The critical evaluation of a comprehensive mass spectral library

A matrix-centered view of mass spectrometry platform innovation for volatilome research

Volatile organic compounds (VOCs) are carbon-containing molecules with high vapor pressure and low water solubility that are released from biotic and abiotic matrices. Because they are in the gaseous phase, these compounds tend to remain undetected when using conventional metabolomic profiling methods. Despite this omission, efforts to profile VOCs can provide useful information related to metabolic status and identify potential signaling pathways or toxicological impacts in natural or engineered environments. Over the past several decades mass spectrometry (MS) platform innovation has instigated new opportunities for VOC detection from previously intractable matrices. In parallel, volatilome research linking VOC profiles to other forms of multi-omic information (DNA, RNA, protein, and other metabolites) has gained prominence in resolving genotype/phenotype relationships at different levels of biological organization. This review explores both on-line and off-line methods used in VOC profiling with MS from different matrices. On-line methods involve direct sample injection into the MS platform without any prior compound separation, while off-line methods involve chromatographic separation prior to sample injection and analyte detection. Attention is given to the technical evolution of platforms needed for increasingly resolved VOC profiles, tracing technical progress over time with particular emphasis on emerging microbiome and diagnostic applications.

A complementary approach for detecting biological signals through a semi-automated feature selection tool

Introduction

Untargeted metabolomics is often used in studies that aim to trace the metabolic profile in a broad context, with the data-dependent acquisition (DDA) mode being the most commonly used method. However, this approach has the limitation that not all detected ions are fragmented in the data acquisition process, in addition to the lack of specificity regarding the process of fragmentation of biological signals. The present work aims to extend the detection of biological signals and contribute to overcoming the fragmentation limits of the DDA mode with a dynamic procedure that combines experimental and in silico approaches.

Methods

Metabolomic analysis was performed on three different species of actinomycetes using liquid chromatography coupled with mass spectrometry. The data obtained were preprocessed by the MZmine software and processed by the custom package RegFilter.

Results and Discussion

RegFilter allowed the coverage of the entire chromatographic run and the selection of precursor ions for fragmentation that were previously missed in DDA mode. Most of the ions selected by the tool could be annotated through three levels of annotation, presenting biologically relevant candidates. In addition, the tool offers the possibility of creating local spectral libraries curated according to the user's interests. Thus, the adoption of a dynamic analysis flow using RegFilter allowed for detection optimization and curation of potential biological signals, previously absent in the DDA mode, being a good complementary approach to the current mode of data acquisition. In addition, this workflow enables the creation and search of in-house tailored custom libraries.

Quantification of the composition of pyrolysis oils of complex plastic waste by gas chromatography coupled with mass spectrometer detector

Waste valorisation through pyrolysis generates solid, liquid and gaseous fractions that need to be deeply characterised in order to try to recover secondary raw materials or chemicals. Depending on the waste and the process conditions, the liquid fraction obtained (so-called pyrolysis oil) can be very complex. This work proposes a method to quantitatively measure the composition of pyrolysis oils coming from three types of polymeric waste: (1) plastic packaging from sorting plants of municipal solid waste, (2) plastic rich fractions rejected from sorting plants of waste of electrical and electronic equipment and (3) end-of-life carbon/glass fibre reinforced thermoset polymers. The proposed methodology uses a gas chromatography (GC) coupled with mass spectrometer detector (MS) analytical technique, a certified saturated alkanes' mix, an internal standard and fourteen model compounds. Validation of the methodology concluded that the average relative error was between -59 wt% and +62 wt% (with standard deviations between 0 wt% and 13 wt%). Considering that the state-of-the-art scenario to quantify complex plastic pyrolysis oils as a whole is almost none and that they are usually evaluated only qualitatively based on the area percentage of the GC-MS chromatograms, the presented quantification methodology implies a clear step forward towards complex pyrolysis oil compositional quantification in a cost-effective way. Besides, this quantification methodology enables determining what proportion is being detected by GC-MS with respect to the total oil. Finally, the presented work includes all the Kováts RI for complex temperature-program gas chromatography of all the signals identified in the analysed pyrolysis oils, to be readily available to other researchers towards the identification of chemical compounds in their studies.

AIRI: Predicting Retention Indices and Their Uncertainties Using Artificial Intelligence

The Kováts retention index (RI) is a quantity measured using gas chromatography and is commonly used in the identification of chemical structures. Creating libraries of observed RI values is a laborious task, so we explore the use of a deep neural network for predicting RI values from structure for standard semipolar columns. This network generated predictions with a mean absolute error of 15.1 and, in a quantification of the tail of the error distribution, a 95th percentile absolute error of 46.5. Because of the Artificial Intelligence Retention Indices (AIRI) network's accuracy, it was used to predict RI values for the NIST EI-MS spectral libraries. These RI values are used to improve chemical identification methods and the quality of the library. Estimating uncertainty is an important practical need when using prediction models. To quantify the uncertainty of our network for each individual prediction, we used the outputs of an ensemble of 8 networks to calculate a predicted standard deviation for each RI value prediction. This predicted standard deviation was corrected to follow the error between the observed and predicted RI values. The Z scores using these predicted standard deviations had a standard deviation of 1.52 and a 95th percentile absolute Z score corresponding to a mean RI value of 42.6.

Accurate de novo peptide sequencing using fully convolutional neural networks

De novo peptide sequencing, which does not rely on a comprehensive target sequence database, provides us with a way to identify novel peptides from tandem mass spectra. However, current de novo sequencing algorithms suffer from low accuracy and coverage, which hinders their application in proteomics. In this paper, we present PepNet, a fully convolutional neural network for high accuracy de novo peptide sequencing. PepNet takes an MS/MS spectrum (represented as a high-dimensional vector) as input, and outputs the optimal peptide sequence along with its confidence score. The PepNet model is trained using a total of 3 million high-energy collisional dissociation MS/MS spectra from multiple human peptide spectral libraries. Evaluation results show that PepNet significantly outperforms current best-performing de novo sequencing algorithms (e.g. PointNovo and DeepNovo) in both peptide-level accuracy and positional-level accuracy. PepNet can sequence a large fraction of spectra that were not identified by database search engines, and thus could be used as a complementary tool to database search engines for peptide identification in proteomics. In addition, PepNet runs around 3x and 7x faster than PointNovo and DeepNovo on GPUs, respectively, thus being more suitable for the analysis of large-scale proteomics data.

Mass Spectrometry-Based Approach to Compute Electron-Impact Partial Ionization Cross-Sections of Methane, Water and Nitromethane from Threshold to 5 keV

The electron impact partial ionization cross-sections of molecules such as methane, water and nitromethane are computed using a modified form of the binary encounter Bethe (BEB) formula. The modified form of the BEB model works on rescaling the molecular binding energies of the orbitals and the scaling of cross-sections using the electron ionization mass spectrometry data. The computed partial ionization cross-sections are consistent with the recommended data and are better than several experimental and theoretical results. The summed partial ionization cross-sections of different fragments also agree with the total ionization cross-sections obtained from BEB and the experimental data. This work highlights the utility of mass spectrometry in the modeling and interpretation of the ionization cross-section data. The limitations and the advantages of the modified form of the BEB model are also discussed.

PyFragMS-A Web Tool for the Investigation of the Collision-Induced Fragmentation Pathways

Dissociation induced by the accumulation of internal energy via collisions of ions with neutral molecules is one of the most important fragmentation techniques in mass spectrometry (MS), and the identification of small singly charged molecules is based mainly on the consideration of the fragmentation spectrum. Many research studies have been dedicated to the creation of databases of experimentally measured tandem mass spectrometry (MS/MS) spectra (such as MzCloud, Metlin, etc.) and developing software for predicting MS/MS fragments in silico from the molecular structure (such as MetFrag, CFM-ID, CSI:FingerID, etc.). However, the fragmentation mechanisms and pathways are still not fully understood. One of the limiting obstacles is that protomers (positive ions protonated at different sites) produce different fragmentation spectra, and these spectra overlap in the case of the presence of different protomers. Here, we are proposing to use a combination of two powerful approaches: computing fragmentation trees that carry information of all consecutive fragmentations and consideration of the MS/MS data of isotopically labeled compounds. We have created PyFragMS-a web tool consisting of a database of annotated MS/MS spectra of isotopically labeled molecules (after H/D and/or ¹⁶O/¹⁸O exchange) and a collection of instruments for computing fragmentation trees for an arbitrary molecule. Using PyFragMS, we investigated how the site of protonation influences the fragmentation pathway for small molecules. Also, PyFragMS offers capabilities for performing database search when MS/MS data of the isotopically labeled compounds are taken into account.

Marine bacteria-based polyvinyl chloride (PVC) degradation by-products: Toxicity analysis on Vigna radiata and edible seaweed Ulva lactuca

Biodegradation of polyvinyl chloride (PVC) by marine bacteria is a sustainable approach that leads to the production of different by-products but their toxicity needs to be evaluated. In the present study, polyvinyl chloride degradation products (PVCDP) produced by three marine bacterial isolates (T-1.3, BP-4.3 and S-237) in the culture supernatant were evaluated for toxicity on the germination of Vigna radiata and growth of Ulva lactuca. A total of 24 compounds comprising of benzene, fatty acid, ether, ester and plastic stabilizer (tris (2, 4-di-tert-butylphenyl) phosphate) were identified by GC-MS using diethyl ether solvent extraction from the supernatant. The per cent germination rate of the seed treated with PVCDP showed no significant effect but germination index and elongation inhibition rate were influenced significantly by PVCDP treatments. In seaweed (U. lactuca), PVCDP showed improvement in the daily growth rate. After ten days of treatment with PVCDP, pigment contents were improved in seaweed and PVCDP (2%) of isolate T-1.3 recorded the highest chlorophyll-a and chlorophyll-b.

Evaluating the Accuracy of the QCEIMS Approach for Computational Prediction of Electron Ionization Mass Spectra of Purines and Pyrimidines

Mass spectrometry is the most commonly used method for compound annotation in metabolomics. However, most mass spectra in untargeted assays cannot be annotated with specific compound structures because reference mass spectral libraries are far smaller than the complement of known molecules. Theoretically predicted mass spectra might be used as a substitute for experimental spectra especially for compounds that are not commercially available. For example, the Quantum Chemistry Electron Ionization Mass Spectra (QCEIMS) method can predict 70 eV electron ionization mass spectra from any given input molecular structure. In this work, we investigated the accuracy of QCEIMS predictions of electron ionization (EI) mass spectra for 80 purine and pyrimidine derivatives in comparison to experimental data in the NIST 17 database. Similarity scores between every pair of predicted and experimental spectra revealed that 45% of the compounds were found as the correct top hit when QCEIMS predicted spectra were matched against the NIST17 library of >267,000 EI spectra, and 74% of the compounds were found within the top 10 hits. We then investigated the impact of matching, missing, and additional fragment ions in predicted EI mass spectra versus ion abundances in MS similarity scores. We further include detailed studies of fragmentation pathways such as retro Diels-Alder reactions to predict neutral losses of (iso)cyanic acid, hydrogen cyanide, or cyanamide in the mass spectra of purines and pyrimidines. We describe how trends in prediction accuracy correlate with the chemistry of the input compounds to better understand how mechanisms of QCEIMS predictions could be improved in future developments. We conclude that QCEIMS is useful for generating large-scale predicted mass spectral libraries for identification of compounds that are absent from experimental libraries and that are not commercially available.

A hydrolytically stable amide polar stationary phase for hydrophilic interaction chromatography

A novel amide functionalized polar stationary phase has been described by grafting acrylamide polymer coating onto hydrolysed poly (glycidyl methacrylate-divinylbenzene) (GMA-DVB) microsphere. The grafting of acrylamide coating is performed via free radical polymerization of acrylamide with pendant double bonds of hydrolysed GMA-DVB microsphere. The obtained stationary phase (G-pAM) possesses favorable hydrophilicity, as proved by strong retention and good separation ability towards several types of model polar analytes (e.g. 71913 plates/m plate count of sucrose), and excellent hydrolytically stability, as indicated by extremely low bleed level (much superior to commercial ones, ~23.7-fold-~77.4-fold lower). Negligible baseline drift under gradient elution (water fraction even up to 50%) was observed. It also exhibits good selectivity in the separation of isomers and homologue sugars.

Open, High-Resolution EI+ Spectral Library of Anthropogenic Compounds

To address the lack of high-resolution electron ionisation mass spectral libraries (HR-[EI+]-MS) for environmental chemicals, a retention-indexed HR-[EI+]-MS library has been constructed following analysis of authentic compounds via GC-Orbitrap MS. The library is freely provided alongside a compound database of predicted physicochemical properties. Currently, the library contains over 350 compounds from 56 compound classes and includes a range of legacy and emerging contaminants. The RECETOX Exposome HR-[EI+]-MS library expands the number of freely available resources for use in full-scan chemical exposure studies and is available at: https://doi.org/10.5281/zenodo.4471217.

Photo transformation of 5-methylbenzotriazole and 5-chlorobenzotriazole by UV irradiation: Influences of pH, salinity, metal species and humic acid

5-methylbenzotriazole (5-TTri) and 5-chlorobenzotriazole (CBT) are two benzotriazole derivatives widely used in various industrial and domestic applications. This paper reports on the photochemical behaviour of 5-TTri and CBT in aqueous solutions under UV radiation at 254 nm and the influences of pH, salinity, metal species and humic acid (HA) on their photo-transformation processes. The photolysis of 5-TTri and CBT under the exposure to UV light were found to follow the first-order reaction kinetic in all cases with half-lives ranging from 7.1 h to 24.3 h for 5-TTri and 5.1 h-20.5 h for CBT in various aqueous solutions containing metal ions and HA. The photolysis rates for both 5-TTri and CBT were strongly dependent on the solution pH value, and decreased with increasing solution pH. Salinity, metal species Cu²⁺ and Fe³⁺, and especially HA had inhibitory effects on the photolysis of 5-TTri and CBT under UV light irradiation at 254 nm. We proposed the tentative photo transformation schemes for both 5-TTri and CBT, which involved two photoproducts (4-methylaniline and N, N-diethylaniline- p-toluidine) and three photoproducts (4-chloroaniline, Aniline and 2,6-diethylaniline), respectively, via N-N and N-NH bond scission and dechlorination process.

High-field and benchtop NMR spectroscopy for the characterization of new psychoactive substances

New psychoactive substances (NPS) have become a serious threat to public health in Europe due to their ability to be sold in the street or on the darknet. Regulating NPS is an urgent priority but comes with a number of analytical challenges since they are structurally similar to legal products. A number of analytical techniques can be used for identifying NPS, among which NMR spectroscopy is a gold standard. High field NMR is typically used for structural elucidation in combination with others techniques like GC-MS, Infrared spectroscopy, together with databases. In addition to their strong ability to elucidate molecular structures, high field NMR techniques are the gold standard for quantification without any physical isolation procedure and with a single internal standard. However, high field NMR remains expensive and emerging "benchtop" NMR apparatus which are cheaper and transportable can be considered as valuable alternatives to high field NMR. Indeed, benchtop NMR, which emerged about ten years ago, makes it possible to carry out structural elucidation and quantification of NPS despite the gap in resolution and sensitivity as compared to high field NMR. This review describes recent advances in the field of NMR applied to the characterization of NPS. High-field NMR methods are first described in view of their complementarity with other analytical methods, focusing on both structural and quantitative aspects. The second part of the review highlights how emerging benchtop NMR approaches could act as a game changer in the field of forensics.

Relationships in Gas Chromatography—Fourier Transform Infrared Spectroscopy—Comprehensive and Multilinear Analysis

Molecular spectroscopic detection techniques, such as Fourier transform infrared spectroscopy (FTIR), provides additional specificity for isomers where often mass spectrometry (MS) fails, due to similar fragmentation patterns. A hyphenated system of gas chromatography (GC) with FTIR via a light-pipe interface is reported in this study to explore a number of GC–FTIR analytical capabilities. Various compound classes were analyzed—aromatics, essential oils and oximes. Variation in chromatographic peak parameters due to the light-pipe was observed via sequentially-located flame ionization detection data. Unique FTIR spectra were observed for separated mixtures of essential oil isomers having similar mass spectra. Presentation of GC×FTIR allows a ‘comprehensive’-style experiment to be developed. This was used to obtain spectroscopic/separation profiles for interconverting oxime species with their individual spectra in the overlap region being displayed on a color contour plot. Partial least square regression provides multivariate quantitative analysis of co-eluting cresol isomers derived from GC–FTIR data. The model resulted in an R2 of 0.99. Prediction was obtained with R2 prediction value of 0.88 and RMSEP of 0.57, confirming the method’s suitability. This study explores the potential of GC–FTIR hyphenation and re-iterates its value to derive unambiguous and detailed molecular information which is complementary to MS.

Decomposition of electron ionization mass spectra for space application using a Monte-Carlo approach

RATIONALE

Quadrupole mass spectrometers equipped with an electron ionization (EI) sources have been widely used in space exploration to investigate the composition of planetary surfaces and atmospheres. However, the complexity of the samples and the minimal calibration for the fragmentation of molecules in the ionization chambers have prevented the deconvolution of the majority of the mass spectra obtained at different targets, thus limiting the determination of the exact composition of the samples analyzed. We propose a Monte-Carlo approach to solve this issue mathematically.

METHODS

We decomposed simulated mass spectra of mixtures acquired with unit resolving power mass spectrometers and EI sources into the sum of the single components fragmentation patterns weighted by their relative concentration using interior-point least-square fitting. To fit compounds with poorly known fragmentation patterns, we used a Monte-Carlo method to vary the intensity of individual fragment ions. We then decomposed the spectrum thousands of times to obtain a statistical distribution.

RESULTS

By performing the deconvolution on a mixture of seven different molecules with interfering fragmentation patterns (H₂ O, O₂ , CH₄ , Ar, N₂ , C₂ H₄ , and C₂ H₆ ) we show that this approach retrieves the mixing ratio of the individual components more accurately than regular mass spectra decomposition methods that rely on fragmentation patterns from general databases. It also provides the probability density function for each species's mixing ratio.

CONCLUSIONS

By removing the solution degeneracy in the decomposition of mass spectra, the method described herein could significantly increase the scientific retrieval from archived space flight mass spectrometry data, where calibration of the ionization source is no longer an option.

Combining thermal hydrolysis and methylation-gas chromatography/mass spectrometry with X-ray photoelectron spectroscopy to characterise complex organic assemblages in geological material

What follows is a method applicable generically to the analysis of low levels of organic matter that is embedded in either loose fine-grained or solid geological material. Initially, the range of organic compounds that could be detected in a geological sample using conventional pyrolysis chromatography/mass spectrometry was compared to the range that was detected using thermally assisted hydrolysis and methylation-gas chromatography/mass spectrometry (THM-GC/MS). This method was used to validate the synthetic components fitted to X-ray photoelectron spectroscopy (XPS) carbon spectra of the sample. Reciprocally, XPS analysis was able to identify the constituent carbon-carbon, carbon-oxygen and carbon-nitrogen bonds of the functional groups in the compounds identified by THM-GC/MS. The two independently derived outputs from the THM-GC/MS and the XPS techniques mutually validated the identification of organic compounds in our geological samples. We describe in detail the improvements to: •The preparation of geological samples for analysis by XPS.•Measurements of organic material in geological samples using GC/MS.•The use of THM-GC/MS and XPS data used together to characterise low levels of organic material in geological samples.

Predicting the absence of an unknown compound in a mass spectral database

Only a small subset of known organic compounds (amenable for gas chromatography/mass spectrometry) is present in the largest mass spectral databases (such as NIST or Wiley). Nevertheless, library search algorithms available in the market are not able to predict the absence of a compound in the database. In the present work, we have tried to implement such prediction by means of supervised classification. Training and validation set contained 1500 and 750 compounds, respectively. Two prediction sets (containing 750 and about 3000 mass spectra) were considered. The easiest-to-use models were built with only one input variable: match factor of the best candidate or InLib factor (both parameters were calculated within MS Search (NIST) software). Multivariate classification models were built by partial least squares discriminant analysis (PLS-DA); match factors of top n candidates were used as input variables. PLS-DA was found to be the most effective approach. The prediction efficiency strongly depended on the 'uniqueness' of mass spectra presented in the test set. PLS-DA model was able to correctly predict the absence of a compound in the database in 29.9% for prediction set #1 and in 74.4% for prediction set #2 (only 1.3% and 2.5% of compounds actually presented in the database were wrongly classified).

Challenges in Identifying the Dark Molecules of Life

Metabolomics is the study of the metabolome, the collection of small molecules in living organisms, cells, tissues, and biofluids. Technological advances in mass spectrometry, liquid- and gas-phase separations, nuclear magnetic resonance spectroscopy, and big data analytics have now made it possible to study metabolism at an omics or systems level. The significance of this burgeoning scientific field cannot be overstated: It impacts disciplines ranging from biomedicine to plant science. Despite these advances, the central bottleneck in metabolomics remains the identification of key metabolites that play a class-discriminant role. Because metabolites do not follow a molecular alphabet as proteins and nucleic acids do, their identification is much more time consuming, with a high failure rate. In this review, we critically discuss the state-of-the-art in metabolite identification with specific applications in metabolomics and how technologies such as mass spectrometry, ion mobility, chromatography, and nuclear magnetic resonance currently contribute to this challenging task.

The organic stratigraphy of Ontong Java Plateau Tuff correlated with the depth-related presence and absence of putative microbial alteration structures

Structures in geological samples are often interpreted as fossilised life; however, such interpretations are equivocal, as abiotic processes can be invoked to explain their presence. Thus, additional lines of chemical evidence are invaluable in confirming or refuting such morphological evidence. Glass shards in tuff from the Ontong Java Plateau (OJP) contain microtubular structures that are in close proximity to functionalised nitrogen substituted aromatic compounds that may be indicative of the chemical remnants of biological activity. The organic composition of the OJP tuff containing microtubular alteration structures was compared with tuff without such features. In addition, organic matter associated with horizons with compacted remnants of woody material buried in the OJP tuff and overlying pelagic calcareous foraminifer sediment were also characterised, to ascertain the provenance of the organic matter found in the OJP tuff. As a further control, the organic material in submarine and terrestrial basalts from other locations were also characterised providing further evidence to support the view that the organic matter in the OJP tuff is authigenic. Carbon-nitrogen chemistry was detected across all OJP tuff samples irrespective of the presence or absence of microtubular features, but was not detected in either the wood material, the overlying pelagic sediments or in the basalts from other locations. The results indicate no direct link between the OJP nitrogenous organic compounds and the presence or absence of microtubular features.

A comprehensive review of feature based methods for drug target interaction prediction

Drug target interaction is a prominent research area in the field of drug discovery. It refers to the recognition of interactions between chemical compounds and the protein targets in the human body. Wet lab experiments to identify these interactions are expensive as well as time consuming. The computational methods of interaction prediction help limit the search space for these experiments. These computational methods can be divided into ligand based approaches, docking approaches and chemogenomic approaches. In this review, we aim to describe the various feature based chemogenomic methods for drug target interaction prediction. It provides a comprehensive overview of the various techniques, datasets, tools and metrics. The feature based methods have been categorized, explained and compared. A novel framework for drug target interaction prediction has also been proposed that aims to improve the performance of existing methods. To the best of our knowledge, this is the first comprehensive review focusing only on feature based methods of drug target interaction.

Gas Chromatography-Mass Spectrometry Microbial Metabolomics for Applications in Strain Optimization

Metabolomics is the systems-scale measurement of biochemical intermediates in biological systems; by virtue of its deep and broad study of metabolism, it has great potential for applications in metabolic engineering. While a number of the analytical techniques used widely in metabolomics are familiar to metabolic engineers performing post hoc analyses of product titers, the requirements for accurately capturing metabolism at a systems scale rather than just measuring a single secreted product are much more complicated. Nonetheless, metabolomics (which is still not widely available as an affordable consumer service like many molecular biology services) is within reach of many properly equipped metabolic engineering groups. To this end, we present a detailed metabolomics protocol with application to strain optimization. Specifically, we focus on characterizing metabolism in the yeast Saccharomyces cerevisiae using gas chromatography coupled to mass spectrometry. The measurement of metabolic intermediates that results from such approaches has the potential to enable more informed and rational efforts towards pathway engineering and strain optimization.

LC-HRMS-Database Screening Metrics for Rapid Prioritization of Samples to Accelerate the Discovery of Structurally New Natural Products

In order to accelerate the isolation and characterization of structurally new or novel secondary metabolites, it is crucial to develop efficient strategies that prioritize samples with greatest promise early in the workflow so that resources can be utilized in a more efficient and cost-effective manner. We have developed a metrics-based prioritization approach using exact LC-HRMS, which uses data for 24 618 marine natural products held in the PharmaSea database. Each sample was evaluated and allocated a metric score by a software algorithm based on the ratio of new masses over the total (sample novelty), ratio of known masses over the total (chemical novelty), number of peaks above a defined peak area threshold (sample complexity), and peak area (sample diversity). Samples were then ranked and prioritized based on these metric scores. To validate the approach, eight marine sponges and six tunicate samples collected from the Fiji Islands were analyzed, metric scores calculated, and samples targeted for isolation and characterization of new compounds. Structures of new compounds were elucidated by spectroscopic techniques, including 1D and 2D NMR, MS, and MS/MS. Structures were confirmed by computer-assisted structure elucidation methods (CASE) using the ACD/Structure Elucidator Suite.

Flavor formation in different production steps during the processing of cold-smoked Spanish mackerel

This study aimed to determine the changes in the flavor profile during different operating units when making cold-smoked Spanish mackerel. Sensory evaluation and instrumental analysis of tenderness were applied to optimize the processing parameters. For the overall odor perception, distinct odor among fresh and processed samples could be distinguished using electronic nose (e-Nose). Purge-and-trap (P&T) extraction combined with gas chromatography-mass spectrometry (GC-MS) was used to identify volatile compounds. Alcohols were the major volatiles in Spanish mackerel over the whole processing, while 2-butanol was found in the highest concentration. Curing was an effective way to remove fishy odor. Drying decreased the concentration of volatiles, especially sulfur compounds. Odor formation in cold-smoked mackerel could be divided into four steps (curing, drying, cold smoking and heating) as a result of partial least squares discriminant analysis (PLS-DA).

Comprehensive comparative compositional study of the vapour phase of cigarette mainstream tobacco smoke and tobacco heating product aerosol

A simple direct sample collection/dilution and introduction method was developed using quartz wool and Tenax/sulficarb sorbents for thermal desorption and comprehensive two-dimensional gas chromatography (TD-GC × GC) analyses of volatile organic compounds from vapour phase (VP) fractions of aerosol produced by tobacco heating products (THP1.0) and 3R4F mainstream tobacco smoke (MTS). Analyses were carried out using flame ionisation detection (FID) for semi-quantification and both low and high resolution time-of-flight mass spectrometry (LR/HR-TOFMS) for qualitative comparison and peak assignment. Qualitative analysis was carried out by combining identification data based on linear retention indices (LRIs) with a match window of ±10 index units, mass spectral forward and reverse library searches (from LR and HRTOFMS spectra) with a match factor threshold of >700 (both forward and reverse), and accurate mass values of ± 3 ppm for increased confidence in peak identification. Using this comprehensive approach of data mining, a total of 79 out of 85 compounds and a total of 198 out of 202 compounds were identified in THP1.0 aerosol and in 3R4F MTS, respectively. Among the identified analytes, a set of 35 compounds was found in both VP sample types. Semi-quantitative analyses were carried out using a chemical class-based external calibration method. Acyclic, alicyclic, aromatic hydrocarbons and ketones appeared to be prominent in 3R4F MTS VP, whereas larger amounts of aldehydes, ketones, heterocyclic hydrocarbons and esters were present in THP1.0 aerosol VP. The results demontsrate the capability and versatility of the method for the characterization and comparison of complex aerosol samples and highlighted the relative chemical simplicity of THP1.0 aerosol in comparison to MTS.

Identification of small molecules using accurate mass MS/MS search

Tandem mass spectral library search (MS/MS) is the fastest way to correctly annotate MS/MS spectra from screening small molecules in fields such as environmental analysis, drug screening, lipid analysis, and metabolomics. The confidence in MS/MS-based annotation of chemical structures is impacted by instrumental settings and requirements, data acquisition modes including data-dependent and data-independent methods, library scoring algorithms, as well as post-curation steps. We critically discuss parameters that influence search results, such as mass accuracy, precursor ion isolation width, intensity thresholds, centroiding algorithms, and acquisition speed. A range of publicly and commercially available MS/MS databases such as NIST, MassBank, MoNA, LipidBlast, Wiley MSforID, and METLIN are surveyed. In addition, software tools including NIST MS Search, MS-DIAL, Mass Frontier, SmileMS, Mass++, and XCMS2 to perform fast MS/MS search are discussed. MS/MS scoring algorithms and challenges during compound annotation are reviewed. Advanced methods such as the in silico generation of tandem mass spectra using quantum chemistry and machine learning methods are covered. Community efforts for curation and sharing of tandem mass spectra that will allow for faster distribution of scientific discoveries are discussed.

Metabolomic spectral libraries for data-independent SWATH liquid chromatography mass spectrometry acquisition

High-quality mass spectral libraries have become crucial in mass spectrometry-based metabolomics. Here, we investigate a workflow to generate accurate mass discrete and composite spectral libraries for metabolite identification and for SWATH mass spectrometry data processing. Discrete collision energy (5-100 eV) accurate mass spectra were collected for 532 metabolites from the human metabolome database (HMDB) by flow injection analysis and compiled into composite spectra over a large collision energy range (e.g., 10-70 eV). Full scan response factors were also calculated. Software tools based on accurate mass and predictive fragmentation were specially developed and found to be essential for construction and quality control of the spectral library. First, elemental compositions constrained by the elemental composition of the precursor ion were calculated for all fragments. Secondly, all possible fragments were generated from the compound structure and were filtered based on their elemental compositions. From the discrete spectra, it was possible to analyze the specific fragment form at each collision energy and it was found that a relatively large collision energy range (10-70 eV) gives informative MS/MS spectra for library searches. From the composite spectra, it was possible to characterize specific neutral losses as radical losses using in silico fragmentation. Radical losses (generating radical cations) were found to be more prominent than expected. From 532 metabolites, 489 provided a signal in positive mode [M+H]⁺ and 483 in negative mode [M-H]^-. MS/MS spectra were obtained for 399 compounds in positive mode and for 462 in negative mode; 329 metabolites generated suitable spectra in both modes. Using the spectral library, LC retention time, response factors to analyze data-independent LC-SWATH-MS data allowed the identification of 39 (positive mode) and 72 (negative mode) metabolites in a plasma pool sample (total 92 metabolites) where 81 previously were reported in HMDB to be found in plasma. Graphical abstract Library generation workflow for LC-SWATH MS, using collision energy spread, accurate mass, and fragment annotation.

Citrus reticulata peel oil inhibits non-small cell lung cancer cell proliferation in culture and implanted in nude mice

Mandarin peel oil exerts an antiproliferative effect on in vitro and in vivo human tumour cells without toxicity effects.

Partial Ionization Cross Sections of Organic Molecules

Partial ionization cross sections are the absolute yields of specific ions from an electron-molecule collision. They are necessary for modeling plasmas and for determining the sensitivity of mass spectrometers, among other applications. One mass-spectrometric application is estimating the abundance of organic compounds on Mars, as sampled by the rover Curiosity. This is a report of semitheoretical data obtained for a collection of organic molecules identified as possible biomarkers in this exotic context.

Mass Spectral Library Quality Assurance by Inter-Library Comparison

A method to discover and correct errors in mass spectral libraries is described. Comparing across a set of highly curated reference libraries compounds that have the same chemical structure quickly identifies entries that are outliers. In cases where three or more entries for the same compound are compared, the outlier as determined by visual inspection was almost always found to contain the error. These errors were either in the spectrum itself or in the chemical descriptors that accompanied it. The method is demonstrated on finding errors in compounds of forensic interest in the NIST/EPA/NIH Mass Spectral Library. The target list of compounds checked was the Scientific Working Group for the Analysis of Seized Drugs (SWGDRUG) mass spectral library. Some examples of errors found are described. A checklist of errors that curators should look for when performing inter-library comparisons is provided. Graphical Abstract ᅟ.

Analysis of Volatile Compounds by Advanced Analytical Techniques and Multivariate Chemometrics

Smelling is one of the five senses, which plays an important role in our everyday lives. Volatile compounds are, for example, characteristics of food where some of them can be perceivable by humans because of their aroma. They have a great influence on the decision making of consumers when they choose to use a product or not. In the case where a product has an offensive and strong aroma, many consumers might not appreciate it. On the contrary, soft and fresh natural aromas definitely increase the acceptance of a given product. These properties can drastically influence the economy; thus, it has been of great importance to manufacturers that the aroma of their food product is characterized by analytical means to provide a basis for further optimization processes. A lot of research has been devoted to this domain in order to link the quality of, e.g., a food to its aroma. By knowing the aromatic profile of a food, one can understand the nature of a given product leading to developing new products, which are more acceptable by consumers. There are two ways to analyze volatiles: one is to use human senses and/or sensory instruments, and the other is based on advanced analytical techniques. This work focuses on the latter. Although requirements are simple, low-cost technology is an attractive research target in this domain; most of the data are generated with very high-resolution analytical instruments. Such data gathered based on different analytical instruments normally have broad, overlapping sensitivity profiles and require substantial data analysis. In this review, we have addressed not only the question of the application of chemometrics for aroma analysis but also of the use of different analytical instruments in this field, highlighting the research needed for future focus.

Temperature-programmed Deoxygenation of Acetic Acid on Molybdenum Carbide Catalysts

Temperature programmed reaction (TPRxn) is a simple yet powerful tool for screening solid catalyst performance at a variety of conditions. A TPRxn system includes a reactor, furnace, gas and vapor sources, flow control, instrumentation to quantify reaction products (e.g., gas chromatograph), and instrumentation to monitor the reaction in real time (e.g., mass spectrometer). Here, we apply the TPRxn methodology to study molybdenum carbide catalysts for the deoxygenation of acetic acid, an important reaction among many in the upgrading/stabilization of biomass pyrolysis vapors. TPRxn is used to evaluate catalyst activity and selectivity and to test hypothetical reaction pathways (e.g., decarbonylation, ketonization, and hydrogenation). The results of the TPRxn study of acetic acid deoxygenation show that molybdenum carbide is an active catalyst for this reaction at temperatures above ca. 300 °C and that the reaction favors deoxygenation (i.e., C-O bond-breaking) products at temperatures below ca. 400 °C and decarbonylation (i.e., C-C bond-breaking) products at temperatures above ca. 400 °C.

Integrating ion mobility spectrometry into mass spectrometry-based exposome measurements: what can it add and how far can it go?

Measuring the exposome remains a challenge due to the range and number of anthropogenic molecules that are encountered in our daily lives, as well as the complex systemic responses to these exposures. One option for improving the coverage, dynamic range and throughput of measurements is to incorporate ion mobility spectrometry (IMS) into current MS-based analytical methods. The implementation of IMS in exposomics studies will lead to more frequent observations of previously undetected chemicals and metabolites. LC-IMS-MS will provide increased overall measurement dynamic range, resulting in detections of lower abundance molecules. Alternatively, the throughput of IMS-MS alone will provide the opportunity to analyze many thousands of longitudinal samples over lifetimes of exposure, capturing evidence of transitory accumulations of chemicals or metabolites. The volume of data corresponding to these new chemical observations will almost certainly outpace the generation of reference data to enable their confident identification. In this perspective, we briefly review the state-of-the-art in measuring the exposome, and discuss the potential use for IMS-MS and the physico-chemical property of collisional cross section in both exposure assessment and molecular identification.

Metabarcoding and metabolome analyses of copepod grazing reveal feeding preference and linkage to metabolite classes in dynamic microbial plankton communities

In order to characterize copepod feeding in relation to microbial plankton community dynamics, we combined metabarcoding and metabolome analyses during a 22-day seawater mesocosm experiment. Nutrient amendment of mesocosms promoted the development of haptophyte (Phaeocystis pouchetii)- and diatom (Skeletonema marinoi)-dominated plankton communities in mesocosms, in which Calanus sp. copepods were incubated for 24 h in flow-through chambers to allow access to prey particles (<500 μm). Copepods and mesocosm water sampled six times spanning the experiment were analysed using metabarcoding, while intracellular metabolite profiles of mesocosm plankton communities were generated for all experimental days. Taxon-specific metabarcoding ratios (ratio of consumed prey to available prey in the surrounding seawater) revealed diverse and dynamic copepod feeding selection, with positive selection on large diatoms, heterotrophic nanoflagellates and fungi, while smaller phytoplankton, including P. pouchetii, were passively consumed or even negatively selected according to our indicator. Our analysis of the relationship between Calanus grazing ratios and intracellular metabolite profiles indicates the importance of carbohydrates and lipids in plankton succession and copepod-prey interactions. This molecular characterization of Calanus sp. grazing therefore provides new evidence for selective feeding in mixed plankton assemblages and corroborates previous findings that copepod grazing may be coupled to the developmental and metabolic stage of the entire prey community rather than to individual prey abundances.

Dereplication, sequencing and identification of peptidic natural products: from genome mining to peptidogenomics to spectral networks

Covering: 2000 to 2015. While recent breakthroughs in the discovery of peptide antibiotics and other Peptidic Natural Products (PNPs) raise a challenge for developing new algorithms for their analyses, the computational technologies for high-throughput PNP discovery are still lacking. We discuss the computational bottlenecks in analyzing PNPs and review recent advances in genome mining, peptidogenomics, and spectral networks that are now enabling the discovery of new PNPs via mass spectrometry. We further describe the connections between these advances and the new generation of software tools for PNP dereplication, de novo sequencing, and identification.

Alkaloid variation in New Zealand kōwhai, Sophora species

Alkaloid contents of leaf and seed samples of eight species of Sophora native to New Zealand, plus Sophora cassioides from Chile are reported. Fifty-six leaf and forty-two seed samples were analysed for alkaloid content by proton nuclear magnetic resonance spectroscopy, which showed major alkaloids as cytisine, N-methyl cytisine and matrine. GC analyses quantified these and identified further alkaloid components. The alkaloids identified were cytisine, sparteine, and matrine-types common to Sophora from other regions of the world. Cytisine, N-methyl cytisine, and matrine were generally the most abundant alkaloids across all species with seeds containing the highest concentrations of alkaloids. However, there was no clear taxonomic grouping based on alkaloid composition. A quantitative analysis of various parts of two Sophora microphylla trees showed that the seeds were the richest source of alkaloids (total 0.4-0.5% DM), followed by leaf and twig (0.1-0.3%) and then bark (0.04-0.06%), with only low amounts (<0.02%) found in the roots. This study represents the most comprehensive phytochemical investigation of New Zealand Sophora species to date and presents data for three species of Sophora for which no prior chemistry has been reported.

Green Synthesis of Silver and Titanium Dioxide Nanoparticles Using Euphorbia prostrata Extract Shows Shift from Apoptosis to G0/G1 Arrest followed by Necrotic Cell Death in Leishmania donovani

The aim of the present study was to synthesize silver (Ag) and titanium dioxide (TiO2) nanoparticles (NPs) using green synthesis from aqueous leaf extract of Euphorbia prostrata as antileishmanial agents and to explore the underlying molecular mechanism of induced cell death. In vitro antileishmanial activity of synthesized NPs was tested against promastigotes of Leishmania donovani by alamarBlue and propidium iodide uptake assays. Antileishmanial activity of synthesized NPs on intracellular amastigotes was assessed by Giemsa staining. The leishmanicidal effect of synthesized Ag NPs was further confirmed by DNA fragmentation assay and by cell cycle progression and transmission electron microscopy (TEM) of the treated parasites. TEM analysis of the synthesized Ag NPs showed a spherical shape with an average size of 12.82 ± 2.50 nm, and in comparison to synthesized TiO2 NPs, synthesized Ag NPs were found to be most active against Leishmania parasites after 24 h exposure, with 50% inhibitory concentrations (IC50) of 14.94 μg/ml and 3.89 μg/ml in promastigotes and intracellular amastigotes, respectively. A significant increase in G0/G1 phase of the cell cycle with a subsequent decrease in S (synthesis) and G2/M phases compared to controls was observed. The growth-inhibitory effect of synthesized Ag NPs was attributed to increased length of S phase. A decreased reactive oxygen species level was also observed, which could be responsible for the caspase-independent shift from apoptosis (G0/G1 arrest) to massive necrosis. High-molecular-weight DNA fragmentation as a positive consequence of necrotic cell death was also visualized. We also report that the unique trypanothione/trypanothione reductase (TR) system of Leishmania cells was significantly inhibited by synthesized Ag NPs. The green-synthesized Ag NPs may provide promising leads for the development of cost-effective and safer alternative treatment against visceral leishmaniasis.

Phenotypic diversity of diploid and haploid Emiliania huxleyi cells and of cells in different growth phases revealed by comparative metabolomics

In phytoplankton a high species diversity of microalgae co-exists at a given time. But diversity is not only reflected by the species composition. Within these species different life phases as well as different metabolic states can cause additional diversity. One important example is the coccolithophore Emiliania huxleyi. Diploid cells play an important role in marine ecosystems since they can form massively abundant algal blooms but in addition the less abundant haploid life phase of E. huxleyi occurs in lower quantities. Both life phases may fulfill different functions in the plankton. We hypothesize that in addition to the functional diversity caused by this life phase transition the growth stage of cells can also influence the metabolic composition and thus the ecological impact of E. huxleyi. Here we introduce a metabolomic survey in dependence of life phases as well as different growth phases to reveal such changes. The comparative metabolomic approach is based on the extraction of intracellular metabolites from intact microalgae, derivatization and analysis by gas chromatography coupled to mass spectrometry (GC-MS). Automated data processing and statistical analysis using canonical analysis of principal coordinates (CAP) revealed unique metabolic profiles for each life phase. Concerning the correlations of metabolites to growth phases, complex patterns were observed. As for example the saccharide mannitol showed its highest concentration in the exponential phase, whereas fatty acids were correlated to stationary and sterols to declining phase. These results are indicative for specific ecological roles of these stages of E. huxleyi and are discussed in the context of previous physiological and ecological studies.

Unexpected peaks in tandem mass spectra due to reaction of product ions with residual water in mass spectrometer collision cells

RATIONALE

Certain product ions in electrospray ionization tandem mass spectrometry are found to react with residual water in the collision cell. This reaction often leads to the formation of ions that cannot be formed directly from the precursor ions, and this complicates the mass spectra and may distort MRM (multiple reaction monitoring) results.

METHODS

Various drugs, pesticides, metabolites, and other compounds were dissolved in acetonitrile/water/formic acid and studied by electrospray ionization mass spectrometry to record their MS(2) and MS(n) spectra in several mass spectrometers (QqQ, QTOF, IT, and Orbitrap HCD). Certain product ions were found to react with residual water in collision cells. The reaction was confirmed by MS(n) studies and the rate of reaction was determined in the IT instrument using zero collision energy and variable activation times.

RESULTS

Examples of product ions reacting with water include phenyl and certain substituted phenyl cations, benzoyl-type cations formed from protonated folic acid and similar compounds by loss of the glutamate moiety, product ions formed from protonated cyclic siloxanes by loss of methane, product ions formed from organic phosphates, and certain negative ions. The reactions of product ions with residual water varied greatly in their rate constant and in the extent of reaction (due to isomerization).

CONCLUSIONS

Various types of product ions react with residual water in mass spectrometer collision cells. As a result, tandem mass spectra may contain unexplained peaks and MRM results may be distorted by the occurrence of such reactions. These often unavoidable reactions must be taken into account when annotating peaks in tandem mass spectra and when interpreting MRM results. Published in 2014. This article is a U.S. Government work and is in the public domain in the USA.