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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

Plots of sample mass in milligrams against intensity for 9 annotated metabolites.A) taurine B) hypoxanthine C) glutamate D) pantothenate E) aspartate F) glcosylceramide (36:1) G) phosphatidylethanolamine (38:4) H) ceramide (38:1) I) triglyceride (48:3).
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pone.0122883.g007: Plots of sample mass in milligrams against intensity for 9 annotated metabolites.A) taurine B) hypoxanthine C) glutamate D) pantothenate E) aspartate F) glcosylceramide (36:1) G) phosphatidylethanolamine (38:4) H) ceramide (38:1) I) triglyceride (48:3).

Mentions: Whilst all samples are compositionally similar it is important to determine the effect of the extracted tissue mass on metabolite composition. Looking at the PCA scores plot (Fig 6A) it can be seen that there is no bias in the distribution of samples based on the tissue mass, with low and high mass samples clustering together within the plot showing that they possess high levels of compositional similarity. As well as looking at the effect of sample mass on the compositional similarity it is important to assess its effect on the abundance of individual metabolites. Fig 7 shows the abundance of 9 annotated metabolites from both HILIC and reversed phase methods plotted against the tissue mass, these plots show no relationship between metabolite abundance and sample mass, with the strongest correlation being for glutamate (r = -0.24). This data shows that using between 3–17mg of sample material has no effect on the overall sample composition or the abundance of individual metabolites, showing this method can provide broad metabolite coverage when sample material is limited.


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)

Plots of sample mass in milligrams against intensity for 9 annotated metabolites.A) taurine B) hypoxanthine C) glutamate D) pantothenate E) aspartate F) glcosylceramide (36:1) G) phosphatidylethanolamine (38:4) H) ceramide (38:1) I) triglyceride (48:3).
© Copyright Policy
Related In: Results  -  Collection

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

pone.0122883.g007: Plots of sample mass in milligrams against intensity for 9 annotated metabolites.A) taurine B) hypoxanthine C) glutamate D) pantothenate E) aspartate F) glcosylceramide (36:1) G) phosphatidylethanolamine (38:4) H) ceramide (38:1) I) triglyceride (48:3).
Mentions: Whilst all samples are compositionally similar it is important to determine the effect of the extracted tissue mass on metabolite composition. Looking at the PCA scores plot (Fig 6A) it can be seen that there is no bias in the distribution of samples based on the tissue mass, with low and high mass samples clustering together within the plot showing that they possess high levels of compositional similarity. As well as looking at the effect of sample mass on the compositional similarity it is important to assess its effect on the abundance of individual metabolites. Fig 7 shows the abundance of 9 annotated metabolites from both HILIC and reversed phase methods plotted against the tissue mass, these plots show no relationship between metabolite abundance and sample mass, with the strongest correlation being for glutamate (r = -0.24). This data shows that using between 3–17mg of sample material has no effect on the overall sample composition or the abundance of individual metabolites, showing this method can provide broad metabolite coverage when sample material is limited.

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