Compound Identification Strategies in Mass Spectrometry-Based Metabolomics and Pharmacometabolomics.

The metabolome is composed of a vast array of molecules, including endogenous metabolites and lipids, diet- and microbiome-derived substances, pharmaceuticals and supplements, and exposome chemicals. Correct identification of compounds from this diversity of classes is essential to derive biologically relevant insights from metabolomics data. In this chapter, we aim to provide a practical overview of compound identification strategies for mass spectrometry-based metabolomics, with a particular eye toward pharmacologically-relevant studies. First, we describe routine compound identification strategies applicable to targeted metabolomics. Next, we discuss both experimental (data acquisition-focused) and computational (software-focused) strategies used to identify unknown compounds in untargeted metabolomics data. We then discuss the importance of, and methods for, assessing and reporting the level of confidence of compound identifications. Throughout the chapter, we discuss how these steps can be implemented using today's technology, but also highlight research underway to further improve accuracy and certainty of compound identification. For readers interested in interpreting metabolomics data already collected, this chapter will supply important context regarding the origin of the metabolite names assigned to features in the data and help them assess the certainty of the identifications. For those planning new data acquisition, the chapter supplies guidance for designing experiments and selecting analysis methods to enable accurate compound identification, and it will point the reader toward best-practice data analysis and reporting strategies to allow sound biological and pharmacological interpretation.

How Well Do We Handle the Sample Preparation, FT-ICR Mass Spectrometry Analysis, and Data Treatment of Atmospheric Waters?

FT-ICR MS (Fourier-transform ion cyclotron resonance mass spectrometry) analysis has shown great potential to aid in the understanding of the extremely high molecular diversity of cloud water samples. The main goal of this work was to determine the differences in terms of formula assignment for analytical (i.e., measurement replicates) and experimental replicates of a given cloud water sample. The experimental replicates, obtained by solid phase extraction, were also compared to the results obtained for freeze-dried samples to evaluate whether the presence of salts interferes with the analysis. Two S/N ratios, generally adopted for atmospheric samples, were evaluated, and three different algorithms were used for assignment: DataAnalysis 5.3 (Bruker), Composer (Sierra Analytics), and MFAssignR (Chemical Advanced Resolution Methods Lab). In contrast to other works, we wanted to treat this comparison from the point of view of users, who usually must deal with a simple list of m/z ratios and intensity with limited access to the mass spectrum characteristics. The aim of this study was to establish a methodology for the treatment of atmospheric aqueous samples in light of the comparison of three different software programs, to enhance the possibility of data comparison within samples.

MFAssignR: Molecular formula assignment software for ultrahigh resolution mass spectrometry analysis of environmental complex mixtures.

Ultrahigh resolution mass spectrometry is widely used for nontargeted analysis of complex environmental and biological mixtures, such as dissolved organic matter, due to its unparalleled ability to provide accurate mass measurements. Accurate and efficient characterization of these mixtures is critical to being better able to evaluate their effect on human health and climate. This characterization requires accurate mass signals free from isobaric interferences, instrument noise, and mass measurement biases, allowing for molecular formula identification. To address this need, an open source post-processing pipeline for ultrahigh resolution mass spectra of environmental complex mixtures software was developed. MFAssignR contains functions that perform noise estimation, 13C and 34S polyisotopic mass filtering, mass measurement recalibration, and molecular formula assignment as part of a consistent data processing environment. Novel applications of mass defect analysis were used in the functions for noise estimation and isotope pair identification. Using formula extensions, exact mass measurements are converted to unambiguous molecular formulas via data dependent pathways, reducing a priori decisions. Optional molecular formula ambiguity and multiple non-oxygen heteroatoms are provided for custom user applications, including isotopically labeled reactive species, halogen-containing species, or tandem ultrahigh resolution mass spectrometry. This represents uncommon flexibility for an open-source software package. To evaluate the performance of MFAssignR, it was used to characterize a sample of biomass burning influenced organic aerosol and the results were compared to those from other available methods of molecular formula assignment and noise estimation. The differences between the methods are described here. Overall, the inclusion of a full pipeline of data preparation functions and the data-dependent ambiguity reductions in MFAssignR render excellent results and make MFAssignR well-suited for the consistent and efficient analysis of environmental complex mixtures. MFAssignR is publicly available via GitHub.

Recent (2000-2015) developments in the analysis of minor unknown natural products based on characteristic fragment information using LC-MS.

Liquid chromatography-Mass Spectrometry (LC-MS) has been widely used in natural product analysis. Global detection and identification of nontargeted components are desirable in natural product research, for example, in quality control of Chinese herbal medicine. Nontargeted components analysis continues to expand to exciting life science application domains such as metabonomics. With this background, the present review summarizes recent developments in the analysis of minor unknown natural products using LC-MS and mainly focuses on the determination of the molecular formulae, selection of precursor ions, and characteristic fragmentation patterns of the known compounds. This review consists of three parts. Firstly, the methods used to determine unique molecular formula of unknown compounds such as accurate mass measurements, MSn spectra, or relative isotopic abundance information, are introduced. Secondly, the methods improving signal-to-noise ratio of MS/MS spectra by manual-MS/MS or workflow targeting-only signals were elucidated; pure precursor ions can be selected by changing the precursor ion isolated window. Lastly, characteristic fragmentation patterns such as Retro-Diels-Alder (RDA), McLafferty rearrangements, "internal residue loss," and so on, occurring in the molecular ions of natural products are summarized. Classical application of characteristic fragmentation patterns in identifying unknown compounds in extracts and relevant fragmentation mechanisms are presented (RDA reactions occurring readily in the molecular ions of flavanones or isoflavanones, McLafferty-type fragmentation reactions of some natural products such as epipolythiodioxopiperazines; fragmentation by "internal residue loss" possibly involving ion-neutral complex intermediates). © 2016 Wiley Periodicals, Inc. Mass Spec Rev 37:202-216, 2018.

Dimensionally reduced machine learning model for predicting single component octanol-water partition coefficients.

MF-LOGP, a new method for determining a single component octanol-water partition coefficients ([Formula: see text]) is presented which uses molecular formula as the only input. Octanol-water partition coefficients are useful in many applications, ranging from environmental fate and drug delivery. Currently, partition coefficients are either experimentally measured or predicted as a function of structural fragments, topological descriptors, or thermodynamic properties known or calculated from precise molecular structures. The MF-LOGP method presented here differs from classical methods as it does not require any structural information and uses molecular formula as the sole model input. MF-LOGP is therefore useful for situations in which the structure is unknown or where the use of a low dimensional, easily automatable, and computationally inexpensive calculations is required. MF-LOGP is a random forest algorithm that is trained and tested on 15,377 data points, using 10 features derived from the molecular formula to make [Formula: see text] predictions. Using an independent validation set of 2713 data points, MF-LOGP was found to have an average [Formula: see text] = 0.77 ± 0.007, [Formula: see text] = 0.52 ± 0.003, and [Formula: see text] = 0.83 ± 0.003. This performance fell within the spectrum of performances reported in the published literature for conventional higher dimensional models ([Formula: see text] = 0.42-1.54, [Formula: see text] = 0.09-1.07, and [Formula: see text] = 0.32-0.95). Compared with existing models, MF-LOGP requires a maximum of ten features and no structural information, thereby providing a practical and yet predictive tool. The development of MF-LOGP provides the groundwork for development of more physical prediction models leveraging big data analytical methods or complex multicomponent mixtures.

Determining the parent and associated fragment formulae in mass spectrometry via the parent subformula graph.

BACKGROUND: Identifying the molecular formula and fragmentation reactions of an unknown compound from its mass spectrum is crucial in areas such as natural product chemistry and metabolomics. We propose a method for identifying the correct candidate formula of an unidentified natural product from its mass spectrum. The method involves scoring the plausibility of parent candidate formulae based on a parent subformula graph (PSG), and two possible metrics relating to the number of edges in the PSG. This method is applicable to both electron-impact mass spectrometry (EI-MS) and tandem mass spectrometry (MS/MS) data. Additionally, this work introduces the two-dimensional fragmentation plot (2DFP) for visualizing PSGs. RESULTS: Our results suggest that incorporating information regarding the edges of the PSG results in enhanced performance in correctly identifying parent formulae, in comparison to the more well-accepted "MS/MS score", on the 2016 Computational Assessment of Small Molecule Identification (CASMI 2016) data set (76.3 vs 58.9% correct formula identification) and the Research Centre for Toxic Compounds in the Environment (RECETOX) data set (66.2% vs 59.4% correct formula identification). In the extension of our method to identify the correct candidate formula from complex EI-MS data of semiochemicals, our method again performed better (correct formula appearing in the top 4 candidates in 20/23 vs 7/23 cases) than the MS/MS score, and enables the rapid identification of both the correct parent ion mass and the correct parent formula with minimal expert intervention. CONCLUSION: Our method reliably identifies the correct parent formula even when the mass information is ambiguous. Furthermore, should parent formula identification be successful, the majority of associated fragment formulae can also be correctly identified. Our method can also identify the parent ion and its associated fragments in EI-MS spectra where the identity of the parent ion is unclear due to low quantities and overlapping compounds. Finally, our method does not inherently require empirical fitting of parameters or statistical learning, meaning it is easy to implement and extend upon. SCIENTIFIC CONTRIBUTION: Developed, implemented and tested new metrics for assessing plausibility of candidate molecular formulae obtained from HR-MS data.

Machine-learning assisted molecular formula assignment to high-resolution mass spectrometry data of dissolved organic matter.

High-resolution mass spectrometry (HRMS) provides molecular compositional information of dissolved organic matter (DOM) through isotopic assignment from the molecular mass. However, due to the inevitable deviation of molecular mass measurement and the limitation of resolving power, multiple possible solutions frequently occur for a given molecular mass. Lowering the mass deviation threshold and adding assignment restriction rules are often applied to exclude the incorrect solutions, which generally involves time-consuming manual post-processing of mass data. To improve the result accuracy in an automated manner, we developed a molecular formula assignment algorithm based on machine-learning technology. The method integrated a logistic regression model using manually corrected isotopic composition and the peak features of HRMS data (m/z, signal-to-noise ratio, isotope type, and number, etc.) as training data. The developed model can evaluate the correctness of a candidate formula for the given mass peak based on the peak features. The method was verified by various DOM samples FT-ICR MS data (direct infusion negative mode electrospray), achieving a ∼90% accuracy (compared to the traditional approach) for formula assignment. The method was applied to a series of NOM samples and showed a significant improvement in formula assignment compared with the mass matching method.

Phytochemical Screening and Antioxidant Potential of Selected Extracts from Betula alba var. pendula Roth., Glycyrrhiza glabra L., and Avena sativa L.

The aim of the present study was to obtain, characterize, and evaluate the antioxidant potential of some extracts obtained from the bark of Betula alba var. pendula Roth., the root of Glycyrrhiza glabra L., and the green herb of the Avena sativa. The results revealed that the lowest IC50 value, determined by all three methods, was obtained for Betulae extractum (BE) (73.6 µg/mL-DPPH method, 11.2 µg/mL-ABTS method, and 58.7 µg/mL-FRAP method), followed by Liquiritiae extractum (LE) (805.6 µg/mL, 92.1 µg/mL, and 722 µg/mL) and Avenae extractum (1.13 mg/mL-DPPH method, 99.7 µg/mL-ABTS method, and 135.1 µg/mL-FRAP method). These results correlate with total polyphenols content (expressed in g tannic acid/100 g dry extract), with BE having more polyphenols than LE and AE (47.96 ± 9.7083 for BE, compared with 9.31 ± 0.9913 for LE and 40.55 ± 6.3715 for AE). The total flavonoid content (expressed as g rutoside/100 g dry extract) is similar for BE and LE (3.75 ± 0.3140 and 3.44 ± 0.3037) and smaller for AE (1.95 ± 0.0526). Therefore, Betulae extractum has the strongest antioxidant action, with an IC50 value very close to the standard used as a reference (ascorbic acid-16.5 µg/mL solution). The FT-ICR-MS analysis confirmed the presence of the major compounds in all three extracts. The antioxidant properties of the studied extracts were further supported by molecular docking experiments that revealed the potential of the analyzed phytochemicals to act as both noncovalent and covalent activators of the Nrf2 signaling pathway, with promising benefits in treating various skin disorders.

Ultrahigh performance LC/FT-MS non-targeted screening for biomass burning organic aerosol with MZmine2 and MFAssignR.

In recent years, ultrahigh performance liquid chromatography Fourier transform mass spectrometry (LC/FT-MS) based non-targeted screening (NTS) methods have become increasingly popular for comprehensive analysis of complex organic mixtures. However, applying these methods for environmental complex mixture analysis is challenging due to the extreme complexity of natural samples and a lack of standard samples or surrogates for environmental complex mixtures. Furthermore, limited molecular markers in the databases and insufficient data processing software workflows make the application of these methods more challenging for environmental complex mixtures. In this work, we implement a new NTS data processing workflow to process data collected from ultrahigh performance liquid chromatography and Fourier transform Orbitrap Elite Mass Spectrometry (LC/FT-MS) by combining MZmine2 and MFAssignR, two opensource data processing tools and commercial Mesquite liquid smoke as a surrogate for biomass burning organic aerosol. MZmine2.53 data extraction followed MFAssignR molecular formula assignment offered noise free and highly accurate 1733 individual molecular formulas presented in liquid smoke with 4906 molecular species, including isomers. The results of this new approach were consistent with the results of direct infusion FT-MS analysis confirming its reliability. Over 90% of the molecular formulas presented in mesquite liquid smoke were matched with the molecular formulas of ambient biomass burning organic aerosol. This suggests the potential use of commercial liquid smoke is an acceptable surrogate for biomass burning organic aerosol research. The presented method significantly improves the identification of the molecular composition of biomass burning organic aerosol by successfully addressing some of the limitations related to the data analysis and giving a semi quantitative insight into the analysis.

First insights into the molecular characteristics of atmospheric organic aerosols from Iasi, Romania: Behavior of biogenic versus anthropogenic contributions and potential implications.

The present study reports first data on the organic molecular composition and evolution of secondary organic aerosols (SOAs) markers in aerosol samples from an urban environment in Romania. Targeted and non-targeted approaches of liquid chromatography tandem with time-of-flight mass spectrometry (LC-ToF-MS) were used as powerful analytical approaches for aerosol characterization at the molecular level. Four distinct organic molecular groups (CHO, CHON, CHONS, and CHOS) were classified as relevant for both warm (with 847 assigned molecular formulae) and cold (with 432 assigned molecular formulae) periods. Different formation mechanisms, physico-chemical processing, meteorological conditions, and sources origin or strengths (biogenic versus anthropogenic), were identified as governing factors of the mass concentration size distribution for the first generation and second-generation oxidation products of α-/ß-pinene and two nitroaromatics (i.e., 4-nitrophenol and 4-nitrocatechol). Aromaticity equivalent (XC), carbon oxidation state (OSC), H/C and O/C ratios, and van Krevelen diagrams, were used to discriminate between: i) the aliphatic or aromatic nature of the identified organic aerosol constituents, ii) the oxidation state of the aerosol samples (e.g., more oxidized molecular formulae during the highly insolated period, more intense photochemistry), and iii) sources role in controlling OAs constituents abundances and behavior (e.g., higher relative contributions of aliphatic CHO formulae with a wider range of carbon numbers and CHOS molecular group with higher contribution during the warm period due to increased biogenic emissions or secondary formation from the biogenic precursors). Since in the present study >88 % of the 4-nitrocatechol and 4-nitrophenol was determined in the aerosol size fraction below 1 µm, it is believed that determination of their abundances and size distribution in ambient aerosols might provide direction for future studies such as to enhance the knowledge on their toxic potential levels for the human health.

De Novo Molecular Formula Annotation and Structure Elucidation Using SIRIUS 4.

SIRIUS 4 is the best-in-class computational tool for metabolite identification from high-resolution tandem mass spectrometry data. It offers de novo molecular formula annotation with outstanding accuracy. When searching fragmentation spectra in a structure database, it reaches over 70% correct identifications. A predicted fingerprint, which indicates the presence or absence of thousands of molecular properties, helps to deduce information about the compound of interest even if it is not contained in any structure database. Here, we present best practices and describe how to leverage the full potential of SIRIUS 4, how to incorporate it into your own workflow, and how it adds value to the analysis of mass spectrometry data beyond spectral library search.

Kendrick Mass Defect Approach Combined to NORINE Database for Molecular Formula Assignment of Nonribosomal Peptides.

The identification of known (dereplication) or unknown nonribosomal peptides (NRPs) produced by microorganisms is a time consuming, expensive, and challenging task where mass spectrometry and nuclear magnetic resonance play a key role. The first step of the identification process always involves the establishment of a molecular formula. Unfortunately, the number of potential molecular formulae increases significantly with higher molecular masses and the lower precision of their measurements. In the present article, we demonstrate that molecular formula assignment can be achieved by a combined approach using the regular Kendrick mass defect (RKMD) and NORINE, the reference curated database of NRPs. We observed that irrespective of the molecular formula, the addition and subtraction of a given atom or atom group always leads to the same RKMD variation and nominal Kendrick mass (NKM). Graphically, these variations translated into a vector mesh can be used to connect an unknown molecule to a known NRP of the NORINE database and establish its molecular formula. We explain and illustrate this concept through the high-resolution mass spectrometry analysis of a commercially available mixture composed of four surfactins. The Kendrick approach enriched with the NORINE database content is a fast, useful, and easy-to-use tool for molecular mass assignment of known and unknown NRP structures.

Analytical dataset on the molecular compositional changes of dissolved organic matter during hyperthermophilic composting.

The aim of this research work was to determine the molecular compositional changes of dissolved organic matter (DOM) taken from different phases of the hyperthermophilic composting (HTC) process. The DOM samples were extracted by the standard protocol of C18 extraction methodology, and then analyzed by electrospray ionization coupled with Fourier transform ion cyclotron resonance mass spectrometry (ESI FT-ICR MS). The profiles of negative ion mass spectrum and DOM molecular formulas of four compost samples were reported. Data related to the molecular compositional changes of DOM during HTC were also presented. Further interpretation and discussion on these datasets can be found in the related article entitled "Molecular insights into the transformation of dissolved organic matter during hyperthermophilic composting using ESI FT-ICR MS" [1].

Extraction and cleansing of data for a non-targeted analysis of high-resolution mass spectrometry data of wastewater.

We provide a workflow to extract unidentified signals from chromatography-high resolution mass spectrometry (LC-HRMS) data of wastewater samples as a pre-step of a non-targeted analysis of dissolved organic matter (DOM). We provide detailed methodology on data processing and cleanup using MS processing software MZmine 2 and an own set of functions in R developed for wastewater analysis. The processing involves signal extraction, linear mass correction, reduction of noise, grouping of isotopologues, molecular formula assignment and merging of replicates. The article contains software settings and reasoning behind the choice of data extraction options. The supplementary information contains a script for the correction of signal masses using internal standards and templates of internal standard lists. We included a reproducible example as an R notebook with data cleansing workflow and data exported from MZmine. The data were used according to the described methodology in the article "A non-targeted high-resolution mass spectrometry data analysis of dissolved organic matter in wastewater treatment" by Verkh et al., 2018. •Includes a linear mass correction algorithm for LC-HRMS signals.•Describes a pipeline of non-targeted processing of LC-HRMS data of wastewater using free software.•Provides tests and reasons for parameter choice in non-targeted LC-HRMS wastewater data extraction.

High-throughput detection of antioxidants in mulberry fruit using correlations between high-resolution mass and activity profiles of chromatographic fractions.

BACKGROUND: Plant extracts contain a huge variety of pharmacologically active substances. Conventionally, various chromatographic methods must be applied several times to purify functional compounds to measure their functional activity. However, conventional purification methods are time-consuming and expensive due to the laborious purification process. Recently, a high-throughput discovery method that replaces such time-consuming purification processes was introduced; this method uses 15 T ultra-high-resolution Fourier transform ion cyclotron resonance mass spectrometry (15 T FT-ICR MS) and a high-throughput screening method. This 15 T FT-ICR MS provides unparalleled resolution and sub-ppm accuracy in mass measurements, while simultaneously detecting multiple compounds without separation. The high-throughput, simultaneous multi-component discovery method known as Scaling of Correlations between Activity and Mass Profiles (SCAMP) was used to detect functional compounds in a plant extract. We validated the performance of SCAMP using 33 fractions from antioxidant-rich mulberry ethyl acetate extract and known standard antioxidants. RESULTS: The mulberry fruit was first separated into 33 fractions by LC and analyzed using high-resolution mass spectrometry. The antioxidative strength of the 33 fractions and standard antioxidants was measured. To validate the efficiency of this antioxidant discovery method, correlations between the antioxidation activity profile and changes in mass intensity of components within the 33 fractions were calculated to provide relative scores for the antioxidant candidate list. Enrichment curves and area under the curve (AUC) values were then calculated to compare the performance of the methods. Using this improved scoring method, five strong antioxidants, chlorogenic acid (14.2 ng), dihydoxy quercetin (46.2 ng), rutin (154.0 ng), quercetin (71.7 ng) and luteolin (3.5 ng) in 2 kg mulberry fruit, were found within the top 20 candidates. CONCLUSIONS: We calculated AUCs in order to compare scoring methods quantitatively. Scoring systems were compared and calculated AUCs, where the AUCs for new scoring systems (0.98 and 0.99) were higher than the previously used correlation coefficient (AUC = 0.89). Using the new scoring algorithms, we successfully enriched thirteen unknown strong antioxidant candidates in addition to known antioxidants, methyl syringin and naringenin (3.5 ng) in mulberry extract. Targeted purification of these unknown candidates will significantly reduce purification time and labor.

High-Resolution Mass Spectrometry for Untargeted Drug Screening.

While gas chromatography-mass spectrometry (GC/MS) continues to be the forensic standard for toxicology, liquid chromatography coupled to tandem MS offers significant operational advantages for targeted confirmatory analysis. LC-high-resolution (HR)-MS has recently been available that offers advantages for untargeted analysis. HR-MS analyzers include the Orbitrap and time-of-flight MS. These instruments are capable of detecting 1 ppm mass resolution. Following soft ionization, this enables the assignment of exact molecular formula, limiting the number of candidate compounds. With this technique, presumptive identification of unknowns can be conducted without the need to match MS library spectra or comparison against known standards. For clinical toxicology, this can greatly expand on the number of drugs and metabolites that can be detected and reported on a presumptive basis. Definitive assignments of the compound's identity can be retrospectively determined with acquisition of the appropriate reference standard.

Broad screening and identification of ß-agonists in feed and animal body fluid and tissues using ultra-high performance liquid chromatography-quadrupole-orbitrap high resolution mass spectrometry combined with spectra library search.

Broad screening and identification of ß-agonists in feed, serum, urine, muscle and liver samples was achieved in a quick and highly sensitive manner using ultra high performance liquid chromatography-quadrupole-orbitrap high resolution mass spectrometry (UHPLC-Q-Orbitrap HRMS) combined with a spectra library search. Solid-phase extraction technology was employed for sample purification and enrichment. After extraction and purification, the samples were analyzed using a Q-Orbitrap high-resolution mass spectrometer under full-scan and data-dependent MS/MS mode. The acquired mass spectra were compared with an in-house library (compound library and MS/MS mass spectral library) built with TraceFinder Software which contained the M/Z of the precursor ion, chemical formula, retention time, character fragment ions and the entire MS/MS spectra of 32 ß-agonist standards. Screening was achieved by comparing 5 key mass spectral results and positive matches were marked. Using the developed method, the identification results from 10 spiked samples and 238 actual samples indicated that only 2% of acquired mass spectra produced false identities. The method validation results showed that the limit of detection ranged from 0.021-3.854 µg kg(-1)and 0.015-1.198 ng mL(-1) for solid and liquid samples, respectively.

Molecular Formula Identification Using Isotope Pattern Analysis and Calculation of Fragmentation Trees.

We present the results of a fully automated de novo approach for identification of molecular formulas in the CASMI 2013 contest. Only results for Category 1 (molecular formula identification) were submitted. Our approach combines isotope pattern analysis and fragmentation pattern analysis and is completely independent from any (spectral and structural) database. We correctly identified the molecular formula for ten out of twelve challenges, being the best automated method competing in this category.

Characterization of TATP gas phase product ion chemistry via isotope labeling experiments using ion mobility spectrometry interfaced with a triple quadrupole mass spectrometer.

Identification of the fragment ion species associated with the ion reaction mechanism of triacetone triperoxide (TATP), a homemade peroxide-based explosive, is presented. Ion mobility spectrometry (IMS) has proven to be a key analytical technique in the detection of trace explosive material. Unfortunately, IMS alone does not provide chemical identification of the ions detected; therefore, it is unknown what ion species are actually formed and separated by the IMS. In IMS, ions are primarily characterized by their drift time, which is dependent on the ion׳s mass and molecular cross-section; thus, IMS as a standalone technique does not provide structural signatures, which is in sharp contrast to the chemical and molecular information that is generally obtained from other customary analytical techniques, such as NMR, Raman and IR spectroscopy and mass spectrometry. To help study the ion chemistry that gives rise to the peaks observed in IMS, the hardware of two different commercial IMS instruments has been directly coupled to triple quadrupole (QQQ) mass spectrometers, in order to ascertain each ion׳s corresponding mass/charge (m/z) ratios with different dopants at two temperatures. Isotope labeling was then used to help identify and confirm the molecular identity of the explosive fragment and adduct ions of TATP. The m/z values and isotope labeling experiments were used to help propose probable molecular formulas for the ion fragments. In this report, the fragment and adduct ions m/z 58 and 240 of TATP have been confirmed to be [C3H6NH·H](+) and [TATP·NH4](+), respectively; while the fragment ions m/z 73 and 89 of TATP are identified as having the molecular formulas [C4H9NH2](+) and [C4H9O2](+), respectively. It is anticipated that the work in this area will not only help to facilitate improvements in mobility-based detection (IMS and MS), but also aid in the development and optimization of MS-based detection algorithms for TATP.

Simultaneous molecular formula determinations of natural compounds in a plant extract using 15 T Fourier transform ion cyclotron resonance mass spectrometry.

BACKGROUND: Plant extracts are a reservoir of pharmacologically active substances; however, conventional analytical methods can analyze only a small portion of an extract. Here, we report a high-throughput analytical method capable of determining most phytochemicals in a plant extract and of providing their molecular formulae from a single experiment using ultra-high-resolution electrospray ionization mass spectrometry (UHR ESI MS). UHR mass profiling was used to analyze natural compounds in a 70% ethanol ginseng extract, which was directly infused into a 15 T Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometer for less than 10 min without a separation process. RESULTS: The UHR FT-ICR MS yielded a mass accuracy of 0.5 ppm and a mass resolving power (m/Δm) of 1,000,000-270,000 for the range m/z 290-1,100. The mass resolution was sufficient to resolve the isotopic fine structure (IFS) of many compounds in the extract. After noise removal from 1,552 peaks, 405 compounds were detected. The molecular formulae of 123 compounds, including 33 ginsenosides, were determined using the observed IFS, exact monoisotopic mass, and exact mass difference. Liquid chromatography (LC)/FT-ICR MS of the extract was performed to compare the high-throughput performance of UHR ESI FT-ICR MS. The LC/FT-ICR MS detected only 129 compounds, including 19 ginsenosides. The result showed that UHR ESI FT-ICR MS identified three times more compounds than LC/FT-ICR MS and in a relatively shorter time. The molecular formula determination by UHR FT-ICR MS was validated by LC and tandem MS analyses of three known ginsenosides. CONCLUSIONS: UHR mass profiling of a plant extract by 15 T FT-ICR MS showed that multiple compounds were simultaneously detected and their molecular formulae were decisively determined by a single experiment with ultra-high mass resolution and mass accuracy. Simultaneous molecular determination of multiple natural products by UHR ESI FT-ICR MS would be a powerful method to profile a wide range of natural compounds.