Mining molecular structure databases: Identification of small molecules based on fragmentation mass spectrometry data

Rapid screening and identification of targeted and non-targeted illegal added drugs in functional foods by MRSIT-HRMS based on NIST screening database

The last few decades have witnessed the increasing consumption of functional foods, leading to the expansion of the worldwide market. However, the illegal addition drugs in functional foods remains incessant despite repeated prohibition, making it a key focus of strict crackdowns by regulatory authorities. Effective analytical tools and procedures are desperately needed to rapidly screen and identify illegally added drugs in a large number of samples, given the growing amount and diversity of these substances in functional foods. The MRSIT-HRMS (Multiple Sample Rapid Introduction combined with High Resolution Mass Spectrometry) without chromatographic separation, after direct sampling, utilizes NIST software (National Institute of Standards and Technology) matching with a home-built library to target identification and non-targeted screen of illegal additives. When applied to 50 batches of suspicious samples, the targeted method detected illegal added drugs in 41 batches of samples, while the non-targeted method screened a new phosphodiesterase-5 (PDE-5) inhibitor type structural derivative. The positive results obtained by the targeted method were consistent with LC-MS/MS (QQQ). The novel MRSIT-HRMS with a limit of quantification (LOD) of 1 μg/mL achieved 100 % correct identification for all 50 batches of actual samples, demonstrating its potential as a highly promising and powerful tool for fast screening of illegally added drugs in functional food, especially when compared to traditional LC-MS/MS methods. This is essential for ensuring drug safety and public health.

Chemometric-based drug discovery approaches from natural origins using hyphenated chromatographic techniques

INTRODUCTION

Isolation and characterization of bioactive components from complex matrices of marine or terrestrial biological origins are the most challenging issues for natural product chemists. Biochemometric is a new potential scope in natural product analytical science, and it is a methodology to find the compound's correlation to their bioactivity with the help of hyphenated chromatographic techniques and chemometric tools.

OBJECTIVES

The present review aims to evaluate the application of chemometric tools coupled to chromatographic techniques for drug discovery from natural resources.

METHODS

The searching keywords "biochemometric," "chemometric," "chromatography," "natural products bioassay," and "bioassay" were selected to search the published articles between 2010-2023 using different search engines including "Pubmed", "Web of Science," "ScienceDirect," and "Google scholar."

RESULTS

An initial stage in natural product analysis is applying the chromatographic hyphenated techniques in conjunction with biochemometric approaches. Among the applied chromatographic techniques, liquid chromatography (LC) techniques, have taken up more than half (53%) and also, mass spectroscopy (MS)-based chromatographic techniques such as LC-MS are the most widely used techniques applied in combination with chemometric methods for natural products bioassay. Considering the complexity of dataset achieved from chromatographic hyphenated techniques, chemometric tools have been increasingly employed for phytochemical studies in the context of determining botanicals geographical origin, quality control, and detection of bioactive compounds.

CONCLUSION

Biochemometric application is expected to be further improved with advancing in data acquisition methods, new efficient preprocessing, model validation and variable selection methods which would guarantee that the applied model to have good prediction ability in compound relation to its bioactivity.

Advancing PROTAC Characterization: Structural Insights through Adducts and Multimodal Tandem-MS Strategies

Proteolysis targeting chimeras (PROTACs) are specialized molecules that bind to a target protein and a ubiquitin ligase to facilitate protein degradation. Despite their significance, native PROTACs have not undergone tandem mass spectrometry (MS) analysis. To address this gap, we conducted a pioneering investigation on the fragmentation patterns of two PROTACs in development, dBET1 and VZ185. Employing diverse cations (sodium, lithium, and silver) and multiple tandem-MS techniques, we enhanced their structural characterization. Notably, lithium cations facilitated comprehensive positive-mode coverage for dBET1, while negative polarity mode offered richer insights. Employing de novo structure determination on 2DMS data from degradation studies yielded crucial insights. In the case of VZ185, various charge states were observed, with [M + 2H]2+ revealing fewer moieties than [M + H]+ due to charge-related factors. Augmenting structural details through silver adducts suggested both charge-directed and charge-remote fragmentation. This comprehensive investigation identifies frequently dissociated bonds across multiple fragmentation techniques, pinpointing optimal approaches for elucidating PROTAC structures. The findings contribute to advancing our understanding of PROTACs, pivotal for their continued development as promising therapeutic agents.

Differentiation of free d-amino acids and amino acid isomers in solution using tandem mass spectrometry of hydrogen-bonded clusters

Free d-amino acids and amino acid isomers were differentiated using tandem mass spectrometry without chromatographic separation. Ultraviolet photodissociation and water adsorption of leucine (Leu) and isoleucine (Ile) enantiomers hydrogen-bonded with tryptophan (Trp) were investigated at 8 K in the gas phase. The enantiomer-selective Cα-Cβ bond cleavage of Trp was observed in the product ion spectra obtained by 285 nm photoexcitation, where the abundance of NH2CHCOOH-eliminated ion of heterochiral H+(d-Trp)(l-Leu) was higher than that of homochiral H+(l-Trp)(l-Leu). When comparing water adsorption on the surfaces of the heterochiral and homochiral clusters in a cold ion trap, the number of water molecules adsorbed on the heterochiral cluster was greater than that adsorbed on the homochiral cluster. These results indicate that the stronger intermolecular interactions within the homochiral H+(l-Trp)(l-Leu) compared to the heterochiral cluster inhibit enantiomer-selective photodissociation. Leu and Ile were differentiated by the isomer-selective Cα-Cβ bond cleavage of Trp in the clusters. Calibration curves for the differentiation of isomeric amino acids and their enantiomers were developed using monitoring isomer- and enantiomer-selective photodissociation, indicating that the molar fractions in solution could be determined from a single product ion spectrum.

Cumulative Neutral Loss Model for Fragment Deconvolution in Electrospray Ionization High-Resolution Mass Spectrometry Data

Clean high-resolution mass spectra (HRMS) are essential to a successful structural elucidation of an unknown feature during nontarget analysis (NTA) workflows. This is a crucial step, particularly for the spectra generated during data-independent acquisition or during direct infusion experiments. The most commonly available tools only take advantage of the time domain for spectral cleanup. Here, we present an algorithm that combines the time domain and mass domain information to perform spectral deconvolution. The algorithm employs a probability-based cumulative neutral loss (CNL) model for fragment deconvolution. The optimized model, with a mass tolerance of 0.005 Da and a scoreCNL threshold of 0.00, was able to achieve a true positive rate (TPr) of 95.0%, a false discovery rate (FDr) of 20.6%, and a reduction rate of 35.4%. Additionally, the CNL model was extensively tested on real samples containing predominantly pesticides at different concentration levels and with matrix effects. Overall, the model was able to obtain a TPr above 88.8% with FD rates between 33 and 79% and reduction rates between 9 and 45%. Finally, the CNL model was compared with the retention time difference method and peak shape correlation analysis, showing that a combination of correlation analysis and the CNL model was the most effective for fragment deconvolution, obtaining a TPr of 84.7%, an FDr of 54.4%, and a reduction rate of 51.0%.

Study on 2-arylbenzo[b]furans from Itea omeiensis and their fragmentation patterns with Q-Orbitrap mass spectrometry

One new 2-arylbenzo[b]furan named iteafuranal F (1) as well as two known analogues (2-3) were isolated from the 95% EtOH extract of aerial parts of Itea omeiensis. Their chemical structures were constructed based on extensive analyses of UV, IR, 1D/2D NMR and HRMS spectra. Antioxidant assays revealed significant superoxide anion radical scavenging capacity of 1 with IC₅₀ value of 0.66 mg/mL, which was comparable to the efficiency of positive control of luteolin. In addition, the preliminary MS fragmentation patterns in negative ion mode were established to distinguish 2-arylbenzo[b]furans with C-10 in different oxidation states: the characteristic loss of CO molecule [M-H-28]^- was observed for 3-formyl-2-arylbenzo[b]furans, and the loss of CH₂O fragment [M-H-30]^- for 3-hydroxymethyl-2-arylbenzo[b]furans, and the loss of CO₂ fragment [M-H-44]^- for 2-arylbenzo[b]furan-3-carboxylic acids.

MAD HATTER Correctly Annotates 98% of Small Molecule Tandem Mass Spectra Searching in PubChem

Metabolites provide a direct functional signature of cellular state. Untargeted metabolomics usually relies on mass spectrometry, a technology capable of detecting thousands of compounds in a biological sample. Metabolite annotation is executed using tandem mass spectrometry. Spectral library search is far from comprehensive, and numerous compounds remain unannotated. So-called in silico methods allow us to overcome the restrictions of spectral libraries, by searching in much larger molecular structure databases. Yet, after more than a decade of method development, in silico methods still do not reach the correct annotation rates that users would wish for. Here, we present a novel computational method called Mad Hatter for this task. Mad Hatter combines CSI:FingerID results with information from the searched structure database via a metascore. Compound information includes the melting point, and the number of words in the compound description starting with the letter ‘u’. We then show that Mad Hatter reaches a stunning 97.6% correct annotations when searching PubChem, one of the largest and most comprehensive molecular structure databases. Unfortunately, Mad Hatter is not a real method. Rather, we developed Mad Hatter solely for the purpose of demonstrating common issues in computational method development and evaluation. We explain what evaluation glitches were necessary for Mad Hatter to reach this annotation level, what is wrong with similar metascores in general, and why metascores may screw up not only method evaluations but also the analysis of biological experiments. This paper may serve as an example of problems in the development and evaluation of machine learning models for metabolite annotation.

Targeted Small-Molecule Identification Using Heartcutting Liquid Chromatography-Infrared Ion Spectroscopy

Infrared ion spectroscopy (IRIS) can be used to identify molecular structures detected in mass spectrometry (MS) experiments and has potential applications in a wide range of analytical fields. However, MS-based approaches are often combined with orthogonal separation techniques, in many cases liquid chromatography (LC). The direct coupling of LC and IRIS is challenging due to the mismatching timescales of the two technologies: an IRIS experiment typically takes several minutes, whereas an LC fraction typically elutes in several seconds. To resolve this discrepancy, we present a heartcutting LC-IRIS approach using a setup consisting of two switching valves and two sample loops as an alternative to direct online LC-IRIS coupling. We show that this automated setup enables us to record multiple IR spectra for two LC-features from a single injection without degrading the LC-separation performance. We demonstrate the setup for application in drug metabolism research by recording six m/z-selective IR spectra for two drug metabolites from a single 2 μL sample of cell incubation extract. Additionally, we measure the IR spectra of two closely eluting diastereomeric biomarkers for the inborn error of metabolism pyridoxine-dependent epilepsy (PDE-ALDH7A1), which shows that the heartcutting LC-IRIS setup has good sensitivity (requiring ∼μL injections of ∼μM samples) and that the separation between closely eluting isomers is maintained. We envision applications in a range of research fields, where the identification of molecular structures detected by LC-MS is required.

Electroactive Polymer-Based Spray Ionization for Direct Mass Spectrometric Analysis

Electrochemically deposited electroactive polymer (EAP) films were investigated for their potential to enhance the performance of ambient ionization mass spectrometry (MS). Several EAPs of varying hydrophobicity were evaluated, including the superhydrophobic polymer poly[3,4-(2-dodecylethylenedioxy)thiophene] (PEDOT-C₁₂). The EAPs were electropolymerized onto indium tin oxide-coated glass, placed in front of the inlet of a mass spectrometer, and charged to 3.5-4.5 kV. Analyte solutions were then applied to the surface, initiating ionization events. Analytes including peptides and small molecule pharmaceuticals were studied in 0.1% formic acid in methanol/water ("spray solvent") as well as in synthetic biological fluid matrices, using both EAP spray ionization (EAPSI) and paper spray ionization (PSI). Each EAPSI analysis required as little as 0.1 μL of solution, and the resulting sprays were stable and reproducible. The sensitivity, limit of detection (LOD), and limit of quantification (LOQ) were evaluated using bradykinin, cannabinol, and cannabidiol, which were prepared in pure solvents, artificial urine, and artificial saliva. The limits of detection and quantitation for EAPSI were improved relative to PSI by 1-2 orders of magnitude for analytes prepared in methanol/water and on the same order of magnitude as PSI for analytes prepared in artificial saliva and urine. This EAP-based spray ionization technique offers possibilities for rapid MS analysis with small sample sizes, high accuracy, and miniaturization of MS instruments.

Multi-Task Neural Networks and Molecular Fingerprints to Enhance Compound Identification from LC-MS/MS Data

Mass spectrometry (MS) is widely used for the identification of chemical compounds by matching the experimentally acquired mass spectrum against a database of reference spectra. However, this approach suffers from a limited coverage of the existing databases causing a failure in the identification of a compound not present in the database. Among the computational approaches for mining metabolite structures based on MS data, one option is to predict molecular fingerprints from the mass spectra by means of chemometric strategies and then use them to screen compound libraries. This can be carried out by calibrating multi-task artificial neural networks from large datasets of mass spectra, used as inputs, and molecular fingerprints as outputs. In this study, we prepared a large LC-MS/MS dataset from an on-line open repository. These data were used to train and evaluate deep-learning-based approaches to predict molecular fingerprints and retrieve the structure of unknown compounds from their LC-MS/MS spectra. Effects of data sparseness and the impact of different strategies of data curing and dimensionality reduction on the output accuracy have been evaluated. Moreover, extensive diagnostics have been carried out to evaluate modelling advantages and drawbacks as a function of the explored chemical space.

Sodium adduct formation with graph-based machine learning can aid structural elucidation in non-targeted LC/ESI/HRMS

Non-targeted screening with LC/ESI/HRMS aims to identify the structure of the detected compounds using their retention time, exact mass, and fragmentation pattern. Challenges remain in differentiating between isomeric compounds. One untapped possibility to facilitate identification of isomers relies on different ionic species formed in electrospray. In positive ESI mode, both protonated molecules and adducts can be formed; however, not all isomeric structures form the same ionic species. The complicated mechanism of adduct formation has hindered the use of this molecular characteristic in the structural elucidation in non-targeted screening. Here, we have studied the adduct formation for 94 small molecules with ion mobility spectra and compared collision cross-sections of the respective ions. Based on the results we developed a fast support vector machine classifier with polynomial kernels for accurately predicting the sodium adduct formation in ESI/HRMS. The model is trained on five independent data sets from different laboratories and uses the graph-based connectivity of functional groups and PubChem fingerprints to predict the sodium adduct formation in ESI/HRMS. The validation of the model showed an accuracy of 74.7% (balanced accuracy 70.0%) on a dataset from an independent laboratory, which was not used in the training of the model. Lastly, we applied the classification algorithm to the SusDat database by NORMAN network to evaluate the proportion of isomeric compounds that could be distinguished based on predicted sodium adduct formation. It was observed that sodium adduct formation probability can provide additional selectivity for about one quarter of the exact masses and, therefore, shows practical utility for structural assignment in non-targeted screening.

Fragmentation pathways of deprotonated ortho-hydroxybenzyl alcohol

The ortho, meta, and para isomers of hydroxybenzyl alcohol can be unequivocally distinguished by the collision-induced dissociation mass spectra of their anions. The presence of a prominent peak at m/z 121 for an elimination of a dihydrogen molecule renders the ortho-isomer spectrum markedly different from those of its meta and para congeners. Investigations carried out with deuterium-labeled isotopologues of the ortho isomer verified that the labile hydrogen atom on the hydroxyl group and one of the benzylic hydrogen atoms are specifically removed in the formation of the m/z 121 ion. The ortho-isomer spectrum also showed a prominent peak at m/z 93. Experimental data indicated that the m/z 93 product ion originates either from a two-step H2 and CO elimination mechanism or from a direct loss of a HCHO molecule from the precursor anion. The intensity ratio of the m/z 93 and 94 peaks in the spectrum recorded from the m/z 124 ion generated from a sample of o-hydroxybenzyl alcohol dissolved in D2 O supported the notion that the direct HCHO loss is the more dominant pathway for the generation of the phenolate ion under low activation conditions. In contrast, the two-step mechanism becomes the more dominant pathway under high collisional activation conditions. The spectrum also showed a weak peak at m/z 105 for a water loss. Based on computational data, the m/z 105 ion generated in this way appears to be a composite generated from a common ion-neutral complex intermediate in which a hydroxyl anion is positioned equidistantly between one of the benzylic hydrogens and a nearby hydrogen atom of the benzene ring. Upon activation, the complex dissociates to form either a phenide or a quinone methide anion. The reaction forming a carbon dioxide adduct under ion-mobility conditions was used to support the proposed water-loss mechanism.

Profiling Phenolic Composition in Pomegranate Peel From Nine Selected Cultivars Using UHPLC-QTOF-MS and UPLC-QQQ-MS

Pomegranate is widely cultivated across China, and the phenolics in its peel are principal components associated with health benefits. Ultra-high performance liquid chromatography coupled to a quadrupole time-of-flight mass spectrometer (UHPLC-QTOF-MS) and ultra-performance liquid chromatography coupled to a triple quadrupole mass spectrometer (UPLC-QQQ-MS) were used in this study, aiming at profiling the total phenolic composition in pomegranate peel from nine selected cultivars in 7 production areas. Sixty-four phenolic compounds were identified or annotated, and 23 of them were firstly reported in pomegranate peel. Principal component analysis (PCA) plots show differences and similarities of phenolics among nine cultivars. Furthermore, 15 phenolic compounds were quantified with the standards, and punicalagin, ellagic acid, gallocatechin, punicalin, catechin, and corilagin were found to be dominant. Punicalagin weighed the highest content (28.03–104.14 mg/g). This study can provide a deeper and more detailed insight into the phenolic composition in pomegranate peel and facilitate the health-promoting utilization of phenolics.

Strategies for structure elucidation of small molecules based on LC–MS/MS data from complex biological samples

LC–MS/MS is a major analytical platform for metabolomics, which has become a recent hotspot in the research fields of life and environmental sciences. By contrast, structure elucidation of small molecules based on LC–MS/MS data remains a major challenge in the chemical and biological interpretation of untargeted metabolomics datasets. In recent years, several strategies for structure elucidation using LC–MS/MS data from complex biological samples have been proposed, these strategies can be simply categorized into two types, one based on structure annotation of mass spectra and for the other on retention time prediction. These strategies have helped many scientists conduct research in metabolite-related fields and are indispensable for the development of future tools. Here, we summarized the characteristics of the current tools and strategies for structure elucidation of small molecules based on LC–MS/MS data, and further discussed the directions and perspectives to improve the power of the tools or strategies for structure elucidation.

Metabolite Identification Using Infrared Ion Spectroscopy─Novel Biomarkers for Pyridoxine-Dependent Epilepsy

Untargeted liquid chromatography-mass spectrometry (LC-MS)-based metabolomics strategies are being increasingly applied in metabolite screening for a wide variety of medical conditions. The long-standing "grand challenge" in the utilization of this approach is metabolite identification─confidently determining the chemical structures of m/z-detected unknowns. Here, we use a novel workflow based on the detection of molecular features of interest by high-throughput untargeted LC-MS analysis of patient body fluids combined with targeted molecular identification of those features using infrared ion spectroscopy (IRIS), effectively providing diagnostic IR fingerprints for mass-isolated targets. A significant advantage of this approach is that in silico-predicted IR spectra of candidate chemical structures can be used to suggest the molecular structure of unknown features, thus mitigating the need for the synthesis of a broad range of physical reference standards. Pyridoxine-dependent epilepsy (PDE-ALDH7A1) is an inborn error of lysine metabolism, resulting from a mutation in the ALDH7A1 gene that leads to an accumulation of toxic levels of α-aminoadipic semialdehyde (α-AASA), piperideine-6-carboxylate (P6C), and pipecolic acid in body fluids. While α-AASA and P6C are known biomarkers for PDE in urine, their instability makes them poor candidates for diagnostic analysis from blood, which would be required for application in newborn screening protocols. Here, we use combined untargeted metabolomics-IRIS to identify several new biomarkers for PDE-ALDH7A1 that can be used for diagnostic analysis in urine, plasma, and cerebrospinal fluids and that are compatible with analysis in dried blood spots for newborn screening. The identification of these novel metabolites has directly provided novel insights into the pathophysiology of PDE-ALDH7A1.

Evaluation of Sample Preparation Methods for Non-Target Screening of Organic Micropollutants in Urban Waters Using High-Resolution Mass Spectrometry

Non-target screening (NTS) has gained interest in recent years for environmental monitoring purposes because it enables the analysis of a large number of pollutants without predefined lists of molecules. However, sample preparation methods are diverse, and few have been systematically compared in terms of the amount and relevance of the information obtained by subsequent NTS analysis. The goal of this work was to compare a large number of sample extraction methods for the unknown screening of urban waters. Various phases were tested for the solid-phase extraction of micropollutants from these waters. The evaluation of the different phases was assessed by statistical analysis based on the number of detected molecules, their range, and physicochemical properties (molecular weight, standard recoveries, polarity, and optical properties). Though each cartridge provided its own advantages, a multilayer cartridge combining several phases gathered more information in one single extraction by benefiting from the specificity of each one of its layers.

Normal-phase chromatographic separation of pigmented wine tannin by nano-HPLC quadrupole time-of-flight tandem mass spectrometry and identification of candidate molecular features

BACKGROUND

As a wine ages, altered sensory properties lead to changes in perceived quality and value. Concurrent modifications of anthocyanin and tannin occur forming pigmented tannin, softening astringency and retaining persistent color. Wine tannin extracts of 1990 and 2010 vintages of Oakville Station Cabernet Sauvignon have been analyzed using normal-phase chromatography with tandem quadrupole time-of-flight mass spectrometry (QToF) to investigate the compositional differences in their pigmented tannin fractions.

RESULTS

The older wine demonstrates much greater structural diversity and a range of more polar compounds, while the younger wine contains fewer observed ion peaks. Several hundred molecular features are observable, and, as expected, there is progression to higher molecular weights after long aging. Between 7% and 16% of molecular features could be matched to a database of anticipated pigmented tannin compounds. Many signals had multiple possible isomeric identities, but fragmentation to resolve their identity was stymied by low sensitivity of the tandem mass spectrometric capability provided by QToF, so isomeric disambiguation is incomplete.

CONCLUSIONS

The chromatography displayed a high degree of resolution in aged wines, separating many of the known pigment types, including aldehyde bridged compounds, pyranoanthocyanins and direct condensation products among others, as well as resolving a great number of unknown compounds. Expanding our understanding of red wine pigments will lead to better wines as winemakers will be able to associate quality with particular wine pigment profiles once we can distinguish the relevant patterns in those pigments. © 2021 Society of Chemical Industry.

Ion mobility-high resolution mass spectrometry in doping control analysis. Part II: Comparison of acquisition modes with and without ion mobility

In the second part of this study, a systematic comparison was made between two ion fragmentation acquisition modes, namely data-independent acquisition (DIA) and DIA with ion mobility spectrometry (IMS) technology. These two approaches were applied to the analysis of 192 doping agents in urine. Group I included 102 compounds such as stimulants, diuretics, narcotics, and β2-agonists, while Group II contained 90 compounds included steroids, glucocorticoids, and hormone and metabolic modulators. Important method parameters were examined and compared, including the fragmentation, sensitivity, and assignment capability with the minimum occurrence of false positive hits. The results differed between Group I and II in number of detected fragments when exploring the MS/MS spectra. In Group I only 13%, while in the Group II 64% of the substances had a higher number of fragments in DIA-IMS mode vs. DIA. In terms of sensitivity, the performance of the two modes with and without activated IMS dimension was identical for about 50% of the doping agents. The sensitivity was higher without IMS, i.e. in simple DIA mode, for 20-40% of remaining doping agents. Despite this sensitivity reduction with IMS, 82% of compounds from both Groups met the minimum required performance level (MRPL) criteria of the World Anti-Doping Agency (WADA) when the DIA-IMS mode was applied. Automated data processing is important in routine doping analysis. Therefore, processing methods were optimized and evaluated for the prevalence of false peak assignments by analysing the target substances at different concentrations in urine samples. Overall, a significantly higher number of misidentified compounds was observed in Group II, with an almost 2-fold higher number of misidentifications in DIA compared to DIA-IMS. This result highlights the benefit of the IMS dimension to reduce the rate of false positive in screening analysis. The optimized UHPLC-IM-HRMS method was finally applied to the analysis of urine samples from administration studies including nine doping agents from both Groups. However, to limit the number of interferences from the biological matrix, an emphasis is needed on the adequate settings of the data processing method.

Systematic, Modifying Group-Assisted Strategy Expanding Coverage of Metabolite Annotation in Liquid Chromatography-Mass Spectrometry-Based Nontargeted Metabolomics Studies

From microbes to human beings, nontargeted metabolic profiling by liquid chromatography (LC)-mass spectrometry (MS) has been commonly used to investigate metabolic alterations. Still, a major challenge is the annotation of metabolites from thousands of detected features. The aim of our research was to go beyond coverage of metabolite annotation in common nontargeted metabolomics studies by an integrated multistep strategy applying data-dependent acquisition (DDA)-based ultrahigh-performance liquid chromatography (UHPLC)-high-resolution mass spectrometry (HRMS) analysis followed by comprehensive neutral loss matches for characteristic metabolite modifications and database searches in a successive manner. Using pooled human urine as a model sample for method establishment, we found 22% of the detected compounds having modifying structures. Major types of metabolite modifications in urine were glucuronidation (33%), sulfation (20%), and acetylation (6%). Among the 383 annotated metabolites, 100 were confirmed by standard compounds and 50 modified metabolites not present in common databases such as human metabolite database (HMDB) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were structurally elucidated. Practicability was tested by the investigation of urines from pregnant women diagnosed with gestational diabetes mellitus vs healthy controls. Overall, 83 differential metabolites were annotated and 67% of them were modified metabolites including five previously unreported compounds. To conclude, the systematic modifying group-assisted strategy can be taken as a useful tool to extend the number of annotated metabolites in biological and biomedical nontargeted studies.

Enabling LIFDI-MS measurements of highly air sensitive organometallic compounds: a combined MS/glovebox technique

A new setup combining a ThermoFisher Exactive Plus Orbitrap Mass Spectrometer with a liquid injection field desorption ionization (LIFDI) source directly connected to an inert atmosphere glovebox is presented. The described setup allows for the analysis of very air- and moisture sensitive samples. Furthermore, the soft nature of LIFDI ionization gives access to the molecular ions of fragile molecules. This new setup is therefore especially useful for sensitive organometallic complexes. The functionality of the new setup is tested against [(Cp)2TiCl]˙, which is known for its notorious sensitivity to air and moisture. Its drastic colour change from green to orange upon exposure to air further supports the easy detection of traces of oxygen during the experiment. In addition, we applied this setup to the mass spectrometric analysis of the qualitative composition of a Cu/Al cluster mixture, which is not accessible by other analytical methods.

Gas-Phase Adsorption of N2 on Protonated Molecules and Its Application to the Structural Elucidation of Small Molecules

The gas-phase adsorption of N₂ on protonated serine (Ser, C₃H₇NO₃), threonine (Thr, C₄H₉NO₃), glycine (Gly, C₂H₅NO₂), and 2-aminoethanol (C₂H₇NO) was investigated using a tandem mass spectrometer equipped with an electrospray ionization source and a cold ion trap. N₂ molecules were adsorbed on the free X–H (X=O and N) groups of protonated molecules. Gas-phase N₂ adsorption-mass spectrometry detected the presence of free X–H groups in the molecular structures, and was applied to the structural elucidation of small molecules. When the 93 structures with an elemental composition of C₃H₇NO₃ were filtered using the gas-phase N₂ adsorption-mass spectrometry results for Ser, the number of possible molecular structures was reduced to 8 via the quantification of the X–H groups. Restricting and minimizing the number of possible candidates were effective steps in the structural elucidation process. Gas-phase N₂ adsorption-mass spectrometry combined with mass spectrometry-based techniques has the potential for being useful for elucidating the molecular structures of a variety of molecules.

Mass spectrometry-based identification of ortho-, meta- and para-isomers using infrared ion spectroscopy

Distinguishing positional isomers, such as compounds having different substitution patterns on an aromatic ring, presents a significant challenge for mass spectrometric analyses and is a frequently encountered difficulty in, for example, drug metabolism research. In contrast to mass spectrometry, IR spectroscopy is a well-known and powerful tool in the distinction of ortho-, meta- and para-isomers, but is not applicable to low-abundance compounds in complex mixtures such as often targeted in bioanalytical studies. Here, we demonstrate the use of infrared ion spectroscopy (IRIS) as a novel method that facilitates the differentiation between positional isomers of disubstituted phenyl-containing compounds and that can be applied in mass spectrometry-based complex mixture analysis. By analyzing different substitution patterns over several sets of isomeric compounds, we show that IRIS is able to consistently probe the diagnostic CH out-of-plane vibrations that are sensitive to positional isomerism. We show that these modes are largely independent of the chemical functionality contained in the ring substituents and of the type of ionization. We also show that IRIS spectra often identify the positional isomer directly, even in the absence of reference spectra obtained from physical standards or from computational prediction. We foresee that this method will be generally applicable to the identification of disubstituted phenyl-containing compounds.

Current and future deep learning algorithms for tandem mass spectrometry (MS/MS)-based small molecule structure elucidation

RATIONALE

Structure elucidation of small molecules has been one of the cornerstone applications of mass spectrometry for decades. Despite the increasing availability of software tools, structure elucidation from tandem mass spectrometry (MS/MS) data remains a challenging task, leaving many spectra unidentified. However, as an increasing number of reference MS/MS spectra are being curated at a repository scale and shared on public servers, there is an exciting opportunity to develop powerful new deep learning (DL) models for automated structure elucidation.

ARCHITECTURES

Recent early-stage DL frameworks mostly follow a "two-step approach" that translates MS/MS spectra to database structures after first predicting molecular descriptors. The related architectures could suffer from: (1) computational complexity because of the separate training of descriptor-specific classifiers, (2) the high dimensional nature of mass spectral data and information loss due to data preprocessing, (3) low substructure coverage and class imbalance problem of predefined molecular fingerprints. Inspired by successful DL frameworks employed in drug discovery fields, we have conceptualized and designed hypothetical DL architectures to tackle the above issues. For (1), we recommend multitask learning to achieve better performance with fewer classifiers by grouping structurally related descriptors. For (2) and (3), we introduce feature engineering to extract condensed and higher-order information from spectra and structure data. For instance, encoding spectra with subtrees and pre-calculated spectral patterns add peak interactions to the model input. Encoding structures with graph convolutional networks incorporates connectivity within a molecule. The joint embedding of spectra and structures can enable simultaneous spectral library and molecular database search.

CONCLUSIONS

In principle, given enough training data, adapted DL architectures, optimal hyperparameters and computing power, DL frameworks can predict small molecule structures, completely or at least partially, from MS/MS spectra. However, their performance and general applicability should be fairly evaluated against classical machine learning frameworks.

TOMATOMET: A metabolome database consists of 7118 accurate mass values detected in mature fruits of 25 tomato cultivars

The total number of low-molecular-weight compounds in the plant kingdom, most of which are secondary metabolites, is hypothesized to be over one million, although only a limited number of plant compounds have been characterized. Untargeted analysis, especially using mass spectrometry (MS), has been useful for understanding the plant metabolome; however, due to the limited availability of authentic compounds for MS-based identification, the identities of most of the ion peaks detected by MS remain unknown. Accurate mass values of peaks obtained by high accuracy mass measurement and, if available, MS/MS fragmentation patterns provide abundant annotation for each peak. Here, we carried out an untargeted analysis of compounds in the mature fruit of 25 tomato cultivars using liquid chromatography-Orbitrap MS for accurate mass measurement, followed by manual curation to construct the metabolome database TOMATOMET (http://metabolites.in/tomato-fruits/). The database contains 7,118 peaks with accurate mass values, in which 1,577 ion peaks are annotated as members of a chemical group. Remarkably, 71% of the mass values are not found in the accurate masses detected previously in Arabidopsis thaliana, Medicago truncatula or Jatropha curcas, indicating significant chemical diversity among plant species that remains to be solved. Interestingly, substantial chemical diversity exists also among tomato cultivars, indicating that chemical profiling from distinct cultivars contributes towards understanding the metabolome, even in a single organ of a species, and can prioritize some desirable metabolic targets for further applications such as breeding.

A map of mass spectrometry-based in silico fragmentation prediction and compound identification in metabolomics

Metabolomics, the comprehensive study of the metabolome, and lipidomics-the large-scale study of pathways and networks of cellular lipids-are major driving forces in enabling personalized medicine. Complicated and error-prone data analysis still remains a bottleneck, however, especially for identifying novel metabolites. Comparing experimental mass spectra to curated databases containing reference spectra has been the gold standard for identification of compounds, but constructing such databases is a costly and time-demanding task. Many software applications try to circumvent this process by utilizing cutting-edge advances in computational methods-including quantum chemistry and machine learning-and simulate mass spectra by performing theoretical, so called in silico fragmentations of compounds. Other solutions concentrate directly on experimental spectra and try to identify structural properties by investigating reoccurring patterns and the relationships between them. The considerable progress made in the field allows recent approaches to provide valuable clues to expedite annotation of experimental mass spectra. This review sheds light on individual strengths and weaknesses of these tools, and attempts to evaluate them-especially in view of lipidomics, when considering complex mixtures found in biological samples as well as mass spectrometer inter-instrument variability.

Non-targeted screening of trace organic contaminants in surface waters by a multi-tool approach based on combinatorial analysis of tandem mass spectra and open access databases

Non-targeted screening (NTS) in mass spectrometry (MS) helps alleviate the shortcoming of targeted analysis such as missing the presence of concerning compounds that are not monitored and its lack of retrospective analysis to subsequently look for new contaminants. Most NTS workflows include high resolution tandem mass spectrometry (HRMS²) and structure annotation with libraries which are still limited. However, in silico combinatorial fragmentation tools that simulate MS² spectra are available to help close the gap of missing compounds in empirical libraries. Three NTS tools were combined and used to detect and identify unknown contaminants at ultra-trace levels in surface waters in real samples in this qualitative study. Two of them were based on combinatorial fragmentation databases, MetFrag and the Similar Partition Searching algorithm (SPS), and the third, the Global Natural Products Social Networking (GNPS), was an ensemble of empirical databases. The three NTS tools were applied to the analysis of real samples from a local river. A total of 253 contaminants were identified by combining all three tools: 209 were assigned a probable structure and 44 were confirmed using reference standards. The two major classes of contaminants observed were pharmaceuticals and consumer product additives. Among the confirmed compounds, octylphenol ethoxylates, denatonium, irbesartan and telmisartan are reported for the first time in surface waters in Canada. The workflow presented in this work uses three highly complementary NTS tools and it is a powerful approach to help identify and strategically select contaminants and their transformation products for subsequent targeted analysis and uncover new trends in surface water contamination.

Seeing the forest for the trees: Retrieving plant secondary biochemical pathways from metabolome networks

Over the last decade, a giant leap forward has been made in resolving the main bottleneck in metabolomics, i.e., the structural characterization of the many unknowns. This has led to the next challenge in this research field: retrieving biochemical pathway information from the various types of networks that can be constructed from metabolome data. Searching putative biochemical pathways, referred to as biotransformation paths, is complicated because several flaws occur during the construction of metabolome networks. Multiple network analysis tools have been developed to deal with these flaws, while in silico retrosynthesis is appearing as an alternative approach. In this review, the different types of metabolome networks, their flaws, and the various tools to trace these biotransformation paths are discussed.

Ion mobility collision cross-section atlas for known and unknown metabolite annotation in untargeted metabolomics

The metabolome includes not just known but also unknown metabolites; however, metabolite annotation remains the bottleneck in untargeted metabolomics. Ion mobility - mass spectrometry (IM-MS) has emerged as a promising technology by providing multi-dimensional characterizations of metabolites. Here, we curate an ion mobility CCS atlas, namely AllCCS, and develop an integrated strategy for metabolite annotation using known or unknown chemical structures. The AllCCS atlas covers vast chemical structures with >5000 experimental CCS records and ~12 million calculated CCS values for >1.6 million small molecules. We demonstrate the high accuracy and wide applicability of AllCCS with medium relative errors of 0.5-2% for a broad spectrum of small molecules. AllCCS combined with in silico MS/MS spectra facilitates multi-dimensional match and substantially improves the accuracy and coverage of both known and unknown metabolite annotation from biological samples. Together, AllCCS is a versatile resource that enables confident metabolite annotation, revealing comprehensive chemical and metabolic insights towards biological processes.

Nontargeted Screening of Water Samples Using Data-Dependent Acquisition with Similar Partition Searching

A rapid screening method for the detection of nontargeted compounds in surface water samples was developed using MS-MS high-resolution mass spectrometry and data-dependent acquisition. The key parameters for the acquisition method were optimized using five model compounds with diverse chemical characteristics. The parameter selection required optimization between the total number of precursor ions that could be selected in an LC-MS run, the quality of each MS (full range) spectrum, and the quality of each MS-MS fragmentation spectrum. After the acquisition method was optimized, 18 surface water samples from rivers, reservoirs, and effluents from wastewater treatment plants were analyzed, generating 41625 MS-MS spectra in about 14 h. The raw data were then converted into two generic formats using the open-access program MSConvert. A combinatorial approach, similar partition searching (SPS), was then used to putatively identify analytes from the accurate mass of each analyte (adjusted for the adduct mass) and the corresponding MS-MS spectra were obtained. In this approach, the structures of about 250000 common compounds, stored in a large database as mathematical partitions of their exact mass, were compared directly to each MS-MS spectrum. Compounds with a similar mass and retention time were grouped together and labeled as "Analytes", using an Excel Add-In. The isotope ratio data from the MS spectrum, the corresponding MS-MS spectra, and the putative identifications were then imported into an Access relational database to facilitate sorting, searching, filtering, and querying the results. This allowed final inspection to assess confidence of the identifications made through the nontargeted screening.

Formation of Protonated ortho-Quinonimide from ortho-Iodoaniline in the Gas Phase by a Molecular-Oxygen-Mediated, ortho-Isomer-Specific Fragmentation Mechanism

Upon collisional activation under mass spectrometric conditions, protonated 2-, 3-, and 4-iodoanilines lose an iodine radical to generate primarily dehydroanilinium radical cations (m/z 93), which are the distonic counterparts of the conventional molecular ion of aniline. When briefly accumulated in the Trap region of a Triwave cell in a SYNAPT G2 instrument, before being released for ion-mobility separation, these dehydroanilinium cations react readily with traces of oxygen present in the mobility gas to form peroxyl radical cations. Although all three isomeric dehydroanilinium ions showed avid affinity for O₂, their reactivities were distinctly different. For example, the product-ion spectra recorded from mass-selected m/z 93 ion from 3- and 4-iodoanilines showed a peak at m/z 125 for the respective peroxylbenzenaminium ion. In contrast, an analogous peak at m/z 125 was absent in the spectrum of the 2-dehydroanilinium ion generated from 2-iodoaniline. Evidently, the 2-peroxylbenzenaminium ion generated from the 2-dehydroanilinium ion immediately loses a ^•OH to form protonated ortho-quinonimide (m/z 108).

Development of a DNA Adductome Mass Spectral Database

Mass spectrometry-based DNA adductomics is an emerging approach for the human biomonitoring of hazardous chemicals. A mass spectral database of DNA adducts will be created for the scientific community to investigate the associations between chemical exposures, DNA damage, and disease risk.

Spectral deep learning for prediction and prospective validation of functional groups

State-of-the-art identification of the functional groups present in an unknown chemical entity requires the expertise of a skilled spectroscopist to analyse and interpret Fourier transform infra-red (FTIR), mass spectroscopy (MS) and/or nuclear magnetic resonance (NMR) data. This process can be time-consuming and error-prone, especially for complex chemical entities that are poorly characterised in the literature, or inefficient to use with synthetic robots producing molecules at an accelerated rate. Herein, we introduce a fast, multi-label deep neural network for accurately identifying all the functional groups of unknown compounds using a combination of FTIR and MS spectra. We do not use any database, pre-established rules, procedures, or peak-matching methods. Our trained neural network reveals patterns typically used by human chemists to identify standard groups. Finally, we experimentally validated our neural network, trained on single compounds, to predict functional groups in compound mixtures. Our methodology showcases practical utility for future use in autonomous analytical detection.

In silico MS/MS spectra for identifying unknowns: a critical examination using CFM-ID algorithms and ENTACT mixture samples

High-resolution mass spectrometry (HRMS) enables rapid chemical annotation via accurate mass measurements and matching of experimentally derived spectra with reference spectra. Reference libraries are generated from chemical standards and are therefore limited in size relative to known chemical space. To address this limitation, in silico spectra (i.e., MS/MS or MS2 spectra), predicted via Competitive Fragmentation Modeling-ID (CFM-ID) algorithms, were generated for compounds within the U.S. Environmental Protection Agency's (EPA) Distributed Structure-Searchable Toxicity (DSSTox) database (totaling, at the time of analysis, ~ 765,000 substances). Experimental spectra from EPA's Non-Targeted Analysis Collaborative Trial (ENTACT) mixtures (n = 10) were then used to evaluate the performance of the in silico spectra. Overall, MS2 spectra were acquired for 377 unique compounds from the ENTACT mixtures. Approximately 53% of these compounds were correctly identified using a commercial reference library, whereas up to 50% were correctly identified as the top hit using the in silico library. Together, the reference and in silico libraries were able to correctly identify 73% of the 377 ENTACT substances. When using the in silico spectra for candidate filtering, an examination of binary classifiers showed a true positive rate (TPR) of 0.90 associated with false positive rates (FPRs) of 0.10 to 0.85, depending on the sample and method of candidate filtering. Taken together, these findings show the abilities of in silico spectra to correctly identify true positives in complex samples (at rates comparable to those observed with reference spectra), and efficiently filter large numbers of potential false positives from further consideration. Graphical abstract.

A Perspective and Framework for Developing Sample Type Specific Databases for LC/MS-Based Clinical Metabolomics

Metabolomics has the potential to greatly impact biomedical research in areas such as biomarker discovery and understanding molecular mechanisms of disease. However, compound identification (ID) remains a major challenge in liquid chromatography mass spectrometry-based metabolomics. This is partly due to a lack of specificity in metabolomics databases. Though impressive in depth and breadth, the sheer magnitude of currently available databases is in part what makes them ineffective for many metabolomics studies. While still in pilot phases, our experience suggests that custom-built databases, developed using empirical data from specific sample types, can significantly improve confidence in IDs. While the concept of sample type specific databases (STSDBs) and spectral libraries is not entirely new, inclusion of unique descriptors such as detection frequency and quality scores, can be used to increase confidence in results. These features can be used alone to judge the quality of a database entry, or together to provide filtering capabilities. STSDBs rely on and build upon several available tools for compound ID and are therefore compatible with current compound ID strategies. Overall, STSDBs can potentially result in a new paradigm for translational metabolomics, whereby investigators confidently know the identity of compounds following a simple, single STSDB search.

Omics Forecasting: Predictive Calculations Permit the Rapid Interpretation of High-Resolution Mass Spectral Data from Complex Mixtures

For some complex mixtures, chromatographic techniques are insufficient to separate the large numbers of compounds present. In addition, these mixtures often contain compounds with similar or identical molecular masses and shared fragmentation transitions. Advancements in mass spectrometry have provided more and more detailed molecular profiles with significant increases in resolution. This has led to a capacity to distinguish a very large number of compounds in complex mixtures, providing overwhelming data sets. The approach of calculating molecular formulas from a mass list has become more and more problematic as the number of signals has increased exponentially, to the point that it has become impossible to manually interpret the thousands of mass signals. The current approach is to calculate a list of possible formulas that fall within a specific mass error of the observed signal. Then, one must look for possible structures that can be derived from each entry on the list of formulas. However, an alternative approach is to anticipate the possible structures of a particular set of compounds, such as red wine pigments, and then compare the ion signals against a predicted list. To that end, starting with known wine pigment types, we have generated a set of expected wine pigment variants based on known derivatives of condensed tannin oligomers, anthocyanins, and fermentation products. After the ability to distinguish compounds by mass spectrometry was accounted for, over 1 million results were generated consisting of known and anticipated wine pigments. A comparison with a small sample of wine phenolic fractions show a large number of matches, suggesting that this approach may be helpful.

Marine Bacteria from Rocas Atoll as a Rich Source of Pharmacologically Active Compounds

Rocas Atoll is a unique environment in the equatorial Atlantic Ocean, hosting a large number of endemic species, however, studies on the chemical diversity emerging from this biota are rather scarce. Therefore, the present work aims to assess the metabolomic diversity and pharmacological potential of the microbiota from Rocas Atoll. A total of 76 bacteria were isolated and cultured in liquid culture media to obtain crude extracts. About one third (34%) of these extracts were recognized as cytotoxic against human colon adenocarcinoma HCT-116 cell line. 16S rRNA gene sequencing analyses revealed that the bacteria producing cytotoxic extracts were mainly from the Actinobacteria phylum, including Streptomyces, Salinispora, Nocardiopsis, and Brevibacterium genera, and in a smaller proportion from Firmicutes phylum (Bacillus). The search in the spectral library in GNPS (Global Natural Products Social Molecular Networking) unveiled a high chemodiversity being produced by these bacteria, including rifamycins, antimycins, desferrioxamines, ferrioxamines, surfactins, surugamides, staurosporines, and saliniketals, along with several unidentified compounds. Using an original approach, molecular networking successfully highlighted groups of compounds responsible for the cytotoxicity of crude extracts. Application of DEREPLICATOR+ (GNPS) allowed the annotation of macrolide novonestimycin derivatives as the cytotoxic compounds existing in the extracts produced by Streptomyces BRB-298 and BRB-302. Overall, these results highlighted the pharmacological potential of bacteria from this singular atoll.

Cyberecoethnopharmacolomics

ETHNOPHARMACOLOGICAL RELEVANCE

Development of a new term which describes the contemporary, composite, constituent sciences of ethnopharmacology.

AIM OF THE STUDY

To discuss the polysyllabic term cyberecoethnopharmacolomics in the context of the future of ethnopharmacology in global health care.

MATERIALS AND METHODS

Literature background and assessment from the prior literature, diverse databases, and personal discussions.

RESULTS

The profiles and literature background with contemporary and future thoughts regarding the concepts and practices of cyber-, eco-, ethno-, pharmacol-, and -omics, and their impact in ethnopharmacology for the future are presented in the context of integrated health care systems.

CONCLUSIONS

Ethnopharmacology has a major role to play in global health care if the relevant sciences and cutting-edge technologies can coalesce synergistically as a responsive, evidence-based health care practice.

An overview of tools, software, and methods for natural product fragment and mass spectral analysis

One major challenge in natural product (NP) discovery is the determination of the chemical structure of unknown metabolites using automated software tools from either GC–mass spectrometry (MS) or liquid chromatography–MS/MS data only. This chapter reviews the existing spectral libraries and predictive computational tools used in MS-based untargeted metabolomics, which is currently a hot topic in NP structure elucidation. We begin by focusing on spectral databases and the general workflow of MS annotation. We then describe software and tools used in MS, particularly those used to predict fragmentation patterns, mass spectral classifiers, and tools for fragmentation trees analysis. We then round up the chapter by looking at more advanced approaches implemented in tools for competitive fragmentation modeling and quantum chemical approaches.

Ambient Metabolic Profiling and Imaging of Biological Samples with Ultrahigh Molecular Resolution Using Laser Ablation Electrospray Ionization 21 Tesla FTICR Mass Spectrometry

Mass spectrometry (MS) is an indispensable analytical tool to capture the array of metabolites within complex biological systems. However, conventional MS-based metabolomic workflows require extensive sample processing and separation resulting in limited throughput and potential alteration of the native molecular states in these systems. Ambient ionization methods, capable of sampling directly from tissues, circumvent some of these issues but require high-performance MS to resolve the molecular complexity within these samples. Here, we demonstrate a unique combination of laser ablation electrospray ionization (LAESI) coupled with a 21 tesla Fourier transform ion cyclotron resonance (21T-FTICR) for direct MS analysis and imaging applications. This analytical platform provides isotopic fine structure information directly from biological tissues, enabling the rapid assignment of molecular formulas and delivering a higher degree of confidence for molecular identification.

CFM-ID 3.0: Significantly Improved ESI-MS/MS Prediction and Compound Identification

Metabolite identification for untargeted metabolomics is often hampered by the lack of experimentally collected reference spectra from tandem mass spectrometry (MS/MS). To circumvent this problem, Competitive Fragmentation Modeling-ID (CFM-ID) was developed to accurately predict electrospray ionization-MS/MS (ESI-MS/MS) spectra from chemical structures and to aid in compound identification via MS/MS spectral matching. While earlier versions of CFM-ID performed very well, CFM-ID's performance for predicting the MS/MS spectra of certain classes of compounds, including many lipids, was quite poor. Furthermore, CFM-ID's compound identification capabilities were limited because it did not use experimentally available MS/MS spectra nor did it exploit metadata in its spectral matching algorithm. Here, we describe significant improvements to CFM-ID's performance and speed. These include (1) the implementation of a rule-based fragmentation approach for lipid MS/MS spectral prediction, which greatly improves the speed and accuracy of CFM-ID; (2) the inclusion of experimental MS/MS spectra and other metadata to enhance CFM-ID's compound identification abilities; (3) the development of new scoring functions that improves CFM-ID's accuracy by 21.1%; and (4) the implementation of a chemical classification algorithm that correctly classifies unknown chemicals (based on their MS/MS spectra) in >80% of the cases. This improved version called CFM-ID 3.0 is freely available as a web server. Its source code is also accessible online.

Evaluation of an untargeted nano-liquid chromatography-mass spectrometry approach to expand coverage of low molecular weight dissolved organic matter in Arctic soil

Characterizing low molecular weight (LMW) dissolved organic matter (DOM) in soils and evaluating the availability of this labile pool is critical to understanding the underlying mechanisms that control carbon storage or release across terrestrial systems. However, due to wide-ranging physicochemical diversity, characterizing this complex mixture of small molecules and how it varies across space remains an analytical challenge. Here, we evaluate an untargeted approach to detect qualitative and relative-quantitative variations in LMW DOM with depth using water extracts from a soil core from the Alaskan Arctic, a unique system that contains nearly half the Earth's terrestrial carbon and is rapidly warming due to climate change. We combined reversed-phase and hydrophilic interaction liquid chromatography, and nano-electrospray ionization coupled with high-resolution tandem mass spectrometry in positive- and negative-ionization mode. The optimized conditions were sensitive, robust, highly complementary, and enabled detection and putative annotations of a wide range of compounds (e.g. amino acids, plant/microbial metabolites, sugars, lipids, peptides). Furthermore, multivariate statistical analyses revealed subtle but consistent and significant variations with depth. Thus, this platform is useful not only for characterizing LMW DOM, but also for quantifying relative variations in LMW DOM availability across space, revealing hotspots of biogeochemical activity for further evaluation.

Expanding the Use of Spectral Libraries in Proteomics

The 2017 Dagstuhl Seminar on Computational Proteomics provided an opportunity for a broad discussion on the current state and future directions of the generation and use of peptide tandem mass spectrometry spectral libraries. Their use in proteomics is growing slowly, but there are multiple challenges in the field that must be addressed to further increase the adoption of spectral libraries and related techniques. The primary bottlenecks are the paucity of high quality and comprehensive libraries and the general difficulty of adopting spectral library searching into existing workflows. There are several existing spectral library formats, but none captures a satisfactory level of metadata; therefore, a logical next improvement is to design a more advanced, Proteomics Standards Initiative-approved spectral library format that can encode all of the desired metadata. The group discussed a series of metadata requirements organized into three designations of completeness or quality, tentatively dubbed bronze, silver, and gold. The metadata can be organized at four different levels of granularity: at the collection (library) level, at the individual entry (peptide ion) level, at the peak (fragment ion) level, and at the peak annotation level. Strategies for encoding mass modifications in a consistent manner and the requirement for encoding high-quality and commonly seen but as-yet-unidentified spectra were discussed. The group also discussed related topics, including strategies for comparing two spectra, techniques for generating representative spectra for a library, approaches for selection of optimal signature ions for targeted workflows, and issues surrounding the merging of two or more libraries into one. We present here a review of this field and the challenges that the community must address in order to accelerate the adoption of spectral libraries in routine analysis of proteomics datasets.