Accurate data are needed on threshold doses for allergenic foods. Data are also needed to demonstrate that available methods of analysis can detect and quantify allergens in foods at or around these threshold levels and that they are robust and fit for purpose. This is of major concern as no curative treatment is currently available for food allergy and accidental ingestion of the culprit food can lead to severe clinical symptoms. Elimination of the problem food ingredient from the diet reduces the risk of allergic reactions but this can lead to other issues such as deficiencies, eating disorders and growth retardation. Emergency medication is available including Antihistamines (H1 blockers), EpiPen (adrenaline-autoinjector) and Corticosteroids. This presentation describes investigations into peanut allergens and their quantitation in highly complex samples.

1. ©2015 Waters Corporation 1
Discovery and Analysis of Peanut Allergens
using Proteomic Approaches with Ion
Mobility and High Resolution Mass
Spectrometry

2. ©2015 Waters Corporation 2
Contents
 General Introduction
– Food allergy
– Regulatory aspects
 Extraction Methods
– RapiGest protocol
 LC/MS Analysis
– DIA strategy (HDMSE)
– Label-free quantification
– Progenesis QI-P
 Conclusions
Drift time
m/z

3. ©2015 Waters Corporation 3
Immunological Aspects of Food Allergy
 Food allergic reaction is an IgE mediated reaction to specific
food proteins
– Prevalent in c. 2% of the adult and 8% of child population
– Symptoms can range from mild to severe (life-threatening)

4. ©2015 Waters Corporation 4
Food Allergy – avoidance &
preventative actions?
 No curative treatment is available for food allergy
 Accidental ingestion of the culprit food can lead to severe clinical
symptoms
– Elimination diet
o Reduce the risk of allergic reactions
o Disadvantages: deficiencies, eating disorders, growth retardation
– Emergency medication
o Antihistamines (H1 blockers)
o EpiPen (adrenaline-autoinjector)
o Corticosteroids
Preventative actions?
Effective tools for detection & quantitation are
needed for effective labelling

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EU perspective – Statutory Food
Labelling Laws
 The rules for pre-packed foods establish a list of 14 food
allergens, which must be indicated by reference to the source
allergen whenever they, or ingredients made from them, are used at
any level in pre-packed foods, including alcoholic drinks
 Labelling rules in European Directives 2003/89/EC & 2006/142/EC
ensure that all consumers are given comprehensive ingredient
listing information and make it easier for people with food
allergies to identify ingredients they need to avoid
 Food Information for Consumers Regulation (EU) No. 1169/2011 builds
on current allergen labelling provisions for pre-packed foods &
introduces a new requirement for allergen information to be provided
for foods sold non-packed or pre-packed for direct sale
– Allergen labelling rules will be changing in December 2014

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Allergen Classification
EU 14 major priorities
Cereals containing gluten, crustaceans, molluscs, eggs,
fish, peanuts, nuts, soybeans, milk, celery, mustard,
sesame, lupin and sulfur dioxide (at levels >10mg/kg or 10
mg/litre, expressed as SO2 )

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Establishment of Threshold Doses
 Threshold dose establishment – ongoing research activity
– Safety assessment LOAEL or NOAEL
 Commission Regulation (EC) No. 41/2009 established levels of
gluten for foods claiming to be either 'gluten-free' or 'very low
gluten‘ (January 2012)
– 'gluten-free': at 20 parts per million of gluten or less
– 'very low gluten': at 100 parts per million of gluten or less -
however, only foods with cereal ingredients that have been
specially processed to remove the gluten may make a 'very low
gluten' claim
 These regulations apply to all foods, pre-packed or sold loose,
such as in health food stores or in catering establishments

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Sample preparation strategy
 Solubilise and extract protein using aq buffer from complex matrix
 Protein denaturation using detergents & chaotrophic agents
(RapiGest™) to linearise the 3D structure
 Proteolytic digestion using trypsin to cleave the protein into
reproducible and peptide sequences (6 – 12 amino acids)
 Additional sample clean-up & enrichment
– SPE
– Immuno-affinity column using specific anti-peptide IgG
 Filtration & dilution in mobile phase A prior to LC-MS analysis

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Example Extraction Protocol
Sonicate in 60⁰C heated ultrasonic
water bath for 15 min/vortex every 5
min
Extraction Buffer 50mM Tris.HCl pH 8.8,
50mM DTT & 0.04% Rapigest
High speed centrifugation
(10000rpm,10min, RT)
Collect the supernatant and
store at -20⁰C prior to analysis
RapiGest™ SF is a reagent used
to enhance enzymatic digestion of
proteins
RapiGest SF helps solubilize
proteins, making them more
susceptible to enzymatic cleavage
without inhibiting enzyme activity

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SDS-PAGE
(peanut flour extracts)
Lane Sample
A Raw
B Raw
C Raw
D Roasted
E Roasted
F Roasted
Provided by Phil Johnson, Anuradha Balasundaram, Rebekah Sayers,
Justin Marsh and Clare Mills (University of Manchester)

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Extraction Methodology Comparison
Provided by Phil Johnson, Anuradha Balasundaram, Rebekah Sayers,
Justin Marsh and Clare Mills (University of Manchester)

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How do we deal with this complexity?
 Increase ‘peak capacity’ of analytical system
 Separate analytes before ID with MS/MS
– LC dimension
o UPLC
o Multi-dimension LC
– MS dimension
o Mass resolution
o Ion mobility
 Use strategies which will work with multiplex spectra
– LC-MSE / LC-HDMSE (data-independent acquisition)
– Bateman et al. JASMS (2002);13:792
– Advocacy increasing
“some form of multiplexing of MS/MS in high resolution format
will most likely need to be a component of future shotgun
proteomics strategies”
Michalski, Cox, Mann. JPR (2011);10:1785

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Why DIA and not DDA?
 Only most intense peptides are fragmented
 Only most intense peptides can be identified
 These may not be the peptides of interest
 Other peptides may be eluting while in MS/MS mode
 Not fragmented, not identified
 These may be the peptides of interest
 Quantification is difficult
 Cannot use survey data as we do not sample peak effectively
 No survey data is being collected while in MS/MS mode
 Run-to-run reproducibility is poor

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2
IMS Increases Peak Capacity:
The Datacube
 Peak capacity = NLC x NIM x Nm/z
 BUT: LC, m/z and IM not completely orthogonal
 BUT: datacube non-uniformly populated
 Nm/z > NLC > NIM [10,000’s > 1000’s (2D-LC) or 100’s (1D-LC) > 10’s]

15. ©2015 Waters Corporation 15
Increasing system peak capacity
by ion mobility separation (IMS)
CID
TRAP
ION MOBILITY
SEPARATION
TRANSFER
HELIUM CELL
Drift time
m/z

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Ion mobility: introduction
 Ion mobility leads to separation based on molecular
conformation
030709_CYTOTEST2_G2.raw: 1
100
0.00 2.00 4.00 6.00 8.00 10.00 12.00
%
0
100
Cytochrome C
+8 charge
Synapt G2
Separation of isobaric ions
Isobaric
ions
Drift time
m/z

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Ionized
Precursors
Precursors
Transferred to TOF
MS
UPLC/HDMSE
…deconvoluting chimericy
Co-Eluting
Peptides

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Ionized
Precursors
Precursors &
Products Time
Aligned
UPLC/HDMSE
…deconvoluting chimericy
Co-Eluting
Peptides

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Concept of high-definition (HD)-MSE
19
Retention time aligned precursor and product ions
Drift time aligned precursor and product ions
ION MOBILITY SEPARATION
DB SEARCH

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Experimental Details (LC/MS)
 LC
– nanoAcquity UPLC
– Solvent A: Water/0.1% FA
– Solvent B: ACN/0.1% FA
– 300 nL/min flow rate
– Trapping configuration (3 mins @ 5 µL/min)
– 1 µL partial loop injection
 MS
– Synapt G2-Si
– HDMSE acquisition mode (0.5s scan rate)
– Resolution mode (25,000 resolution)
– LE: 4 eV; HE: 19-45 eV
– Lockmass correction: Glu-fibrinopeptide (m/z 785.8426)

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Main principles of quantitative
‘discovery proteomics’ using MS
Mueller LN et al. JPR (2008);7:51
• ‘Labelled’ methods
• Compare peak areas
across peptide peak pairs
separated by ‘tag’ mass
• ‘Label-free’
methods
• Label-free quant
• Compare peptide peak
volumes across LC-MS
runs
• Spectral counting
• Compare number of
MS/MS measurements
for a peptide peak across
LC-MS runs

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‘Labelled’ vs ‘Label-free’
 Label-free needs no sample modification / manipulation
 Can be applied to any samples, including non-growing
 No constraints on experimental designs
 New samples can be compared to historical data
 No reagent costs (iTRAQ is $400/sample!)1
 No time for sample preparation reactions
 No variability introduced due to preparation reactions
1.Dekkers DHW et al. Curr Proteomics (2010);7:108

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Label free protein quant via the
Waters method
Relative quantitation via comparison of normalised peak volumes
- only been possible following introduction of reproducible nanoUPLC

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The Waters method also gives
absolute quantification
[ADH] = x mol
[BSA] = x mol
[HBA] = 0.5 x mol
[HBB] = 0.5 x mol
• Serendipitous discovery
• Protein standard development work
Silva JC et al. MCP (2006);5:144

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The Waters method also gives
absolute quantification
 The intensity response under electrospray conditions of the
three most intense peptides is a function of the molar amount
infused in the mass spectrometer
– The estimated absolute amount of a protein can be calculated from
the intensity of the top three ionizing tryptic peptides
o Assumption is that the “best ionizing peptides” have similar chemical
composition
– The absolute amount can be calculated for every identified protein
Silva JC et al. MCP (2006);5:144
 fmol/µL50
spike][Proteinintensitypeptide
interest]of[Proteinintensitypeptide
3
1i
3
1i





Conc =

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Data processing – Progenesis QI-P
alignment
peak
detection
identification
protein
quantitation
statistics
peptide
quantitation

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Progenesis QI-P Workflow
27,313
features
Alignment
Normalization
Peak
Picking
Database Searching
Protein Output
(including label-free quantitation)

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Processing/Search Parameters
 Progenesis QI-P
– Apex 3D processing parameters
o Low energy threshold = 150 counts
o High energy threshold = 30 counts
o Lockmass correction = 785.8426 m/z (GFP)
– IA database search parameters
o Minimum fragment ions per peptide = 1
o Minimum fragment ions per protein = 3
o Minimum peptide per protein = 1
o Uniprot database (A. Hypogaea) reviewed sequences
o False discovery rate = 4%

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Qualitative Overview – GPC flour
(peanut allergen families)
Average # peptides
Summed (log) intensity of top3 peptides
Average % sequence coverage

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Normalized Relative Abundance
(dynamic range of the peanut proteome)
Nearly 4 orders of magnitude dynamic range

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Relative Quantification
(significant isoforms)
Ara h 1 Ara h 3 Ara h 10,11

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Batch-to-batch consistency
(GPC flour)
Sequences showing greatest variation :
all Ara h 3 with the exception of P43237
(Ara h 1)
# flour batches analyzed = 7 (in triplicate)
Batches 2 & 4

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From discovery to targeted…..

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Conclusions
 Optimized protein extraction protocol
– Using acid labile detergent (RapiGest)
– Applicable to other categories of allergens
 Comprehensive characterization of the peanut proteome
 Achieved using a DIA approach utilizing ion mobility
– Over 300 proteins identified with high % sequence coverage (per
injection)
– Label-free quantification of all identified proteins
 Potential target peptides identified for absolute quantification

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Acknowledgement
 University of Manchester
– Phil Johnson
– Anuradha Balasundaram
– Rebekah Sayers
– Justin Marsh
– Clare Mills