Quantitative detection of metabolites using matrix-assisted laser desorption/ionization mass spectrometry with 9-aminoacridine as the matrix

Quantitative detection of metabolites is a highly desirable feature in metabolome analyses. Recently, the successful detection of multiple metabolites using matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) in the negative ion mode employing 9-aminoacridine as the organic matrix was reported (Edwards JL, Kennedy RT. Anal. Chem. 2005; 77: 2201-2209). However, there is little information available on quantitative detection of multiple metabolites using MALDI-MS and in particular the influence changes in metabolite levels have on such detections. We investigated this aspect by spiking a synthetic metabolite cocktail (consisting of 39 metabolites including amino acids, organic acids and phospho-metabolites) with five representative metabolites at increasing concentrations, one metabolite at a time, and assessed the signals from replicate determinations. It was possible to detect quantitative changes in the spiked metabolites. Although analyte suppression was observed, it was possible to observe scenarios where the spiked metabolite had little or no influence on the quantitative detection of some metabolites. It appears that the mass spectral response of the metabolite is suppressed only when the spiked chemical species are relatively similar in chemical terms. This suggests that quantitation is possible in scenarios where changes in a specific metabolite or a class of metabolites are monitored following appropriate analyte separation strategies, and that careful interpretations must be made when using the technique for quantitative analysis in unbiased metabolomic approaches.     

Comparative study of machine-learning and chemometric tools for analysis of in-vivo high-throughput screening data

High-throughput screening (HTS) has become a central tool of many pharmaceutical and crop-protection discovery operations. If HTS screening is carried out at the level of the intact organism, as is commonly done in crop protection, this strategy has the potential of uncovering a completely new mechanism of actions. The challenge in running a cost-effective HTS operation is to identify ways in which to improve the overall success rate in discovering new biologically active compounds. To this end, we describe our efforts directed at making full use of the data stream arising from HTS. This paper describes a comparative study in which several machine learning and chemometric methodologies were used to develop classifiers on the same data sets derived from in vivo HTS campaigns and their predictive performances compared in terms of false negative and false positive error profiles.     

Mn–Zn ferrite nanoparticles for ferrofluid preparation: Study on thermal–magnetic properties

Mn_1−xZn_xFe₂O₄ (with x   varying from 0.1 to 0.5) ferrite nanoparticles used for ferrofluid preparation have been prepared by chemical co-precipitation method and characterized. Characterization techniques like elemental analysis by atomic absorption spectroscopy and spectrophotometry, thermal analysis using simultaneous TG-DTA, XRD, TEM, VSM and Mossbauer spectroscopy have been utilized. The final cation contents estimated agree with the initial degree of substitution. The Curie temperature (_Tc$T_{\mathrm{c}}$) and particle size decrease with the increase in zinc substitution. In the case of particles with higher zinc concentration, both ferrimagnetic nanoparticles and particles exhibiting superparamagnetic behavior at room temperature are present. In addition, some of the results obtained by slightly altering the preparation condition are also discussed. The precipitated particles were used for ferrofluid preparation. The fine particles were suitably dispersed in heptane using oleic acid as the surfactant. The volatile nature of the carrier chosen helps in altering the number concentration of the magnetic particles in a ferrofluid. Magnetic properties of the fine particles and ferrofluids are discussed. Ferrofluids having Mn_0.5Zn_0.5Fe₂O₄ particles can be used for the energy conversion application utilizing the magnetically induced convection for thermal dissipation.     

Matrix-suppressed laser desorption/ionisation mass spectrometry and its suitability for metabolome analyses

Matrix-assisted laser desorption/ionisation (MALDI) mass spectrometry was investigated for the simultaneous detection of several metabolites, as applicable to global metabolite analysis (metabolomics). The commonly employed organic matrices alpha-cyano-4-hydroxycinnamic acid and 3,5-dihydroxybenzoic acid, in both the crystalline and ionic liquid forms, were investigated. The employment of a low matrix-to-analyte molar ratio suppressed matrix peaks and was effective in detecting all the metabolites with a unique mass in a 30-metabolite synthetic cocktail, albeit to varying degrees. These matrix-suppressed laser desorption/ionisation (MSLDI) analyses were performed in the positive ion mode, and metabolites were detected as the protonated [M+H]+, sodiated [M+Na]+ or potassiated [M+K]+ species. The spectral signals were dominated by basic metabolites. It was possible to detect components of a synthetic cocktail when it was spiked quantitatively into a microbial extract, demonstrating the feasibility of using the technique for detecting metabolite signals in a complex biological matrix. However, analyte suppression effects were noted when the relative proportion of one analyte was allowed to increasingly dominate the others in a mixture. The implications of the findings with respect to applications in metabolomic investigations are discussed.     

A solvation-based screening approach for metabolite arrays

This paper explores a new method for screening metabolites in an array format based on relative polarity using selective solvent dissolution. A synthetic cocktail of metabolites was spotted onto a hydrophobic silicon surface, and solubilised with solvents of varying polarity. The metabolites retained on the silicon surface after the solvent treatments were detected using time-of-flight static secondary ion mass spectrometry (ToF-sSIMS). Solvent-specific metabolite retention was clearly evident on multivariate analysis of the dataset, using principal component analysis. Selective removal of metabolites was observed when solvents with different polarity were used, with the metabolite retention or removal in most cases correlating to the polarity of the solvent used, although consideration of other forces in operation may be needed to arrive at fully predictable behaviours. This approach provides the basis for development of a technique to separate complex metabolites into simpler constituents in a metabolite array prior to identification and quantification using mass spectrometry. It is an analytical approach that is intermediate between the more rapid but less informative direct analysis methods (such as DIMS) that do not involve any analyte separations and the more comprehensive but time consuming methods (such as GC- and LC-MS) that involve chromatographic or electrophoretic separations. The approach has the potential to be successfully developed for rapid, yet informative screening of metabolomes.     

A selective metabolite array for the detection of phosphometabolites

Immobilised metal ion affinity (IMA) has been traditionally used specifically for the separation of phosphorylated proteins and nucleic acids, in proteomics and genomics, respectively. This report describes the novel application of IMA in metabolomics for the development of metabolite arrays to detect phosphometabolites using a plasma polymer-modified surface. Immobilisation of gallium, zirconium, cobalt, copper, zinc, nickel, iron, and chromium to acrylic acid plasma polymer followed by subsequent exposure to metabolites (phospho- and non-phosphometabolites) was investigated. Results analysed using ToF-SIMS suggests that gallium and zirconium exhibit higher phosphometabolite affinity and specificity compared to other metals, and can be used to develop metabolite arrays for the detection of phosphometabolites.     

Photoluminescence study of native defects in annealed GaAs

Low temperature (6°K) photoluminescence measurements have been performed on GaAs annealed under various conditions, to study defects generated by outdiffusion of the constituent atoms. Several defect-related luminescence peaks have been observed and associated with Ga and As outdiffusion. The outdiffusion of these elements during annealing to 850°C in vacuum and with Ga or As overpressure and SiO₂ coatings is studied by monitoring the intensities of these peaks.     

Simultaneous assay of pigments,carbohydrates, proteins and lipids in microalgae

Biochemical compositional analysis of microbial biomass is a useful tool that can provide insight into the behaviour of an organism and its adaptational response to changes in its environment. To some extent, it reflects the physiological and metabolic status of the organism. Conventional methods to estimate biochemical composition often employ different sample pretreatment strategies and analytical steps for analysing each major component, such as total proteins, carbohydrates, and lipids, making it labour-, time- and sample-intensive. Such analyses when carried out individually can also result in uncertainties of estimates as different pre-treatment or extraction conditions are employed for each of the component estimations and these are not necessarily standardised for the organism, resulting in observations that are not easy to compare within the experimental set-up or between laboratories. We recently reported a method to estimate total lipids in microalgae (Chen, Vaidyanathan, Anal. Chim. Acta, 724, 67–72). Here, we propose a unified method for the simultaneous estimation of the principal biological components, proteins, carbohydrates, lipids, chlorophyll and carotenoids, in a single microalgae culture sample that incorporates the earlier published lipid assay. The proposed methodology adopts an alternative strategy for pigment assay that has a high sensitivity. The unified assay is shown to conserve sample (by 79%), time (67%), chemicals (34%) and energy (58%) when compared to the corresponding assay for each component, carried out individually on different samples. The method can also be applied to other microorganisms, especially those with recalcitrant cell walls.