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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

Principal component analysis (components = 2, R2X – 0.439, Q2–0.210) of metabolite features identified in at least 70% of samples in experiment 1.A) scores plot and B) Hotelling’s T2 and C) DModX plot, showing that sample mass has no effect on overall metabolite composition.
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pone.0122883.g004: Principal component analysis (components = 2, R2X – 0.439, Q2–0.210) of metabolite features identified in at least 70% of samples in experiment 1.A) scores plot and B) Hotelling’s T2 and C) DModX plot, showing that sample mass has no effect on overall metabolite composition.

Mentions: Having considered the behaviour of individual metabolite peaks, the final step in assessing the method performance is to look at the similarity of the overall composition of the analysed samples. Principal component analysis (PCA) was performed on all 12,274 metabolite features that were identified in at least 70% of samples (Fig 4). This PCA revealed little structure within the data with the first component accounting for only 25.3% of the total variability with a predictive performance of Q2 = -0.10, with the first two components accounting for just 43.9% of variability with a predictive performance of Q2 = -0.21. The distance of a samples metabolite composition to a calculated average composition was assessed using the Hotelling’s T2 range plot (Fig 4B). This plot shows that all of the samples are compositionally similar both to each other and the calculated average, with all samples having a T2 of < 5 with the 95% confidence interval set at 13.88. The distance of samples to the model was assessed using the DModX plot (Fig 4C), which shows that the samples have a low residual of difference to the fitted model with all of the observations falling below the Dcritical(0.05) threshold. This combined with the Hotelling’s T2 show that all of the samples are compositionally similar and that there are no outliers to the model.


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)

Principal component analysis (components = 2, R2X – 0.439, Q2–0.210) of metabolite features identified in at least 70% of samples in experiment 1.A) scores plot and B) Hotelling’s T2 and C) DModX plot, showing that sample mass has no effect on overall metabolite composition.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4390242&req=5

pone.0122883.g004: Principal component analysis (components = 2, R2X – 0.439, Q2–0.210) of metabolite features identified in at least 70% of samples in experiment 1.A) scores plot and B) Hotelling’s T2 and C) DModX plot, showing that sample mass has no effect on overall metabolite composition.
Mentions: Having considered the behaviour of individual metabolite peaks, the final step in assessing the method performance is to look at the similarity of the overall composition of the analysed samples. Principal component analysis (PCA) was performed on all 12,274 metabolite features that were identified in at least 70% of samples (Fig 4). This PCA revealed little structure within the data with the first component accounting for only 25.3% of the total variability with a predictive performance of Q2 = -0.10, with the first two components accounting for just 43.9% of variability with a predictive performance of Q2 = -0.21. The distance of a samples metabolite composition to a calculated average composition was assessed using the Hotelling’s T2 range plot (Fig 4B). This plot shows that all of the samples are compositionally similar both to each other and the calculated average, with all samples having a T2 of < 5 with the 95% confidence interval set at 13.88. The distance of samples to the model was assessed using the DModX plot (Fig 4C), which shows that the samples have a low residual of difference to the fitted model with all of the observations falling below the Dcritical(0.05) threshold. This combined with the Hotelling’s T2 show that all of the samples are compositionally similar and that there are no outliers to the model.

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