Explore the vast range of titles available.

Microcystins (MCs) are cyclic peptides produced by cyanobacteria, which can be harmful to humans and animals when ingested. Differences in the coding of the non‑ribosomal peptide synthetase/polyketide synthase enzyme complex responsible for microcystin production have resulted in more than 100 microcystin variants being reported to date. The microcystin diversity of Microcystis CAWBG11 was investigated using matrix-assisted laser desorption/ionization-time of flight mass spectrometry and liquid chromatography-mass spectrometry. This revealed that CAWBG11 simultaneously produced 21 known microcystins and six new congeners: [Asp3] MC-RA, [Asp3] MC-RAba, [Asp3] MC-FA, [Asp3] MC-WA, MC-FAba and MC-FL. The new congeners were putatively characterized by tandem mass spectrometry and chemical derivatization. A survey of the microcystin congeners produced by 49 cyanobacterial strains documented in scientific literature showed that cyanobacteria generally produce four microcystin congeners, but strains which produce up to 47 microcystin congeners have been reported. Microcystis CAWBG11 (which produces at least 27 congeners) was positioned in the top ten percentile of the strains surveyed, and showed fluidity of the amino acids incorporated into both position two and position four.

In polymicrobial infections, microbes can interact with both the host immune system and one another through direct contact or the secretion of metabolites, affecting disease progression and treatment options. The thick mucus in the lungs of patients with cystic fibrosis is highly susceptible to polymicrobial infections by opportunistic pathogens, including the bacterium Pseudomonas aeruginosa and the fungus Aspergillus fumigatus. Unravelling the hidden molecular interactions within such polymicrobial communities and their metabolic exchange processes will require effective enabling technologies applied to model systems. In the present study, MALDI-TOF and MALDI-FT-ICR imaging mass spectrometry (MALDI-IMS) combined with MS/MS networking were used to provide insight into the interkingdom interaction between P. aeruginosa and A. fumigatus at the molecular level. The combination of these technologies enabled the visualization and identification of metabolites secreted by these microorganisms grown on agar. A complex molecular interplay was revealed involving suppression, increased production, and biotransformation of a range of metabolites. Of particular interest is the observation that P. aeruginosa phenazine metabolites were converted by A. fumigatus into other chemical entities with alternative properties, including enhanced toxicities and the ability to induce fungal siderophores. This work highlights the capabilities of MALDI-IMS and MS/MS network analysis to study interkingdom interactions and provides insight into the complex nature of polymicrobial metabolic exchange and biotransformations.

Untargeted mass spectrometry (MS) experiments produce complex, multidimensional data that are practically impossible to investigate manually. For this reason, computational pipelines are needed to extract relevant information from raw spectral data and convert it into a more comprehensible format. Depending on the sample type and/or goal of the study, a variety of MS platforms can be used for such analysis. MZmine is an open-source software for the processing of raw spectral data generated by different MS platforms. Examples include liquid chromatography–MS, gas chromatography–MS and MS–imaging. These data might typically be associated with various applications including metabolomics and lipidomics. Moreover, the third version of the software, described herein, supports the processing of ion mobility spectrometry (IMS) data. The present protocol provides three distinct procedures to perform feature detection and annotation of untargeted MS data produced by different instrumental setups: liquid chromatography–(IMS–)MS, gas chromatography–MS and (IMS–)MS imaging. For training purposes, example datasets are provided together with configuration batch files (i.e., list of processing steps and parameters) to allow new users to easily replicate the described workflows. Depending on the number of data files and available computing resources, we anticipate this to take between 2 and 24 h for new MZmine users and nonexperts. Within each procedure, we provide a detailed description for all processing parameters together with instructions/recommendations for their optimization. The main generated outputs are represented by aligned feature tables and fragmentation spectra lists that can be used by other third-party tools for further downstream analysis. Key points MZmine is a program designed to process data from untargeted mass spectrometry (MS) experiments acquired in data-dependent acquisition mode; specifically, collision-induced dissociation and higher-energy collisional dissociation. This protocol provides three distinct procedures to perform feature detection and annotation of untargeted MS data produced by instrumental setups: liquid chromatography–(ion mobility spectrometry–)MS, gas chromatography–MS and (ion mobility spectrometry–)MS imaging. Untargeted mass spectrometry (MS) produces complex, multidimensional data. The MZmine open-source project enables processing of spectral data from various MS platforms, e.g., liquid chromatography–MS, gas chromatography–MS, MS–imaging and ion mobility spectrometry–MS, and is specialized for metabolomics.

Over several millennia, various native plant species in South America have been used for their healing and psychoactive properties. Chemical analysis of archaeological artifacts provides an opportunity to study the use of psychoactive plants in the past and to better understand ancient botanical knowledge systems. Liquid chromatography tandem mass spectrometry (LC-MS/MS) was used to analyze organic residues from a ritual bundle, radiocarbon dated to approximately 1,000 C.E., recovered from archaeological excavations in a rock shelter located in the Lípez Altiplano of southwestern Bolivia. The site is located at an elevation of ∼3,900 m above sea level and contains evidence of intermittent human occupations during the last 4,000 years. Chemical traces of bufotenine, dimethyltryptamine, harmine, and cocaine, including its degradation product benzoylecgonine, were identified, suggesting that at least three plants containing these compounds were part of the shamanic paraphernalia dating back 1,000 years ago, the largest number of compounds recovered from a single artifact from this area of the world, to date. This is also a documented case of a ritual bundle containing both harmine and dimethyltryptamine, the two primary ingredients of ayahuasca. The presence of multiple plants that come from disparate and distant ecological areas in South America suggests that hallucinogenic plants moved across significant distances and that an intricate botanical knowledge was intrinsic to pre-Columbian ritual practices.

Obtaining comprehensive, untargeted metabolic profiles for complex solid samples, e.g., animal tissues, requires sample preparation and access to information-rich analytical methodologies such as mass spectrometry (MS). Here we describe a practical two-step process for tissue samples that is based on extraction into 'aqueous' and 'organic' phases for polar and nonpolar metabolites. Separation methods such as ultraperformance liquid chromatography (UPLC) in combination with MS are needed to obtain sufficient resolution to create diagnostic metabolic profiles and identify candidate biomarkers. We provide detailed protocols for sample preparation, chromatographic procedures, multivariate analysis and metabolite identification via tandem MS (MS/MS) techniques and high-resolution MS. By using these optimized approaches, analysis of a set of samples using a 96-well plate format would take ∼48 h: 1 h for system setup, 8–10 h for sample preparation, 34 h for UPLC-MS analysis and 2–3 h for preliminary/exploratory data processing, representing a robust method for untargeted metabolic screening of tissue samples.

The use of a single enzyme such as trypsin for shotgun proteomics limits the ability to cover the whole proteome and all protein post-translational modifications. This protocol describes the use of six alternative proteases that complement trypsin to increase the coverage of the proteome. Protein digestion using a dedicated protease represents a key element in a typical mass spectrometry (MS)-based shotgun proteomics experiment. Up to now, digestion has been predominantly performed with trypsin, mainly because of its high specificity, widespread availability and ease of use. Lately, it has become apparent that the sole use of trypsin in bottom-up proteomics may impose certain limits in our ability to grasp the full proteome, missing out particular sites of post-translational modifications, protein segments or even subsets of proteins. To overcome this problem, the proteomics community has begun to explore alternative proteases to complement trypsin. However, protocols, as well as expected results generated from these alternative proteases, have not been systematically documented. Therefore, here we provide an optimized protocol for six alternative proteases that have already shown promise in their applicability in proteomics, namely chymotrypsin, LysC, LysN, AspN, GluC and ArgC. This protocol is formulated to promote ease of use and robustness, which enable parallel digestion with each of the six tested proteases. We present data on protease availability and usage including recommendations for reagent preparation. We additionally describe the appropriate MS data analysis methods and the anticipated results in the case of the analysis of a single protein (BSA) and a more complex cellular lysate ( Escherichia coli ). The digestion protocol presented here is convenient and robust and can be completed in ∼2 d.

Mass spectrometry is a central analytical technique for protein research and for the study of biomolecules in general. Driven by the need to identify, characterize, and quantify proteins at ever increasing sensitivity and in ever more complex samples, a wide range of new mass spectrometry-based analytical platforms and experimental strategies have emerged. Here we review recent advances in mass spectrometry instrumentation in the context of current and emerging research strategies in protein science.

Serum vitamin D metabolite levels are of interest as biomarkers for vitamin D status, which has influence on numerous body functions and pathologies. The determination of vitamin D metabolite levels by liquid chromatography/mass spectrometry (LC/MS) is challenging due to their low concentrations and relatively low ionization efficiencies. Three ionization sources, dielectric barrier discharge ionization (DBDI), atmospheric pressure chemical ionization (APCI), and electrospray ionization (ESI), were compared regarding achievable limits of detection and occurring matrix effects. The latter were mainly caused by phospholipids. Therefore, in addition to a conventional solid phase extraction (SPE) stationary phase, a material for selective removal of phospholipids was examined. The selective removal of phospholipids significantly reduced observed matrix effects, especially when ESI was applied. Achievable limits of detection and observed matrix effects were lowest for APCI and with some limitations, also for DBDI.

Metabolism has an essential role in biological systems. Identification and quantitation of the compounds in the metabolome is defined as metabolic profiling, and it is applied to define metabolic changes related to genetic differences, environmental influences and disease or drug perturbations. Chromatography–mass spectrometry (MS) platforms are frequently used to provide the sensitive and reproducible detection of hundreds to thousands of metabolites in a single biofluid or tissue sample. Here we describe the experimental workflow for long-term and large-scale metabolomic studies involving thousands of human samples with data acquired for multiple analytical batches over many months and years. Protocols for serum- and plasma-based metabolic profiling applying gas chromatography–MS (GC-MS) and ultraperformance liquid chromatography–MS (UPLC-MS) are described. These include biofluid collection, sample preparation, data acquisition, data pre-processing and quality assurance. Methods for quality control–based robust LOESS signal correction to provide signal correction and integration of data from multiple analytical batches are also described.

Metabolomic results on human blood plasma largely depend on the sample preparation protocols employed for protein precipitation and metabolite extraction. Five different extraction methods were examined, which can be grouped into two categories, liquid-liquid extraction and protein precipitation methods, including long-standing protocols such as the Folch extraction and Bligh-Dyer extraction in comparison to modern methods such as the Matyash protocol and two global metabolite extraction methods. Extracts were subjected to analysis of blood plasma lipids and primary metabolites by using chip-based direct infusion nanoelectrospray tandem mass spectrometry and gas chromatography coupled to time-of-flight mass spectrometry, respectively. Optimal extraction schemes were evaluated based on the number of identified metabolites, extraction efficiency, compound diversity, reproducibility, and convenience for high-throughput sample preparations. Results showed that Folch and Matyash methods were equally valid and robust for lipidomic assessments while primary metabolites were better assessed by the protein precipitation methods with organic solvent mixtures. Graphical Abstract Schematic workflow of five extraction methods and subsequent mass spectrometry analysis using GC-TOF MS and nanoelectrospray direct-infusion ion trap MS/MSᅟ