label free quantitative proteomics - spc...
TRANSCRIPT
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Label Free Quantitative Proteomics
Mi-Youn Brusniak, Ph.D.
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• Principles of quantitative proteomics
• Labeling vs. non-labeling approaches for quantitative proteomics
• SpecArray: software tool for quantitative proteomics without isotopic labeling
• Corra: Framework to generate candidate biomarkers using non labeling methods
Outline
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Summary of LC-ESI-MS/MS
• Protein mixtures are digested into peptides
• Peptides are concentrated and fractionated by separation technologies such as SCX, IEF, RP, etc.
• While eluting from RP column, peptides are ionized by ESI and analyzed by MS/MS
• Peptides are identified from CID spectra
• Peptides are mostly quantified from MS signatures except in the case of iTRAQ
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Electrospray Ionization
• Multiple charge states: from +1 to +4
M + z H+ = M(H+)z m/z = (M+z*H)/z
HV+ -
LC
time
ESI
+4
+3
+2
+1
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Reversed-Phase Chromatography
• Separate peptides by hydrophobicity
• Reproducible
• Automated, coupled online with MS
time
Analytical Column (100 µµµµm i.d.)
Grounded
Peek micro-cross
Tapered frit
Precolumnwaste
6-way Divert Valve
close
- 2 to 4 kV
Peptide eluting profiling
% A
cN
% A
cN
R.A
.(%
)R
.A.(
%)
00
5050
100100
00
100100
5050
Time (min)Time (min)1010 2020 4040 5050 6060 70703030
HPLC
UV detector
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Single Ion Chromatogram
m/z
inte
nsi
ty
2D view: m/z, intensity
intensity
scan #
m/z=1000.2
Single Ion Current (SIC) Trace
3D view: m/z, intensity, time
MS scans
time (scan #)in
t en
s ity
m/zm/z
m/z
m/z
1000.2
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Peptide Quantification
• Area of SIC is proportional to peptide abundance
• Ionization efficiency of each peptide is different
– Depends on the peptide molecular properties (e.g. number of basic residues)
• MS Technology is NOT quantitative
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• Principles of quantitative proteomics
• Labeling vs. non-labeling approaches for quantitative proteomics
• SpecArray: software tool for quantitative proteomics without isotopic labeling
• Corra: Framework to generate candidate biomarkers using non labeling methods
Outline
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Stable Isotopic Labeling and Quantitative Proteomics
• Samples labeled with different stable isotopes
• Chemically identical
• Distinguishable by MS in mass shift
• Peptide abundance ratio measured by ratio of SIC areas
• Peptides are identified before quantification
Area=1.21x109
Area=1.01x109
d0d0
d8d8 d0/d8=1/0.83d0/d8=1/0.83
ASAPRatio
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Typical LC-MS/MS Analysis
Features: 2720
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Typical LC-MS/MS Analysis
CIDs: 1633
Features: 2720
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Typical LC-MS/MS Analysis
Features: 2720
CIDs: 1633
IDs: 363
ID/CID: 22%
ID/feature: 13%
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Limitations of LC-MS/MS Approach to Large-Scale Protein Profiling
• Sample size limited:
– ICAT (2), iTRAQ (4), …
• Difficult to trace protein abundance across a large number of samples
• Most peptides cannot be identified
• Difficult to identify & quantify low-abundance proteins
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LC-MS Approach
1 10
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LC-MS Approach
1 10
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LC-MS Platform for Large-Scale Protein Profiling
• Samples are NOT labeled
• Samples are analyzed under identical
settings
• Peptide abundance is evaluated by MS signal
intensity in different runs
• Reproducibility in LC-MS analysis critical
• Peptide alignment crucial
• Followed by target LC-MS/MS
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Challenges in LC-MS Platform
• Highly reproducible LC-MS analysis– Retention time shift
– Fluctuations in MS signal intensity
– Peptide identification in separated MS/MS
• Complex samples– Overlapping signals
– Misaligned peptides
• Large sample size– Column degradation
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• Principles of quantitative proteomics
• Labeling vs. non-labeling approaches for quantitative proteomics
• SpecArray: software tool for quantitative proteomics without isotopic labeling
• Corra: Framework to generate candidate biomarkers using non labeling methods
Outline
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• Software Suite for the Generation and
Comparison of Peptide Arrays from Sets of Data
Collected by Liquid Chromatography-Mass
Spectrometry
• Xiao-Jun Li et. al. Molecular & Cellular
Proteomics 4.9, 2005
• SpecArray v1.2.0 is available in
http://sourceforge.net/projects/sashimi or
http://tools.proteomecenter.org/software.php
SpecArray
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SpecArray Software Suite
Process raw data
Extract peptide features
Align peptides across samples
Generate peptide array
Pep3D
mzXML2dat
PepArray
PepMatch
PepList
Collect LC-ESI-MS data
Evaluate data quality
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Collect LC-MS Data
Data collection:
� LC-MS in profiling mode
� No or minimal MS/MS
� Peptide abundance evaluated by MS signal intensity (after proper normalization)
� Converted into mzXML file format
Process raw data
Extract peptide features
Align peptides across samples
Generate peptide array
Pep3D
mzXML2dat
PepArray
PepMatch
PepList
Collect LC-ESI-MS data
Evaluate data quality
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Evaluate Data Quality
Pep3D
Process raw data
Extract peptide features
Align peptides across samples
Generate peptide array
Pep3D
mzXML2dat
PepArray
PepMatch
PepList
Collect LC-ESI-MS data
Evaluate data quality
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LC-MS Data of Same Sample
badgood
QTOFMicroTOF
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Process Raw Data
mzXML2dat:
� Smoothing, denoising, centroiding, subtracting background, estimating S/N
� ~2GB ���� ~5MB
Process raw data
Extract peptide features
Align peptides across samples
Generate peptide array
Pep3D
mzXML2dat
PepArray
PepMatch
PepList
Collect LC-ESI-MS data
Evaluate data quality
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Pep3D Images
Before
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Pep3D Images
After
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Extract Peptide Features
PepList:
� Peptide isotopic distribution
� Peptide elution profile
� Data range: m/z, time
I0
I1
I2
I3
I4
Process raw data
Extract peptide features
Align peptides across samples
Generate peptide array
Pep3D
mzXML2dat
PepArray
PepMatch
PepList
Collect LC-ESI-MS data
Evaluate data quality
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Peptide Feature Detection
Mass: 2177.8Time: 78 minCharge: +2
Peptide isotopic distribution
Peptide elution profile
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Align Peptides Across Samples
PepMatch:
�Align peptides by mass, charge, retention time
� Reproducibility
m/z: Highly reproducible
retention time: Relative order reproducible
signal intensity: Relative intensity
reproducible
� Align multiple samples
� Combine aligned peptides
Process raw data
Extract peptide features
Align peptides across samples
Generate peptide array
Pep3D
mzXML2dat
PepArray
PepMatch
PepList
Collect LC-ESI-MS data
Evaluate data quality
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Peptide Alignment Between Two Samples
retention time intensity
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Generating Peptide Expression Array
PepArray:
� Sample-dependent normalization
� Search missed features
� User specified information
Minimal sample size of each group
Process raw data
Extract peptide features
Align peptides across samples
Generate peptide array
Pep3D
mzXML2dat
PepArray
PepMatch
PepList
Collect LC-ESI-MS data
Evaluate data quality
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• Principles of quantitative proteomics
• Labeling vs. non-labeling approaches for quantitative proteomics
• Introduction to ASAPRatio: software tool for quantitative proteomics using isotopic labeling
• Introduction to SpecArray: software tool for quantitative proteomics without isotopic labeling
• Corra: Framework to generate candidate biomarkers.
Outline
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What is Corra?
• Corra is a Scottish Goddess of prophecy.
• Corra is a framework to generate candidate biomarkers in high throughput MS data processing environment.
• Corra’s goal is to detect differentially expressed features by maximizing the number of such features while controlling the false discovery rate.
• Corra uses LC-MS (and LC-MS/MS) and experiment design information
• Work in progress– Validation of work flow using Latin Square data
– Validation of reproducibility
What is Corra?
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Corra: MS1 Data Analysis(A Discovery-Based Approach)
LC-MS of sample pools
of cases and controlsAlign LC-MS data
Differential analysis
PCA and clustering
Peak inclusion list of
discriminatory features
Peptide/protein
identification by MS/MS
Candidate validation
in larger sample pool
Discriminatory peptide features initially determined just from MS1 data analysis
Follow-up MS/MS subsequently determines peptide identity
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Corra Framework Overview
APML(feature lists)Annotated Peptide
Markup Language
LC-MS Feature Picking
LC-MS alignment
R: Statistical Analysis Validation and candidate for targeted analysis
Bioconductor Annotated
Data Set
TPP Processes
INTERACT
PeptideProphet
mzXML
ProteinProphet
Pep3DDatabase search
(SEQUEST/Mascot)
DB
Annotation of picked
peptide features with
sequence assignments
LC-MS
DBLC-MS/MS
LC-MS and MS/MS (mzXML)
MS/MS
MS
APML (aligned feature lists)Annotated Peptide
Markup Language
Corra Framework Overflow
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Corra Framework Overview
-20 -10 0 10 20
-15
-10
-50
51
01
5
PCA on all common features
PC1
PC
2
DB
DB
DB
DB
NGT
NGT
NGT
IGT
IGT
IGT
NGT
IGT
NGT
NGTNGT
IGT
IGT
DB
NGT
NGT
DB
NGT
IGT
NGT
DB
IGT
IGT
IGT
NGT
NGT
IGT
IGT
DB
DBIGT
Corra
•Quick Quality Control on the fly
•LC-MS or LC-MS/MS Process
•Annotation of Samples and aligned Features
•Simple and Basic Statistical Analysis (PCA, Volcanoplot)•Simple Cross-validation
Outputs:
•APML for Feature Lists•APML for Aligned Feature Lists
•Candidate Target Analysis Lists (feature or peptide or protein)
•Weighted Panels of Proteins which has a certain statistical predictability
Inputs:
•mzXML
•Search Result Files
•Sample Information
m.z rt charge M t P.Value adj.P.Val B
343 439.468 35.666 4 1.309641 5.747842 5.92E-06 0.010247 3.929592
285 1164.557 121.915 4 -1.55887 -5.54884 9.78E-06 0.010247 3.480909
742 734.352 65.715 3 -2.03654 -5.30905 2.34E-05 0.016372 2.687205
152 974.18 80.755 4 -1.41841 -4.82714 6.15E-05 0.032238 1.825438
421 992.613 16.686 4 -1.26019 -4.53599 0.00013 0.040325 1.150351
255 981.175 45.484 4 -1.32148 -4.52185 0.000135 0.040325 1.11754
1753 669.88 90.033 2 -2.03223 -4.39905 0.000293 0.071341 0.442843
1367 861.883 47.297 2 -3.3339 -6.34749 0.000107 0.040325 0.299392
453 1104.57 112.804 4 -1.17287 -4.15356 0.000346 0.071341 0.264205
680 758.056 67.722 3 -1.21069 -4.10977 0.000387 0.071341 0.163092
332 739.094 92.16 4 -1.07081 -4.06259 0.000437 0.071341 0.05428
326 987.174 45.883 4 -1.25356 -3.99535 0.000518 0.071341 -0.10052
183 726.855 82.543 4 -1.12979 -3.94417 0.00059 0.071341 -0.2181
1252 905.114 81.87 3 -1.18671 -3.93147 0.00061 0.071341 -0.24723
324 923.705 131.279 4 -1.10673 -3.9304 0.000611 0.071341 -0.24969
668 889.396 67.162 3 -1.31687 -3.90338 0.000655 0.071341 -0.31164
1179 448.137 25.178 3 -1.25939 -3.8901 0.000677 0.071341 -0.34206
234 531.773 102.71 4 -1.05553 -3.88587 0.000684 0.071341 -0.35175
370 448.18 24.596 4 -1.05587 -3.88262 0.00069 0.071341 -0.35918
644 892.412 71.75 3 -1.13239 -3.91765 0.000715 0.071341 -0.36621
Histogram of cor(run1all, use = "pairwise.complete.obs")
cor(run1all, use = "pairwise.complete.obs")
Fre
qu
en
cy
0.70 0.75 0.80 0.85 0.90 0.95 1.00
05
01
00
15
02
00
25
0
Quick Monitoring
0
500
1000
1500
2000
2500
3000
2670
0304
-1.p
epList
2670
0457-
1.pe
pList
2680
0013
-1.p
epList
2680
0017-
2.pe
pList
2680
0025-1
.pep
List
2680
0046
-2.p
epList
2680
0055-
1.pe
pList
2680
0063
-2.p
epList
2680
0068-
1.pe
pList
2680
0079
-1.p
epList
2680
0101-
1.pe
pList
2680
0112
-2.p
epLi
st
2680
0134-1
.pepL
ist
2680
0213-
2.pe
pList
2680
0273
-2.p
epList
2680
0454-
1.pe
pList
2680
0552
-2.p
epList
2680
0753-
1.pe
pList
Sample
No
Featu
res
Series1
Series2
Corra Framework Overview
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Corra Application Examples(LC-MS: Diabetes Pilot Study)
24 runs = 8 individuals x 3 replicate runs
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Corra Application Examples(LC-MS: NCI Mouse Skin Cancer)
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Summary
• Label free quantitative LC-MS and LC-MS/MS is a powerful method for identifying and quantifying proteins in complex samples
• Corra framework is very promising in large-scale protein profiling
• Software suites have been developed for both LC-MS and LC-MS/MS platforms