Utilizing Skyline to analyze lipidomics data containing liquid chromatography, ion mobility spectrometry and mass spectrometry dimensions.

Lipidomics studies suffer from analytical and annotation challenges because of the great structural similarity of many of the lipid species. To improve lipid characterization and annotation capabilities beyond those afforded by traditional mass spectrometry (MS)-based methods, multidimensional separation methods such as those integrating liquid chromatography, ion mobility spectrometry, collision-induced dissociation and MS (LC-IMS-CID-MS) may be used. Although LC-IMS-CID-MS and other multidimensional methods offer valuable hydrophobicity, structural and mass information, the files are also complex and difficult to assess. Thus, the development of software tools to rapidly process and facilitate confident lipid annotations is essential. In this Protocol Extension, we use the freely available, vendor-neutral and open-source software Skyline to process and annotate multidimensional lipidomic data. Although Skyline ( https://skyline.ms/skyline.url ) was established for targeted processing of LC-MS-based proteomics data, it has since been extended such that it can be used to analyze small-molecule data as well as data containing the IMS dimension. This protocol uses Skyline's recently expanded capabilities, including small-molecule spectral libraries, indexed retention time and ion mobility filtering, and provides a step-by-step description for importing data, predicting retention times, validating lipid annotations, exporting results and editing our manually validated 500+ lipid library. Although the time required to complete the steps outlined here varies on the basis of multiple factors such as dataset size and familiarity with Skyline, this protocol takes ~5.5 h to complete when annotations are rigorously verified for maximum confidence.

Uncovering PFAS and Other Xenobiotics in the Dark Metabolome Using Ion Mobility Spectrometry, Mass Defect Analysis, and Machine Learning.

The identification of xenobiotics in nontargeted metabolomic analyses is a vital step in understanding human exposure. Xenobiotic metabolism, transformation, excretion, and coexistence with other endogenous molecules, however, greatly complicate the interpretation of features detected in nontargeted studies. While mass spectrometry (MS)-based platforms are commonly used in metabolomic measurements, deconvoluting endogenous metabolites from xenobiotics is also often challenged by the lack of xenobiotic parent and metabolite standards as well as the numerous isomers possible for each small molecule m/z feature. Here, we evaluate a xenobiotic structural annotation workflow using ion mobility spectrometry coupled with MS (IMS-MS), mass defect filtering, and machine learning to uncover potential xenobiotic classes and species in large metabolomic feature lists. Xenobiotic classes examined included those of known high toxicities, including per- and polyfluoroalkyl substances (PFAS), polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs), and pesticides. Specifically, when the workflow was applied to identify PFAS in the NIST SRM 1957 and 909c human serum samples, it greatly reduced the hundreds of detected liquid chromatography (LC)-IMS-MS features by utilizing both mass defect filtering and m/z versus IMS collision cross sections relationships. These potential PFAS features were then compared to the EPA CompTox entries, and while some matched within specific m/z tolerances, there were still many unknowns illustrating the importance of nontargeted studies for detecting new molecules with known chemical characteristics. Additionally, this workflow can also be utilized to evaluate other xenobiotics and enable more confident annotations from nontargeted studies.

Utilizing Pine Needles to Temporally and Spatially Profile Per- and Polyfluoroalkyl Substances (PFAS).

As concerns over exposure to per- and polyfluoroalkyl substances (PFAS) are continually increasing, novel methods to monitor their presence and modifications are greatly needed, as some have known toxic and bioaccumulative characteristics while most have unknown effects. This task however is not simple, as the Environmental Protection Agency (EPA) CompTox PFAS list contains more than 9000 substances as of September 2020 with additional substances added continually. Nontargeted analyses are therefore crucial to investigating the presence of this immense list of possible PFAS. Here, we utilized archived and field-sampled pine needles as widely available passive samplers and a novel nontargeted, multidimensional analytical method coupling liquid chromatography, ion mobility spectrometry, and mass spectrometry (LC-IMS-MS) to evaluate the temporal and spatial presence of numerous PFAS. Over 70 PFAS were detected in the pine needles from this study, including both traditionally monitored legacy perfluoroalkyl acids (PFAAs) and their emerging replacements such as chlorinated derivatives, ultrashort chain PFAAs, perfluoroalkyl ether acids including hexafluoropropylene oxide dimer acid (HFPO-DA, "GenX") and Nafion byproduct 2, and a cyclic perfluorooctanesulfonic acid (PFOS) analog. Results from this study provide critical insight related to PFAS transport, contamination, and reduction efforts over the past six decades.

Development and Application of Multidimensional Lipid Libraries to Investigate Lipidomic Dysregulation Related to Smoke Inhalation Injury Severity.

The implication of lipid dysregulation in diseases, toxic exposure outcomes, and inflammation has brought great interest to lipidomic studies. However, lipids have proven to be analytically challenging due to their highly isomeric nature and vast concentration ranges in biological matrices. Therefore, multidimensional techniques such as those integrating liquid chromatography, ion mobility spectrometry, collision-induced dissociation, and mass spectrometry (LC-IMS-CID-MS) have been implemented to separate lipid isomers as well as provide structural information and increased identification confidence. These data sets are however extremely large and complex, resulting in challenges for data processing and annotation. Here, we have overcome these challenges by developing sample-specific multidimensional lipid libraries using the freely available software Skyline. Specifically, the human plasma library developed for this work contains over 500 unique lipids and is combined with adapted Skyline functions such as indexed retention time (iRT) for retention time prediction and IMS drift time filtering for enhanced selectivity. For comparison with other studies, this database was used to annotate LC-IMS-CID-MS data from a NIST SRM 1950 extract. The same workflow was then utilized to assess plasma and bronchoalveolar lavage fluid (BALF) samples from patients with varying degrees of smoke inhalation injury to identify lipid-based patient prognostic and diagnostic markers.

Metabolite Profiling Reveals Predictive Biomarkers and the Absence of ß-Methyl Amino-l-alanine in Plasma from Individuals Diagnosed with Amyotrophic Lateral Sclerosis.

By employing chip-based capillary zone electrophoresis coupled to high-resolution mass spectrometry, we profiled the plasma metabolome of 134 patients diagnosed with sporadic amyotrophic lateral sclerosis (ALS) (81 males and 53 females) and 118 individuals deemed healthy (49 males and 69 females). The most significant markers (p < 0.01) were creatine, which was 49% elevated, and creatinine and methylhistidine, which were decreased by 20 and 24%, respectively, in ALS patients. The ratio of creatine versus creatinine increased 370 and 200% for male and female ALS patients, respectively. In addition, male ALS patients on an average had 5-13% lower amounts of seven essential amino acids, whereas females did not significantly differ from healthy controls. We developed two models using the metabolite abundances: (1) a classification model for the separation of ALS and healthy samples and (2) a classification model for the prediction of disease progression based on the ALS functional rating score. Utilizing a Monte Carlo cross-validation approach, a linear discriminant analysis model achieved a mean area under the receiver operating characteristic curve (AUC) of 0.85 (0.06) with a mean sensitivity of 80% (9%) and specificity of 78% (10%) for the separation of ALS and controls, respectively. A support vector machine classifier predicted progression categories with an AUC of 0.90 (0.06) with a mean sensitivity of 73% (10%) and a specificity of 86% (5%). Lastly, using a previously reported assay with a stable isotope-labeled (13C315N2) spike-in standard, we were unable to detect the exogenous neurotoxic metabolite, ß-methylamino-l-alanine, in the free or protein-bound fraction of any of the 252 plasma samples.

A novel integrated strategy for the detection and quantification of the neurotoxin ß-N-methylamino-L-alanine in environmental samples.

We describe a set of new tools for the detection and quantification of ß-N-methylamino-L-alanine (BMAA) which includes a novel stable isotope-labeled BMAA standard (13C3,15N2) and a chip-based capillary electrophoresis mass spectrometry platform for separation and detection. Baseline resolution of BMAA from its potentially confounding structural isomers N-2-aminoethylglycine (AEG) and 2,4-diaminobutyric acid (2,4-DAB) is achieved using the chip-based CE-MS system in less than 1 min. Detection and linearity of response are demonstrated across > 3.5 orders of dynamic range using parallel reaction monitoring (PRM). The lower limit of detection and quantification were calculated for BMAA detection at 40 nM (4.8 ng/mL) and 400 nM (48 ng/mL), respectively. Finally, the strategy was applied to detect BMAA in seafood samples purchased at a local market in Raleigh, NC where their harvest location was known. BMAA was detected in a sea scallop sample. Because the BMAA/stable isotope-labeled 13C3,15N2-BMAA (SIL-BMAA) ratio in the scallop sample was below the limit of quantification, a semiquantitative analysis of BMAA content was carried out, and BMAA content was estimated to be approximately 820 ng BMAA/1 g of wet scallop tissue. Identification was verified by high mass measurement accuracy of precursor (< 5 ppm) and product ions (< 10 ppm), comigration with SIL-BMAA spike-in standard, and conservation of ion abundance ratios for product ions between BMAA and SIL-BMAA. Interestingly, BMAA was not identified in the free protein fraction but only detected after protein hydrolysis which suggests that BMAA is tightly bound by and/or incorporated into proteins. Graphical abstract Utilization of novel 13C3,15N2-BMAA and chip-based CE-MS/MS for detection and quantification of BMAA in environmental samples.