the analysis of allergens in raw and roasted peanuts using nanoscale uplc & time of-flight mass...
TRANSCRIPT
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©2015 Waters Corporation 1
The Analysis Of Allergens in Raw and
Roasted Peanuts using nanoscale UPLC &
Time-of-Flight Mass Spectrometry
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Presentation Overview
Allergens Background
Technologies for Allergen Analysis
Experimental Workflow
– Sample Prep
– Instrument set-up
– Software
Results
Conclusions
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Background: Allergens Overview
Incidence of food allergy in industrialised populations is
increasing
Effects for suffers can be fatal
Food Regulations WW
– Reduce cross-contamination in factory
– Food Labelling
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Background: Allergens Type of Food Categories
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Analysis of Allergens Technologies
ELISA
PCR
MS
In
creasin
g C
urren
t U
sag
e
In
creasin
g I
nstr
um
en
t C
om
ple
xit
y &
Pric
e
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Workflow
SAMPLE PREPARATION (1) Protein extraction (2) Tryptic digest
DATA ACQUISITION Acquire data-independent MSE Data
SOFTWARE PROCESSING PLGS
INSTRUMENT SET-UP (1) nanoACQUITY UPLC ® (2) XevoTM QTof MS
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Workflow (1)
SAMPLE PREPARATION
PART 1:
— Ara h1 protein extraction from raw and roasted peanut
PART 2:
— Tryptic digest of raw and roasted Ara h1 extract (RapiGest™ SF)
(PART 3:)
— Ara h1 identification and quantification in matrix (additional use
of ADH)
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nanoACQUITY UPLC
Column:
– nanoACQUITY™ BEH C18, 75 mm x
150 mm
Flow Rate:
– 300 nL/min
Mobile Phase:
– A: 0.1% FA in Water
– B: 0.1% FA in Acetonitrile
Gradient:
Workflow (2)
INSTRUMENT SET-UP (1) nanoACQUITY UPLC ® (2) XevoTM QTof MS
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nanoACQUITY data Raw peanut sample
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Xevo QTof MS
Ionisation Mode:
– Electrospray Positive Ion
Capillary:
– 3.3 V
Cone:
– 25 V
Source Temperature:
– 100oC
LC/MSE Conditions:
– MS scan (Low CE): 6 V
– MSE scan (High CE ): 15 – 40 V
– Scan time: 0.6 sec
Workflow (2)
INSTRUMENT SET-UP (1) nanoACQUITY UPLC ® (2) XevoTM QTof MS
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UPLC-MSE provides ‘all the data, all the time’
– More information from a single analysis
UPLC-MSE employs a simple methodology which
– Uses generic methods of acquisition
– Uses relevant Application Manager to mine data set
– ‘Acquire your data, Ask questions later!’
Workflow
DATA ACQUISITION Acquire data-independent MSE Data
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Advantages of MSE with PLGS Time-aligned spectra
Low energy fragmentation
High energy fragmentation *
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Workflow
SOFTWARE PROCESSING PLGS (ProteinLynx Global SERVER™ )
Databank: SwissProt
False Positive Rate: 4%
Fixed modification: Carbamidomethyl C
Variable modification: Acetyl N-Term, Deamidation N, Deamidation Q, Met-Oxidation, Hydroxy P
N-Linked Glycosylation
~10 ppm window for precursor ions;
~25 ppm window for fragment ions
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ProteinLynx Global SERVER™ Parameters
•Data preparation
Default parameters were used. Lock mass correction: 785.8426.
•Work flow
Databank: SwissProt
False Positive Rate: 4%
Fixed modification: Carbamidomethyl C
Variable modification: Acetyl N-Term, Deamidation N, Deamidation Q, Met-Oxidation, Hydroxy P
N-Linked Glycosylation
~10 ppm window for precursor ions;
~25 ppm window for fragment ions
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Peptide Sequence Identification: GSEEDITNPINLR
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Peptide sequences observed in both raw & roasted samples
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Relative intensities of peptide sequences present in both samples
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Peptide Coverage Raw and Roasted Samples
Signal peptide: 1-25
Raw Sample Roasted Sample
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Quantification of Ara H1
in Matrix
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Experimental Overview
Aim:
– To identify and quantify Ara H1 in complex
matrix
Samples:
Two samples investigated – different
concentrations:
– Sample A – 1 : 3 (v/v) Sample 1 vs Standard
solution
– Sample B – 1 : 200 (v/v) Sample 1 vs Standard
solution
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Workflow
SAMPLE PREPARATION
PART 1:
— Ara h1 protein extraction from raw and roasted peanut
PART 2:
— Tryptic digest of raw and roasted Ara h1 extract (RapiGest™ SF)
(PART 3:)
— Ara h1 identification and quantification in matrix (additional use
of ADH)
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Sample A – 1 : 3 dilution
MS – Low collision energy data
MSE – High collision energy data
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Sample B – 1 : 200 dilution
MS – Low collision energy data
MSE – High collision energy data
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Sample B – 1 : 200 dilution Peptide Coverage
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©2015 Waters Corporation 26
Summary
Challenges for analysis of allergenic proteins – many different
approaches already used
– Complexity added – Typical food processing (e.g. food processing)
can alter the markers peptides present / amount that they are
present in the samples
Analytical tools and software can support confidence in the
results obtained
– ProteinLynx Global Server (PLGS) with intelligent filtering and
scoring routines minimizes occurrence of false positive results
– UPLC-MSE fragment ion exact mass data provides greater
confidence in protein identification in food samples