The short and long of it: shorter chromatographic analysis suffice for sample classification during UHPLC-MS-based metabolic fingerprinting

Madala, NETugizimana, FSteenkamp, PAPiater, LADubery, IA2013-04-172013-04-172013-03Madala, N.E, Tugizimana, F, Steenkamp, P.A, Piater, L.A and Dubery, I.A. 2012. The short and long of it: Shorter chromatographic analysis suffice for sample classification during UHPLC-MS-based metabolic fingerprinting. Chromatographia, vol. 76, (5-6), pp 279-2850009-5893http://link.springer.com/article/10.1007%2Fs10337-012-2336-zhttp://hdl.handle.net/10204/6675Copyright: 2012 Springer-Verlag. This is an ABSTRACT ONLY. The definitive version is published in Chromatographia, vol.76,(5-6), pp 279-285Ultra 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.enData miningUltra high-performance liquid chromatography-mass spectrometryUHPLC-MSMetabolic fingerprintingMetabolomicsMultivariate data analysisThe short and long of it: shorter chromatographic analysis suffice for sample classification during UHPLC-MS-based metabolic fingerprintingArticleMadala, N., Tugizimana, F., Steenkamp, P., Piater, L., & Dubery, I. (2013). The short and long of it: shorter chromatographic analysis suffice for sample classification during UHPLC-MS-based metabolic fingerprinting. http://hdl.handle.net/10204/6675Madala, NE, F Tugizimana, PA Steenkamp, LA Piater, and IA Dubery "The short and long of it: shorter chromatographic analysis suffice for sample classification during UHPLC-MS-based metabolic fingerprinting." (2013) http://hdl.handle.net/10204/6675Madala N, Tugizimana F, Steenkamp P, Piater L, Dubery I. The short and long of it: shorter chromatographic analysis suffice for sample classification during UHPLC-MS-based metabolic fingerprinting. 2013; http://hdl.handle.net/10204/6675.TY - Article AU - Madala, NE AU - Tugizimana, F AU - Steenkamp, PA AU - Piater, LA AU - Dubery, IA AB - 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. DA - 2013-03 DB - ResearchSpace DP - CSIR KW - Data mining KW - Ultra high-performance liquid chromatography-mass spectrometry KW - UHPLC-MS KW - Metabolic fingerprinting KW - Metabolomics KW - Multivariate data analysis LK - https://researchspace.csir.co.za PY - 2013 SM - 0009-5893 T1 - The short and long of it: shorter chromatographic analysis suffice for sample classification during UHPLC-MS-based metabolic fingerprinting TI - The short and long of it: shorter chromatographic analysis suffice for sample classification during UHPLC-MS-based metabolic fingerprinting UR - http://hdl.handle.net/10204/6675 ER -