Flow Injection Electrospray Mass Spectrometry Research Articles

Disclosing the chemistry of oil curing by mass spectrometry using methyl linoleate as a model binder

The structure of the polymeric fraction in an oil painting is believed to be strongly connected to the stability of the paint layers over time, but its molecular characterisation is extremely difficult given the complex composition of a vegetable oil-based polymer.In this study we report the implementation of a methodological approach for the systematic mass spectrometric investigation of the molecular features of the products of oxidative degradation and cross-linking of oil paint layers upon curing. The approach is based on the use of methyl linoleate as a simplified model of an oil paint binder. Gas-chromatography coupled with mass spectrometry, solid phase microextraction gas chromatography mass spectrometry, flow injection electrospray mass spectrometry and evolved gas analysis mass spectrometry, are used to analyse the evolution of compounds produced over seven months of natural ageing, from the volatile products to the macromolecular and cross-linked fractions. The aim is to improve our fundamental molecular understanding of the curing process of oil paints, and to investigate the balance between oxidative degradation and cross-linking when specific binder-pigment combinations are in place.Model paint layers were prepared using lead white and ultramarine blue as pigments. These two pigments are known to produce paint layers with different stability over time. The use of methyl linoleate as model oil binder greatly simplifies the mass spectral features of the lipid paint fraction, enabling the detection of products of oxidation and cross-linking with a new high level of molecular detail. Data clearly show that crucial differences between paints containing the two pigments establish with time, which are mostly related to the cross-linked fraction.

Chemical and physicochemical properties of the high cohesive silicone gel from Poly Implant Prothèse (PIP) breast prostheses after explantation: A preliminary, comparative analytical investigation

Aim of this work was to gain a deeper insight into the analytical profile of the macromolecular and LMW fractions of polymeric silicones present in breast implants.The study was conducted on silicone gel samples from (i) breast prostheses (Poly Implant Prothèse, PIP) explanted from a patient that needed their therapeutical removal, (ii) from a virgin Mc Ghan 410 MX prosthesis and (iii) from a sample of technical-grade non-cohesive silicone. The gels were analysed using rheological techniques, attenuated total reflectance infrared spectroscopy (ATR-FT-IR), nuclear magnetic resonance (1H NMR), gas chromatography coupled to mass spectrometry (GC–MS) and flow injection electrospray mass spectrometry (FI-ESI-MS). Our results demonstrate that, compared to the virgin McGhan gel, the silicone present the PIP prostheses lacks a significant part of the cross-linking sites necessary for the high-cohesive properties of the gel, significant amounts of cholesterol have been absorbed from the breast tissue by the silicone material, demonstrating the lack of impermeability of its elastomer shell. The potential implications and consequences of these analytical results are discussed.

Characterization of flavonol glycosides in individual Arabidopsis root tips by flow injection electrospray mass spectrometry

Developments in mass spectrometry-based technologies are offering insights into the complexity and dynamic nature of plant metabolism. However, the ability to generate reliable metabolic profiles at high spatial resolution is still limited by the need of most technologies for large sample sizes or time-intensive extraction and detection methods. Here we describe the use of flow injection electrospray mass spectrometry for the rapid identification and semi-quantitative analysis of flavonol glycosides in individual root tips. This method uncovered spatial and temporal differences in metabolic profiles that were masked in analyses of whole roots or seedlings, while showing that individual biological replicates can be extremely consistent.

Enhancement of Plant Metabolite Fingerprinting by Machine Learning

Metabolite fingerprinting of Arabidopsis (Arabidopsis thaliana) mutants with known or predicted metabolic lesions was performed by (1)H-nuclear magnetic resonance, Fourier transform infrared, and flow injection electrospray-mass spectrometry. Fingerprinting enabled processing of five times more plants than conventional chromatographic profiling and was competitive for discriminating mutants, other than those affected in only low-abundance metabolites. Despite their rapidity and complexity, fingerprints yielded metabolomic insights (e.g. that effects of single lesions were usually not confined to individual pathways). Among fingerprint techniques, (1)H-nuclear magnetic resonance discriminated the most mutant phenotypes from the wild type and Fourier transform infrared discriminated the fewest. To maximize information from fingerprints, data analysis was crucial. One-third of distinctive phenotypes might have been overlooked had data models been confined to principal component analysis score plots. Among several methods tested, machine learning (ML) algorithms, namely support vector machine or random forest (RF) classifiers, were unsurpassed for phenotype discrimination. Support vector machines were often the best performing classifiers, but RFs yielded some particularly informative measures. First, RFs estimated margins between mutant phenotypes, whose relations could then be visualized by Sammon mapping or hierarchical clustering. Second, RFs provided importance scores for the features within fingerprints that discriminated mutants. These scores correlated with analysis of variance F values (as did Kruskal-Wallis tests, true- and false-positive measures, mutual information, and the Relief feature selection algorithm). ML classifiers, as models trained on one data set to predict another, were ideal for focused metabolomic queries, such as the distinctiveness and consistency of mutant phenotypes. Accessible software for use of ML in plant physiology is highlighted.

Measurement of dietary exposure: a challenging problem which may be overcome thanks to metabolomics?

The diet is an important environmental exposure, and its measurement is an essential component of much health-related research. However, conventional tools for measuring dietary exposure have significant limitations being subject to an unknown degree of misreporting and dependent upon food composition tables to allow estimation of intakes of energy, nutrients and non-nutrient food constituents. In addition, such tools may be inappropriate for use with certain groups of people. As an alternative approach, the recent techniques of metabolite profiling or fingerprinting, which allows simultaneous monitoring of multiple and dynamic components of biological fluids, may provide metabolic signals indicative of food intake. Samples can be analysed through numerous analytical platforms, followed by multivariate data analysis. In humans, metabolomics has been applied successfully in pharmacology, toxicology and medical screening, but nutritional metabolomics is still in its infancy. Biomarkers of a small number of specific foods and nutrients have been developed successfully but less targeted and more high-throughput methods, that do not need prior knowledge of which signals might be discriminatory, and which may allow a more global characterisation of dietary intake, remain to be tested. A proof a principle project (the MEDE Study) is currently underway in our laboratories to test the hypothesis that high-throughput, non-targeted metabolite fingerprinting using flow injection electrospray mass spectrometry can be applied to human biofluids (blood and urine) to characterise dietary exposure in humans.

High-throughput, nontargeted metabolite fingerprinting using nominal mass flow injection electrospray mass spectrometry.

Flow injection electrospray-mass spectrometry (FIE-MS) is finding utility as a first-pass metabolite fingerprinting tool in many research fields. We provide a protocol that has proved reliable in large-scale research projects involving diverse sample matrices originating from plants, microbes and mammalian biofluids. Using Brachypodium leaf material as an example matrix all steps are summarized from sample extraction to data quality assessment. Alternative procedures for dealing with other common matrices such as bloods and urine are included. With little sample pretreatment, no chromatography and instrument cycle times of <5 min it is feasible to analyze >1,000 samples per week. Analysis of a typical batch of 240 samples (including first-pass data analysis) can be accomplished within 48 h.

Explanatory signal interpretation and metabolite identification strategies for nominal mass FIE-MS metabolite fingerprints

Flow injection electrospray mass spectrometry (FIE-MS) metabolite fingerprinting is widely used as a 'first pass' screen for compositional differences, where discrimination between samples can be achieved without any preconceptions. Powerful data analysis algorithms can be used to select and rank FIE-MS fingerprint variables highly explanatory of the biological problem under investigation. We describe how to create a species-specific FIE-MS/MS(n) metabolite database and how to then query the database to predict the identity of highly significant variables within FIE-MS fingerprints. The protocol details how to interpret m/z signals within the explanatory variable list based on a correlation analysis in conjunction with an investigation of mathematical relationships regarding (de)protonated molecular ions, salt adducts, neutral losses and dimeric associations routinely observed in FIE-MS fingerprints. Although designed for use by biologists/analytical chemists, collaboration with data-mining experts is generally advised. The protocol is applicable in any areas of bioscience research involving FIE-MS fingerprinting.

Preprocessing, classification modeling and feature selection using flow injection electrospray mass spectrometry metabolite fingerprint data

Metabolome analysis by flow injection electrospray mass spectrometry (FIE-MS) fingerprinting generates measurements relating to large numbers of m/z signals. Such data sets often exhibit high variance with a paucity of replicates, thus providing a challenge for data mining. We describe data preprocessing and modeling methods that have proved reliable in projects involving samples from a range of organisms. The protocols interact with software resources specifically for metabolomics provided in a Web-accessible data analysis package FIEmspro (http://users.aber.ac.uk/jhd) written in the R environment and requiring a moderate knowledge of R command-line usage. Specific emphasis is placed on describing the outcome of modeling experiments using FIE-MS data that require further preprocessing to improve quality. The salient features of both poor and robust (i.e., highly generalizable) multivariate models are outlined together with advice on validating classifiers and avoiding false discovery when seeking explanatory variables.

Discovery of novel inhibitors of Bcl-xL using multiple high-throughput screening platforms

Bcl-xL is a member of the Bcl-2 family of proteins that are implicated to play a vital role in several diseases including cancer. Bcl-xL suppresses apoptosis; thus the inhibition of Bcl-xL function could restore the apoptotic process. To identify antagonists of Bcl-xL function, two ultra-high-throughput screens were implemented. An activity assay utilized fluorescence polarization, based on the binding of fluorescein-labeled peptide [the BH3 domain of BAD protein (F-Bad 6)] to Bcl-xL. A 384-well plate assay with mixtures of 10 drug compounds per well, combined with a fast plate reader, resulted in a throughput of 46,080 data points/day. Utilizing this screening format, 370,400 compounds were screened in duplicate and 425 inhibitors with an IC 50 below 100 μM were identified. The second assay format, affinity selection/mass spectrometry (ASMS), used ultrafiltration to separate Bcl-xL binders from nonbinders in mixtures of 2400 compounds. The bound species were subsequently separated from the protein and analyzed by flow injection electrospray mass spectrometry. Utilizing the ASMS format, 263,382 compounds were screened in duplicate and 29 binders with affinities below 100 μM were identified. Two novel classes of Bcl-xL inhibitors were identified by both methods and confirmed to bind 13C-labeled Bcl-xL using heteronuclear magnetic resonance spectroscopy.

Determination of diamino acids in peptidoglycans from anaerobic bacteria.

Peptidoglycans isolated from two Fusobacterium species of anaerobic bacteria were analyzed for constituent amino acids. Hydrolysis conditions were varied to optimize the yield of diamino acids from peptidoglycan. The o-phthalaldehyde derivatives of the diamino acid stereoisomers were separated by high-performance liquid chromatography (OPA-HPLC), and variations in the relative areas of the two peaks noticed during analysis of solid samples were attributed to sampling errors. Co-derivatization/injection experiments using standards of the meso and rac forms separated from commercial mixtures demonstrated that meso-2,6-diaminopimelic acid and meso-lanthionine were peptidoglycan components in Fusobacterium varium and Fusobacterium nucleatum, respectively. The protonated molecules of 2,6-diaminopimelic acid and lanthionine were detected in peptidoglycan hydrolyzates by off-line, flow-injection electrospray mass spectrometry (ESI-MS). In ESI-MS-MS experiments under identical collision-induced dissociation (CID) conditions, peptidoglycan-derived and standard diamino acids exhibited similar fragmentations. Fragmentation pathways are proposed for each diamino acid. The results confirm that the meso forms of two different diamino acids are utilized in the peptidoglycans of Fusobacterium species.

Determination of perchlorate at trace levels in drinking water by ion-pair extraction with electrospray ionization mass spectrometry.

Perchlorate has been added to the U.S. Environmental Protection Agency's Drinking Water Contaminant Candidate List (CCL). The present work describes the analysis of perchlorate in water by liquid-liquid extraction followed by flow injection electrospray mass spectrometry (ESI/MS). Cationic surfactants, mostly alkyltrimethyl-ammonium salts, are used to ion-pair aqueous perchlorate, forming extractable ion pairs. The cationic surfactant associates with the perchlorate ion to form a complex detectable by ESI/MS. The selectivity of the extraction and the mass spectrometric detection increases confidence in the identification of perchlorate. The method detection limit for perchlorate based on 3.14 sigma n-1 of seven replicate injections was 100 ng L-1 (parts per trillion). Standard addition was used to quantitate perchlorate in a drinking water sample from a contaminated source, and the concentration determined agreed within experimental error with the concentration determined by ion chromatography.

Letter: Detection of reaction intermediates by flow injection electrospray ionization mass spectrometry: reaction of chemiluminescent N-sulfonylacridinium-9-carboxamides with hydrogen peroxide

Flow injection electrospray mass spectrometry was used to detect the intermediates and products formed during the reaction of chemiluminescent acridinium salts under the conditions necessary for light emission. A stream of aqueous alkaline hydrogen peroxide was mixed with an aqueous solution of N-sulfonylacridinium-9-carboxamide salt immediately prior to entering the ESI-MS interface. The resulting negative-ion mass spectra corresponded to the expected 9-hydroperoxide adduct, the acridone end product normally seen in the chemiluminescent reaction, and unreacted acridinium salt, with no indication of the postulated spirodioxetanone intermediate or competing pseudobase.