Abstract
The use of tissue as a matrix to elucidate disease pathology or explore intervention comes with several advantages. It allows investigation of the target alteration directly at the focal location and facilitates the detection of molecules that could become elusive after secretion into biofluids. However, tissue metabolomics/metabonomics comes with challenges not encountered in biofluid analyses. Furthermore, tissue heterogeneity does not allow for tissue aliquoting. Here we describe a multiplatform, multi-method workflow which enables metabolic profiling analysis of tissue samples, while it can deliver enhanced metabolome coverage. After applying a dual consecutive extraction (organic followed by aqueous), tissue extracts are analyzed by reversed-phase (RP-) and hydrophilic interaction liquid chromatography (HILIC-) ultra-performance liquid chromatography coupled to mass spectrometry (UPLC-MS) and nuclear magnetic resonance (NMR) spectroscopy. This pipeline incorporates the required quality control features, enhances versatility, allows provisional aliquoting of tissue extracts for future guided analyses, expands the range of metabolites robustly detected, and supports data integration. It has been successfully employed for the analysis of a wide range of tissue types.
Key words
- Metabolomics
- Metabonomics
- Metabolic profiling
- Metabolic phenotyping
- Lipidomics
- Tissue
- Extraction
- Metabolome
- Lipidome
- Coverage
- Multiplatform
- NMR
- UPLC-MS
- MSE
- HILIC
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References
Lamour SD, Veselkov KA, Posma JM et al (2015) Metabolic, immune, and gut microbial signals mount a systems response to Leishmania major infection. J Proteome Res 14:318–329. https://doi.org/10.1021/pr5008202
Folch J, Lees M, Sloane Stanley GH (1957) A simple method for the isolation and purification of total lipides from animal tissues. J Biol Chem 226:497–509
Bligh EG, Dyer WJ (1959) A rapid method of total lipid extraction and purification. Can J Biochem Physiol 37:911–917
Geier FM, Want EJ, Leroi AM et al (2011) Cross-platform comparison of Caenorhabditis elegans tissue extraction strategies for comprehensive metabolome coverage. Anal Chem 83:3730–3736. https://doi.org/10.1021/ac2001109
Masson P, Spagou K, Nicholson JK et al (2011) Technical and biological variation in UPLC-MS-based untargeted metabolic profiling of liver extracts: application in an experimental toxicity study on galactosamine. Anal Chem 83:1116–1123. https://doi.org/10.1021/ac103011b
Anwar MA, Vorkas P, Li JV et al (2015) Optimization of metabolite extraction of human vein tissue for ultra performance liquid chromatography-mass spectrometry and nuclear magnetic resonance-based untargeted metabolic profiling. Analyst 140:7586–7597
Vorkas PA, Isaac G, Anwar MA et al (2015) Untargeted UPLC-MS profiling pipeline to expand tissue metabolome coverage: application to cardiovascular disease. Anal Chem 87:4184–4193. https://doi.org/10.1021/ac503775m
Vorkas PA, Shalhoub J, Isaac G et al (2015) Metabolic phenotyping of atherosclerotic plaques reveals latent associations between free cholesterol and ceramide metabolism in atherogenesis. J Proteome Res 14:1389–1399. https://doi.org/10.1021/pr5009898
Ashrafian H, Li JV, Spagou K et al (2014) Bariatric surgery modulates circulating and cardiac metabolites. J Proteome Res 13:570–580. https://doi.org/10.1021/pr400748f
Anwar MA, Vorkas PA, Li J et al (2016) Prolonged mechanical circumferential stretch induces metabolic changes in rat inferior vena cava. Eur J Vasc Endovasc 52:544–552. https://doi.org/10.1016/j.ejvs.2016.07.002
Vorkas PA, Shalhoub J, Lewis MR et al (2016) Metabolic phenotypes of carotid atherosclerotic plaques relate to stroke risk: an exploratory study. Eur J Vasc Endovasc 52:5–10. https://doi.org/10.1016/j.ejvs.2016.01.022
Anwar MA, Adesina-Georgiadis KN, Spagou K et al (2017) A comprehensive characterisation of the metabolic profile of varicose veins; implications in elaborating plausible cellular pathways for disease pathogenesis. Sci Rep 7:2989. https://doi.org/10.1038/s41598-017-02529-y
Shannon P, Markiel A, Ozier O et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498–2504. https://doi.org/10.1101/gr.1239303
Kanehisa M, Goto S (2000) KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28:27–30
Beckonert O, Keun HC, Ebbels TM et al (2007) Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts. Nat Protoc 2:2692–2703. https://doi.org/10.1038/nprot.2007.376. nprot.2007.376 [pii]
Nicholson JK, Foxall PJ, Spraul M et al (1995) 750 MHz 1H and 1H-13C NMR spectroscopy of human blood plasma. Anal Chem 67:793–811
Yap IK, Brown IJ, Chan Q et al (2010) Metabolome-wide association study identifies multiple biomarkers that discriminate north and south Chinese populations at differing risks of cardiovascular disease: INTERMAP study. J Proteome Res 9:6647–6654. https://doi.org/10.1021/pr100798r
Saric J, Wang Y, Li J et al (2008) Species variation in the fecal metabolome gives insight into differential gastrointestinal function. J Proteome Res 7:352–360. https://doi.org/10.1021/pr070340k
Lofstedt T, Trygg J (2011) OnPLS-a novel multiblock method for the modelling of predictive and orthogonal variation. J Chemom 25:441–455. https://doi.org/10.1002/cem.1388
Veselkov KA, Vingara LK, Masson P et al (2011) Optimized preprocessing of ultra-performance liquid chromatography/mass spectrometry urinary metabolic profiles for improved information recovery. Anal Chem 83:5864–5872. https://doi.org/10.1021/ac201065j
Acknowledgments
This research was supported by the Royal Society of Chemistry and National Institute for Health Research (NIHR) Biomedical Research Centre (BRC) based at Imperial College Healthcare NHS Trust and Imperial College London. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health. MRAU is funded by the Imperial College President’s PhD Scholarship and the Stratified Medicine Graduate Training Programme in Systems Medicine and Spectroscopic Profiling (STRATiGRAD).
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Vorkas, P.A., Abellona U, M.R., Li, J.V. (2018). Tissue Multiplatform-Based Metabolomics/Metabonomics for Enhanced Metabolome Coverage. In: Theodoridis, G., Gika, H., Wilson, I. (eds) Metabolic Profiling. Methods in Molecular Biology, vol 1738. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7643-0_17
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DOI: https://doi.org/10.1007/978-1-4939-7643-0_17
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