A strategy for sensitive, large scale quantitative metabolomics

Journal of Visualized Experiments

Volumn , Issue 87, 2014, Pages

  Liu, Xiaojing ^a   Ser, Zheng ^a   Cluntun, Ahmad A ^a   Mentch, Samantha J ^a   Locasale, Jason W ^a  

^a Department of Obstetrics Gynecology   (United States)

Author keywords

Chemistry; High resolution mass spectrometry; Issue 87; Low mass calibration; Metabolomics; Orbitrap; Positive negative switching

Indexed keywords

EID: 84901611191     PISSN: 1940087X     EISSN: None     Source Type: Journal    
DOI: 10.3791/51358     Document Type: Article

References (22)

1. Fiehn, O. Combining genomics, metabolome analysis, and biochemical modelling to understand metabolic networks. Comparative and Functional Genomics. 2 (3), 155-168, doi: 10. 1002/cfg. 82, (2001).
2. Mulleder, M. et al. A prototrophic deletion mutant collection for yeast metabolomics and systems biology. Nature Biotechnology. 30 (12), 1176-1178. doi: 10. 1038/nbt. 2442, (2012).
3. Patel, V. R., Eckel-Mahan, K., Sassone-Corsi, P., & Baldi, P. CircadiOmics: Integrating circadian genomics, transcriptomics, proteomics and metabolomics. Nature Methods. 9 (8), 772-773. doi: 10. 1038/nmeth. 2111, (2012).
4. Ideker, T. et al. Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. Science. 292 (5518), 929-934. doi: 10. 1126/science. 292. 5518. 929, (2001).
5. Jonsson, P. et al. A strategy for identifying differences in large series of metabolomic samples analyzed by GC/MS. Analytical Chemistry. 76 (6), 1738-1745. doi: 10. 1021/ac0352427, (2004).
6. Jonsson, P. et al. High-throughput data analysis for detecting and identifying differences between samples in GC/MS-based metabolomic analyses. Analytical Chemistry. 77 (17), 5635-5642. doi: 10. 1021/ac050601e, (2005).
7. 1H NMR metabolomics data. Analytical Chemistry. 78 (13), 4430-4442. doi: 10. 1021/ac060209g, (2006).
8. Wiklund, S. et al. Visualization of GC/TOF-MS-based metabolomics data for identification of biochemically interesting compounds using OPLS class models. Analytical Chemistry. 80 (1), 115-122. doi: 10. 1021/ac0713510, (2008).
9. Xia, J., Bjorndahl, T. C., Tang, P., & Wishart, D. S. MetaboMiner--semi-automated identification of metabolites from 2D NMR spectra of complex biofluids. BMC Bioinformatics. 9, 507, doi: 10. 1186/1471-2105-9-507, (2008).
10. 13C NMR isotopomer analysis reveals a connection between pyruvate cycling and glucose-stimulated insulin secretion (GSIS). Proceedings of the National Academy of Sciences of the United States of America. 99 (5), 2708-2713. doi: 10. 1073/pnas. 052005699, (2002).
11. 13C NMR. Proceedings of the National Academy of Sciences of the United States of America. 96 (14), 8235-8240. doi: 10. 1073/pnas. 96. 14. 8235, (1999).
12. Kitteringham, N. R., Jenkins, R. E., Lane, C. S., Elliott, V. L., & Park, B. K. Multiple reaction monitoring for quantitative biomarker analysis in proteomics and metabolomics. Journal of Chromatography. B, Analytical Technologies in the Biomedical and Life Sciences. 877 (13), 1229-1239. doi: 10. 1016/j. jchromb. 2008. 11. 013, (2009).
13. Locasale, J. W. et al. Metabolomics of human cerebrospinal fluid identifies signatures of malignant glioma. Molecular & Cellular Proteomics: MCP. 11 (6), doi: 10. 1074/mcp. M111. 014688, (2012).
14. Wolf-Yadlin, A., Hautaniemi, S., Lauffenburger, D. A., & White, F. M. Multiple reaction monitoring for robust quantitative proteomic analysis of cellular signaling networks. Proceedings of the National Academy of Sciences of the United States of America. 104 (14), 5860-5865. doi: 10. 1073/pnas. 0608638104, (2007).
15. Yuan, M., Breitkopf, S. B., Yang, X., & Asara, J. M. A positive/negative ion-switching, targeted mass spectrometry-based metabolomics platform for bodily fluids, cells, and fresh and fixed tissue. Nature Protocols. 7 (5), 872-881. doi: 10. 1038/nprot. 2012. 024, (2012).
16. Ramanathan, R. et al. It is time for a paradigm shift in drug discovery bioanalysis: From SRM to HRMS. Journal of Mass Spectrometry: JM,. 46 (6), 595-601. doi: 10. 1002/jms. 1921, (2011).
17. Lu, W., Clasquin, M. F., Melamud, E., Amador-Noguez, D., Caudy, A. A., & Rabinowitz, J. D. Metabolomic analysis via reversed-phase ion-pairing liquid chromatography coupled to a stand alone orbitrap mass spectrometer. Analytical Chemistry. 82 (8), 3212-3221. doi: 10. 1021/ac902837x, (2010).
18. Michalski, A. et al. Ultra high resolution linear ion trap orbitrap mass spectrometer (orbitrap elite) facilitates top down LC MS/MS and versatile peptide fragmentation modes. Molecular & Cellular Proteomics: MCP. 11 (3), doi: 10. 1074/mcp. O111. 013698, (2012).
19. Michalski, A. et al. Mass spectrometry-based proteomics using Q exactive, a high-performance benchtop quadrupole orbitrap mass spectrometer. Molecular & Cellular Proteomics: MCP. 10 (9), doi: 10. 1074/mcp. M111. 011015, (2011).
20. Snyder, N. W., Khezam, M., Mesaros, C. A., Worth, A., Blair, I. A. Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry (UPLC-HRMS). J. Vis. Exp. (75), e50433, doi: 10. 3791/50433, (2013).
21. Gika, H. G., Theodoridis, G. A., Wingate, J. E., & Wilson, I. D. Within-day reproducibility of an HPLC-MS-based method for metabonomic analysis: Application to human urine. Journal of Proteome Research. 6(8), 3291-3303. doi: 10. 1021/pr070183p, (2007).
22. Want, E. J. et al. Global metabolic profiling procedures for urine using UPLC-MS. Nature Protocols. 5 (6), 1005-1018. doi: 10. 1038/nprot. 2010. 50; 10. 1038/nprot. 2010. 50, (2010).