increasing ms ifunnel technology for sensitivity for the qqq and … · 2016. 9. 4. ·...
TRANSCRIPT
Increasing MS
Sensitivity for
Proteomics
iFunnel Technology for
the QQQ and Q-TOF
Improving Sensitivity: iFunnel Technology in the
Agilent 6490 QQQ and 6550 Q-TOF
More efficient ionization
• Thermal confinement of
ESI ion plume
• Efficient desolvation to
create gas phase ions
2
Increased ion sampling
• 6 capillary inlets
• Samples 12x more ion rich
gas from the source
Greater ion transfer
• Removes the gas but
captures the ions
• Helps to remove source
generated noise
High
Pressure
Funnel
Low
Pressure
Funnel
MS Inlet
Nebulizer
Heated Sheath
Gas Thermal Gradient Focusing Region
Heat Sink with Forced Air
Cooling
Impact of Ion Funnel on QQQ Sensitivity
Observed a 5-10x increase in
sensitivity
Linear dynamic range (standard flow):
6460: 200 fmol to 25 pmol on-column
6490: 20 amol to 25 pmol on-column
6460 QQQ
6490 QQQ with iFunnel technology
1 fmol on-column
LVNEVTEFAK
575.5 937.5
3
Robust, Reproducible Quantitation in Digested
Plasma Using the UHPLC/QQQ Protein
Response
%RSD
Ret. Time
%RSD
Adiponectin:
IFYNQQNHYDGSTGK 9.8 0.13
Antithrombin-III :
DDLYVSDAFHK 4.7 0.16
Apolipoprotein A-II precursor:
SPELQAEAK 6.7 0.12
Apolipoprotein C-III:
GWVTDGFSSLK 2.3 0.08
Ceruloplasmin :
EYTDASFTNR 9.6 0.14
Heparin cofactor II:
TLEAQLTPR 6.1 0.15
Histidine-rich glycoprotein:
DGYLFQLLR 3.4 0.02
Kininogen-1:
TVGSDTFYSFK 3.3 0.13
L-selectin:
AEIEYLEK 9.5 0.15
Plasminogen:
LFLEPTR 2.2 0.13
Vitamin D-binding protein:
THLPEVFLSK 3.0 0.12
von Willebrand Factor:
ILAGPAGDSNVVK 9.5 0.15
The samples were provided by Derek Smith and Christoph H. Borchers from the UVic-Genome BC Proteomics Centre
2.2% RSD
n=110
4.7% RSD, n=4
7.9% RSD, n=4
12.3% RSD, n=4
Plasminogen LFLEPTR
4
HPLC-Chip/Q-TOF Increases Sensitivity for Protein
Identification
MS MS/MS
From 4-8 unique peptides (n=3)
10 amol BSA Digest On-column
5
Protein Discovery with
the 6550 Q-TOF
High Sensitivity Protein
Identification
Protein Identification
Identify Proteins
Shotgun:
find all proteins
present
Differentially
expressed (label and
label free)
Targeted
identification
Spectrum Mill
Scaffold MPP and Pathway Analysis
7
Spectrum Mill B.04.00: New Release
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Spectrum Mill B.04 Just Released: New Features
• Automated workflows with saved parameter files
• Autovalidation with FDR and new “Auto Thresholding” strategies
• Variable Modification Localization (VML) probability scoring for PTMs
• MRM Selector for creating MRM methods from protein id results
• Peptide Selector for exporting targeted QQQ MRM, QTOF inclusion lists,
and accurate mass csv databases from a list of protein accession numbers
• Integration with MPP workflows via AMRT export and ID Browser
• Integrated Biology with MPP Pathways
• Support for Scaffold, PepXML export (Skyline, TPP)
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Spectrum Mill B.04: Post-translational Modification
(PTM) Site Localization
VML score = Difference in Score of same identified sequences with different variable modification localizations
10
New Proteomics Software Suite Including Scaffold
• Scaffold Proteomics Software
– Scaffold Software from Proteomics Software of Portland, Oregon
– Allows users to combine results from multiple search engines (Spectrum Mill,
Mascot, SEQUEST, etc.) for increased confidence in protein identifications
– Scaffold contains tools to produce Venn diagrams of protein searches and
classify protein identifications by GO categories:
11
Spectrum Mill – MPP Data Exchange: A Unique
Agilent Advantage
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Filtered data projected on curated pathways
Multi-Omics Analysis in MPP
Protein Data
Overlay
Metabolite
Data Overlay
13
Export a list of proteins based on pathway analysis
Protein Discovery to Targeted Protein Analysis
LBMSDG
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• Protein data base search
• Protein-protein comparison of all results
• Export of protein “abundance” in MPP format Spectrum Mill
• Statistical analysis and visualization of differential proteins
• Pathway analysis of differential features
• Export of protein accession numbers
Mass Profiler Pro
• Use discovery data to develop MRM or inclusion list from protein accession numbers
• In silico prediction of peptides based on protein accession numbers
Spectrum Mill
• Inclusion list (with RT) or targeted mode on Q-TOF
• MRM or DMRM method on QQQ
• Export Quant results to MPP for analysis
Target Analysis (Q-TOF or
QQQ)
Genomics/Metabolomics Discovery to Target
Proteins Analysis
LBMSDG
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• Process genomics/metabolomics data to find significant differences
• Map differential features to pathways
• Export protein accession numbers
Mass Profiler Pro or GeneSpring
• Peptide Selector predicts possible peptides and precursors for target proteins
• Export as inclusion list for data directed LC/MS/MS analysis
Spectrum Mill
• Acquire data on QTOF using inclusion list and data-directed mode
Q-TOF
• Search data in Spectrum Mill and find one-hit and missing
• Loop until satisfied with protein coverage and peptide surrogates
Spectrum Mill
Label-free Protein Discovery Results for HeLa Cell
Lysates
LBMSDG
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• Treated HeLa cell lysates (Millipore) trypsinized then analyzed by LC/MS/MS (n=4)
• Protein database search in Spectrum Mill
• Protein-protein comparison in Spectrum Mill groups proteins across the entire set
• Color coding = abundance (based on EIC of peptides assigned to the protein)
• Export results to MPP
Protein
Group
Control HS-Ars IFN TNF
Injection
Treatments:
HS-Ars = heat-shock + arsenite
IFN = interferon
TNF = tumor necrosis factor
Statistical Analysis and Visualization of Protein
Identification Results
LBMSDG
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One-way ANOVA followed by PCA of differential proteins
Statistical Analysis and Visualization of Protein
Identification Results
LBMSDG
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Pathway Analysis of the Differential Proteins
Between HeLa Cell Lysates: Apoptotic Pathway
LBMSDG
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Pathway Directed Experiment:
Target Protein List Is Exported To Spectrum Mill
Create list of target peptides for
proteomics study
• Measure changes in protein
expression level
• Detect post-translational
modifications
Copy protein accession numbers
from Pathway Architect
Generate peptide lists for:
• QQQ MRM
• Q-TOF target list
LBMSDG
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Development of MRM-based Methods for Non-
Detected Target Proteins
LBMSDG
21
Improvements for Increasing Protein Identification
Increased
identifications
Q-TOF mass
spectrometer Chromatography
Data dependent
acquisition (DDA)
Recognize peptide
isotope pattern
Select “pure”
precursors
Optimize MS/MS
accumulation time
Better phase (Polaris)
Improved chip
manufacturing
Increase sensitivity
Increase speed to
acquire more MS/MS
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DDA: On-the fly Determination of Precursor Purity
30% precursor
purity threshold
Intended precursor must be at least 30%
of precursor signal in the isolation window
600 601 602 603 604
Isolation window
Precursor
below purity
TH. MS/MS
will not be
done
Meets 30%
criteria –
mixed
MS/MS but
can ID
Dominant
precursor –
will give
good
MS/MS
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MS precursor abundance
Acc.
Tim
e (
ms)
DDA: On-the fly Optimization of MS/MS Acquisition
Time Based on Precursor Intensity
Power ramp relationship
Increased protein
identified
Increased # of
MS/MS
Faster cycle times
24
Improved Chromatography With Polaris C18, 3 µm
Stationary Phase
Zorbax 300SB-C18
Jupiter
MagicAQ
SB-AQ/Reprosil
Extend C18/Reprosil
MetaSil
Polaris
609.7877 2+
982.9878 2+
706.3977 3+
Improved Protein ID
Best of tested phases Narrower peaks than
existing HPLC-Chips Good performance
with high sample load
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Increase in Protein Identification With Increased
Sensitivity
10x less sample! 10x less sample!
Same number of identifications with 10x less sample!
0
200
400
600
800
1000
1200
0 100 200 300 400 500 600
Pro
tein
Id
en
tifi
ed
ng loaded
6530 vs. 6550: Proteins
6530
6550
0
1000
2000
3000
4000
5000
6000
7000
8000
0 100 200 300 400 500 600
Un
iqu
e P
ep
tid
es I
den
tifi
ed
ng loaded
6530 vs. 6550: Peptides
6530
6550
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Effect of Gradient Time on Protein Identification:
E. coli Lysate
7000
7500
8000
8500
9000
9500
10000
0 50 100 150 200
Un
iqu
e P
ep
tid
es I
den
tifi
ed
Gradient Time
Unique Peptides
1000
1050
1100
1150
1200
1250
1300
1350
1400
1450
0 50 100 150 200
Un
iqu
e P
rote
ins I
den
tifi
ed
Gradient Time
Proteins
500 ng E. coli lysate
1% FDR
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Targeted Proteomics
with the 6490 QQQ
High Sensitivity Peptide
Quantitation
Protein Discovery and Targeted Proteomic Workflows
Page 29
Target
Proteins
Proteomics Experiment
Literature and other sources
Metabolomics Experiment Genomics
Experiment
Mass
Profiler
Pro
Metlin
PCDL
Pathway
Architect
GeneSpring
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Spectrum
Mill Mass
Profiler
Pro
Scaffold
Pathway
Architect
Skyline Targeted Proteomics Environment
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• Open Source software
• Multi-vendor software
• Funded with CPTAC project
• Rapidly evolved using feedback
from top-labs
• Widely used and highly regarded
Human SRM Atlas
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SISCAPA: Enrich Target Peptides and Decrease
Sample Complexity
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Increasing Throughput Using SISCAPA to Enrich
Target Peptides and Reduce Sample Complexity
Standard flow ion funnel QQQ-MS
Mesothelin curves (log/log)
0.001
0.01
0.1
1
10
100
0.001 0.01 0.1 1 10 100 1000
L/H
(fw
d)
or
H/L
(re
v)
Are
a R
ati
o
fmol Spiked Varying Peptide
Agilent 6490 (400ul/min) + Bravo
Bravo1-Forward-Meso
Bravo1-Reverse-Meso
Endogenous level: 3fmol/10ul = 16ng/ml
0.001
0.01
0.1
1
10
100
0.001 0.01 0.1 1 10 100 1000
L/H
(fw
d)
or
H/L
(re
v)
Are
a R
ati
o
fmol Spiked Varying Peptide
AB 4000 Qtrap (300nl/min) + Kingfisher
Meso:Xlink:Reverse
Meso:Xlink:Forward
Endogenous level: 3fmol/10ul = 16ng/ml
Lorne Conference
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Reducing analysis time to 3 min
Meso
CA
12
5
Her-
2
sT
fR
Tg
1 Tg
2
OP
N
FL
C
LPSBP
PC
I
AF
P
Magnetic Bead Implementation of SISCAPA Assay
Technology
Sample peptide
Labeled standard
MRM Chromatogram
Biomarker concentration
Agilent Bravo
34
42
Avg. Δ R.T ~ 2.4 s across 8 replicates over a 60-minute gradient