Version 1
: Received: 13 April 2017 / Approved: 14 April 2017 / Online: 14 April 2017 (04:41:05 CEST)
How to cite:
Ebshiana, A.A.; Snowden, S.G.; Legido-Quigley, C. Multimodal Metabolomics Combining UPLC-qToF-MS and GC-MS Data in Plasma and Brain Tissue. Preprints2017, 2017040080. https://doi.org/10.20944/preprints201704.0080.v1
Ebshiana, A.A.; Snowden, S.G.; Legido-Quigley, C. Multimodal Metabolomics Combining UPLC-qToF-MS and GC-MS Data in Plasma and Brain Tissue. Preprints 2017, 2017040080. https://doi.org/10.20944/preprints201704.0080.v1
Ebshiana, A.A.; Snowden, S.G.; Legido-Quigley, C. Multimodal Metabolomics Combining UPLC-qToF-MS and GC-MS Data in Plasma and Brain Tissue. Preprints2017, 2017040080. https://doi.org/10.20944/preprints201704.0080.v1
APA Style
Ebshiana, A.A., Snowden, S.G., & Legido-Quigley, C. (2017). Multimodal Metabolomics Combining UPLC-qToF-MS and GC-MS Data in Plasma and Brain Tissue. Preprints. https://doi.org/10.20944/preprints201704.0080.v1
Chicago/Turabian Style
Ebshiana, A.A., Stuart G. Snowden and Cristina Legido-Quigley. 2017 "Multimodal Metabolomics Combining UPLC-qToF-MS and GC-MS Data in Plasma and Brain Tissue" Preprints. https://doi.org/10.20944/preprints201704.0080.v1
Abstract
Metabolomic analysis of biological fluids and tissues has become an increasingly routine tool in the biological toolbox. However, challenges remain to be overcome, including developing strategies to maximise coverage of the metabolome without requiring large sample volumes. Here we describe a multimodal strategy that combines data using both LC-MS and GC-MS from a unique vial with a sample of plasma (20µl) or a sample of brain tissue (3mg). Using a split phase extraction the non-aqueous phase was analyzed by reversed phase (RP) LC-MS, whilst the aqueous phase was analyzed using hydrophilic liquid interaction chromatography (HILIC)LC-MS, with both phases also analysed using GC-MS after derivatization of the extract. Analytical performance was assessed in 7 rat cerebellum samples and a pilot study of 40 plasma samples (20 vs. 20: AD vs. healthy controls). The method, which uses four hours of instrument time, measured 20,707 metabolite features in brain samples and 17,266 in plasma samples, from those 44.1% features displayed CV’s below 15% and 75.2% below 30%. The method has potential to resolve subtle biological differences and to correlate metabolite composition directly to clinical outcomes including MMSE, age and ADCS-ADL. This method can acquire in the order of 20K metabolic features when low volumes are available.
Keywords
metabolomics; LC-MS; GC-MS; plasma; brain tissue
Subject
Chemistry and Materials Science, Analytical Chemistry
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
