard to develop IMS-MS method. Heparin DP10 which has molecular weight around 3000 Da has shown a 2D IMS-MS spectrum with trend lines for charge +2 and +3 and m/z range from 1000 to 2000. Preliminary data of PPS showed 2D IMS-MS profiles with charge states from +1 to +5 and m/z range from 300 to 2500. These results show that IMS-MS can reduce the complexity of sulfated polysaccharide spectra by additional separation of different charge states and polysaccharide sizes. However the spectra are still complex for peak assignment without any pre-treatment. The uses of ion exchange resin and ion-pairs have shown improved sensitivity and separation in IMS-MS.<p></p

Building a Library with >1000 Ion Mobility Collision Cross Sections for Ultrafast Small Molecule Analyses (ASMS 2017)

Metabolomics and exposomics studies have been growing rapidly during the past decade and providing insights into cellular metabolism and how a system responds to stimuli. Ion mobility spectrometry-mass spectrometry (IMS-MS) has become an appealing tool for small molecule studies as it enables the analysis and separation of the many isomeric species. However, to facilitate the identification of endogenous metabolites and xenobiotics from complex samples, a library with accurate collision cross sections (CCS) is needed. In this work, we generated a CCS library for >1000 small molecule standards using triplicate analyses in positive and negative polarity. This library was then coupled with ultrafast small molecule analyses using a sub-minute online SPE-IMS-MS method for characterization of different metabolic and xenobiotic conditions.IMS-MS is capable of separating molecules that have the same mass-to-charge (m/z) ratios, but different sizes, shapes or conformations. In this work an Agilent drift tube-IMS-QTOF MS platform was used to characterize over one thousand endogenous metabolites and xenobiotics. The CCS values were measured using seven stepped fields in both positive and negative polarity. For small molecule studies, the samples were analyzed by coupling Rapidfire, an online SPE system to the IMS-MS platform which enables sub-minute ultrafast analyses. Different SPE cartridges such as C18, graphitic carbon, HILIC and phenyl cartridges were explored to effectively extract different classes of molecules. In addition, different ionization sources including ESI, APCI and APPI were evaluated for efficient small molecule studies. A large scale ion mobility CCS library including over 800 endogenous metabolites and over 200 xenobiotics was generated using drift tube IMS-MS. Different classes of metabolites displayed different trend lines for their CCS values. For instance, fatty acids show larger CCS values than carbohydrates or amino acids. We were also able to map the CCS values for key metabolites and intermediates in important metabolite pathways such as glycolysis/gluconeogenesis, pentose phosphate pathway and citrate cycle, etc. These results are essential for future studies aimed at identification of metabolites within different structure classes and pathways. To evaluate the use of this CCS library for metabolite identification, we analyzed how small molecules change in patients with and without exercises, as well as those with and without ethanol use, and compared the results from the SPE-IMS-MS method with those from LC-MS method. <div>Xenobiotics that are important for the evaluation of environmental exposure such as polycyclic aromatic hydrocarbons (PAHs) and polychlorinated biphenyls (PCBs) exposures were also studied. Different ionization sources including ESI, APCI and APPI, were evaluated for efficient characterization and detection of the PAHs and PCBs and their metabolites. Preliminary data showed that we were able to detect PAHs in [M∙]+ and [M + H]+ forms and PCBs in the [M-Cl+O]- form using APCI/APPI methods. We also showed that the pre-separation, clean-up and enrichment steps by Rapidfire SPE greatly improved the sensitivity, enabling ultrafast and simultaneous analyses of PAHs and PCBs and their metabolites in complex biological and environmental samples.</div

Development of an Ion Mobility Spectrometry-Orbitrap Mass Spectrometer Platform

Complex samples benefit from multidimensional measurements where higher resolution enables more complete characterization of biological and environmental systems. To address this challenge, we developed a drift tube-based ion mobility spectrometry-Orbitrap mass spectrometer (IMS-Orbitrap MS) platform. To circumvent the time scale disparity between the fast IMS separation and the much slower Orbitrap MS acquisition, we utilized a dual gate and pseudorandom sequences to multiplex the injection of ions and allow operation in signal averaging (SA), single multiplexing (SM), and double multiplexing (DM) IMS modes to optimize the signal-to-noise ratio of the measurements. For the SM measurements, a previously developed algorithm was used to reconstruct the IMS data. A new algorithm was developed for the DM analyses involving a two-step process that first recovers the SM data and then decodes the SM data. The algorithm also performs multiple refining procedures to minimize demultiplexing artifacts. The new IMS-Orbitrap MS platform was demonstrated by the analysis of proteomic and petroleum samples, where the integration of IMS and high mass resolution proved essential for accurate assignment of molecular formulas

Compression Ratio Ion Mobility Programming in Structures for Lossless Ion Manipulations (ASMS 2017)

Structures for Lossless Ion Manipulations (SLIM) technology has enabled very long path length IM separations using traveling waves (TW) in serpentine and multi-pass designs, but resolutions achievable are limited by peak broadening phenomena, which increasingly inhibit detection due to peak dilution. In this work we developed a new approach for spatial and temporal peak compression that can mitigate many of the negative effects of peak broadening and demonstrate its application for the collapse of the ion distributions into tighter packets to provide higher sensitivity. The nature of fields and ion dynamics enabling peak compression will be presented. The implications of compression ratio squeezing of ion packets and programming for IM separations and other applications will be discussed.Theoretical and simulation methods are used to study the process of peak compression in TW SLIM. In-house computational models were used to study effects of compression. SIMION ion trajectory simulations were used to demonstrate proof-of-concept, to predict experimental performance and optimize SLIM designs. Software package OpenFOAM was used to visualize the ion confinement fields and model the ion dynamics by treating ion motion using advection-diffusion equation. Experimental implementation was performed on a 13 m long serpentine path length SLIM device with multi-pass capability, coupled to an Agilent qTOF MS.We demonstrate peak compression using a SLIM device with a TW region (R1) and another region where a stuttering wave moves only intermittently (R2). As the ions pass the interface between R1 and R2, the ion packets spanning a number of TW-created traveling traps (TT) are redistributed into fewer TT, resulting in spatial compression. The degree of spatial compression is controllable and determined by the ratio of stationary time of the TW in the second region to its moving time. This compression ratio ion mobility programming (CRIMP) approach has been implemented using SLIM in conjunction with a TOF-MS. CRIMP with the SLIM IM-MS platform is shown to provide increased peak intensities, reduced peak widths, and improved S/N ratios with MS detection. The increase in peak height is equivalent to the applied compression ratio (CR) until such a point that space charge effects lead to ion activation and/or losses. SLIM TTs keep ions confined as long as the TW is in the surfing mode, and TW produce a ion peak bin “quantization” effect which allows peak compression with integer CR. The effect of such peak compression on IM separation and resolution will be discussed from theoretical standpoint and correlated to experimental observations. TW SLIM IM separation of milk oligosaccharide isomers in conjunction with peak compression shows that two species with very similar mobilities can be fully separated by combined application of separation and compression. Also CRIMP allows injecting a wide pulse of ions that can be separated and then compressed to enable high resolution IM separations at high sensitivity. Further, insights from ion trajectories modeling on the effects of space charge during the CRIMP process will be discussed