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Waelkens Sept4 EW.PPT
Transcript of Waelkens Sept4 EW.PPT
Proteomics/Peptidomics
E. Waelkens
System biology tools and preclinical
models for translational research in
endometriosis, ESHRE Campus
workshop, 4-5 September 2009
Proteomics: What?Proteomics: What?
Proteins
Proteomics = the study of the protein library
- under certain well-definedconditions
- in certain cells/tissues/organisms
ProteomicsProteomics
Proteins AA level
Protein level
•Proteolytic maturation
•Methionine processing
•Sugars, Ubiquitination
•…….
MODIFICATIONS !
Challenges in Proteomics/PeptidomicsChallenges in Proteomics/Peptidomics
• Labile modifications (eg phosphorylation)
• Dynamic changing (not static)
• Handling dependent (sample (pre)treatment !)
• Wide concentration range (no PCR equivalent)• Wide concentration range (no PCR equivalent)
• Localization dependent
• Better technology = more data
• Differential analysis and quantification
Proteomics/Peptidomics: How?Proteomics/Peptidomics: How?
Various ways
examples:
• (2D)- gel electrophoresis (old, old fashioned, but it still
climbs up the mountains) climbs up the mountains)
•immunological techniques [western blot ,
immunohistochemistry, fluorescence,...]
• chromatography: UV profiling• fluorescent tagged proteins [full- length,
(sub)cellular localization]
• Protein (Peptide) arrays• Mass spectrometry
Proteomics/Peptidomics: How?Proteomics/Peptidomics: How?
Thanks to 2 “soft ionization” techniques(ESI and MALDI)
Mass spectrometryMass spectrometryMass spectrometry
Flow Chart: Proteomics/PeptidomicsFlow Chart: Proteomics/Peptidomics
Separation
Analysis
Data processing
SeparationSeparation
• 1D Gel (SDS-PAGE)• 2D GEL -> 2D DIGE
GEL based separations
Biringer & Amato, 2002
3535
SeparationSeparation
• 2D DIGE = labeling with fluorescing cyanine dyes:Cy2 (green) Cy3 (orange) Cy5 (red)
GEL based separations
2D-DIGE,2D-DIGE,
SeparationSeparation
• Special affinity based separations (special affinity matrixes: eg for serum samples/ membrane proteins/ phoshorylated peptides, antibodies based separations, dendrimer capture). Popular application: magnetic beads
• solid phase extraction
• new separations in the MS analyser itself: ion mobility
• new LC based separations:
• Advion Robot• UPLC chip based separations• robotic MALDI MS spotters• mixed functional phase columns
Serum ProteomicsSerum Proteomics
Depletion methods (2-20 proteins):
• high dynamic range of protein concentrations
• dominated by a limited number of high-abundance proteins, constituting
up 95–99% of the total protein
Immunodepletion methods
Affinity methods
- old methods: Cibacron Blue (Blue Sepharose): albumin; Protein G
(A): IgG
- new methods: combinatorial chemistry: eg short peptide fragments
(hexapeptides)
Serum ProteomicsSerum Proteomics
Low Abundant
High Abundant
Up to 6 fold increase in identification !
SeparationSeparation
MORE SEPARATION = MORE
IDENTIFICATION
���� Combine (pre-) fractionation steps: immuno-affinity based, subcellular fractionation, serum depletion kits, ….
Special SeparationSpecial Separation
Protein tryptic peptidesAnalysis
- label proteins at rarely occurring AA’s (eg cysteine or methionine)
- analyze labeled peptides only --> reducing complexity
Protein tryptic peptidesAnalysis
ICAT (isotope coded affinity tag) , COFRADIC
Analysis: HistoryAnalysis: History
obtained from:
Yergey A.L., Yergey A.K.
Preparative Scale Mass Spectrometry: A Brief History of the Calutron
JASMS, 1997, V8(N9), p943-953
AnalysisAnalysis
Huge improvement of mass spectrometers:
- end 80’s: Soft ionization methods : ESI and MALDI (Nobel price 2002)
- end 90’s: hybrid MS instruments, nanospray, MALDI TOF/TOF
- begin 21 century: exponential growth of MS equipment:
TOF/TOF with real CID, linear iontrap, orbitrap, 9.4 Tesla FT ICR MS, ECD and ETD dissociation, new hybrids (eg MALDI-Iontrap; 3Q-Iontrap..), ion mobility
- CPU power
MilestonesMilestones
1897: Sir J.J Thomson, Cavendish Laboratory , University of Cambridge, discovery of the electron
1906: Thomson: Nobel Prize for this studies on the conduction of electricity by gasses (ion movements)
1912: Thompson: First mass spectrometer
1946: William E. Stephens: concept of time-of-flight analyzer
1953-58: Wolfgang Paul: quadrupole analyzer
1983: first commercial ion trap
1989: Paul: Nobel Prize in Physics
1968: Malcolm Dole: first concept of ESI1968: Malcolm Dole: first concept of ESI
1974: Comisarow & Marshall: FTMS
1984-88: John Fenn: ESI for biomolecule analysis
Wong, S.F., Meng, C.K. and Fenn, J.B., J. Phys. Chem., 92, 546 (1988)Meng, C.K., Mann, M. and Fenn, J.B., Z. Phys.D., 10, 361 (1988)
1988: Tanaka (Japan) and Franz Hillenkamp/Michael Karas (Germany): MALDI
M. Karas and F. Hillenkamp, Anal. Chem. 60, 2299-2301 (1988)K. Tanaka et al., Rapid Commun. Mass Spectrom. 2, 151-153 (1988)
Analysis: MSAnalysis: MS
Turbo Engine behind Proteomics/Peptidomics = Mass Spectrometry (MS)
What should we know about MS ?
1. create ions : ionization
2. separate the ions : analyzer3. measure the ions : detector
Basic MS instrumentBasic MS instrument
ESI or MALDI e.g. IonTrapor TOF (time of flight)
source analyzer detector
Examples: •ESI IonTrap
•MALDI TOF
Electrospray Ionization (ESI)Electrospray Ionization (ESI)
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Rela
tive A
bundance
+271075.8
+281037.5
+291001.7
+30968.4
+31937.2+32
907.9
+33880.5
+34854.6
+35830.3
+36807.3
Averaged spectrum of 2 components
Multiple charged ions
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Rela
tive A
bundance
+251161.7
+261117.1
1075.8
+37785.5
+38764.8
+39745.3
+40726.6
DeconvolutionDeconvolution
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Re
lative A
bund
an
ce
29021.0
46668.0
Carbonic Anhydrase
Reported MW1 = 29,022
Yeast Enolase
Reported MW1 = 46,671
Deconvoluted Spectrum
5000 10000 15000 20000 25000 30000 35000 40000 45000
mass
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Re
lative A
bund
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29105.0
47085.0
Analyser: separate ionsAnalyser: separate ions
ESI or MALDI e.g. IonTrapor TOF (time of flight)
source analyzer detector
Examples: •ESI IonTrap
•MALDI TOF
Analyzer: separate ionsAnalyzer: separate ions
• Ion-trap MS• Quadrupole MS• Time-of-flight MS• Time-of-flight MS• Fourier-transform MS• Magnetic-sector MS
More complex MS instrumentsMore complex MS instruments
ESI or MALDI e.g. quadrupoleor time-of-flight
e.g. quadrupoleor TOF or Iontrap
source analyzer detectoranalyzer
Collision cell
Examples: • ESI Q TOF
• ESI Q Iontrap
• MALDI Iontrap
• MALDI TOF TOF
MS/MS: AA sequence MS/MS: AA sequence
Note: manual interaction or de novo sequence
MS/MS of m/z 730.4
ALGSFR
Empirical formula (composition)Empirical formula (composition)
Azo_1 #257-284 RT: 4.99-5.27 AV: 17 NL: 1.54E6T: FTMS - p NSI Full ms [ 85.00-1000.00]
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Re
lative
Ab
un
da
nc
e
241.1199C11 H17 O 4 N2
C12 H13 N6
181.0509C9 H 9 O4 227.1042
C10 H15 O4 N2
C11 H11 N6
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m/z
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Re
lative
Ab
un
da
nc
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135.0454C8 H 7 O2
195.0667C10 H 11 O4
174.0564C10 H 8 O2 N1
216.0519C8 H10 O6 N 1
C7 H 4 O1 N8
259.0766C18 H11 O2
C4 H13 O 8 N5
168.0431C8 H8 O4
145.9380103.3712 132.0555
Top Down AnalysisTop Down Analysis
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14+12+
893.5290
893.6815
600 700 800 900 1000 1100 1200 1300 1400m/z
Rela
tive A
bundance
928.6396
929.9735
720.3784823.9249
916.4733769.9116 955.7405
1008.5148
1097.6791676.8584
1229.5820 1358.6410900.2117663.0114588.3018 1115.5698 1412.59381348.38761239.3015
Sequence Analysis of m/z 893
MS/MS of m/z 893
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tive A
bundance
829.7762967.9076
1055.8090
774.5244
1161.3905 1451.3701726.1155
829.4963
1064.82191175.4320 1301.2075 1463.2484682.5838
Top Down AnalysisTop Down Analysis
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Rela
tive A
bundance
928.6396
929.9735
720.3784823.9249
916.4733769.9116
955.7405
1008.5148
1097.6791676.8584
1229.5820 1358.6410900.2117663.0114588.3018 1115.56981412.59381348.38761239.3015
MS/MS of m/z 893
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Rela
tive A
bundance
11124.3263
3713.7782
8769.17634027.9395
5079.5063 9822.72111700.8490
10058.84647947.83059667.67734738.40322778.3347 6858.2459
600 700 800 900 1000 1100 1200 1300 1400
m/z
Zero Charge Monoisotopic Data
Data ProcessingData Processing
• Protein/peptide identification = search engine to interrogate database(s)
• Find the differences: Biomarker discovery
Clinical Proteomics: BiomarkersClinical Proteomics: Biomarkers
• Diagnostic
• Prognostic
• Therapeutic• Therapeutic
Clinical Proteomics: BiomarkersClinical Proteomics: Biomarkers
LabelingLabeling
2 different strategies:2 different strategies:
How to find differences ?How to find differences ?
LabelingLabeling
Label freeLabel free
Clinical Proteomics: BiomarkersClinical Proteomics: Biomarkers
LabelingLabeling Based on stable isotopes (not radioactive): C12 � C13
N14 � N15
Examples: SILAC (stable isotope labelling )
ICATunbiased
ICAT
ITRAQ
O18 labelling
AQUA (=targetted approach)
unbiased
Labeling: ICATLabeling: ICAT
ICAT (Isotope-coded affinity tag)
= Duplex Labeling:
2 tags (“light” and “heavy” linker, 9x C12 and 9x C13, ∆ mass= 9 Da)
has a biotin tag
labeling = at the protein level (at cysteines)labeling = at the protein level (at cysteines)
analysis = only labeled peptides � reduced complexity
BIOTINLINKER C12
BIOTINLINKER C13
ICATICAT
Step 4: remove Biotin tag (= cleavable
Isotope-Coded Affinity Tag (cICAT))
BIOTINLINKER C12
BIOTINLINKER C13
Labeling for SeparationLabeling for Separation
Protein tryptic peptidesAnalysis
- label proteins at rarely occurring AA’s (eg cysteine or methionine)
- analyze labeled peptides only --> reducing complexity
Protein tryptic peptidesAnalysis
ICAT (isotope coded affinity tag) , COFRADIC
Labeling:Labeling: SILACSILAC
Stable isotope labeling with amino acids in cell culture (SILAC)
eg: 13C labeled L-lysine :
- 2 populations (“light” and “heavy” amino acid in cell culture)
12C L-lysine 13C labeled L-lysine
Duplex Labeling technique
Labeling in vivo
Labeling:Labeling: AQUAAQUA
AQUA peptide: known
concentration, isotope labeled
Digestion
Q2
Q3Q1Source
M I X
MRM analysis
Set parent ion Set fragment ion
Labeling: iTRAQLabeling: iTRAQ
Charged Neutral loss
Isobaric Tag
(Total mass = 145)
Reporter Balance PRG
� Gives strong signature ion in MS/MS� Gives good b- and y-ion series� Maintains charge state � Maintains ionization efficiency�Signature ion masses lie in quiet region�Signature ion masses lie in quiet region
= MS/MS Fragmentation Site
Reporter
(Mass = 114 thru 117)
Peptide Reactive
Group
Balance
(Mass = 31 thru 28)
iTRAQ: MultiplexingiTRAQ: Multiplexing
PEPTIDE
MIX ���� MS
114
115 PEPTIDE
31
30
1347.0 1349.6 1352.2 1354.8 1357.4 1360.0
Mass (m/z)
1352.84116
117
PEPTIDE
PEPTIDE
29
28
1 M/Z peak !
iTRAQ: MultiplexingiTRAQ: Multiplexing
1347.0 1349.6 1352.2 1354.8 1357.4 1360.0
Mass (m/z)
1352.84
1 M/Z peak !100
1 M/Z peak !
111.0 112.8 114.6 116.4 118.2 120.0
Mass (m/z)
5186.0
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% I
nte
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ityMS/MS
4 # fragments !
iTRAQ:iTRAQ: Absolute QuantificationAbsolute Quantification
Upf1 ∆∆∆∆
lysate
Reduce/
Block Cysteines
Trypsin digest
React with
Reduce/
Block Cysteines
Trypsin digest
Reduce/
Block Cysteines
Trypsin digest
Xrn1 ∆∆∆∆
lysate
Synthetic
peptides
wt
lysate
React with React with React with React with iTRAQ™ Reagent
114
MIX
SCX
LC -MS/MS
React with iTRAQ™ Reagent
115
React with iTRAQ™ Reagent
116
React with iTRAQ™ Reagent
117
• Single 2D LC analysis for combined samples (4-plex)
• Add at known concentration
Clinical Proteomics: BiomarkersClinical Proteomics: Biomarkers
LabelingLabeling
• At protein level or at peptide level
• relative quantification and absolute quantification (using standards)• relative quantification and absolute quantification (using standards)
• allows MULTIPLEXING
Note:
2D DIGE = also multiplexing and relative quantification
Clinical Proteomics: BiomarkersClinical Proteomics: Biomarkers
LabelLabel--freefree NO MULTIPLEXING
DATA Processing !!!!
Clinical Proteomics: BiomarkersClinical Proteomics: Biomarkers
LabelLabel--freefree Profiling
“GEL” view matching approach
1D GEL view:
2D Gel view2D Gel view
LabelLabel--freefree “GEL” view matching approach
2D GEL view: mapping using retention time and m/z value
LabelLabel--free: MRM approachfree: MRM approach
MRM= Multiple Reaction MonitoringDigestion
Q2
Q3Q1Source
Set parent ion Set fragment ion
Clinical Proteomics: BiomarkersClinical Proteomics: Biomarkers
LabelLabel--freefree
• At protein level or at peptide level
• only relative quantification (using standards)
• no MULTIPLEXING
• Cost effective and easy
Examples: SELDI-MS
2D LC MS/MS with mapping of the data
using retention time and m/z value
MRM methods
Appetizer: Imaging Mass SpectrometryAppetizer: Imaging Mass Spectrometry
Matrix Coating on Tissue
Optical Image
Appetizer: Tissue Proteomics using IMSAppetizer: Tissue Proteomics using IMS
Tissue Biomarker DiscoveryAlterations in Hippocampus:
KO mouse
“Fingerprint of striatum” Alterations in Hippocampus:
WT mouse
General considerationsGeneral considerations
Clinical Proteomics/Peptidomics
- Many possible approaches
- More separation = more identification
- Serum/plasma proteomics: use depletion methods
- Biomarker discovery: start with the target sample- Biomarker discovery: start with the target sample
- Bioinformatics !
- Future perspectives:
genomics + proteomics + peptidomics + metabolomics + …
� Personalized medicine
AcknowledgementsAcknowledgements
Prof R. Derua
K. Schildermans
S. Vandoninck
Prof B. De Moor
R. Van de Plas
Prof T. D’Hooghe
Prof I. Vergote
Prof D. Timmerman