The Impact of Metabolomics on Flavor Chemistry

1. The Impact of Metabolomics on Flavor Chemistry Josephine Charve and Gary Reineccius Department of Food Science and Nutrition University of Minnesota

2. Flavoromics • My definition - The application of chemometrics to the study of a broad array of chemical stimuli involved in forming human flavor perception.

3. Historically – volatiles were the primary interest of the flavor chemist

4. Chewing gum - menthone, sucrose and perceived intensity(Davidson et. al, 2000) Aroma Sensory and Sucrose

5. Perception is multimodal

6. Mouthfeel Olfaction Taste Texture Perception Sound Appearance Experience

7. Why take this approach – value? • Improved prediction of sensory properties • Better product characterization • Discovery - statistically linking stimuli to perception • New contributors to perception • Understanding of pathways leading to stimuli

8. Why now? • Developments in “omics” are driving the development (and availability) of instrumentation, approaches, and data handling and analysis. • Our University • Two new metabolomics faculty • Over $2,000,000 in advanced MS and some nmr instrumentation • Staffing with data handling/analysis experts from Super Computing Center

9. Presentation • What is metabolomics bringing us? • Sample preparation/isolation for analysis • Data collection (instrumentation) • Data handling • Data analysis • Many similar challenges we face

10. Preparation/Isolation • Volatiles – not much help. • We recognize the limitations of any extraction/isolation method • Help with instrumentation for volatiles

11. Non-volatiles – helping us • Searching for “best” method for us. • Going through a host of methods evaluating each for sensitivity and breath

12. Non-volatiles – common approach • Solvent extraction of solid tissues • Mechanical disruption of the tissue (grinding, vibration or other methods on a frozen sample.) • Solvent selection varies widely with compounds of interest; • polar compounds being best extracted with isopropanol, ethanol, methanol, acidic methanol, acetonitrile, water, and methanol:water. • Non-polar compounds are most often extracted with chloroform or ethyl acetate • (Dettmer et al., 2007).

13. Polar substances in potatoes • Polar substances – (potatoes) best extraction method • methanol and heating (for enzyme deactivation) • Methoxylation of sugars and silylation • GC-MS profiling resulted in 150 polar compounds, 77 of which were identified • (Roessner et al. 2000)

14. Secondary plant metabolites • Extraction with acidified aqueous methanol. (75% methanol, 0.1% formic acid, water from the sample or added as necessary). • Key factor is simplicity • De Vos et al. (2007)

15. Analysis • MS –GC/GC, LC (UPLC), direct analysis • Long history of application of basic techniques to foods • Impressive evolution in methods • nmr • Applied to foods in related work for more than two decades. • Historically, emphasis was to detect adulteration and fraud

16. GCxGC • Better resolution • Better sensitivity – no back ground • Better quantitative data • Going from unique use to standard method

17. GC-MS • Broad use of TOF instruments • Improved sensitivity (4X vs. quad) • Fast scans – permits peak deconvolution (peaks with 1 sec difference in peak apex) • Also minor peaks not well separated from major peaks • Accurate mass at fast scan • Leco – GC/MS and GCxGC/MS - low resolution MS but peak deconvolution due to high sampling rate • Thermo – triple quad also high resolution magnetic sector • Waters – GCxGC with accurate mass TOF

18. UPLC or Capillary LC • UPLC – sub -2μm stationary phase and high linear velocity of the mobile phase • Faster analysis (high throughput) • Better peak resolution • Higher peak capacity • Must be interfaced with fast MS – often TOF • (Plumb et al., 2005).

19. Ambient ionization – gas analysis • PTR-MS or API-MS • Ideally suited to providing an ion profile of a gaseous sample • Many current applications in flavor chemistry

20. Ambient Ionization (AI) Methods • Ionization methods allow for analysis of samples under ambient conditions • Many AI methods require no sample preparation • Currently, AI methods are receiving considerable attention

21. HV solvent N2 spray capillary inlet of mass spectrometer gas jet spray desorbed ions nebulizer capillary dt-s a surface b sample DESI Instrumentation • Implementation of DESI • DESI spray head – generation of charged droplets/primary ions • Atmospheric pressure ionization compatible mass spectrometer – mass analysis of generated secondary ions • Some ancillary equipment/supplies – either necessary for experiment or convenience • To date, majority of DESI data has been generated using linear quadrupole ion traps • DESI has been demonstrated on nearly all mass analyzers • Several different API interfaces

22. Example - Detection of lysozyme DESI spectrum of lysozyme present on PTFE surface; average surface concentration 50 ng/cm2 Microbial contamination on food processing surfaces?

23. nmr • MS offers sensitivity and capacity to detect compounds in mixtures but are limited to ionizable species, have difficulties resolving isomers, and usually require standard compounds for quantification. • NMR has the capacity to characterize chemical structure and quantity but is limited to the 20-50 most abundant compounds in a given sample without isotope labeling. (several other limitations) • Quantitative and reproducible – statistical analysis • Sensitivity not limited by same factors as MS • High throughput – 500 samples per day • Hegeman et al. Anal. Chem. 2007, 79, 6912-6921, Hegeman for isotope labeling studies; (Pan and Raftery, 2007; (Lindon et al., 2004

24. Simple sample preparation • Freeze sample • Freeze dry • Reconstitute in 80:20 D2O:CD3OD containing 0.05% w/v TSP-d4 (sodium salt of trimethylsilylpropionic acid) • Sample heated (50C – 10 min) • Micro centrifugation • Ward J, Harris C, Lewis J, Beale MH, Phytochemistry 62 (2003) 949–957

25. Data handling/Analysis • Multivariate statistical projection methods (partial least squares, principle component analysis) are commonly used (as starting point) • Lend themselves well to biological data because of their ability to correlate multiple variables in a robust and easily interpretable fashion • (Jonsson et al., 2005).

26. Statistical heterospectroscopy (SHY) • New technique used to correlate nmr data with UPLC-MS results by cross-assigning the signals. • (Crockford et al., 2006). (Pan and Raftery, 2007). • Technique used to combine nmr with DESI-MS to correlate known biomarkers with specific metabolites • (Pan et al., 2007). • http://www.nmr.ch/ CARA

27. MetAlignTM • Used in numerous publications for data extraction from GC-MS and LC-MS data sets. • Pulls out all of the masses and sorts which are the same in all of the data sets and which could differentiate the data. • Allows the user to look for unique peaks in the sample set above a chosen noise threshold • (Lomen et al., 2006).

28. MetAlign • “MetAlign is a software program for full scan LC-MS and GC-MS comparisons and was designed and written by Arjen Lommen of RIKILT-Institute of Food Safety. It has been extensively tested in collaboration with Plant Research International.” • http://www.metalign.nl/UK/

29. Commercial packages • For example – Marker Lynx (Waters), Xaminer™ Thermo and ?

30. Umetrics SIMCA-P • http://www.umetrics.com/default.asp/pagename/software_simcapplus/c/4 • Select compounds to focus efforts on – not try to identify everything

31. Identifications • Most existing metabolite libraries are either proprietary, insufficiently comprehensive, collected under non-standardized conditions or unsearchable by computers. • Exceptions: Human Metabolome Database (HMDB; http://www.hmdb.ca/) (>20,000 metabolites) and

32. Madison Metabolomics Consortium (MMC) Database (MMCD; http://mmcd. nmrfam.wisc.edu/), a web-based tool that contains data pertaining to biologically relevant small molecules from a variety of species.

33. Conclusions • Not much help in isolation methods for volatiles – information on semi or non—volatiles • New instrumentation – GC/GC, MS and sample interfaces, nmr, LC-MS • New tools for data handling and analysis • Also combining instrument data e.g. nmr w/ MS • Establishing public databases

34. Greatest contribution? • Availability • Methodologies

35. Will demand collaboration

37. Non-volatiles/Semi-volatiles • Extraction • MeOH/H20 Descriptive Sensory Analysis • Volatiles/Semi-volatiles • Extraction • SAFE ? • SPME • Instrumental Analysis • LC-MS-TOF MS/MS • NMR ? • Instrumental Analysis • GC-TOF-MS • Accurate mass Collect LC fractions for descriptive sensory analysis • Data Analysis • MetAlign • PLS with DA • PCA • SHY

39. Figure 1. GC-MS-based metabolomics. A, Analyticalapproach used B, Conventional approach. C, Alternative, unbiased approach toGCMS data analysis. Tikunov Y, Lommen A, Ric de Vos CH, Verhoeven HA, Bino RJ, Hall RD, Bovy AJ Plant Physiology, November 2005, Vol. 139, pp. 1125–1137

45. Formatted to export data • For example - to SIMCA-P

46. Case study

47. Strawberry metabolites • Fourier Transform Ion Cyclotron Mass Spectrometry (FTMS) • FTMS - only MS system capable of routinely achieving ultra high resolution at high acquisition rate (100–1,000 amu scan/sec) allowing multiple scans in (1–2 min). • Separation of the metabolites achieved solely by ultra-high mass resolution, eliminating the need for time consuming chromatography and derivatization. Aarón A, Ric De Vos, Verhoeven HA, Maliepaard CA, Kruppa G, Bino R, Goodenowe DB. Omics 6(3), 2002 (217-234)

48. Method • 4 stages of ripeness for berries • Extraction –50/50 MeOH/0.1% formic acid or 100% acetonitrile (AN) • Direct injection into FTMS • MS ionization - electrospray (ESI + or -) or atmospheric pressure chemical ionization (APCI + or -)

49. Strawberry metabolite 1ppm mass accuracy 10 ppm mass accuracy (2 possible only 1 is logical)

50. Result • total of 5,250 unique 12C masses were obtained from extracts of the four different developmental fruit stages, ionized in the four ionization modes. • In the red stage extract, 55% of the masses were assigned a single empirical formula, 10% two formulae, and 35% three or more formulae. • Went to data databases for help - 159,000 natural products (Chapman and Hall, Dictionary of Natural Products)