For personal use only. Please do not reuse or reproduce without the author s permission MASS SPECTRMETRY IN METABLMICS Pavel Aronov Stanford Mass Spectrometry Users Meeting August 21, 2008
rigin of Metabolomics 2
Metabolomics and Proteomics Quantitation Proteomics Under development Metabolomics Well established Identification SILAC, AQUA, itraq Relative quantitation (x fold) Well established De novo sequencing * H N N R PTMs still 1 H a challenge R 2 H N R 3 * Classical Analytical Chemistry Absolute (nm, pg/ml) or relative (x fold) quantitation Under development Huge diversity of structures, NMR often required Moderate success with mass spectral libraries 3
Types of Experiments in Metabolomics targeted non-targeted quantitative semi-quantitative Number of analyzed metabolites is limited by the number of available standards Absolute quantitation of metabolites (nm, mg/ml) Number of analyzed metabolites is limited by the number of available library spectra Relative quantitation of metabolites (fold) Number of analyzed metabolites is limited by capacity of analytical instrumentation Relative quantitation of metabolites (fold) 4
Applications of Metabolomics Studies of biochemical pathways bservational studies ( hypothesis generating studies) Phenotyping Search of diagnostic biomarkers of disease Early detection of toxic effects of drug candidates 5
GC-MS Analysis of Metabolites: verview 50-600 (300) amu mass range mono- and disaccharides, amino acids, fatty acids (mostly primary metabolites) Derivatization required Metabolite libraries are available due to instrumentindependent and well understood nature of electron ionization that generates extensive fragmentation and information reach spectra Advantageous for flux analysis using 13 C labeling 6
GC-MS Analysis of Metabolites: Workflow Sample preparation: -depletion of abundant metabolites (urine: urease treatment) -extraction, homogenization, or lyophilization -oximation (sugars), and silylation GC-MS analysis -disposable glass liners are preferred to eliminate carry-over -retention index (RI) standards can be used to aid identification Deconvolution of mass spectra using libraries -AMDIS (freeware from NIST) 7
GC-MS Profile of Urine SH-15 H G-603-SH-15-H 100 14.24 14.48 21.25 22.83 34.55 36.58 Scan EI+ TIC 1.34e6 23.34 35.50 23.42 25.42 27.15 22.55 % 15.00 26.66 9.40 13.07 26.09 49.65 40.57 9.87 10.03 12.44 13.69 15.06 16.23 21.78 16.10 18.05 18.37 20.99 19.1120.01 16.90 23.98 28.83 30.66 32.95 27.31 30.81 34.39 28.22 30.07 31.32 32.04 33.47 37.38 37.62 38.49 39.92 42.58 41.92 42.80 0 10.00 12.50 15.00 17.50 20.00 22.50 25.00 27.50 30.00 32.50 35.00 37.50 40.00 42.50 45.00 47.50 50.00 Time 8
LC-MS Analysis of Metabolites: verview 100-2000 amu mass range sugars, amino acids, peptides, lipids, secondary plant metabolites No derivatization required Low efficiency of LC especially for polar compounds Metabolite mass spectral libraries are missing instrument-dependent nature of collision induced dissociation, insufficient fragmentation Ultra-high resolution MS (FT ICR, rbitrap, TF) may aid identification 9
LC-MS Analysis of Metabolites: Workflow Sample preparation: -depletion of abundant metabolites (urine: urease treatment) -extraction or lyophilization; automation with online extraction LC-MS analysis -combination of ionization modes is preferred (ESI, APCI, +, -) -reverse phase LC for non-polar metabolites and hydrophilic interaction chromatography (HILIC) for polar metabolites Detection of spectral features (molecular ions) using metabolomics software -freeware XCMS and MZmine Identification based on retention time, accurate mass, and fragmentation (libraries) 10
9.40 9.87 10.03 12.44 14.24 13.07 13.69 14.48 21.25 22.83 15.00 15.06 16.23 16.90 22.55 21.78 23.34 23.42 23.98 25.42 26.66 26.09 27.15 27.31 28.22 28.83 34.55 30.66 32.95 30.81 34.39 30.07 31.32 33.47 32.04 36.58 35.50 37.38 37.62 38.49 40.57 39.92 41.92 42.58 42.80 49.65 HILIC-MS Profile of Urine 100 1.10 1.44 3.57 SH-15 H G-603-SH-15-H 100 % GC-MS Scan EI+ TIC 1.34e6 16.10 18.05 18.37 20.99 19.1120.01 0 10.00 12.50 15.00 17.50 20.00 22.50 25.00 27.50 30.00 32.50 35.00 37.50 40.00 42.50 45.00 47.50 50.00 Time 11.57 14.30 % 18.32 17.63 6.10 7.77 9.26 10.06 TIC XIC (m/z 217) 0 Time 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 9.00 10.00 11.00 12.00 13.00 14.00 15.00 16.00 17.00 18.00 19.00 11
Lipid Analysis by ESI-MS Many classes of lipids are too heavy and nonvolatile for GC separation ( > 500 amu) Surface activity of polar lipids is advantageous for electrospray ionization (ESI) Some lipids have simple structure based on glycerol or sphingoid base scaffold Peptides (proteomics) Sphingo- and phospholipids (metabolomics) H * H N R 1 N H R 2 H N R 3 * C 13 H 27 R 1 N H X R 1 R 2 H P X 12
Identification of Phospholipids Using ESI-MS 100 % 183.9 [C 5 H 15 NP 4 ] + Di-C15:0-PC C 38 H 76 N 8 P 705.530857 CH 3 P CH 2 CH 2 N CH 3 CH 3 [M+H] + 706.4 185.1 0 50 100 150 200 250 300 350 400 450 500 550 600 650 700 m/z 100 690.2 [M-CH 3 ] - % [C 14 H 27 C - ] 241.1 0 [M+HC] - 242.3 466.00 750.4 50 100 150 200 250 300 350 400 450 500 550 600 650 700 750 m/z 13
Summary Three metabolomics platforms can be provided at SUMS in the future: 1. Profiling of phospholipids and sphingolipids using nanoesi-ms-qtf 2. Profiling of primary metabolites including 13 C flux analysis using GC-MS 3. Profiling of polar (HILIC) and non-polar (C18, C4) metabolites using LC-ESI-MS-QTF #1 and #2 may be automated in the future to provide simple sample drop-in service 14
References verview: http://fiehnlab.ucdavis.edu/ http://masspec.scripps.edu/index.php Metabolomics: Methods and Protocols http://www.springerprotocols.com/book Toc/doi/10.1007/978-1-59745-244-1 15
Metabolomics Webinar Metabolomics: Analytics, Tools, and Applications (Free!) Broadcast Date Thursday, August 28, 2008 Time 1:00-2:00 pm EDT Key learning points for this webinar: Introduction to and definition of metabolomics Examples of metabolomes and where they fit into the larger biological picture Analytical techniques for uncovering and exploiting metabolomics capabilities and limitations Implications of metabolomics for biomedicine, drug discovery, and development Metabolomics case studies in diabetes and prostate cancer Because the metabolome is reflected in small molecules typically generated in low abundance and with a wide dynamic range, analytical science has been scrambling to devise methods for meeting the metabolomic challenge. Two techniques in particular gas chromatography and high-performance liquid chromatography in tandem with mass spectrometry have become the de facto platforms for analyzing the metabolome. Your hosts for this webinar, Steven Fischer, Ph.D. (Agilent Technologies), Chris Beecher, Ph.D. (University of Michigan), and Christopher Newgard, Ph.D. (Duke University), bring years of combined experience surrounding metabolomics. They will present specific examples of how metabolomics can be applied to translational medicine, diabetes, and prostate cancer. A live Q&A session will follow the presentations, offering you a chance to pose questions to our expert panelists. 16
Acknowledgements Metabolomics Researchers at UC Davis -Bruce Hammock -Robert Weiss -Katja Dettmer -Vladimir Tolstikov -liver Fiehn Mass Spectrometrists at Stanford -Allis Chien -Theresa McLaughlin -Karolina Krasinska -Chris Adams 17