AutonoMS: Automated Ion Mobility Metabolomic Fingerprinting.

Automation is dramatically changing the nature of laboratory life science. Robotic lab hardware that can perform manual operations with greater speed, endurance, and reproducibility opens an avenue for faster scientific discovery with less time spent on laborious repetitive tasks. A major bottleneck remains in integrating cutting-edge laboratory equipment into automated workflows, notably specialized analytical equipment, which is designed for human usage. Here we present AutonoMS, a platform for automatically running, processing, and analyzing high-throughput mass spectrometry experiments. AutonoMS is currently written around an ion mobility mass spectrometry (IM-MS) platform and can be adapted to additional analytical instruments and data processing flows. AutonoMS enables automated software agent-controlled end-to-end measurement and analysis runs from experimental specification files that can be produced by human users or upstream software processes. We demonstrate the use and abilities of AutonoMS in a high-throughput flow-injection ion mobility configuration with 5 s sample analysis time, processing robotically prepared chemical standards and cultured yeast samples in targeted and untargeted metabolomics applications. The platform exhibited consistency, reliability, and ease of use while eliminating the need for human intervention in the process of sample injection, data processing, and analysis. The platform paves the way toward a more fully automated mass spectrometry analysis and ultimately closed-loop laboratory workflows involving automated experimentation and analysis coupled to AI-driven experimentation utilizing cutting-edge analytical instrumentation. AutonoMS documentation is available at https://autonoms.readthedocs.io.

Evaluation of Surface-Induced Dissociation Ion Mobility-Mass Spectrometry for Lipid Structural Characterization.

The complexity of the lipidome has necessitated the development of novel analytical approaches for the identification and structural analysis of morphologically diverse classes of lipids. At this time, a variety of dissociation techniques have been utilized to probe lipid decomposition pathways in search of structurally diagnostic fragment ions. Here, we investigate the application of surface-induced dissociation (SID), a fragmentation technique that imparts energy to the target molecule via collision with a coated surface, for the fragmentation of seven lipids across four major lipid subclasses. We have developed a tuning methodology for guiding the efficient operation of a previously developed custom SID device for molecules as small as ca. 300 Da with ion mobility analysis of the fragmentation products. SID fragmentation of the various lipids analyzed was found to generate fragment ions similar to those observed in CID spectra, but fragment ion lab frame onset energies were lower in SID due to the higher energy deposition via a more massive target. For the largest lipid evaluated (cardiolipin 18:1), SID produced chain fragment ions, which yielded analytically useful information regarding the composition of the acyl tails. Ion mobility provided an orthogonal dimension of separation and aided in assigning product ions to their precursors. Overall, the combination of SID and IM-MS is another potential methodology in the analytical toolkit for lipid structural analysis.

Deficiency of the lipid flippase ATP10A causes diet-induced dyslipidemia in female mice.

Genetic association studies have linked ATP10A and closely related type IV P-type ATPases (P4-ATPases) to insulin resistance and vascular complications, such as atherosclerosis. ATP10A translocates phosphatidylcholine and glucosylceramide across cell membranes, and these lipids or their metabolites play important roles in signal transduction pathways regulating metabolism. However, the influence of ATP10A on lipid metabolism in mice has not been explored. Here, we generated gene-specific Atp10A knockout mice and show that Atp10A-/- mice fed a high-fat diet did not gain excess weight relative to wild-type littermates. However, Atp10A-/- mice displayed female-specific dyslipidemia characterized by elevated plasma triglycerides, free fatty acids and cholesterol, as well as altered VLDL and HDL properties. We also observed increased circulating levels of several sphingolipid species along with reduced levels of eicosanoids and bile acids. The Atp10A-/- mice also displayed hepatic insulin resistance without perturbations to whole-body glucose homeostasis. Thus, ATP10A has a sex-specific role in regulating plasma lipid composition and maintaining hepatic liver insulin sensitivity in mice.

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.

Deficiency of the lipid flippase ATP10A causes diet-induced dyslipidemia in female mice.

Genetic association studies have linked ATP10A and closely related type IV P-type ATPases (P4-ATPases) to insulin resistance and vascular complications, such as atherosclerosis. ATP10A translocates phosphatidylcholine and glucosylceramide across cell membranes, and these lipids or their metabolites play important roles in signal transduction pathways regulating metabolism. However, the influence of ATP10A on lipid metabolism in mice has not been explored. Here, we generated gene-specific Atp10A knockout mice and show that Atp10A-/- mice fed a high-fat diet did not gain excess weight relative to wild-type littermates. However, Atp10A-/- mice displayed female-specific dyslipidemia characterized by elevated plasma triglycerides, free fatty acids and cholesterol, as well as altered VLDL and HDL properties. We also observed increased circulating levels of several sphingolipid species along with reduced levels of eicosanoids and bile acids. The Atp10A-/- mice also displayed hepatic insulin resistance without perturbations to whole-body glucose homeostasis. Thus, ATP10A has a sex-specific role in regulating plasma lipid composition and maintaining hepatic liver insulin sensitivity in mice.

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.

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.

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.

Collision Cross-Section Calibration Strategy for Lipid Measurements in SLIM-Based High-Resolution Ion Mobility.

Structures for lossless ion manipulation-based high-resolution ion mobility (HRIM) interfaced with mass spectrometry has emerged as a powerful tool for the separation and analysis of many isomeric systems. IM-derived collision cross section (CCS) is increasingly used as a molecular descriptor for structural analysis and feature annotation, but there are few studies on the calibration of CCS from HRIM measurements. Here, we examine the accuracy, reproducibility, and practical applicability of CCS calibration strategies for a broad range of lipid subclasses and develop a straightforward and generalizable framework for obtaining high-resolution CCS values. We explore the utility of using structurally similar custom calibrant sets as well as lipid subclass-specific empirically derived correction factors. While the lipid calibrant sets lowered overall bias of reference CCS values from ∼2-3% to ∼0.5%, application of the subclass-specific correction to values calibrated with a broadly available general calibrant set resulted in biases <0.4%. Using this method, we generated a high-resolution CCS database containing over 90 lipid values with HRIM. To test the applicability of this method to a broader class range typical of lipidomics experiments, a standard lipid mix was analyzed. The results highlight the importance of both class and arrival time range when correcting or scaling CCS values and provide guidance for implementation of the method for more general applications.

Enantiomer Differentiation of Amino Acid Stereoisomers by Structural Mass Spectrometry Using Noncovalent Trinuclear Copper Complexes.

Previous work has demonstrated that copper complexation strategies can be used with tandem MS (MS/MS) and, more recently, ion mobility-mass spectrometry (IM-MS) to differentiate chiral isomers based upon enantiomeric-specific binding. In this study, we investigate the separation of chiral amino acids (AAs) forming trinuclear complexes that can be directly resolved by IM-MS analyses. Twenty standard AAs of both d- and l-chirality were investigated. Specific AAs including d/l-histidine, d/l-proline, d/l-glutamine, d/l-tyrosine, and d/l-tryptophan were evaluated as "chiral selectors" that, when combined with copper, were found to promote selective complexation with specific AA enantiomers. Significant enantiomer differentiation was observed in the IM spectra for hydrophobic AAs acids with peak-to-peak resolutions ranging from 0.63 to 1.15. Among the chiral selectors investigated, histidine provided the best enantioselectivity, followed by tryptophan, suggesting the aromatic structure plays an important role in forming chiral-specific ion complexes. Unlike MS/MS methods where chiral selectors with l-stereochemistry enhance the differentiation, the chirality of the selector was found to have no significant effect on observed IM separation with both d- and l-selectors providing similar resolutions but with inverted IM arrival time ordering. To investigate the structural differences between resolvable chiral complexes, a combination of MS/MS, collision cross-section (CCS) measurements, and molecular mechanics techniques was used. Candidate trinuclear structures of the stoichiometry [(Cu2+)3(d/lIle)3(lHis)2 - 5H]+ were constructed with guidance from empirical MS/MS results. Of the 48 theoretical structures generated, one enantiomeric cluster pair yielded close correlation (<1%) with experimental CCS measurements, suggesting the most enantioselective ion complexes observed in this work are bridged by three coppers.

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.

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.

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.

Chlorpyrifos Disrupts Acetylcholine Metabolism Across Model Blood-Brain Barrier.

Despite the significant progress in both scientific understanding and regulations, the safety of agricultural pesticides continues to be called into question. The need for complementary analytics to identify dysregulation events associated with chemical exposure and leverage this information to predict biological responses remains. Here, we present a platform that combines a model organ-on-chip neurovascular unit (NVU) with targeted mass spectrometry (MS) and electrochemical analysis to assess the impact of organophosphate (OP) exposure on blood-brain barrier (BBB) function. Using the NVU to simulate exposure, an escalating dose of the organophosphate chlorpyrifos (CPF) was administered. With up to 10 µM, neither CPF nor its metabolites were detected across the BBB (limit of quantitation 0.1 µM). At 30 µM CPF and above, targeted MS detected the main urinary metabolite, trichloropyridinol (TCP), across the BBB (0.025 µM) and no other metabolites. In the vascular chamber where CPF was directly applied, two primary metabolites of CPF, TCP and diethylthiophosphate (DETP), were both detected (0.1-5.7 µM). In a second experiment, a constant dose of 10 µM CPF was administered to the NVU, and though neither CPF nor its metabolites were detected across the BBB after 24 h, electrochemical analysis detected increases in acetylcholine levels on both sides of the BBB (up to 24.8 ± 3.4 µM) and these levels remained high over the course of treatment. In the vascular chamber where CPF was directly applied, only TCP was detected (ranging from 0.06 µM at 2 h to 0.19 µM at 24 h). These results provide chemical evidence of the substantial disruption induced by this widely used commercial pesticide. This work reinforces previously observed OP metabolism and mechanisms of impact, validates the use of the NVU for OP toxicology testing, and provides a model platform for analyzing these organotypic systems.

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.

High Confidence Shotgun Lipidomics Using Structurally Selective Ion Mobility-Mass Spectrometry.

Ion mobility (IM) is a gas phase separation strategy that can either supplement or serve as a high-throughput alternative to liquid chromatography (LC) in shotgun lipidomics. Incorporating the IM dimension in untargeted lipidomics workflows can help resolve isomeric lipids, and the collision cross section (CCS) values obtained from the IM measurements can provide an additional molecular descriptor to increase lipid identification confidence. This chapter provides a broad overview of an untargeted ion mobility-mass spectrometry (IM-MS) workflow using a commercial drift tube ion mobility-quadrupole-time-of-flight mass spectrometer (IM-QTOF) for high confidence lipidomics.

Resolving Power and Collision Cross Section Measurement Accuracy of a Prototype High-Resolution Ion Mobility Platform Incorporating Structures for Lossless Ion Manipulation.

A production prototype structures for lossless ion manipulation ion mobility (SLIM IM) platform interfaced to a commercial high-resolution mass spectrometer (MS) is described. The SLIM IM implements the traveling wave ion mobility technique across a ∼13m path length for high-resolution IM (HRIM) separations. The resolving power (CCS/ΔCCS) of the SLIM IM stage was benchmarked across various parameters (traveling wave speeds, amplitudes, and waveforms), and results indicated that resolving powers in excess of 200 can be accessed for a broad range of masses. For several cases, resolving powers greater than 300 were achieved, notably under wave conditions where ions transition from a nonselective "surfing" motion to a mobility-selective ion drift, that corresponded to ion speeds approximately 30-70% of the traveling wave speed. The separation capabilities were evaluated on a series of isomeric and isobaric compounds that cannot be resolved by MS alone, including reversed-sequence peptides (SDGRG and GRGDS), triglyceride double-bond positional isomers (TG 3, 6, 9 and TG 6, 9, 12), trisaccharides (melezitose, raffinose, isomaltotriose, and maltotriose), and ganglioside lipids (GD1b and GD1a). The SLIM IM platform resolved the corresponding isomeric mixtures, which were unresolvable using the standard resolution of a drift-tube instrument (∼50). In general, the SLIM IM-MS platform is capable of resolving peaks separated by as little as ∼0.6% without the need to target a specific separation window or drift time. Low CCS measurement biases <0.5% were obtained under high resolving power conditions. Importantly, all the analytes surveyed are able to access high-resolution conditions (>200), demonstrating that this instrument is well-suited for broadband HRIM separations important in global untargeted applications.

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.

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.