Mass Spectral Library Matching Research Articles

Metabolomics-based profiling of five Salvia L. (Lamiaceae) species using untargeted data analysis workflow.

The genus Salvia L., a member of the family Lamiaceae, is a keystone genus with a wide range of medicinal properties. It possesses a rich metabolite source that has long been used to treat different disorders. Due to a deficiency of untargeted metabolomic profiling in the genus Salvia, this work attempts to investigate a comprehensive mass spectral library matching, computational data annotations, exclusive biomarkers, specific chemotypes, intraspecific metabolite profile variation, and metabolite enrichment by a case study of five medicinal species of Salvia. Aerial parts of each species were subjected to QTRAP liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis workflow based on untargeted metabolites. A comprehensive and multivariate analysis was acquired on the metabolite dataset utilizing MetaboAnalyst 6.0 and the Global Natural Products Social Molecular Networking (GNPS) Web Platform. The untargeted approach empowered the identification of 117 metabolites by library matching and 92 nodes annotated by automated matching. A machine learning algorithm as substructural topic modeling, MS2LDA, was further implemented to explore the metabolite substructures, resulting in four Mass2Motifs. The automated library newly discovered a total of 23 metabolites. In addition, 87 verified biomarkers of library matching, 58 biomarkers of GNPS annotations, and 11 specific chemotypes were screened. Integrative spectral library matching and automated annotation by the GNPS platform provide comprehensive metabolite profiling through a workflow. In addition, QTRAP LC-MS/MS with multivariate analysis unveiled reliable information about inter and intraspecific levels of differentiation. The rigorous investigation of metabolite profiling presents a large-scale overview and new insights for chemotaxonomy and pharmaceutical studies.

Untargeted metabolite profiling: A comprehensive study using data analysis workflow in Salvia L. species (Lamiaceae)

The genus Salvia L., a member of the Lamiaceae family, possesses remarkable medicinal properties for treating various disorders due to its rich natural product resources. Despite the genus's significance, there is a dearth of reports on complete metabolite characterization at intraspecific levels within Salvia. This study endeavors to bridge this gap by conducting a comprehensive investigation encompassing mass spectral library matching, biomarker assessment, chemotype specification, metabolite enrichment detection, and computational metabolite annotation. Dried aerial parts from three distinct Salvia species served as the primary material for untargeted quadrupole linear/ion trap (QTRAP) liquid chromatography (LC)- mass spectrometry (MS/MS)-based metabolite analysis. The metabolite datasets underwent rigorous processing, utilizing multivariate analysis methods through MetaboAnalyst 5.0 and the Global Natural Products Social Molecular Networking (GNPS) Web Platform. Our investigation identified 160 metabolites through library matching, and an additional 57 annotated nodes were obtained through automated spectral matching. Employing annotation tools such as DEREPLICATOR+ and substructure discovery tools like MS2LDA (a Bayesian Latent Dirichlet Allocation (LDA) model) led to the discovery of four metabolites and three Mass2Motifs. Furthermore, 48 metabolites were explored for the first time using GNPS libraries. The investigation unveiled specific chemotypes and identified 98 potential biomarkers. Using library and GNPS spectral matching proved to be high-throughput supplementary strategies for annotating and identifying various metabolites. Moreover, integrating QTRAP LC-MS/MS with a multivariate statistical method grounded in biomarker and functional analysis provided differential features at specific and intraspecific levels. This comprehensive overview of metabolites in Salvia species offers valuable insights for quality profiling.

Machine learning-assisted non-target analysis of a highly complex mixture of possible toxic unsymmetrical dimethylhydrazine transformation products with chromatography-mass spectrometry

Unsymmetrical dimethylhydrazine (UDMH) is a toxic and environmentally hostile compound that was massively introduced to the environment during previous decades due to its use in the space and rocket industry. The compound forms multiple transformation products, and many of them are as dangerous as UDMH or even more dangerous. The danger includes, but is not limited to, acute toxicity, chronic health hazards, carcinogenicity, and environmental damage. UDMH transformation products are poorly investigated. In this work, the mixture formed by long storage of the waste that contained UDMH was studied. Even a preliminary screening of such a mixture is a complex task. It consists of dozens of compounds, and most of them are missing in chemical and spectral databases. The complete preparative separation of such a mixture is very laborious. We applied several methods of gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry, and several machine learning and chemoinformatics methods to make a preliminary but informative screening of the mixture. Machine learning allowed predicting retention indices and mass spectra of candidate structures. The combination of various ion sources and a comparison of the observed with the predicted spectra and retention was used to propose confident structures for 24 compounds. It was demonstrated that neither high-resolution mass spectrometry nor mass spectral library matching is enough to elucidate the structures of unknown UDMH transformation products. At the same time, the use of machine learning and a combination of methods significantly improves the identification power. Finally, machine learning was applied to estimate the acute toxicity of the discovered compounds. It was shown that many of them are comparable to or even more toxic than UDMH itself. Such an extremely wide and still underestimated variety of easily formed derivatives of UDMH can lead to a significant underestimation of the potential hazard of this compound.

Tattoo Pigment Identification in Inks and Skin Biopsies of Adverse Reactions by Complementary Elemental and Molecular Bioimaging with Mass Spectral Library Matching.

Tattooing has become increasingly popular throughout society. Despite the recognized issue of adverse reactions in tattoos, regulations remain challenging with limited data available and a missing positive list. The diverse chemical properties of mostly insoluble inorganic and organic pigments pose an outstanding analytical challenge, which typically requires extensive sample preparation. Here, we present a multimodal bioimaging approach combining micro X-ray fluorescence (μXRF) and laser desorption ionization-mass spectrometry (LDI-MS) to detect the elemental and molecular composition in the same sample. The pigment structures directly absorb the laser energy, eliminating the need for matrix application. A computational data processing workflow clusters spatially resolved LDI-MS scans to merge redundant information into consensus spectra, which are then matched against new open mass spectral libraries of tattoo pigments. When applied to 13 tattoo inks and 68 skin samples from skin biopsies in adverse tattoo reactions, characteristic signal patterns of isotopes, ion adducts, and in-source fragments in LDI-MS1 scans yielded confident compound annotations across various pigment classes. Combined with μXRF, pigment annotations were achieved for all skin samples with 14 unique structures and 2 inorganic pigments, emphasizing the applicability to larger studies. The tattoo-specific spectral libraries and further information are available on the tattoo-analysis.github.io website.

Machine learning model for detecting fentanyl analogs from mass spectra

In recent years, fentanyl and its analogs have been increasingly abused, leading to tragic outcomes. One way to tackle this problem is to rapidly detect fentanyl analog compounds and prevent them from reaching people who could be harmed by them. However, the emergence of novel fentanyl analogs that evade detection by classic mass spectral library matching has exacerbated the problem. We propose supervised machine learning classification models as a complementary approach to library matching for detecting fentanyl analogs from mass spectra. To develop and apply such models, we extract two dozen peak-based and similarity-based input features from each spectrum of interest. Using techniques such as random forests, neural networks, and logistic regression, we identify patterns within these features’ values, resulting in strong detection performance. Within a cross-validation framework, we achieve 99% probability of detection and 1% probability of false alarm on a representative set of several thousand mass spectra. These results suggest that machine learning models may offer a robust complement to library matching. Practitioners from diverse fields, including border security, law enforcement, and military may benefit from this capability to detect drugs of abuse.

Future feed control – Tracing banned bovine material in insect meal

In the present study, we assessed if different legacy and novel molecular analyses approaches can detect and trace prohibited bovine material in insects reared to produce processed animal protein (PAP). Newly hatched black soldier fly (BSF) larvae were fed one of the four diets for seven days; a control feeding medium (Ctl), control feed spiked with bovine hemoglobin powder (BvHb) at 1% (wet weight, w/w) (BvHb 1%, w/w), 5% (BvHb 5%, w/w) and 10% (BvHb 10%, w/w). Another dietary group of BSF larvae, namely *BvHb 10%, was first grown on BvHb 10% (w/w), and after seven days separated from the residual material and placed in another container with control diet for seven additional days. Presence of ruminant material in insect feed and in BSF larvae was assessed in five different laboratories using (i) real time-PCR analysis, (ii) multi-target ultra-high performance liquid chromatography coupled to tandem mass spectrometry (UHPLC-MS/MS), (iii) protein-centric immunoaffinity-LC-MS/MS, (iv) peptide-centric immunoaffinity-LC-MS/MS, (v) tandem mass spectral library matching (SLM), and (vi) compound specific amino acid analysis (CSIA). All methods investigated detected ruminant DNA or BvHb in specific insect feed media and in BSF larvae, respectively. However, each method assessed, displayed distinct shortcomings, which precluded detection of prohibited material versus non-prohibited ruminant material in some instances. Taken together, these findings indicate that detection of prohibited material in the insect-PAP feed chain requires a tiered combined use of complementary molecular analysis approaches. We therefore advocate the use of a combined multi-tier molecular analysis suite for the detection, differentiation and tracing of prohibited material in insect-PAP based feed chains and endorse ongoing efforts to extend the currently available battery of PAP detection approaches with MS based techniques and possibly δ13CAA fingerprinting.

Low Molecular Weight Volatile Organic Compounds Indicate Grazing by the Marine Rotifer Brachionus plicatilis on the Microalgae Microchloropsis salina.

Microalgae produce specific chemicals indicative of stress and/or death. The aim of this study was to perform non-destructive monitoring of algal culture systems, in the presence and absence of grazers, to identify potential biomarkers of incipient pond crashes. Here, we report ten volatile organic compounds (VOCs) that are robustly generated by the marine alga, Microchloropsis salina, in the presence and/or absence of the marine grazer, Brachionus plicatilis. We cultured M. salina with and without B. plicatilis and collected in situ volatile headspace samples using thermal desorption tubes over the course of several days. Data from four experiments were aggregated, deconvoluted, and chromatographically aligned to determine VOCs with tentative identifications made via mass spectral library matching. VOCs generated by algae in the presence of actively grazing rotifers were confirmed via pure analytical standards to be pentane, 3-pentanone, 3-methylhexane, and 2-methylfuran. Six other VOCs were less specifically associated with grazing but were still commonly observed between the four replicate experiments. Through this work, we identified four biomarkers of rotifer grazing that indicate algal stress/death. This will aid machine learning algorithms to chemically define and diagnose algal mass production cultures and save algae cultures from imminent crash to make biofuel an alternative energy possibility.

Identification of polar transformation products and high molecular weight polycyclic aromatic hydrocarbons (PAHs) in contaminated soil following bioremediation

Bioremediation is a technique commonly used to reduce the toxicity associated with polycyclic aromatic hydrocarbons (PAHs) in contaminated soils. However, the efficacy of bioremedial applications is evaluated based on the removal of a subset of parent (or unsubstituted) PAHs and does not incorporate toxic polar transformation products or the more mutagenic high molecular weight PAHs (MW≥302amu or MW302-PAHs). Previously, an effects-directed analysis approach was used to assess the effect of bioremediation on the toxicity of a coal tar-contaminated soil. Increased genotoxicity and developmental toxicity was measured postbioremedation in the more polar soil extract fractions, as compared to the less polar fractions where the targeted PAHs eluted, and could not be attributed to the 88 target PAHs analyzed for (including selected oxygen-containing PAHs). In this study, comprehensive two-dimensional gas chromatography time-of-flight and liquid chromatography quadrupole time-of-flight mass spectrometry were used to characterize transformation products in the soil extract fractions identified as toxic, previously. Additionally, the degradation of 12MW302-PAHs, picene (MW=278) and coronene (MW=300) were evaluated following bioremediation. Non-targeted analysis resulted in the tentative identification of 10 peaks with increased intensity postbioremediation (based on mass spectral library matching and fragmentation patterns from >5000 candidate peaks in the soil extracts). Several of these compounds contained oxygen, suggesting they would be relatively polar. MW302-PAHs were not significantly degraded during bioremediation, suggesting that the carcinogenic potential associated with these PAHs might remain unchanged. The results of this study suggest that polar transformation products, and MW302-PAHs, should be considered for realistic risk assessment of bioremediated soils.

Identification of toxic cyclopeptides based on mass spectral library matching

To gain perspective on the use of tandem mass spectral libraries for identification of toxic cyclic peptides, the new library was built from 263 mass spectra (mainly MS2 spectra) of 59 compounds of that group, such as microcystins, amatoxins, and some related compounds. Mass spectra were extracted from the literature or specially acquired on ESI-Q-ToF and MALDI-ToF/ToF tandem instruments. ESI-MS2 product-ion mass spectra appeared to be rather close to MALDI-ToF/ToF fragment spectra which are uncommon for mass spectral libraries. Testing of the library was based on searches where reference spectra were in turn cross-compared. The percentage of 1st rank correct identifications (true positives) was 70% in a general case and 88–91% without including knowingly defective (‘one-dimension’) spectra as test ones. The percentage of 88–91% is the principal estimate for the overall performance of this library that can be used in a method of choice for identification of individual cyclopeptides and also for group recognition of individual classes of such peptides. The approach to identification of cyclopeptides based on mass spectral library matching proved to be the most effective for abundant toxins. That was confirmed by analysis of extracts from two cyanobacterial strains.

P54 Capillary electrophoresis – mass spectrometry for the forensic analysis of cathinone drugs and metabolites

(For research use only, not for use in diagnostic procedures). In forensic drug analysis there is always a need for increased sensitivity to provide better detection of new synthetic substances in challenging casework. The capability to resolve very complex mixtures and provide confirmation of the identity of polar drugs and metabolites is the strength of Capillary Electrophoresis integrated with Mass Spectrometry. In this work we apply a sheathless interface for Capillary Electrophoresis-ESI-Mass Spectrometry (CESI-MS) for screening of 25 cathinone related designer drugs and metabolites in biofluids. The sample preparation included a pre-treatment step using only a simple dilution of urine. The CE used a 90 cm barefused silica, 150 μm o.d., 30 μm i.d. capillary, at 200-300 V/cm, 2.4 to 2.8 μamps at 25 °C. The background electrolyte (BGE) and conductive liquid were both 50 mM Ammonium Formate, pH 2.85. Sample introduction was either Hydrodynamic; 16s at 34.5 mBar or Electrokinetic; 10kV for 16s. The CE Instrument was a Beckman Coulter CESI 8000 coupled to an AB SCIEX 5600 TripleTOF® mass spectrometer through a sheathless interface High Sensitivity Porous Sprayer (HSPS). The ESI Voltage applied to the interface was 1.2 to 1.8 kV. The capillaries were initially conditioned with MeOH, water, 1N NaOH, water and BGE. A sheathless HSPS was used to interface CE and ESI-MS. The drugs tested included 25 cathinone drugs and metabolites in urine. Acquired MS/MS spectra were searched against an accurate mass drug library for confident compound identification. Combining high resolution MS and MS/MS acquisition with Capillary Electrophoresis separation, allows for retrospective compound identification of the cathinones with highest confidence based on accurate mass product ion specificity, mass spectral library matching as well as mass error, isotopic profile and molecular formula finding. The LOQ values for all components were found to be less than 5 ng/mL. Linearity was achieved between 5-1000 ng/mL for most drugs and % RSDs for different analytes are reported to be less than 10 % in most cases. A sheathless interface for Capillary Electrophoresis- ESI-Mass Spectrometry (CESI-MS) has shown to be a viable alternative for screening of 25 cathinone related designer drugs and metabolites in biofluids.

Characterizing Vaccinium berry Standard Reference Materials by GC‐MS using NIST spectral libraries

A gas chromatography-mass spectrometry (GC-MS)-based method was developed for qualitative characterization of metabolites found in Vaccinium fruit (berry) dietary supplement Standard Reference Materials (SRMs). Definitive identifications are provided for 98 unique metabolites determined among six Vaccinium-related SRMs. Metabolites were enriched using an organic liquid/liquid extraction, and derivatized prior to GC-MS analysis. Electron ionization (EI) fragmentation spectra were searched against EI spectra of authentic standards compiled in the National Institute of Standards and Technology's mass spectral libraries, as well as spectra selected from the literature. Metabolite identifications were further validated using a retention index match along with prior probabilities and were compared with results obtained in a previous effort using collision-induced dissociation (CID) MS/MS datasets from liquid chromatography coupled to mass spectrometry experiments. This manuscript describes a nontargeted metabolite profile of Vaccinium materials, compares results among related materials and from orthogonal experimental platforms, and discusses the feasibility and development of using mass spectral library matching for nontargeted metabolite identification.

Detection of Volatile Spoilage Metabolites in Fermented Cucumbers Using Nontargeted, Comprehensive 2‐Dimensional Gas Chromatography‐Time‐of‐Flight Mass Spectrometry (GC×GC‐TOFMS)

A nontargeted, comprehensive 2-dimensional gas chromatography-time-of-flight mass spectrometry (GC×GC-TOFMS) method was developed for the analysis of fermented cucumber volatiles before and after anaerobic spoilage. Volatile compounds extracted by solid-phase microextraction were separated on a polyethylene glycol 1st-dimension column and 14% cyanopropylphenyl 2nd-dimension column. Among 314 components detected in fermented cucumber brine, 199 had peak areas with coefficients of variation below 30%. Peak identifications established by mass spectral library matching were 92% accurate based on 63 authentic standards. Analysis of variance of analytes' log peak areas revealed 33 metabolites changed in concentration after spoilage (P < 0.05), including increases in acetic, propanoic, and butyric acids, n-propyl acetate, several alcohols, and a decrease in furfural. GC×GC-TOFMS with a nontargeted, semi-automated approach to data analysis made possible the separation, identification, and determination of differences in polar volatile components, facilitating the discovery of several metabolites related to fermented cucumber spoilage. Practical Application: An optimized method for the chemical analysis of volatile food components is described and applied to the profiling of volatile compounds in fermented cucumbers, resulting in the identification of 137 components, many of which are being reported for the first time in fermented cucumbers. This nontargeted GC×GC-TOFMS method and inclusive data analysis platform facilitated the discovery of several metabolites that were formed or utilized during anaerobic spoilage of fermented cucumbers. Further study of these metabolites will enhance our ability to understand and potentially control the metabolism of spoilage bacteria that can degrade lactic acid under the restrictive environmental conditions present in fermented cucumbers.

Thermal decomposition studies of riot control agent ω-chloroacetophenone (CN) by pyrolysis-gas chromatography–mass spectrometry

Pyrolysis-GC/MS system with on-line micro-furnace was used to make rapid evaluation of ω-chloroacetophenone (CN) decomposition under inert thermal atmospheres. The volatile products evolved during pyrolysis were analyzed by thermal gravimetric analysis (TGA) and Py-GC/MS to obtain specific thermogram and pyrogram. Thermal gravimetric analysis results showed that CN undergoes sublimation at 167°C prior to its decomposition at 229°C. Totally 45 degradation products were identified based on mass spectral library matching with the aid of correlation of the values of boiling point (bp) and retention time. A large number of mono-aromatics and polycyclic aromatic hydrocarbons were observed beyond 600°C. In addition to the aromatic hydrocarbons, oxygenated compounds were also observed during the pyrolysis process. The pyrolysis mechanism was proposed based on the determined pyrolysates and their relative abundance with temperature. The investigation results can provide significant information for understanding the thermal behavior of CN and evaluation of the potential influence of the pyrolysates to living being and the environment.

Identification of flame retarding organic alkali metal salt additives in polycarbonate by flow injection electrospray ion trap mass spectrometry

Three commonly used flame retarding organic alkali metal salt additives in polycarbonate, potassium diphenylsulfone sulfonate, potassium perfluorobutane sulfonate and p-toluenesulfonic acid sodium salt could be simultaneously, quickly and conveniently identified in a polycarbonate sample by utilizing electrospray ion trap mass spectroscopy detection in flow injection mode after dissolution and precipitation of the polymer. The resulting method offered advantages of speed, selectivity and precision of identification based on two stage mass spectroscopy fragmentation patterns and mass spectral library matching for the three salts tested, and is likely to be applicable to other compounds of similar class.

High-speed, low-pressure gas chromatography–mass spectrometry for essential oil analysis

Analysis of parsley and fennel essential oils was performed by using low-pressure gas chromatography–mass spectrometry (GC–MS). The low-pressure instrument configuration was achieved by fitting a GC–MS instrument with a 530 μm I.D. capillary column and an appropriate capillary restrictor at the inlet of the column. Comparison of the performance of the low-pressure GC–MS setup was made with fast GC–MS using a narrow-bore capillary column. By comparing the two approaches side-by-side the benefits of low-pressure GC–MS for characterisation of moderately complex essential oils comprising less than 50 detectable components can be fully appreciated. Although efficiency is sacrificed, the improved sample capacity of the 530 μm I.D. column leads to higher peak intensities and in-turn better mass spectral library matching thus providing highly satisfactory results.

Determination of eugenol, anethole, and coumarin in the mainstream cigarette smoke of Indonesian clove cigarettes

Indonesian clove cigarettes (kreteks), typically have the appearance of a conventional domestic cigarette. The unique aspects of kreteks are that in addition to tobacco they contain dried clove buds (15–40%, by wt.), and are flavored with a proprietary “sauce”. Whereas the clove buds contribute to generating high levels of eugenol in the smoke, the “sauce” may also contribute other potentially harmful constituents in addition to those associated with tobacco use. We measured levels of eugenol, trans-anethole (anethole), and coumarin in smoke from 33 brands of clove-flavored cigarettes (filtered and unfiltered) from five kretek manufacturers. In order to provide information for evaluating the delivery of these compounds under standard smoking conditions, a quantification method was developed for their measurement in mainstream cigarette smoke. The method allowed collection of mainstream cigarette smoke particulate matter on a Cambridge filter pad, extraction with methanol, sampling by automated headspace solid-phase microextraction, and subsequent analysis using gas chromatography/mass spectrometry. The presence of these compounds was confirmed in the smoke of kreteks using mass spectral library matching, high-resolution mass spectrometry (±0.0002 amu), and agreement with a relative retention time index, and native standards. We found that when kreteks were smoked according to standardized machine smoke parameters as specified by the International Standards Organization, all 33 clove brands contained levels of eugenol ranging from 2490 to 37,900 μg/cigarette (μg/cig). Anethole was detected in smoke from 13 brands at levels of 22.8–1030 μg/cig, and coumarin was detected in 19 brands at levels ranging from 9.2 to 215 μg/cig. These detected levels are significantly higher than the levels found in commercial cigarette brands available in the United States.