http://hdl.handle.net/10204/896 Wed, 22 May 2013 05:00:07 GMT 2013-05-22T05:00:07Z http://hdl.handle.net/10204/6675 Title: The short and long of it: shorter chromatographic analysis suffice for sample classification during UHPLC-MS-based metabolic fingerprinting Authors: Madala, NE; Tugizimana, F; Steenkamp, PA; Piater, LA; Dubery, IA Abstract: Ultra high-performance liquid chromatography hyphenated to mass spectrometry (UHPLC-MS) technologies has been widely applied in metabolomics, and the high resolution and peak capacity thereof are only some of the key aspects that are exploited in such and related fields. In the current study, we investigated if low resolution chromatography, with the aid of multivariate data analyses, could be sufficient for a metabolic fingerprinting study that aims at discriminating between samples of different biological status or origin. UHPLC-MS data from chemically-treated Arabidopsis thaliana plants were used and chromatograms with different gradient lengths were compared. MarkerLynxTM technology was employed for data mining, followed by principal component analysis (PCA) and orthogonal projections to latent structure discriminant analysis (OPLS-DA) as multivariate statistical interpretations. The results showed that, despite the congestion in low resolution chromatograms (of 5 and 10 min), samples could be classified based on their respective biological background in a similar manner as when using chromatograms with better resolution (of 20 and 40 min). This paper thus underlines that, in a metabolic fingerprinting study, low resolution chromatography together with multivariate data analyses suffice for biological classification of samples. The results also suggest that, depending on the initial objective of the undertaken study, optimisation in chromatographic resolution prior to full scale metabolomics studies is mandatory. Description: Copyright: 2012 Springer-Verlag. This is an ABSTRACT ONLY. The definitive version is published in Chromatographia, vol.76,(5-6), pp 279-285 Fri, 01 Mar 2013 00:00:00 GMT http://hdl.handle.net/10204/6675 2013-03-01T00:00:00Z http://hdl.handle.net/10204/6671 Title: Biotransformation of isonitrosoacetophenone (2-keto-2-phenyl-acetaldoxime) in tobacco cell suspensions Authors: Madala, NE; Steenkamp, PA; Piater, LA; Dubery, IA Abstract: Nicotiana tabacum cell suspensions, 2g wet wt/ml, rapidly took up 1 mM isonitrosoacetophenone (INAP), a plant-derived stress metabolite with anti-oxidative and anti-fungal properties, producing 40-hexopyranosyloxy-30-methoxyisonitrosoacetophenone in 54 %yield over 18 h. Unconverted INAP was at 33 lM. UPLC–MS/MS analyses with MassFragment software were used for metabolite identification. INAP had been hydroxylated at its meta- and para-positions as well as undergoing subsequent methoxylation and glycosylation. INAP is thus recognized by the enzymatic machinery of the phenylpropanoid pathway and is converted to a molecule with a substitution pattern similar to ferulic acid. Description: Copyright: 2012 Springer Netherlands. This is an ABSTRACT ONLY. The definitive version is published in Biotechnology Letters, vol.34(7), pp 1351-1356 Sun, 01 Jul 2012 00:00:00 GMT http://hdl.handle.net/10204/6671 2012-07-01T00:00:00Z http://hdl.handle.net/10204/6670 Title: Collision energy alteration during mass spectrometric acquisition is essential to ensure unbiased metabolomic analysis Authors: Madala, NE; Steenkamp, PA; Piater, LA; Dubery, IA Abstract: Metabolomics entails identification and quantification of all metabolites within a biological system with a given physiological status; as such, it should be unbiased. A variety of techniques are used to measure the metabolite content of living systems, and results differ with the mode of data acquisition and output generation. LC–MS is one of many techniques that has been used to study the metabolomes of different organisms but, although used extensively, it does not provide a complete metabolic picture. Recent developments in technology, for example the introduction of UPLC–ESI–MS, have, however, seen LC–MS become the preferred technique for metabolomics. Here, we show that when MS settings are varied in UPLC–ESI–MS, different metabolite profiles result from the same sample. During use of a Synapt UPLC–high definition MS instrument, the collision energy was continually altered (3, 10, 20, and 30 eV) during MS acquisition. PCA and OPLS-DA analysis of the generated UPLC–MS data of metabolites extracted from elicited tobacco cells revealed different clustering and different distribution patterns. As expected, ion abundance decreases with increasing collision energy, but, more importantly, results in unique multivariate data patterns from the same samples. Our findings suggest that different collision energy settings should be investigated during MS data acquisition because these can contribute to coverage of a wider range of the metabolome by UPLC–ESI–MS and prevent biased results. Description: Copyright: 2012 Springer-Verlag. This is an ABSTRACT ONLY. The definitive version is published in Analytical and Bioanalytical Chemistry, vol. 404(2), pp 367-372 Wed, 01 Aug 2012 00:00:00 GMT http://hdl.handle.net/10204/6670 2012-08-01T00:00:00Z http://hdl.handle.net/10204/6655 Title: Spherical stochastic neighbor embedding of hyperspectral data Authors: Lunga, D; Ersoy, O Abstract: In hyperspectral imagery, low-dimensional representations are sought in order to explain well the nonlinear characteristics that are hidden in high-dimensional spectral channels. While many algorithms have been proposed for dimension reduction and manifold learning in Euclidean spaces, very few attempts have focused on non-Euclidean spaces. Here, we propose a novel approach that embeds hyperspectral data, transformed into bilateral probability similarities, onto a nonlinear unit norm coordinate system. By seeking a unit l2-norm nonlinear manifold, we encode similarity representations onto a space in which important regularities in data are easily captured. In its general application, the technique addresses problems related to dimension reduction and visualization of hyperspectral images. Unlike methods such as multidimensional scaling and spherical embeddings, which are based on the notion of pairwise distance computations, our approach is based on a stochastic objective function of spherical coordinates. This allows the use of an Exit probability distribution to discover the nonlinear characteristics that are inherent in hyperspectral data. In addition, the method directly learns the probability distribution over neighboring pixel maps while computing for the optimal embedding coordinates. As part of evaluation, classification experiments were conducted on the manifold spaces for hyperspectral data acquired by multiple sensors at various spatial resolutions over different types of land cover. Various visualization and classification comparisons to five existing techniques demonstrated the strength of the proposed approach while its algorithmic nature is guaranteed to converge to meaningful factors underlying the data. Description: Copyright: 2012 IEEE Xplore. This is an ABSTRACT ONLY. The definitive version is published in IEEE Transactions on Geoscience and Remote Sensing, vol. 51(2), pp 857- 871 Sun, 01 Jul 2012 00:00:00 GMT http://hdl.handle.net/10204/6655 2012-07-01T00:00:00Z