Metabolomics Analyses of 14 Classical Neurotransmitters by GC-TOF with LC-MS Illustrates Secretion of 9 Cell-Cell Signaling Molecules from Sympathoadrenal Chromaffin Cells in the Presence of Lithium.

The classical small molecule neurotransmitters are essential for cell-cell signaling in the nervous system for regulation of behaviors and physiological functions. Metabolomics approaches are ideal for quantitative analyses of neurotransmitter profiles but have not yet been achieved for the repertoire of 14 classical neurotransmitters. Therefore, this study developed targeted metabolomics analyses by full scan gas chromatography/time-of-flight mass spectrometry (GC-TOF) and hydrophilic interaction chromatography-QTRAP mass spectrometry (HILIC-MS/MS) operated in positive ionization mode for identification and quantitation of 14 neurotransmitters consisting of acetylcholine, adenosine, anandamide, aspartate, dopamine, epinephrine, GABA, glutamate, glycine, histamine, melatonin, norepinephrine, serine, and serotonin. GC-TOF represents a new metabolomics method for neurotransmitter analyses. Sensitive measurements of 11 neurotransmitters were achieved by GC-TOF, and three neurotransmitters were analyzed by LC-MS/MS (acetylcholine, anandamide, and melatonin). The limits of detection (LOD) and limits of quantitation (LOQ) were assessed for linearity for GC-TOF and LC-MS/MS protocols. In neurotransmitter-containing dense core secretory vesicles of adrenal medulla, known as chromaffin granules (CG), metabolomics measured the concentrations of 9 neurotransmitters consisting of the catecholamines dopamine, norepinephrine, and epinephrine, combined with glutamate, serotonin, adenosine, aspartate, glycine, and serine. The CG neurotransmitters were constitutively secreted from sympathoadrenal chromaffin cells in culture. Nicotine- and KCl-stimulated release of the catecholamines and adenosine. Lithium, a drug used for the treatment of bipolar disorder, decreased the constitutive secretion of dopamine and norepinephrine and decreased nicotine-stimulated secretion of epinephrine. Lithium had no effect on other secreted neurotransmitters. Overall, the newly developed GC-TOF with LC-MS/MS metabolomics methods for analyses of 14 neurotransmitters will benefit investigations of neurotransmitter regulation in biological systems and in human disease conditions related to drug treatments.

Mass Spectral Feature List Optimizer (MS-FLO): A Tool To Minimize False Positive Peak Reports in Untargeted Liquid Chromatography-Mass Spectroscopy (LC-MS) Data Processing.

Untargeted metabolomics by liquid chromatography-mass spectrometry generates data-rich chromatograms in the form of m/z-retention time features. Managing such datasets is a bottleneck. Many popular data processing tools, including XCMS-online and MZmine2, yield numerous false-positive peak detections. Flagging and removing such false peaks manually is a time-consuming task and prone to human error. We present a web application, Mass Spectral Feature List Optimizer (MS-FLO), to improve the quality of feature lists after initial processing to expedite the process of data curation. The tool utilizes retention time alignments, accurate mass tolerances, Pearson's correlation analysis, and peak height similarity to identify ion adducts, duplicate peak reports, and isotopic features of the main monoisotopic metabolites. Removing such erroneous peaks reduces the overall number of metabolites in data reports and improves the quality of subsequent statistical investigations. To demonstrate the effectiveness of MS-FLO, we processed 28 biological studies and uploaded raw and results data to the Metabolomics Workbench website ( www.metabolomicsworkbench.org ), encompassing 1481 chromatograms produced by two different data processing programs used in-house (MZmine2 and later MS-DIAL). Post-processing of datasets with MS-FLO yielded a 7.8% automated reduction of total peak features and flagged an additional 7.9% of features, per dataset, for review by the user. When manually curated, 87% of these additional flagged features were verified false positives. MS-FLO is an open source web application that is freely available for use at http://msflo.fiehnlab.ucdavis.edu .

Using MS-FINDER for identifying 19 natural products in the CASMI 2016 contest.

In its fourth year, the CASMI 2016 contest was organized to evaluate current chemical structure identification strategies for 19 natural products using high-resolution LC-MS and LC-MS/MS challenge datasets using automated methods with or without the combination of other tools. These natural products originate from plants, fungi, marine sponges, algae, or micro-algae. Every compound annotation workflow must start with determination of elemental compositions. Of these 19 challenges, one was excluded by the organizers after submission. For the remaining 18 challenges, three software programs were used. MS-FINDER version 1.62 was able to correctly identify 89% of the molecular formulas using an internal database that comprised of 13 metabolomics repositories with 45,181 formulas. SIRIUS correctly identified 61% compositions using PubChem formulas and Seven Golden Rules correctly identified 83% by using the Dictionary of Natural Products as a targeted database. Next, we performed structural dereplication for which we used the consensus formula from the three software programs. We submitted two solution sets for these challenges. In the first solution set, avaniya001, we only used the internal MS-FINDER functions for predicting and ranking structures, correctly identifying 53% of the structures as top-hit, 72% within the top-3 structures, and 78% within the top-10 hits. For our second set, avaniya002, we used both MS-FINDER predictions as well as MS/MS queries against the commercial NIST 14, METLIN, and the public MassBank of North America libraries. Here we correctly identified 78% of the structures as top-hit and 83% within the top-3 hits. Three challenge spectra remained unidentified in either of our submissions within the top-10 hits.