Metabolomic and Lipidomic Characterization of Meningioma Grades Using LC-HRMS and Machine Learning.

Meningiomas are among the most common brain tumors that arise from the leptomeningeal cover of the brain and spinal cord and account for around 37% of all central nervous system tumors. According to the World Health Organization, meningiomas are classified into three histological subtypes: benign, atypical, and anaplastic. Sometimes, meningiomas with a histological diagnosis of benign tumors show clinical characteristics and behavior of aggressive tumors. In this study, we examined the metabolomic and lipidomic profiles of meningioma tumors, focusing on comparing low-grade and high-grade tumors and identifying potential markers that can discriminate between benign and malignant tumors. High-resolution mass spectrometry coupled to liquid chromatography was used for untargeted metabolomics and lipidomics analyses of 85 tumor biopsy samples with different meningioma grades. We then applied feature selection and machine learning techniques to find the features with the highest information to aid in the diagnosis of meningioma grades. Three biomarkers were identified to differentiate low- and high-grade meningioma brain tumors. The use of mass-spectrometry-based metabolomics and lipidomics combined with machine learning analyses to prospect and characterize biomarkers associated with meningioma grades may pave the way for elucidating potential therapeutic and prognostic targets.

Expanding Per- and Polyfluoroalkyl Substances Coverage in Nontargeted Analysis Using Data-Independent Analysis and IonDecon.

Per- and polyfluoroalkyl substances (PFAS) are widespread, persistent environmental contaminants that have been linked to various health issues. Comprehensive PFAS analysis often relies on ultra-high-performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC HRMS) and molecular fragmentation (MS/MS). However, the selection and fragmentation of ions for MS/MS analysis using data-dependent analysis results in only the topmost abundant ions being selected. To overcome these limitations, All Ions fragmentation (AIF) can be used alongside data-dependent analysis. In AIF, ions across the entire m/z range are simultaneously fragmented; hence, precursor-fragment relationships are lost, leading to a high false positive rate. We introduce IonDecon, which filters All Ions data to only those fragments correlating with precursor ions. This software can be used to deconvolute any All Ions files and generates an open source DDA formatted file, which can be used in any downstream nontargeted analysis workflow. In a neat solution, annotation of PFAS standards using IonDecon and All Ions had the exact same false positive rate as when using DDA; this suggests accurate annotation using All Ions and IonDecon. Furthermore, deconvoluted All Ions spectra retained the most abundant peaks also observed in DDA, while filtering out much of the artifact peaks. In complex samples, incorporating AIF and IonDecon into workflows can enhance the MS/MS coverage of PFAS (more than tripling the number of annotations in domestic sewage). Deconvolution in complex samples of All Ions data using IonDecon did retain some false fragments (fragments not observed when using ion selection, which were not isotopes or multimers), and therefore DDA and intelligent acquisition methods should still be acquired when possible alongside All Ions to decrease the false positive rate. Increased coverage of PFAS can inform on the development of regulations to address the entire PFAS problem, including both legacy and newly discovered PFAS.

Characterization of Bile Acid Isomers and the Implementation of High-Resolution Demultiplexing with Ion Mobility-Mass Spectrometry.

Bile acids (BAs) are a complex suite of clinically relevant metabolites that include many isomers. Liquid chromatography coupled to mass spectrometry (LC-MS) is an increasingly popular technique due to its high specificity and sensitivity; nonetheless, acquisition times are generally 10-20 min, and isomers are not always resolved. In this study, the application of ion mobility (IM) spectrometry coupled to MS was investigated to separate, characterize, and measure BAs. A subset of 16 BAs was studied, including three groups of isomers belonging to unconjugated, glycine-conjugated, and taurine-conjugated BA classes. A variety of strategies were explored to increase BA isomer separation such as changing the drift gas, measuring different ionic species (i.e., multimers and cationized species), and enhancing the instrumental resolving power. In general, Ar, N2, and CO2 provided the best peak shape, resolving power (Rp), and separation, especially CO2; He and SF6 were less preferable. Furthermore, measuring dimers versus monomers improved isomer separation due to enhanced gas-phase structural differences. A variety of cation adducts other than sodium were characterized. Mobility arrival times and isomer separation were affected by the choice of adduct, which was shown to be used to target certain BAs. Finally, a novel workflow that involves high-resolution demultiplexing in combination with dipivaloylmethane ion-neutral clusters was implemented to improve Rp dramatically. A maximum Rp increase was observed with lower IM field strengths to obtain longer drift times, increasing Rp from 52 to 187. A combination of these separation enhancement strategies demonstrates great potential for rapid BA analysis.

Segmented Flow Strategies for Integrating Liquid Chromatography-Mass Spectrometry with Nuclear Magnetic Resonance for Lipidomics.

Building an accurate lipid inventory relies on coordinated information from orthogonal analytical capabilities. Integrating the familiar workflow of liquid chromatography (LC), high-resolution mass spectrometry (HRMS), and tandem mass spectrometry (MS/MS) with proton nuclear magnetic resonance spectroscopy (1H NMR) would be ideal for building that inventory. For absolute lipid structural elucidation, LC-HRMS/MS can provide lower-level structural information with superior sensitivity, while 1H NMR can provide invaluable higher-order structural information for the disambiguation of isomers with absolute chemical specificity. Digitization of the LC eluent followed by splitting the microfractions into two flow paths in a defined ratio for HRMS/MS and NMR would be the ideal strategy to permit correlation of the MS and NMR data as a function of chromatographic retention time. Here, we report an active segmentation platform to transform analytical flow rate LC eluent into parallel microliter segmented flow queues for high confidence correlation of the MS, MS/MS, and NMR data. The practical details in implementing this strategy to achieve an integrated LC-MS-NMR platform are presented, including the development of an active segmentation technology using a four-port two-way valve to transform the LC eluent into parallel segmented flows for online MS analysis followed by offline segment-specific 1H NMR and optimization of the detector response toward segmented flow. To demonstrate the practicality of this novel platform, it was tested using lipid mixture samples.

Why tandem mass spectrometry for trace analysis: Concepts of tandem analytical techniques.

RATIONALE: The triple quadrupole mass spectrometer, typically in combination with a gas or liquid chromatograph (GC/MS/MS and LC/MS/MS), is perhaps the most iconic example today of a tandem analytical instrument. Here I present the concepts of tandem or hyphenated techniques for trace analysis (that is, the detection and/or quantitation of one or more analytes present in a mixture at low levels). METHODS: This tutorial presents the principles of tandem trace analytical techniques such as GC/MS/MS and LC/MS/MS, including the capabilities and requirements for such tandem techniques, the role of sensitivity and selectivity in tandem techniques, ways to assess the "informing power" of these techniques, and a comparison of tandem techniques with individual techniques at high resolution. These points are illustrated with several examples of trace analysis using tandem analytical techniques. RESULTS: Several characteristics of the triple quadrupole have made it the "laboratory workhorse" for trace analysis, including the remarkable efficiency of the low-energy collision-induced dissociation (CID) process in a radiofrequency (RF)-only multipole collision cell, the ease of computer control, and the capability for rapid scanning, rapid switching from mass to mass, and high transmission efficiency, enabling a wide variety of MS/MS scans. The efficiency of selected reaction monitoring means that triple quadrupoles dominate MS/MS for detection and quantitation of targeted compounds. CONCLUSIONS: This special issue addresses the intriguing question of how the triple quadrupole mass spectrometer progressed from "bleeding edge" to "the laboratory workhorse" over the last 40 years. This tutorial on the principles of tandem trace analytical techniques provides perspectives and insights into answering that question and should help educate the novice and stimulate the sophisticate.

A rapid and robust method for amino acid quantification using a simple N-hydroxysuccinimide ester derivatization and liquid chromatography-ion mobility-mass spectrometry.

The vast majority of mass spectrometry (MS)-based metabolomics studies employ reversed-phase liquid chromatography (RPLC) to separate analytes prior to MS detection. Highly polar metabolites, such as amino acids (AAs), are poorly retained by RPLC, making quantitation of these key species challenging across the broad concentration ranges typically observed in biological specimens, such as cell extracts. To improve the detection and quantitation of AAs in microglial cell extracts, the implementation of a 4-dimethylaminobenzoylamido acetic acid N-hydroxysuccinimide ester (DBAA-NHS) derivatization agent was explored for its ability to improve both analyte retention and detection limits in RPLC-MS. In addition to the introduction of the DBAA-NHS labeling reagent, a uniformly (U) 13C-labeled yeast extract was also introduced during the sample preparation workflow as an internal standard (IS) to eliminate artifacts and to enable targeted quantitation of AAs, as well as untargeted amine submetabolome profiling. To improve method sensitivity and selectivity, multiplexed drift-tube ion mobility (IM) was integrated into the LC-MS workflow, facilitating the separation of isomeric metabolites, and improving the structural identification of unknown metabolites. Implementation of the U-13C-labeled yeast extract during the multiplexed LC-IM-MS analysis enabled the quantitation of 19 of the 20 common AAs, supporting a linear dynamic range spanning up to three orders of magnitude in concentration for microglial cell extracts, in addition to reducing the required cell count for reliable quantitation from 10 to 5 million cells per sample.

Per- and Polyfluoroalkyl Substances (PFAS) in Street Sweepings.

One hundred and seventeen street sweeping samples were collected and analyzed for per- and polyfluoroalkyl substances (PFAS). Fifty-six samples were collected in one city (Gainesville, Florida) allowing for an in-depth city-wide characterization. Street sweepings from five other urban areas, (Orlando, n = 15; Key West, n = 15; Pensacola, n = 12; Tampa, n = 13; and Daytona Beach, n = 6) were analyzed to provide a city-to-city comparison of PFAS. Within our analytical workflow, 37 PFAS were quantified across all samples, while the maximum number of PFAS quantified at one site was 26. Of those PFAS quantified in Gainesville, 60% were perfluoroalkyl acids (PFAAs) and 33% were precursors to PFAA. Among the PFAAs, short-chain perfluoroalkyl carboxylic acids (PFCAs) were the dominant class representing 26% of the total PFAS by concentration. In the comparison across different urban cities, the dominant compound by concentration and frequency of detection varied; however, perfluorooctanoic acid (PFOA) and linear perfluorooctanesulfonic acid (PFOSlin) were the two PFAS that were detected the most frequently. This study documents the first-time detection of hexadecafluorosebacic acid and perfluoro-3,6,9-trioxaundecane-1,11-dioic acid within environmental samples.

Generation and Release of Neurogranin, Vimentin, and MBP Proteolytic Peptides, Following Traumatic Brain Injury.

Traumatic brain injury (TBI) is a major neurological disorder without FDA-approved therapies. In this study, we have examined the concept that TBI might trigger global brain proteolysis in the acute post-injury phase. Thus, we conducted a systemic proteolytic peptidomics analysis using acute cerebrospinal fluid (CSF) samples from TBI patients and normal control samples. We employed ultrafiltration-based low molecular weight (LMW; < 10 kDa) peptide enrichment, coupled with nano-reversed-phase liquid chromatography/tandem mass spectrometry analysis, followed with orthogonal quantitative immunoblotting-based protein degradation analysis. We indeed identified novel patterns of injury-dependent proteolytic peptides derived from neuronal components (pre- and post-synaptic terminal, dendrites, axons), extracellular matrix, oligodendrocytes, microglial cells, and astrocytes. Among these, post-synaptic protein neurogranin was identified for the first time converted to neurogranin peptides including neurogranin peptide (aa 16-64) that is phosphorylated at Ser-36/48 (P-NG-fragment) in acute human TBI CSF samples vs. normal control with a receiver operating characteristic area under the curve of 0.957. We also identified detailed processing of astroglia protein (vimentin) and oligodendrocyte protein (MBP and Golli-MBP) to protein breakdown products (BDPs) and/or LMW proteolytic peptides after TBI. In addition, using MS/MS selected reaction monitoring method, two C-terminally released MBP peptides TQDENPVVHFF and TQDENPVVHF were found to be elevated in acute and subacute TBI CSF samples as compared to their normal control counterparts. These findings imply that future therapeutic strategies might be placed on the suppression of brain proteolysis as a target. The endogenous proteolytic peptides discovered in human TBI biofluid could represent useful diagnostic and monitoring tools for TBI.

Lipidomics and Redox Lipidomics Indicate Early Stage Alcohol-Induced Liver Damage.

Alcoholic fatty liver disease (AFLD) is characterized by lipid accumulation and inflammation and can progress to cirrhosis and cancer in the liver. AFLD diagnosis currently relies on histological analysis of liver biopsies. Early detection permits interventions that would prevent progression to cirrhosis or later stages of the disease. Herein, we have conducted the first comprehensive time-course study of lipids using novel state-of-the art lipidomics methods in plasma and liver in the early stages of a mouse model of AFLD, i.e., Lieber-DeCarli diet model. In ethanol-treated mice, changes in liver tissue included up-regulation of triglycerides (TGs) and oxidized TGs and down-regulation of phosphatidylcholine, lysophosphatidylcholine, and 20-22-carbon-containing lipid-mediator precursors. An increase in oxidized TGs preceded histological signs of early AFLD, i.e., steatosis, with these changes observed in both the liver and plasma. The major lipid classes dysregulated by ethanol play important roles in hepatic inflammation, steatosis, and oxidative damage. Conclusion: Alcohol consumption alters the liver lipidome before overt histological markers of early AFLD. This introduces the exciting possibility that specific lipids may serve as earlier biomarkers of AFLD than those currently being used.

Metabolomic and lipidomic characterization of an X-chromosome deletion disorder in neural progenitor cells by UHPLC-HRMS.

INTRODUCTION: Intellectual disorders involving deletions of the X chromosome present a difficult task in the determination of a connection between symptoms and metabolites that could lead to treatment options. One specific disorder of X-chromosomal deletion, Fragile X syndrome, is the most frequently occurring of intellectual disabilities. Previous metabolomic studies have been limited to mouse models that may not have sufficiently revealed the full biochemical diversity of the disease in humans. OBJECTIVES: The primary objective of this study was to elucidate the human biochemistry in X-chromosomal deletion disorders through metabolomic and lipidomic profiling, using cells from a X-deletion patient as a representative case. METHODS: Metabolomic and lipidomic analysis was performed by UHPLC-HRMS on neural progenitor (NP) cells isolated from an afflicted female patient versus normal neural progenitor cells. RESULTS: Results showed perturbations in several metabolic pathways, including those of arginine and proline, that significantly impact both neurotransmitter generation and overall brain function. Coincidently, dysregulation was observed for lipids involved in both cellular structure and membrane integrity. The trends of observed metabolomic changes, as well as lipidomic profiling from identified features, are discussed. CONCLUSION: The lipidomic and metabolomic profiles of NP cell samples exhibited significant differentiation associated with partial deletion of the X chromosome. These findings suggest that rare X-chromosomal deletion disorders are not only a mental disorder limited to alterations in local neuronal functions, but are also metabolic diseases.

Characterization and standardization of multiassay platforms for four commonly studied traumatic brain injury protein biomarkers: a TBI Endpoints Development Study.

Aim: There is a critical need to validate biofluid-based biomarkers as diagnostic and drug development tools for traumatic brain injury (TBI). As part of the TBI Endpoints Development Initiative, we identified four potentially predictive and pharmacodynamic biomarkers for TBI: astroglial markers GFAP and S100B and the neuronal markers UCH-L1 and Tau. Materials & methods: Several commonly used platforms for these four biomarkers were identified and compared on analytic performance and ability to detect gold standard recombinant protein antigens and to pool control versus TBI cerebrospinal fluid (CSF). Results: For each marker, only some assay formats could differentiate TBI CSF from the control CSF. Also, different assays for the same biomarker reported divergent biomarker values for the same biosamples. Conclusion: Due to the variability of TBI marker assay in performance and reported values, standardization strategies are recommended when comparing reported biomarker levels across assay platforms.

Lay abstract Traumatic brain injury (TBI) is a leading cause of mortality and morbidity around the world. There is a critical need to validate biofluid-based biomarker tests as diagnostic and drug development tools. For this study, we focused on four brain-derived proteins called GFAP, S100B, UCH-L1 and Tau. To measure these biomarker proteins in human biofluid, one relies on either commercial or home-brew assays. Here, we attempted to compare the performance of 2­4 assay formats for each biomarker. We compared their assay sensitivity, ability to detect 'gold standard' protein analyte we procured, as well as the ability to differentiated pooled TBI cerebrospinal fluid from healthy control cerebrospinal fluid. We found that there are high variabilities among TBI marker assays in assay performance, reported biomarker values and ability to differentiate TBI versus control biofluid. Thus, a standardization strategy is needed when comparing reported biomarker levels across assay platforms.

Evaluation of extraction workflows for quantitative analysis of per- and polyfluoroalkyl substances: A case study using soil adjacent to a landfill.

Specific aspects of previously reported extraction workflows, for measurement of per- and polyfluoroalkyl substances (PFAS) in solid matrices, have not been adequately interrogated. The objective of this study was to explore the importance of each workflow step in providing the most appropriate extraction for a comprehensive set of PFAS (51 different species) in soil. We compared different procedures, including two pre-extraction set ups (overnight handling of samples prior to extraction), two extraction solvents (methanol (MeOH), and acetonitrile (ACN)), two extraction solvent volumes (10 mL and 8.5 mL), and two post-extraction cleanup strategies (ENVI-Carb and ion-pair). Of the 51 species targeted, 21 were at quantifiable levels in soil samples collected adjacent to a landfill, of which 13 PFAS were consistently detected among the different extraction workflows. Overall, results showed no significant difference in PFAS concentration between different extraction solvents and cleanup strategies. Perfluoropentanoic acid, perfluorohexanoic acid, and perfluorooctanoic acid had the highest concentrations in all extraction workflows, accounting for nearly 13%, 38%, and 17% of the total monitored PFAS (ΣPFAS), respectively. While final concentration values were similar across methods, recovery and accuracy studies showed that MeOH had the best recovery, with 88% of the isotopically labeled PFAS standards showing extraction recovery within the acceptable range of 80% to 120% (compared to 14% of isotopically labeled PFAS standards in workflows using ACN). Upon examination of additional cleanup steps, 67% of monitored PFAS (out of 51 total PFAS monitored), on average, exhibited higher accuracy (relative error ≤20%) using ENVI-Carb clean up (in comparison with 51% in workflows using ion pair clean up). Results also demonstrated that larger volumes of MeOH (and subsequent re-extractions) did not yield a better recovery, enabling a reduction in overall analysis time and cost in comparison to many published methods.

Per- and polyfluoroalkyl substances (PFAS) in sediments collected from the Pensacola Bay System watershed.

Sediment samples from 25 locations in the Pensacola Bay System (PBS) watershed were analyzed for the presence of 51 per- and polyfluoroalkyl substances (PFAS) using ultra high-performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS) and selected reaction monitoring. Results revealed quantifiable concentrations of PFAS in all sampling locations. More specifically, perfluorobutanoic acid (PFBA) was present in every sediment sample with a minimum and maximum concentration of 0.04 to 0.48 ng g-1 dry weight, respectively, across the 25 sites with an average of 0.1 ± 0.09 ng g-1. While PFOS, with an average of 0.11 ± 0.14 ng g-1 (range:

Design and Implementation of a Dual-Probe Microsampling Apparatus for the Direct Analysis of Adherent Mammalian Cells by Ion Mobility-Mass Spectrometry.

Atmospheric pressure sampling mass spectrometric methods are ideal platforms for rapidly analyzing the metabolomes of biological specimens. Several liquid extraction-based techniques have been developed for increasing metabolome coverage in direct sampling workflows. Here, we report the construction of a dual-probe microsampling device (DPM), based on the design of the liquid microjunction surface sampling probe, for analyzing the metabolome of live microglial cells by drift-tube ion mobility spectrometry (IMS) quadrupole time-of-flight mass spectrometry. Utilizing two distinct solvent systems in parallel is demonstrated to extract a wide structural variety of metabolites and lipids, enabling a more comprehensive analysis of intracellular metabolism. Employing the DPM-IM-MS method to adherent cells yielded the detection of 73 unique lipids and 79 small molecule metabolites from each optimized solvent system probe, respectively. Integration of multiplexed ion mobility scans is also shown to increase extracted analyte signal intensities between 2- and 10-fold compared to traditional single-pulse IMS, enabling the detection of 38 low-intensity features not previously detected by single-pulse DPM-IM-MS. To examine the ability of the DPM system to differentiate between sample treatment groups, microglia were stimulated with the endotoxin lipopolysaccharide (LPS). Several metabolic alterations were detected between sample treatment groups by DPM-IM-MS, many of which were not previously detected with conventional single-probe liquid microjunction surface sampling.

Environmental lipidomics: understanding the response of organisms and ecosystems to a changing world.

BACKGROUND: Understanding the interaction between organisms and the environment is important for predicting and mitigating the effects of global phenomena such as climate change, and the fate, transport, and health effects of anthropogenic pollutants. By understanding organism and ecosystem responses to environmental stressors at the molecular level, mechanisms of toxicity and adaptation can be determined. This information has important implications in human and environmental health, engineering biotechnologies, and understanding the interaction between anthropogenic induced changes and the biosphere. One class of molecules with unique promise for environmental science are lipids; lipids are highly abundant and ubiquitous across nearly all organisms, and lipid profiles often change drastically in response to external stimuli. These changes allow organisms to maintain essential biological functions, for example, membrane fluidity, as they adapt to a changing climate and chemical environment. Lipidomics can help scientists understand the historical and present biofeedback processes in climate change and the biogeochemical processes affecting nutrient cycles. Lipids can also be used to understand how ecosystems respond to historical environmental changes with lipid signatures dating back to hundreds of millions of years, which can help predict similar changes in the future. In addition, lipids are direct targets of environmental stressors, for example, lipids are easily prone to oxidative damage, which occurs during exposure to most toxins. AIM OF REVIEW: This is the first review to summarize the current efforts to comprehensively measure lipids to better understand the interaction between organisms and their environment. This review focuses on lipidomic applications in the arenas of environmental toxicology and exposure assessment, xenobiotic exposures and health (e.g., obesity), global climate change, and nutrient cycles. Moreover, this review summarizes the use of and the potential for lipidomics in engineering biotechnologies for the remediation of persistent compounds and biofuel production. KEY SCIENTIFIC CONCEPT: With the preservation of certain lipids across millions of years and our ever-increasing understanding of their diverse biological roles, lipidomic-based approaches provide a unique utility to increase our understanding of the contemporary and historical interactions between organisms, ecosystems, and anthropogenically-induced environmental changes.

Lipid Annotator: Towards Accurate Annotation in Non-Targeted Liquid Chromatography High-Resolution Tandem Mass Spectrometry (LC-HRMS/MS) Lipidomics Using A Rapid and User-Friendly Software.

Lipidomics has great promise in various applications; however, a major bottleneck in lipidomics is the accurate and comprehensive annotation of high-resolution tandem mass spectral data. While the number of available lipidomics software has drastically increased over the past five years, the reduction of false positives and the realization of obtaining structurally accurate annotations remains a significant challenge. We introduce Lipid Annotator, which is a user-friendly software for lipidomic analysis of data collected by liquid chromatography high-resolution tandem mass spectrometry (LC-HRMS/MS). We validate annotation accuracy against lipid standards and other lipidomics software. Lipid Annotator was integrated into a workflow applying an iterative exclusion MS/MS acquisition strategy to National Institute of Standards and Technology (NIST) SRM 1950 Metabolites in Frozen Human Plasma using reverse phase LC-HRMS/MS. Lipid Annotator, LipidMatch, and MS-DIAL produced consensus annotations at the level of lipid class for 98% and 96% of features detected in positive and negative mode, respectively. Lipid Annotator provides percentages of fatty acyl constituent species and employs scoring algorithms based on probability theory, which is less subjective than the tolerance and weighted match scores commonly used by available software. Lipid Annotator enables analysis of large sample cohorts and improves data-processing throughput as compared to previous lipidomics software.

Separation of Structurally Similar Anabolic Steroids as Cation Adducts in FAIMS-MS.

Novel synthetic anabolic androgenic steroids have been developed not only to dodge current antidoping tests at the professional sports level, but also for consumption by noncompetitive bodybuilders. These novel anabolic steroids are commonly referred to as "designer steroids" and pose a significant risk to users because of the lack of testing for toxicity and safety in animals or humans. Manufacturers of designer steroids dodge regulation by distributing them as nutritional or dietary supplements. Improving the throughput and accuracy of screening tests would help regulators to stay on top of illicit anabolic steroids. High-field asymmetric-waveform ion mobility spectrometry (FAIMS) utilizes an alternating asymmetric electric field to separate ions by their different mobilities at high- and low-fields as they travel through the separation space. When coupled to mass spectrometry (MS), FAIMS enhances the separation of analytes from other interfering compounds with little to no increase in analysis time. Here we investigate the effects of adding various cation species to sample solutions for the separation of structurally similar or isomeric anabolic androgenic steroids. FAIMS-MS spectra for these cation-modified samples show an increased number of compensation field (CF) peaks, some of which are confirmed to be unique for one steroid isomer over another. The CF peaks observed upon addition of cation species correspond to both monomer steroid-cation adduct ions and larger multimer ion complexes. Notably, the number of CF peaks and their CF shifts do not appear to have a straightforward relationship with cation size or electronegativity. Future directions aim at investigating the structures for these analyte-cation adduct ions for building a predictive model for their FAIMS separations.

Analysis of Tryptophan Metabolites in Serum Using Wide-Isolation Strategies for UHPLC-HRMS/MS.

Current targeted metabolomic workflows are limited by design and thus sacrifice crucial information from a profiling standpoint that could lead to a more fundamental understanding of the metabolic processes of interest. One drawback to performing targeted analysis on ion trapping instruments is the potential for increased variability in analysis when analytes and standards are isolated and trapped individually for fragmentation. In addition, this sequential isolation process increases the duty cycle of the mass spectrometer and reduces the number of points collected across a chromatographic peak. To address this, the use of a wide-isolation window (12 Da) to encompass the target analyte and the isotope standard within a single fragmentation window ensures that fragmentation is consistent when quantitation relies on the ratio of the target to the internal standard. Additionally, the preservation of a faster scan rate ensures that optimal representation of chromatographic peaks is preserved for the purposes of both quantitative and qualitative analyses that require peak integration for statistical analysis. The use of this flexible method is promising in the investigation of pathways that require multiple targets and are highly integrated within the system. Here, we demonstrate the application of this method in a fast ultra-high performance liquid chromatography (UHPLC) analysis to integrate wide-isolation quantitative strategies for high-resolution mass spectrometry (HRMS) combined with profiling qualitative metabolomics for the analysis of tryptophan degradation metabolites in mouse serum. Analysis of tryptophan-deficient states as compared to control in both germ-free or E. coli gut microbiota states was used to quantitate pathway-specific metabolites as well as obtain full profiling information. The quantitative and qualitative results revealed the preservation of the primary pathways of degradation in the kynurenine pathway to potentially produce primary products such as nicotinamide during stress-induced dietary states.