The Impact of Metabolomics on Flavor Chemistry

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The Impact of Metabolomics on Flavor Chemistry

• Josephine Charve and Gary Reineccius
• Department of Food Science and Nutrition
• University of Minnesota

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Flavoromics

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

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Historically volatiles were the primary
interest of the flavor chemist
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Chewing gum - menthone, sucrose and perceived
intensity (Davidson et. al, 2000)
Aroma
Sensory and Sucrose
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Perception is multimodal
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Mouthfeel
Olfaction
Taste
Perception
Texture
Sound
Appearance
Experience
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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

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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

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Presentation

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

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Preparation/Isolation

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

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Non-volatiles helping us

• Searching for best method for us.
• Going through a host of methods evaluating each
  for sensitivity and breath

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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 methanolwater.
• Non-polar compounds are most often extracted with
  chloroform or ethyl acetate
• (Dettmer et al., 2007).

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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)

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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)

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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

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GCxGC

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

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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

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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).

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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

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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

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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

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

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Simple sample preparation

• Freeze sample
• Freeze dry
• Reconstitute in 8020 D2OCD3OD 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) 949957

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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).

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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

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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).

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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/

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Commercial packages

• For example Marker Lynx (Waters), Xaminer
  Thermo and ?

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Umetrics SIMCA-P

• http//www.umetrics.com/default.asp/pagename/softw
  are_simcapplus/c/4
• Select compounds to focus efforts on not try to
  identify everything

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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/) (gt20,000 metabolites)
• and

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• 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.

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Conclusions

• Not much help in isolation methods for volatiles
  information on semi or nonvolatiles
• 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

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Greatest contribution?

• Availability
• Methodologies

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Will demand collaboration
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Descriptive Sensory Analysis

• Non-volatiles/Semi-volatiles
• Extraction
• MeOH/H20

• 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

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Figure 1. GC-MS-based metabolomics. A, Analytical
approach 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. 11251137
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Formatted to export data

• For example - to SIMCA-P

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Case study
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Strawberry metabolites

• Fourier Transform Ion Cyclotron Mass Spectrometry
  (FTMS)
• FTMS - only MS system capable of routinely
  achieving ultra high resolution at high
  acquisition rate (1001,000 amu scan/sec)
  allowing multiple scans in (12 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)
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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 -)

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

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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