Overview of foodomics applications using high resolution mass spectrometry including profiling of natural products, dietary intake studies and an introduction of REIMS direct analysis.

1. ©2015 Waters Corporation 1
Foodomics Applications
Improving consumer well being, health and
knowledge using the latest -omic techniques

2. ©2015 Waters Corporation 2
Overview
 Introduction to foodomics and nutrimetabolomics
– Influence of diet on health (e.g. Dietary flavonoids)
 Metabolomic approaches for molecular characterisation of foods
– Characterisation of nutritionally valuable natural products (Passiflora
example)
– Investigation of flavonoid markers of dietary intake
 Rapid Evaporative Ionisation Mass Spectrometry (REIMS)
– Direct analysis for rapid profiling
 Summary
 Acknowledgements

3. ©2015 Waters Corporation 3
 Foodomics
– Refers to the metabolite profiling of
foods prior to consumption
(biomarkers of consumption)
 Nutritional metabolomics has
emerged with two major goals:
– (1) to determine the effects of
dietary compounds on host
metabolism after consumption
(biomarkers of effect)
– (2) identify metabolic disease that is
influenced by nutrients & to develop
targeted diet-based treatments
(disease risk biomarkers)
Foodomics and nutrimetabolomics
Heart disease
Alheizmers
Cancer
Asthma
Diabetes

4. ©2015 Waters Corporation 5
Links between diet and health…

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Functional foods
 Flavonoids are one of the largest widespread class of plant secondary
metabolites with diverse biological & pharmacological properties
 Highly bioactive and play a wide variety of different roles in health of
pants, animals and human health
 Many flavonoid containing plants are utilized as functional foods &
phytomedicines
 Functional foods represent one of the fastest growing markets with
interest in the systematic characterization of flavonoids in plant crops
Flavonoids
Reduce
blood
pressure
Antioxidant
s
Anti-
inflammator
y action
Improve
endothelial
function
Reduce
platelet
activity
Enzymatic
Modulation

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Characterisation of functional foods
 Functional foods & phytomedicines
– Natural product profiling - flavonoids
– Analytical challenges?
 Technology overview
– Introduction to CCS
– HRAMS with ion mobility
– Reducing sample complexity (spectral cleanup)
– Increased selectivity with ion mobility
 Profiling of Passiflora species (marker flavonoids) using ion
mobility with UNIFI processing

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Natural product profiling
Analytical challenges…
 Sample complexity
 Crude (non-selective) sample preparation
 Generic chromatography conditions (no chromatography?)
– >30k features
 Spectral interpretation
 Isomeric compounds
– different pharmacological affects?
 Structural elucidation of “unknowns”
 Identification of authentic product
– Identification of active components
– Quantitation of active components

8. ©2015 Waters Corporation 10
IMS-MS for natural product profiling
 IMS-MS can help overcome the challenge of matrix complexity
 IMS is a rapid separation approach, orthogonal to UPLC that provides
increased peak capacity
 Separation can help spectral decongestion for complex samples,
improving selectivity & specificity
 Can separate isomeric species and provide structural / confirmatory
information
 Spectral clarity improves confidence in identification and aids in
structural elucidation of unknowns

9. ©2015 Waters Corporation 11
Introduction to ion mobility & Synapt G2-Si
technology

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Ion mobility separation
 Separation of ionic species as they drift through a gas under the
influence of an electric field
 Rate of drift is dependent on ion’s mobility in the gas
 Leads to separation based on 3D molecular conformation,
size & charge
Isobaric
ions
IMS
-9 -8 -7 -6
TOF MS LC
-5 -4 -3 -2 -1 0 1 2 3 4
n
10n seconds

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SYNAPT G2-Si High Definition MS (HDMS)
Orthogonal acceleration QToF
1. Increased
sensitivity
2. Ion mobility 3. Accurate
mass
measurement
15mm
5mm
~11mm
CID & ETD

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HDMSE Unlimited Product Ion Acquisition
Co-eluting
precursor ions
Mobility
separation
Drift time aligned
precursors and products
Drift time
m/z
Drift time
m/z

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MSE – (no mobility separation)
Low Energy
High Energy

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HDMSE – MSE with a mobility
separation
Low Energy
High Energy

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Collision Cross Section (CCS)?
 Measured drift time can be
converted into CCS
 CCS is an important
distinguishing
characteristic of an ion
which is related to:
– chemical structure
– 3-dimensional conformation
– Charge
 CCS is a physicochemical
property of an ion

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Combining analytical discrimination
Analytical measurement
r.t
m/z
d.t
Property of molecule
KD
mass
CCS
UPLC
Mass
spectrometer
Ion
mobility
cell
Peak
capacity

17. ©2015 Waters Corporation 20
Profiling analysis of Passifloraceae spp.
 Also known as passion flower or passion vine
 ~ 500 species of flowering plants
 Passiflora species used in production of food products & dietary
supplements
– Passion fruit juices, teas and confectionery
– Herbal remedies & supplements
 Sedative properties of some species used to alleviate nervous
anxiety and insomnia
Collaboration between Waters, Janete Harumi Yariwake and Cintia Matiucci

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Product profiling - analytical
strategy
Component detection
Determine chemical structures
Assign identifications for known/unknowns; look
for unknowns
Resolve analytes chromatographically
OR
direct analysis?
Component identification
isobaric analytes, isomers, different charge
states, resolve co-eluting analytes...
UPLC separation for sensitivity and resolution
Infusion / ASAP
Synapt G2-Si
ESI [pos & neg]
HDMSE unbiased data acquisition
UNIFI 1.8
Exact mass precursor & fragments; isotope
pattern; r.time; drift time (CCS)
Chemical elucidation toolset
Elemental Composition
Mass Fragment for structure assignment &
confirmation; Chemspider library searches

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Answering the challenge of natural product
characterisation:
Sample complexity

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UPLC separation
Passiflora edularis
>10 000
components
detected

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Ion mobility separation
Increased peak capacity
Conventional UPLC
separation

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Answering the challenge of natural product
characterisation:
Spectral interpretation

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MSE multi component precursor and fragment ion spectra
Vitexin (m/z 431), retention time 8.395 mins.
Conventional HRMS
Vitexin
Low energy
High energy
Vitexin

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HDMSE single component precursor and ion mobility product ion spectra
Vitexin (m/z 431), retention time 8.395 mins, drift time 4.27 ms
Resolved from chromatographically coeluting components using ion mobility
Ion mobility spectral clean-up
Vitexin
Vitexin
Low energy
High energy

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Answering the challenge of natural product
characterisation:
Isomers

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Active marker flavonoid structures
OH
OH
O
OH
O
O
OH
OH
OH
OH
OH
O
OH
O
OH
O
OH
OH
OH
OH
OH
OH
OH
O
OH
O
O
OH
OH
OH
OH
OH
OH
O
OH
O
OH
O
OH
OH
OH
OH
Isoorientin Orientin
Isovitexin Vitexin
6C glycocides 8C glycocides
6
8
6
8
Luteolin-8-C-glucoside
C21H20O11
Luteolin-6-C-glucoside
C21H20O11
Apigenin-6-C-glucoside
C21H20O10
Apigenin-8-C-glucoside
C21H20O10

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Orientin/isoorientin
isomer separation & CCS values
Isoorientin
197.68Ǻ2
Orientin
187.65Ǻ2
Identical twins with shared retention time & accurate mass
BUT different DRIFT times
C21H20O11
448.377
∆ =10 Ǻ2

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Answering the challenge of natural product
characterisation:
Authenticity & active compounds

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Identification of active compounds
CAERULEA EDULIS
INCARNATAALATA
Which species has the most potent
sedative effects?

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Comparative analysis tools:
P.edulis and P.alata
Region of interest
isoorientin & orientin
Region of interest
isovitexin & vitexin
Mirror plots
Species
comparison:
Passiflora edulis
(reference)
Passiflora alata
(control)

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Passiflora edulis
+++
Passiflora alata
+
Vitexin
Isovitexin
Chromatographic view shows intensity differences
for active compounds between species
Comparative analysis tools:
P.edulis and P.alata

32. ©2015 Waters Corporation 35
PE unique
region of
interest m/z
577
Comparative analysis tools:
P.edulis and P.alata
Drift time
m/z

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Passiflora
Edulis
Passiflora
Alata
Different profiles for the markers at m/z 577
Candidates for further investigation
Comparative analysis tools:
P.edulis and P.alata

34. ©2015 Waters Corporation 37
Summary
 Natural product profiling is challenged by complex samples with many isomeric
compounds
 Combined with UPLC and HRMS, ion mobility offers advantages for the
characterisation of these samples
– Ion mobility separation is orthogonal to chromatography, providing enhanced peak
capacity
– The combination of UPLC and IMS helps resolve coeluting compounds and even isomers
– Drift times are measured for all precursor ions
– Simultaneous fragmentation data is obtained for all components
– The added dimension of ion mobility enables spectral cleanup and the ability to generate
single component fragment ion spectra for all precursors
 UNIFI software provides all the required informatics for interpretation of these
comprehensive datasets
– UNIFI automatically generates collision cross sections (CCS) for all components
– HDMS data viewer and comparison tools allows drift time mapping

35. ©2015 Waters Corporation 38
Markers of dietary intake:
Monitoring a flavanol-rich diet
Dr Vanessa Garcia Larsen

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Project background
 Recent epidermiologic evidence has suggested a protective effect of
diets rich in flavonoids against stroke and respiratory disease
 Current methods to assess nutrient intake involve the recall of food
over a prolonged period of time and a more rapid technique would
be advantageous
 More accurate methods to estimate markers of dietary intake,
including flavonoids, will improve our understanding on the
complex relationship between disease and dietary exposures
 Aims
– Can an accurate method be developed to estimate flavonoid markers
of dietary intake?
o Is the use of HR-MS useful for this experiment? (unknown markers in
humans samples)
o Can any biomarkers / metabolites of flavonoid intake in vivo be
identified?
o Is it possible to develop a method of analysis that can be used
routinely in studies in humans?

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Experimental Approach
 Dietary assessment of flavonoid intake
– Food Frequency Questionnaire (FFQ)
– Covered 12 months of dietary intake
– High and low dietary intake
 Sampling
– Serum
 Analytical set-up
– ACQUITY I-Class
– SYNAPT G2-Si HDMS
– Progenesis QI

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 Dietary flavonoid subclasses
– Flavanones (eriodictyol, hesperidin
and naringenin)
– Anthocyanins (cyanidin, delphinidin,
malvidin, pelargononidin, petunidin,
peonidin)
– Flavan-3-ols (catechins, epicatechin)
– Flavonols (quercetin, kaemferol,
myricetin, isohamnetin)
– Flavones (luteolin, apigenin)
– Flavonoid polymers
(proanthocyanidins, theaflavins,
thearubigins)
 Flavonoids & their polymers
constitute a large class of food
constituents & alter metabolic
process & have a positive impact
on health
 Specific groups of foods are rich
sources of 1 or more subclasses
Proposed compound classes
Polyphenolics

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UPLC Chromatogram
ESI negative trace
Flavanoids Non-polar metabolites

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UPLC Chromatogram
ESI negative trace
Flavanoids Non-polar metabolites
Gallic acid
C7H6O5

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Unsupervised PCA analysis
Low vs. high grape consumption

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S-Plot
Low vs. high grape consumption
Potential biomarkers
for the high samples
Potential biomarkers
for the low samples
Visualisation of covariance &
correlation between the
metabolites and the class
designation

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Progenesis QI
Marker identification – database
searching phase

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Initial results & future work
 43,000 compounds of interest = comprehensive data set, to
determine the most appropriate markers for the study
– Data interpretation: Progenesis and Ezinfo
 Further evaluation planned to test the model with a larger study
group
 Use the data to construct a routine assay using MS
– Three biomarkers have already been identified and will be used as an
objective tool to estimate dietary intake of flavonoids

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Sample
REIMS instrument configuration
iKnife and bipolar forceps
Hand-held
sampling device(s)
Diathermy
generator
Informatics system
Source
REIMS is an emerging technique
that allows rapid characterisation of
biological tissues
Xevo G2-XS
Qtof and
Synapt G2Si

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How does it work?

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REIMS workflow
Direct analysis with chemometric profiling
Xevo
G2-XS Qtof or
Synapt G2Si
Model
Generation
Multivariate
statistical
analysis
Different
tissues
Different
species
Different
production
Biomarker ID &
verification
Rapid screening of unknowns
by similarity to database
entries

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Direct analysis for flexible & rapid
screening
Sub-sampling
Determination
(5s)
Data analysis
(2s)
Report
Sample to report in seconds
 Point-of-control analysis

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Genotype influences phenotypic
characteristics
Ca. 22k genes
20 different amino acids
combined to give >100k
expressed proteins
>> post-translational
modifications
Metabolites are close to the phenotype

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Speciation: buffalo vs. bovine spectra
REIMS Tof MS neg ion IPA 150 ul/min 50-1200 m/z

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Polyunsaturated fatty acid (PUFA)
content
Ca. x1.6
Arachidonic acid
Ω6
C20H32O2
Eicosapentaenoic acid
Ω3
C20H30O2
Ca. x7
 Good ratio of Ω3:6
acids

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Multi-species muscle tissue – MVA
models
2 biological replicates of each species, 10 technical
replicates per sample
PCA model
Non-ruminants
Ruminants
Buffalo Bovine
Porcine
Ovine
Gallus
gallus

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PROTOTYPE real-time recogniser
software

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Food Fraud & authenticity applications

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REIMS spectra
Kaempferol
Apigenin
Oleanolic acid /
ursolic acid
Herbs & spices
PDO status Belgian butter
Botanical origin of
honey
Essential Ω6 fatty acid
Ω9
Galangin
Myricetin
Geographical origin of coffee
Caffeine
Tanzania
Peru
Kenyan
Colombian

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Butter fatty acid profile 220-290 m/z
Tof MS neg ion
Myristic acid
C14:0
C14H27O2
[M-H]-
Δ-0.6
Palmitoleic
acid
C16:1 (9Z)
C16H29O2
[M-H]-
Δ 0.7
Palmitic acid
C16:0
C16H31O2
[M-H]-
Δ 0.6
Linoleic acid
C18:2 (9Z,12Z)
C18H31O2
[M-H]-
Δ 2.8
Oleic acid
C18:1 (9Z)
C18H33O2
[M-H]-
Δ 0.8
Stearic acid
C18:0
C18H35O2
[M-H]-
Δ 0.6
Pentadecanoic acid
C15:0
C15H29O2
[M-H]-
Δ 0.2
Heptadecanoic acid
C17H33O2
[M-H]-
Δ 0.5
Butterfat is a triglyceride derived from
fatty acids such as myristic, palmitic, and
oleic acids
Saturated fatty acids
Palmitic acid: 31%
Myristic acid: 12%
Stearic acid: 11%
Lower (at most 12 carbon atoms) saturated
fatty acids: 11%
pentadecanoic acid and heptadecanoic acid:
traces
Unsaturated fatty acids
Oleic acid: 24%
Palmitoleic acid: 4%
Linoleic acid: 3%
α-Linolenic acid: 1%
α-Linoleic acid
C18:3 (9Z,12Z, 15Z)
C18H29O2
[M-H]-
Δ 1.3
Δ mDa error

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Rosmarinus officinalis
Tof MS neg ion
Kaempferol
C15H10O6
-0.16ppm
Apigenin
C15H10O5
-2.2ppm
Oleanolic acid /
ursolic acid
C30H48O3
-1.84ppm
Quinic acid
C7H16O6
1.23ppm
5,7-Dihydroxy-3',4',5'-trimethoxyflavone (ayanin)
C18H16O7
-3.94ppm
Flavonoids
Polyphenols
Terpenes, terpene
alcohols &
terpenoids
2-hydroxybenzoic acid (salicyclic acid)
C7H6O3
1.46ppm

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Rosmarinus officinalis
Tof MS/MS rosmarinic acid 359.1 m/z

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Summary
 There is a link between food and health
– Much research is directed to understand the relationship between food
intake, key metabolites, health issues and disease
– “Development of new products for functional foods, nutraceuticals, and
natural health products sector is one of the fastest expanding areas of
research today” [CORDIS]
 Metabolomic approach using HRMS screening
– Allows a holistic view of food composition & contaminants
– Ion mobility separation is orthogonal to chromatography
o Enhanced peak capacity from ion mobility reduces sample complexity
 REIMS emerging technique for direct analysis
– Non-targeted analysis with real-time chemometric profiling
– Point-of-control analysis

61. ©2015 Waters Corporation 64
Acknowledgements
 Prof Clare Mills
 Mike McCullagh
 Antonietta Wallace
 Lee Gethings
 Martin Wells