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Metabolomic method: UPLC-q-ToF polar and non-polar metabolites in the healthy rat cerebellum using an in-vial dual extraction.

Ebshiana AA, Snowden SG, Thambisetty M, Parsons R, Hye A, Legido-Quigley C - PLoS ONE (2015)

Bottom Line: To date however only a small number of metabolomic studies have been applied to studying the metabolite composition of tissue samples, this is due, in part to a number of technical challenges including scarcity of material and difficulty in extracting metabolites.The aim of this study was to develop a method for maximising the biological information obtained from small tissue samples by optimising sample preparation, LC-MS analysis and metabolite identification.The described metabolomics method includes a database for 200 metabolites, retention time, mass and relative intensity, and presents the basal metabolite composition for brain tissue in the healthy rat cerebellum.

View Article: PubMed Central - PubMed

Affiliation: Institute of Pharmaceutical Sciences, King's College London, Franklin-Wilkins Building, 150 Stamford Street, London, SE1 9NH, United Kingdom.

ABSTRACT
Unbiased metabolomic analysis of biological samples is a powerful and increasingly commonly utilised tool, especially for the analysis of bio-fluids to identify candidate biomarkers. To date however only a small number of metabolomic studies have been applied to studying the metabolite composition of tissue samples, this is due, in part to a number of technical challenges including scarcity of material and difficulty in extracting metabolites. The aim of this study was to develop a method for maximising the biological information obtained from small tissue samples by optimising sample preparation, LC-MS analysis and metabolite identification. Here we describe an in-vial dual extraction (IVDE) method, with reversed phase and hydrophilic liquid interaction chromatography (HILIC) which reproducibly measured over 4,000 metabolite features from as little as 3mg of brain tissue. The aqueous phase was analysed in positive and negative modes following HILIC separation in which 2,838 metabolite features were consistently measured including amino acids, sugars and purine bases. The non-aqueous phase was also analysed in positive and negative modes following reversed phase separation gradients respectively from which 1,183 metabolite features were consistently measured representing metabolites such as phosphatidylcholines, sphingolipids and triacylglycerides. The described metabolomics method includes a database for 200 metabolites, retention time, mass and relative intensity, and presents the basal metabolite composition for brain tissue in the healthy rat cerebellum.

No MeSH data available.


Related in: MedlinePlus

Applied analytical pipeline.Shows the seven steps from tissue sectioning to IVDE and onto data processing and multivariate analysis of variation.
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pone.0122883.g002: Applied analytical pipeline.Shows the seven steps from tissue sectioning to IVDE and onto data processing and multivariate analysis of variation.

Mentions: In this study two primary experiments were performed to assess the precision and sensitivity of the IVDE, instrument methods and tissue homogenisation as well as to determine the effect of sample mass on metabolite recovery. The first experiment was designed to assess the combined variability of the IVDE and instrument methods. This was done by homogenising a single piece (18mg) of rat cerebellum, removing sample mass and tissue homogenisation as sources of variability. The homogenate was split into 7 aliquots of 50μl which underwent parallel extractions prior to injection on both HILIC and reversed phase methods (Fig 1A). The second experiment was designed to assess the effect of the mass of tissue extracted and tissue homegenistation on method sensitivity and precision. Four Sprague-Drawly rat brain were obtained and this material was used to perform Experiment 2. To do this 15 tissue samples ranging from 3–17mg were homogenised and extracted in parallel prior to analysis (Fig 1B). Sensitivity was assessed in terms of the number of metabolite features that are routinely detected, whilst precision will be assessed in terms of the variability (coefficient of variation) of the abundance of internal standard and metabolite peaks as well as the degree of compositional similarity between samples as determined principal component analysis (PCA). A graphical description of the analytical workflow used in this study is shown in Fig 2.


Metabolomic method: UPLC-q-ToF polar and non-polar metabolites in the healthy rat cerebellum using an in-vial dual extraction.

Ebshiana AA, Snowden SG, Thambisetty M, Parsons R, Hye A, Legido-Quigley C - PLoS ONE (2015)

Applied analytical pipeline.Shows the seven steps from tissue sectioning to IVDE and onto data processing and multivariate analysis of variation.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4390242&req=5

pone.0122883.g002: Applied analytical pipeline.Shows the seven steps from tissue sectioning to IVDE and onto data processing and multivariate analysis of variation.
Mentions: In this study two primary experiments were performed to assess the precision and sensitivity of the IVDE, instrument methods and tissue homogenisation as well as to determine the effect of sample mass on metabolite recovery. The first experiment was designed to assess the combined variability of the IVDE and instrument methods. This was done by homogenising a single piece (18mg) of rat cerebellum, removing sample mass and tissue homogenisation as sources of variability. The homogenate was split into 7 aliquots of 50μl which underwent parallel extractions prior to injection on both HILIC and reversed phase methods (Fig 1A). The second experiment was designed to assess the effect of the mass of tissue extracted and tissue homegenistation on method sensitivity and precision. Four Sprague-Drawly rat brain were obtained and this material was used to perform Experiment 2. To do this 15 tissue samples ranging from 3–17mg were homogenised and extracted in parallel prior to analysis (Fig 1B). Sensitivity was assessed in terms of the number of metabolite features that are routinely detected, whilst precision will be assessed in terms of the variability (coefficient of variation) of the abundance of internal standard and metabolite peaks as well as the degree of compositional similarity between samples as determined principal component analysis (PCA). A graphical description of the analytical workflow used in this study is shown in Fig 2.

Bottom Line: To date however only a small number of metabolomic studies have been applied to studying the metabolite composition of tissue samples, this is due, in part to a number of technical challenges including scarcity of material and difficulty in extracting metabolites.The aim of this study was to develop a method for maximising the biological information obtained from small tissue samples by optimising sample preparation, LC-MS analysis and metabolite identification.The described metabolomics method includes a database for 200 metabolites, retention time, mass and relative intensity, and presents the basal metabolite composition for brain tissue in the healthy rat cerebellum.

View Article: PubMed Central - PubMed

Affiliation: Institute of Pharmaceutical Sciences, King's College London, Franklin-Wilkins Building, 150 Stamford Street, London, SE1 9NH, United Kingdom.

ABSTRACT
Unbiased metabolomic analysis of biological samples is a powerful and increasingly commonly utilised tool, especially for the analysis of bio-fluids to identify candidate biomarkers. To date however only a small number of metabolomic studies have been applied to studying the metabolite composition of tissue samples, this is due, in part to a number of technical challenges including scarcity of material and difficulty in extracting metabolites. The aim of this study was to develop a method for maximising the biological information obtained from small tissue samples by optimising sample preparation, LC-MS analysis and metabolite identification. Here we describe an in-vial dual extraction (IVDE) method, with reversed phase and hydrophilic liquid interaction chromatography (HILIC) which reproducibly measured over 4,000 metabolite features from as little as 3mg of brain tissue. The aqueous phase was analysed in positive and negative modes following HILIC separation in which 2,838 metabolite features were consistently measured including amino acids, sugars and purine bases. The non-aqueous phase was also analysed in positive and negative modes following reversed phase separation gradients respectively from which 1,183 metabolite features were consistently measured representing metabolites such as phosphatidylcholines, sphingolipids and triacylglycerides. The described metabolomics method includes a database for 200 metabolites, retention time, mass and relative intensity, and presents the basal metabolite composition for brain tissue in the healthy rat cerebellum.

No MeSH data available.


Related in: MedlinePlus