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Metabolomics provides a unique snapshot into the world of small molecules and the complex biological processes that govern the human, animal, plant, and environmental ecosystems encapsulated by the One Health modeling framework. However, this “molecular snapshot” is only as informative as the number of metabolites confidently identified within it. The spectral similarity (SS) score is traditionally used to identify compound(s) in mass spectrometry approaches to metabolomics, where spectra are matched to reference libraries of candidate spectra. Unfortunately, there is little consensus on which of the dozens of available SS metrics should be used. This lack of standard SS score creates analytic uncertainty and potentially leads to issues in reproducibility, especially as these data are integrated across other domains. In this work, we use metabolomic spectral similarity as a case study to showcase the challenges in consistency within just one piece of the One Health framework that must be addressed to enable data science approaches for One Health problems. Here, using a large cohort of datasets comprising both standard and complex datasets with expert-verified truth annotations, we evaluated the effectiveness of 66 similarity metrics to delineate between correct matches (true positives) and incorrect matches (true negatives). We additionally characterize the families of these metrics to make informed recommendations for their use. Our results indicate that specific families of metrics (the Inner Product, Correlative, and Intersection families of scores) tend to perform better than others, with no single similarity metric performing optimally for all queried spectra. This work and its findings provide an empirically-based resource for researchers to use in their selection of similarity metrics for GC-MS identification, increasing scientific reproducibility through taking steps towards standardizing identification workflows.
Harmful cyanobacterial blooms are becoming more common and persistent around the world. When in bloom, various cyanobacterial strains can produce anatoxins in high concentrations, which, unlike other cyanobacterial toxins, may be present in clear water. Potential human and animal exposures to anatoxins occur mainly through unintentional ingestion of contaminated algal mats and water. To address this public health threat, we developed and validated an LC-MS/MS method to detect anatoxins in human urine to confirm exposures. Pooled urine was fortified with anatoxin-a and dihydroanatoxin at concentrations from 10.0 to 500 ng/mL to create calibrators and quality control samples. Samples were diluted with isotopically labeled anatoxin and solvent prior to LC-MS/MS analysis. This method can accurately quantitate anatoxin-a with inter- and intraday accuracies ranging from 98.5 to 103% and relative standard deviations < 15%, which is within analytical guidelines for mass spectrometry methods. Additionally, this method qualitatively detects a common degradation product of anatoxin, dihydroanatoxin, above 10 ng/mL. We also evaluated a commercial anatoxin-a ELISA kit for potential diagnostic use; however, numerous false positives were detected from unexposed individual human urine samples. In conclusion, we have developed a method to detect anatoxins precisely and accurately in urine samples, addressing a public health area of concern, which can be applied to future exposure events.
Abstract. Aerosols are a source of great uncertainty in radiative forcing predictions and have poorly understood health impacts. Most aerosol mass is formed in the atmosphere from reactive gas-phase organic precursors, forming secondary organic aerosol (SOA). Semivolatile organic compounds (SVOCs) (effective saturation concentration, C*, of 10−1–103 µg m−3) comprise a large fraction of organic aerosol, while intermediate-volatility organic compounds (IVOCs) (C* of 103–106 µg m−3) and volatile organic compounds (VOCs) (C* ≥ 106 µg m−3) are gas-phase precursors to SOA and ozone. The Comprehensive Thermal Desorption Aerosol Gas Chromatograph (cTAG) is the first single instrument simultaneously quantitative for a broad range of compound-specific VOCs, IVOCs and SVOCs. cTAG is a two-channel instrument which measures concentrations of C5–C16 alkane-equivalent-volatility VOCs and IVOCs on one channel and C14–C32 SVOCs on the other coupled to a single high-resolution time-of-flight mass spectrometer, achieving consistent quantification across 15 orders of magnitude of vapor pressure. cTAG obtains concentrations hourly and gas–particle partitioning for SVOCs every other hour, enabling observation of the evolution of these species through oxidation and partitioning into the particle phase. Online derivatization for the SVOC channel enables detection of more polar and oxidized species. In this work we present design details and data evaluating key parameters of instrument performance such as I/VOC collector design optimization, linearity and reproducibility of calibration curves obtained using a custom liquid evaporation system for I/VOCs and the effect of an ozone removal filter on instrument performance. Example timelines of precursors with secondary products are shown, and analysis of a subset of compounds detectable by cTAG demonstrates some of the analytical possibilities with this instrument.
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Pyrolysis of waste plastics can produce a product mixture with a high concentration of olefins (>50 wt%). The olefins, as building blocks in the petroleum industry, are potential precursors for valuable commodity chemicals with higher values (>$2000 per ton). In this work, we produce aldehydes by hydroformylation of the olefins present in pyrolysis oil from colored post-consumer recycled high-density polyethylene (PCR-HDPE). The obtained aldehydes in the oil are then converted into aliphatic amines via reductive amination with a Ru/C catalyst. The aminated oil was characterized by multiple analytical chemistry techniques including elemental analysis, nuclear magnetic resonance spectroscopy, high-resolution liquid chromatography-mass spectrometry, and gas chromatography-mass spectrometry with a Polyarc flame ionization detector. The concentration of metals in the PCR-HDPE and oil changes during the tandem processes, showing limited effects of these elements (e.g., Al, Ca, Fe, Mg, Ti, Zn) on hydroformylation and reductive amination. Additionally, we demonstrated that reductive amination of aldehydes with varied carbon numbers and branching properties can be achieved in the presence of a complex mixture, including paraffins and aromatics. The results indicate that waste plastics have the potential to serve as a renewable source for mono and di aliphatic amines, thereby diminishing reliance on fossil feedstocks as the current primary amine source.
The mission of this CRADA with Agilent was to couple powerful MS platforms (QQQ, IM-QTOFMS) with Agilent’s novel Ultra-High-Performance Liquid Chromatography (UHPLC) fast metabolomic workflows and perform ABF Machine Learning (ML) to generated datasets. Agilent transferred UHPLC methods to PNNL and LBNL and methods were implemented and demonstrated in both labs, achieving total acquisition times of < 10 min. Metabolites analyzed using Agilent’s shared methods included metabolites from central carbon metabolism, common across hosts, and metabolites unique to engineered strains. Standards were acquired in an UHPLC-Drift Tube Ion Mobility Mass Spectrometer (DTIMS) system for the first time within the context of ABF and methods were optimized based on Agilent’s protocols. Samples from ABF hosts Pseudomonas putida, Aspergillus pseudoterreus, Aspergillus niger and Rhodosporidium toruloides were analyzed using the UHPLC-DTIMS platform for a total of 276 runs. A data analysis workflow compatible with the Experimental Data Depot (EDD) and completely shareable was developed for the acquired UHPLC-DTIMS data. Samples were analyzed using a Data Independent Acquisition Approach (DIA), which for most of the standards provided more transitions therefore increasing detection confidence. Using the data acquired by PNNL, LBNL, and Agilent’s specifications from previous ML projects, SNL applied an ensemble ML strategy to pick the best performing model for automated LC-method selection. Finally, with the contribution of the participant labs and Agilent, SNL developed an Automated Method Selection (AMS) software tool to predict the best liquid chromatography method for analysis of any new molecules of interest. Samples with novel pathways and new metabolite targets of interest are generated at a high pace in the ABF. Overall, the project advanced rapid metabolomics by combining liquid chromatography, ion mobility spectrometry, and data-independent mass spectrometry with machine learning. This multidimensional approach uses retention time, collision cross-section, precursor mass, and fragment-ion information to distinguish chemically similar metabolites that can be difficult to resolve using conventional liquid- or gas-chromatography methods. The resulting workflow also provided automated metabolite-identification error estimates, addressing a recognized need for statistical confidence measures in metabolomics.
Biomass can be converted to various types of products in biological and metabolic processes. For an in-depth understanding of biomass conversion, quantitative and qualitative information of products in these conversion processes are essential. Here we introduce analytical techniques including high-performance liquid chromatography (HPLC), gas chromatography (GC), gas chromatography-mass spectrometry (GC-MS), and nuclear magnetic resonance (NMR) for biomass-based products characterization in biological and metabolic processes.
Marcia hiantina (Mollusca, Bivalvia) (Lamarck, 1818), is an edible clam mainly distributed along the tropical coastal regions. Recent researches have demonstrated that clams can possess compounds, including polysaccharides, with a wide range of biological actions including antioxidant, immunomodulatory and antitumor activities. Here an α-glucan was isolated from M. hiantina by hot water, purified by anion exchange chromatography, and its structure was characterized by a combination of multiple nuclear magnetic resonance (NMR) methods (1D 1 H, 1 H- 1 H COSY, 1 H- 1 H TOCSY, 1 H- 1 H NOESY, 1 H- 13 C HSQC and 1 H- 13 C HSQC-NOESY spectra), gas chromatography-mass spectrometry, and high performance size exclusion chromatography (HPSEC). The analysis from NMR, monosaccharide composition, methylation analyses and HPSEC combined with multi angle light scattering (MALS) of M. hiantina-derived α-glycan confirmed a branched polysaccharide exclusively composed of glucose (Glc), mostly 4-linked in its backbone, branched occasionally at 6-positions, and having a molecular weight of ~ 570 kDa. The mollusk α-glucan was subjected to four cell-based assays: (i) viability of three cell lines (RAW264.7, HaCaT, and HT-29), (ii) activity on lipopolysaccharide (LPS)-induced prostaglandin production in RAW264.7 cells, (iii) inhibitory activities of in H 2 O 2 - and LPS-induced reactive oxygen species (ROS) production in HMC3 cells, and (iv) HaCaT cell proliferation. Furthermore, results have indicated no cytotoxicity, potent inhibition of both H 2 O 2 - and LPS-induced ROS, and potent cell proliferative activity.
Ozonation is a common remediation approach to eliminate odors from mold, tobacco and fire damage in buildings. Little information exists to: 1) assess its effectiveness; 2) provide guidance on operation conditions; and 3) identify potential risks associated with the presence of indoor ozone and ozonation byproducts. The goal of this study is to evaluate chemical changes in thirdhand smoke (THS) aerosols induced by high levels of ozone, in comparison with THS aerosols aged under similar conditions in the absence of ozone. Samples representing different stages of smoke aging in the absence of ozone, including freshly emitted secondhand smoke (SHS) and THS, were collected inside an 18-m 3 room-sized chamber over a period of 42 h after six cigarettes were consumed. The experiments involved collection and analysis of gas phase species including volatile organic compounds (VOCs), volatile carbonyls, semivolatile organic compounds (SVOCs), and particulate matter. VOC analysis was carried out by gas chromatography/mass spectrometry with a thermal desorption inlet (TD-GC/MS), and volatile carbonyls were analyzed by on-line derivatization with dinitrophenylhydrazine (DNPH), followed by liquid chromatography with UV/VIS detection. SVOCs were extracted from XAD-coated denuders and Teflon-coated fiberglass filters in the absence of ozone. In those extracts, tobacco-specific nitrosamines (TSNAs) and other SVOCs were analyzed by gas chromatography with positive chemical ionization-triple quadrupole mass spectrometric detection (GC/PCI-QQQ-MS), and polycyclic aromatic hydrocarbons (PAHs) were quantified by gas chromatography with ion trap mass spectrometric detection (GC/IT-MS) in selected ion monitoring mode. Particulate matter concentration was determined gravimetrically. In a second experiment, a 300 mg h -1 commercial ozone generator was operated during 1 h, one day after smoke was generated, to evaluate the remediation of THS by ozonation. VOCs and volatile carbonyls were analyzed before and after ozonation. Extracts from fabrics that were exposed in the chamber before and after ozonation as surrogates for indoor furnishings were analyzed by GC/IT-MS, and aerosol size distribution was studied with a scanning mobility particle sizer. Ozone concentration was measured with a photometric detector. An estimated 175 mg ozone reacted with THS after 1 h of treatment, corresponding to 58% of the total O 3 released during that period. Fabric-bound nicotine was depleted after ozonation, and the surface concentration of PAHs adsorbed to fabric specimens decreased by an order of magnitude due to reaction with ozone, reaching pre-smoking levels. These results suggest that ozonation has the potential to remove harmful THS chemicals from indoor surfaces. However, gas phase concentrations of volatile carbonyls, including formaldehyde, acetaldehyde and acetone were higher immediately after ozonation. Ultrafine particles (UFP, in most cases with size <60 nm) were a major ozonation byproduct. UFP number concentrations peaked shortly after ozonation ended, and remained at higher-than background levels for several hours. Based on these results, minimum re-entry times after ozone treatment were predicted for different indoor scenarios. Clearly defining re-entry times can serve as a practical measure to prevent acute exposures to ozone and harmful ozonation byproducts after treatment. This study evaluated potential benefits and risks associated with THS remediation using ozone, providing insights into this technology.
Abstract Assessing central carbon metabolism in plants can be challenging due to the dynamic range in pool sizes, with low levels of important phosphorylated sugars relative to more abundant sugars and organic acids. Here, we report a sensitive liquid chromatography–mass spectrometry method for analysing central metabolites on a hybrid column, where both anion-exchange and hydrophilic interaction chromatography (HILIC) ligands are embedded in the stationary phase. The liquid chromatography method was developed for enhanced selectivity of 27 central metabolites in a single run with sensitivity at femtomole levels observed for most phosphorylated sugars. The method resolved phosphorylated hexose, pentose, and triose isomers that are otherwise challenging. Compared with a standard HILIC approach, these metabolites had improved peak areas using our approach due to ion enhancement or low ion suppression in the biological sample matrix. The approach was applied to investigate metabolism in high lipid-producing tobacco leaves that exhibited increased levels of acetyl-CoA, a precursor for oil biosynthesis. The application of the method to isotopologue detection and quantification was considered through evaluating 13C-labeled seeds from Camelina sativa. The method provides a means to analyse intermediates more comprehensively in central metabolism of plant tissues.
225 Ac-targeted α-radiotherapy is a promising approach to treating malignancies, including prostate cancer. However, α-emitting isotopes are difficult to image because of low administered activities and a low fraction of suitable γ-emissions. The in vivo generator 134 Ce/ 134 La has been proposed as a potential PET imaging surrogate for the therapeutic nuclides 225 Ac and 227 Th. In this report, we detail efficient radiolabeling methods using the 225 Ac-chelators DOTA and MACROPA. These methods were applied to radiolabeling of prostate cancer imaging agents, including PSMA-617 and MACROPA-PEG 4 -YS5, for evaluation of their in vivo pharmacokinetic characteristics and comparison to the corresponding 225 Ac analogs. Methods: Radiolabeling was performed by mixing DOTA/MACROPA chelates with 134 Ce/ 134 La in NH 4 OAc, pH 8.0, at room temperature, and radiochemical yields were monitored by radio–thin-layer chromatography. In vivo biodistributions of 134 Ce-DOTA/MACROPA.NH 2 complexes were assayed through dynamic small-animal PET/CT imaging and ex vivo biodistribution studies over 1 h in healthy C57BL/6 mice, compared with free 134 CeCl 3 . In vivo, preclinical imaging of 134 Ce-PSMA-617 and 134 Ce-MACROPA-PEG 4 -YS5 was performed on 22Rv1 tumor–bearing male nu/nu-mice. Ex vivo biodistribution was performed for 134 Ce/ 225 Ac-MACROPA-PEG 4 -YS5 conjugates. Results: 134 Ce-MACROPA.NH 2 demonstrated near-quantitative labeling with 1:1 ligand-to-metal ratios at room temperature, whereas a 10:1 ligand-to-metal ratio and elevated temperatures were required for DOTA. Rapid urinary excretion and low liver and bone uptake were seen for 134 Ce/ 225 Ac-DOTA/MACROPA. NH 2 conjugates in comparison to free 134 CeCl 3 confirmed high in vivo stability. An interesting observation during the radiolabeling of tumor-targeting vectors PSMA-617 and MACROPA-PEG 4 -YS5—that the daughter 134 La was expelled from the chelate after the decay of parent 134 Ce—was confirmed through radio–thin-layer chromatography and reverse-phase high-performance liquid chromatography. Both conjugates, 134 Ce-PSMA-617 and 134 Ce-MACROPA-PEG 4 -YS5, displayed tumor uptake in 22Rv1 tumor–bearing mice. The ex vivo biodistribution of 134 Ce-MACROPA.NH 2 , 134 Ce-DOTA and 134 Ce-MACROPA-PEG 4 -YS5 corroborated well with the respective 225 Ac-conjugates. Conclusion: These results demonstrate the PET imaging potential for 134 Ce/ 134 La-labeled small-molecule and antibody agents. The similar 225 Ac and 134 Ce/ 134 La-chemical and pharmacokinetic characteristics suggest that the 134 Ce/ 134 La pair may act as a PET imaging surrogate for 225 Ac-based radioligand therapies.
The lipidome comprises the total content of molecular species of each lipid class, and is measured using the analytical techniques of lipidomics. Many liquid chromatography-mass spectrometry (LC-MS) methods have previously been described to characterize the lipidome. However, many lipidomic approaches may not fully uncover the subtleties of lipid molecular species, such as the full fatty acid (FA) composition of certain lipid classes. Here, we describe a stepwise targeted lipidomics approach to characterize the polar and non-polar lipid classes using complementary LC-MS methods. Our “polar” method measures 260 molecular species across 12 polar lipid classes, and is performed using hydrophilic interaction chromatography (HILIC) on a NH2 column to separate lipid classes by their headgroup. Our “non-polar” method measures 254 molecular species across three non-polar lipid classes, separating molecular species on their FA characteristics by reverse phase (RP) chromatography on a C30 column. Five different extraction methods were compared, with an MTBE-based extraction chosen for the final lipidomics workflow. A state-of-the-art strategy to determine and relatively quantify the FA composition of triacylglycerols is also described. This lipidomics workflow was applied to developing, mature, and germinated pennycress seeds/seedlings and found unexpected changes among several lipid molecular species. During development, diacylglycerols predominantly contained long chain length FAs, which contrasted with the very long chain FAs of triacylglycerols in mature seeds. Potential metabolic explanations are discussed. The lack of very long chain fatty acids in diacylglycerols of germinating seeds may indicate very long chain FAs, such as erucic acid, are preferentially channeled into beta-oxidation for energy production.
T cell function is determined by transcriptional networks that are regulated by epigenetic programming via posttranslational modifications (PTMs) to histone proteins and DNA. Bottom-up mass spectrometry (MS) can identify histone PTMs, whereas intact protein analysis by MS can detect species missed by bottom-up approaches. We used a novel approach of online two-dimensional liquid chromatography-tandem MS with high-resolution reversed-phase liquid chromatography (RPLC), alternating electron transfer dissociation (ETD) and collision-induced dissociation (CID) on precursor ions to maximize fragmentation of uniquely modified species. The first online RPLC separation sorted histone families, then RPLC or weak cation exchange hydrophilic interaction liquid chromatography (WCX-HILIC) separated species heavily clad in PTMs. Tentative identifications were assigned by matching proteoform masses to predicted theoretical masses that were verified with tandem MS. We used this innovative approach for histone-intact protein PTM mapping (HiPTMap) to identify and quantify proteoforms purified from CD8 T cells after in vivo influenza infection. Activation significantly altered PTMs following influenza infection, histone maps changed as T cells migrated to the site of infection, and T cells responding to secondary infections had significantly more transcription enhancing modifications. Thus, HiPTMap identified and quantified proteoforms and determined changes in CD8 T cell histone PTMs over the course of infection.
Gas chromatography (GC) is undoubtedly the analytical technique of choice for analyzing the composition of petroleum-based fuels. Over the past twenty years, as comprehensive two-dimensional gas chromatography (GC×GC) has evolved, fuel analysis has often been highlighted in scientific reports, as their complexity allows for illustration of the impressive peak capacity gains afforded by GC×GC. Indeed, several research groups in recent years have applied GC×GC and chemometrics to demonstrate the potential of these analytical tools to address important compliance (tax evasion, tax credits, physical quality standards) and forensic (arson investigations, oil spills) applications involving fuels. None the less, routine use of GC×GC in forensic laboratories has been limited largely by (1) legal and regulatory guidelines, (2) lack of chemometrics training, and (3) concerns about the reproducibility of GC×GC. The goal of this review is to highlight recent advances in one-dimensional GC (1D-GC) and GC×GC analyses of fuels for compliance and forensic applications, in an effort to assist scientists in overcoming the aforementioned hindrances. An introduction to 1D-GC principles, GC×GC technology (column stationary phases and modulators) and several chemometric techniques will be provided. More specifically, chemometric techniques will be broken down into (1) signal pre-processing, (2) peak decomposition, identification and quantification, and (3) classification and pattern recognition. Examples of compliance and forensic applications will be discussed, with particular emphasis on the demonstrated success of the employed chemometric techniques. Overall, this review will hopefully make 1D-GC and GC×GC coupled with chemometric data analysis tools more accessible to the larger scientific community, and aid in eventual widespread standardization.