Presentation is loading. Please wait.

Presentation is loading. Please wait.

The Impact of Metabolomics on Flavor Chemistry

Similar presentations


Presentation on theme: "The Impact of Metabolomics on Flavor Chemistry"— Presentation transcript:

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

5 Perception is multimodal

6 Perception Mouthfeel Olfaction Taste Texture 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 nmr
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 A small quantity of each sample was centrifuged and 0.1 ml D2O (for NMR ®eld/frequency lock) was added to 1ml of each supernatant. A 0.1% solution of TSP (sodium salt of 3-(trimethylsilyl)propionic-2,2,3,3-d4 acid) in D2O was added to 0.6 ml of each supernatant in a 5mm o.d. NMR tube, to give a ®nal TSP concentration of 0.01%.

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 Method Ionizing Agent Sample State Desorption ElectroSpray Ionization (DESI) Charged droplets/ions Condensed phase, gas phase Desorption Atmospheric Pressure Chemical Ionization (DAPCI) Primary ions via corona discharge or charged droplets Direct Ionization in Real Time (DART) Metastable atoms Atmospheric Pressure Matrix Assisted Laser Desortion Ionization (AP-MALDI) Laser irradiation/Matrix Condensed phase Atmospheric Pressure Solids Ionization Probe (ASAP) Primary ions via corona discharge Electrospray Laser Desorption Ionization (ELDI) Laser irradiation/Charged droplets 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 inlet of mass spectrometer
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 a b surface dt-s gas jet spray capillary spray nebulizer capillary inlet of mass spectrometer sample desorbed ions solvent N2 HV

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 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, , Hegeman for isotope labeling studies; (Pan and Raftery, 2007; (Lindon et al., 2004 There are some issues with the evaluation of biofluids with NMR. Solvent and pH of the samples have an effect on the chemical shift seen in the NMR signal and thus consistent sample preparation is extremely important. Intermolecular interactions such as hydrogen bonding and the presence of metal ions also contribute to shifts in the spectra (Defernez and Colquhoun, 2003). An excessive amount of water present in a biological sample can result in a large water peak that hides other compound peaks. A water suppression pulse sequence can be used to circumvent this problem (Lindon et al., 2004). A problem with NMR that is also seen with MS methods is that the large number of compounds present in the sample may lead to over-lap in some areas with certain compounds being neglected in the analysis as a result. NMR has the additional disadvantage that it is less sensitive than other methods; it has a lower detection limit of 10μM (Pan and Raftery, 2007).

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). Partial least squares (PLS) is a data analysis technique that can relate the independent variables of the samples (type of sample, specific sample characteristics, etc.) to the instrumental response. Clusters of responses seen from PLS can be further differentiated using a technique called discriminant analysis (DA) (Lindon et al., 2004). DA is a categorical technique which uses an algorithm to project data into clusters and can give an indication of which parameters exhibit importance (Hollywood et al., 2006). Principle component analysis (PCA) is a multivariate data handling technique that is valuable in the analysis of large amounts of instrumental data; it serves to locate patterns and trends in large data sets (Jonsson et al., 2005). The amount of variability in a sample is explained by principle components. The first principle component accounts for as much variability as possible and each principle component thereafter further accounts for the variability (Lindon, Holmes, et al., 2004). Jonsson et al. (2005) were able to use PCA in conjunction with PLS to differentiate different types of mice using LC-MS. They developed predictive models to screen large sets of samples. Chen et al. (2006) used PCA to combine NMR and DESI-MS to obtain a 3D score plot which potentially could help differentiate large data sets. Belton et al. (1998) used PCA of NMR data to differentiate apple juice from different cultivars.

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). 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.”

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

30 Umetrics SIMCA-P 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; (>20,000 metabolites) and

32 Madison Metabolomics Consortium
(MMC) Database (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

36

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 37

38

39 Figure 1. GC-MS-based metabolomics
Figure 1. GC-MS-based metabolomics. A, Analytical approach used B, Conventional approach. C, Alternative, unbiased approach to GCMS 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

40

41

42

43

44

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 ( )

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 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)

51

52 Next steps Look for changes or relationships of data to some attribute via multivariate statistics Identify the compounds of interest. HPLC, mass spectrometry (MS/MS) and/or nuclear magnetic resonance (NMR) must be employed. Evaluate result for value


Download ppt "The Impact of Metabolomics on Flavor Chemistry"

Similar presentations


Ads by Google