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A high-throughput platform for detailed lipidomic analysis of a range of mouse and human tissues

Lipidomics is of increasing interest in studies of biological systems. However, high-throughput data collection and processing remains non-trivial, making assessment of phenotypes difficult. We describe a platform for surveying the lipid fraction for a range of tissues. These techniques are demonstr...

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Autores principales: Furse, Samuel, Fernandez-Twinn, Denise S., Jenkins, Benjamin, Meek, Claire L., Williams, Huw E. L., Smith, Gordon C. S., Charnock-Jones, D. Stephen, Ozanne, Susan E., Koulman, Albert
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7196091/
https://www.ncbi.nlm.nih.gov/pubmed/32144454
http://dx.doi.org/10.1007/s00216-020-02511-0
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author Furse, Samuel
Fernandez-Twinn, Denise S.
Jenkins, Benjamin
Meek, Claire L.
Williams, Huw E. L.
Smith, Gordon C. S.
Charnock-Jones, D. Stephen
Ozanne, Susan E.
Koulman, Albert
author_facet Furse, Samuel
Fernandez-Twinn, Denise S.
Jenkins, Benjamin
Meek, Claire L.
Williams, Huw E. L.
Smith, Gordon C. S.
Charnock-Jones, D. Stephen
Ozanne, Susan E.
Koulman, Albert
author_sort Furse, Samuel
collection PubMed
description Lipidomics is of increasing interest in studies of biological systems. However, high-throughput data collection and processing remains non-trivial, making assessment of phenotypes difficult. We describe a platform for surveying the lipid fraction for a range of tissues. These techniques are demonstrated on a set of seven different tissues (serum, brain, heart, kidney, adipose, liver, and vastus lateralis muscle) from post-weaning mouse dams that were either obese (> 12 g fat mass) or lean (<5 g fat mass). This showed that the lipid metabolism in some tissues is affected more by obesity than others. Analysis of human serum (healthy non-pregnant women and pregnant women at 28 weeks’ gestation) showed that the abundance of several phospholipids differed between groups. Human placenta from mothers with high and low BMI showed that lean placentae contain less polyunsaturated lipid. This platform offers a way to map lipid metabolism with immediate application in metabolic research and elsewhere. [Figure: see text] ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00216-020-02511-0) contains supplementary material, which is available to authorized users.
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spelling pubmed-71960912020-05-05 A high-throughput platform for detailed lipidomic analysis of a range of mouse and human tissues Furse, Samuel Fernandez-Twinn, Denise S. Jenkins, Benjamin Meek, Claire L. Williams, Huw E. L. Smith, Gordon C. S. Charnock-Jones, D. Stephen Ozanne, Susan E. Koulman, Albert Anal Bioanal Chem Research Paper Lipidomics is of increasing interest in studies of biological systems. However, high-throughput data collection and processing remains non-trivial, making assessment of phenotypes difficult. We describe a platform for surveying the lipid fraction for a range of tissues. These techniques are demonstrated on a set of seven different tissues (serum, brain, heart, kidney, adipose, liver, and vastus lateralis muscle) from post-weaning mouse dams that were either obese (> 12 g fat mass) or lean (<5 g fat mass). This showed that the lipid metabolism in some tissues is affected more by obesity than others. Analysis of human serum (healthy non-pregnant women and pregnant women at 28 weeks’ gestation) showed that the abundance of several phospholipids differed between groups. Human placenta from mothers with high and low BMI showed that lean placentae contain less polyunsaturated lipid. This platform offers a way to map lipid metabolism with immediate application in metabolic research and elsewhere. [Figure: see text] ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00216-020-02511-0) contains supplementary material, which is available to authorized users. Springer Berlin Heidelberg 2020-03-07 2020 /pmc/articles/PMC7196091/ /pubmed/32144454 http://dx.doi.org/10.1007/s00216-020-02511-0 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Research Paper
Furse, Samuel
Fernandez-Twinn, Denise S.
Jenkins, Benjamin
Meek, Claire L.
Williams, Huw E. L.
Smith, Gordon C. S.
Charnock-Jones, D. Stephen
Ozanne, Susan E.
Koulman, Albert
A high-throughput platform for detailed lipidomic analysis of a range of mouse and human tissues
title A high-throughput platform for detailed lipidomic analysis of a range of mouse and human tissues
title_full A high-throughput platform for detailed lipidomic analysis of a range of mouse and human tissues
title_fullStr A high-throughput platform for detailed lipidomic analysis of a range of mouse and human tissues
title_full_unstemmed A high-throughput platform for detailed lipidomic analysis of a range of mouse and human tissues
title_short A high-throughput platform for detailed lipidomic analysis of a range of mouse and human tissues
title_sort high-throughput platform for detailed lipidomic analysis of a range of mouse and human tissues
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7196091/
https://www.ncbi.nlm.nih.gov/pubmed/32144454
http://dx.doi.org/10.1007/s00216-020-02511-0
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