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

Proteomics: Why ?Proteomics: Why ?

Genomics =/=> Proteomics

ProteinsGenes

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


• 2D DIGE = labeling with fluorescing cyanine dyes:Cy2 (green) Cy3 (orange) Cy5 (red)

GEL based separations

2D-DIGE,2D-DIGE,

Separation: 2D LCSeparation: 2D LC

• 2D –Gel �� 2D LC

• massive amount of data when linked to MS


• 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 !


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


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)

Special ESI: NanospraySpecial ESI: Nanospray

Wilm, 1991

Special ESI: NanospraySpecial ESI: Nanospray

Electrospray Ionization (ESI)Electrospray Ionization (ESI)

55

60

65

70

75

80

85

90

95

100

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

750 800 850 900 950 1000 1050 1100 1150 1200m/z

0

5

10

15

20

25

30

35

40

45

50

55

Rela

tive A

bundance

+251161.7

+261117.1

1075.8

+37785.5

+38764.8

+39745.3

+40726.6

DeconvolutionDeconvolution

55

60

65

70

75

80

85

90

95

100

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

0

5

10

15

20

25

30

35

40

45

50

55

Re

lative A

bund

an

ce

29105.0

47085.0

MALDI ionizationMALDI ionization


MALDIMALDI


MALDIMALDI

Ions are preferentially +1

Special MALDI: SELDISpecial MALDI: SELDI

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

MALDIMALDI--TOF/TOFTOF/TOF

Triple Q Triple Q -- IonTrapIonTrap

Q Q -- TOFTOF

MALDIMALDI--IonTrapIonTrap

Tube Lens

Skimmer

Ion TransferTube

+

++

+

+

Laser

FTFT--MSMS

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]

70

80

90

100

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

120 140 160 180 200 220 240 260

m/z

0

10

20

30

40

50

60

Re

lative

Ab

un

da

nc

e

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

13+

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

14+

15+

12+

11+

10+

Rela

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

600 700 800 900 1000 1100 1200 1300 1400

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

1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 11000

0

10

20

30

40

50

60

70

80

90

100

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


LabelingLabeling

2 different strategies:2 different strategies:

How to find differences ?How to find differences ?

LabelingLabeling

Label freeLabel free


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

BIOTINLINKER C12

BIOTINLINKER C13LINKER C

Step 1: label proteins

Step 2: digest proteins

ICATICAT

BIOTINLINKER C12


Step 2: digest proteins

ICATICAT

BIOTINLINKER C12


Step 3: isolate labeled peptides

ICATICAT

Step 4: remove Biotin tag (= cleavable

Isotope-Coded Affinity Tag (cICAT))

BIOTINLINKER C12

BIOTINLINKER C13

Labeling for SeparationLabeling for Separation


- label proteins at rarely occurring AA’s (eg cysteine or methionine)

- analyze labeled peptides only --> reducing complexity


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

0

10

20

30

40

50

60

70

80

90

% I

nte

ns

ityMS/MS

4 # fragments !

iTRAQiTRAQ

Reporter Group Placement: Selection of ‘Quiet Region’

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


116


117

• Single 2D LC analysis for combined samples (4-plex)

• Add at known concentration


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


LabelLabel--freefree NO MULTIPLEXING

DATA Processing !!!!


LabelLabel--freefree Profiling

“GEL” view matching approach

1D GEL view:

1D Gel view1D Gel view

Using UV spectra: PF2D

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


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: Imaging Mass SpectrometryAppetizer: Imaging Mass Spectrometry

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

Thank You !Thank You !

www.Prometa.be

BioMacS.kuleuven.be

Advanced Data processingAdvanced Data processing

Advanced Data ProcessingAdvanced Data Processing

Female

Hierarchical Clustering

Male

Advanced Data ProcessingAdvanced Data Processing

PCA Classification

femalemale

And finally…And finally…

• Validation of potential candidates

• Design of “kits”

• Many candidates

• Design of “kits”

waelkens sept4 ew.ppt

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