quantitative proteomics: applications and strategies

Quantitative Proteomics: Applications and Strategies

October 2013

Gustavo de SouzaIMM, OUS

A little history…

1985 – First use: up to a 3 kDa peptide could be ionized

1987 – Method to ionize intact proteins (up to 34 kDa) described

Instruments have no sequence capability

1989 – ESI is used for biomolecules (peptides)

Sequence capability, but low sensitivity

1994 – Term «Proteome» is coined

1995 – LC-MS/MS is implemented

«Gold standard» of proteomic analysis

2DE-based approach

2DE-based approach

““I see 1000 spots, but identify 50 only.”I see 1000 spots, but identify 50 only.”

Gradient elution:200 nl/min

Column (75 mm)/spray tip (8 mm)

Reverse-phase C18 beads, 3 mm

Platin-wire2.0 kV

Sample Loading:500 nl/min

No precolumn or split

ESI

15 cm

Fenn et al., Science 246:64-71, 1989.

LC-MS

MS-based quantitation

InletIon

SourceMass

AnalyzerDetector

MALDIES

Time-of-FlightQuadrupole

Ion TrapQuadrupole-TOF

LC

Peak intensities can vary up to 100x between duplicate runs.

Quatitative analysis MUST be carried on a single run.

Ion Intensity = Ion abundance

MS measure m/z

m/z

Inte

nsity

Sample 2Sample 1

Isotopic Labeling

Unlabeled peptide:

Labeled peptide:

a) b) a) b)

18O16O

15N14N

13C12C

2H1H

Stable IsotopeElement

18O16O

15N14N

13C12C

2H1H

Stable IsotopeElement

Enzymatic Labeling

Metabolic Labeling

SILAC

*

m/zm/z

Passage cells to allow incorporation of labelled AA

By 5 cell doublings cells have incorporated

*

m/zm/z

Grow SILAC labelled cells to desired number of cells for experiment

*

m/zm/z

Start SILAC labelling by growing cells in labelling media

(labelled AA / dialized serum)m/zm/z

Media with Normal AA ()

Media with Labelled AA (*)

X 3 X 3

Cells in normal culture media

Ong SE et al., 2002

Chemical Labeling

Biotin Biotin tagtag

Linker Linker (heavy or light)(heavy or light)

Thiol specific Thiol specific reactive groupreactive group

ICAT Reagents:ICAT Reagents: Heavy reagent: d8Heavy reagent: d8--ICAT (ICAT (XX=deuterium)=deuterium)Light reagent: d0Light reagent: d0--ICAT (ICAT (XX=hydrogen)=hydrogen)

S

N N

O

N OO

O N IO OXX

XX

XX

XX

XX

XX

XX

XX

XX

XX

XX

XX

XX

XX

XX

XX

Gygi SP et al., 1999

ICAT (Isotope-Coded Affinity Tag)

ICAT

Cell State 1Cell State 1((All cysteines labeled with All cysteines labeled with

light ICATlight ICAT))

Cell State 2Cell State 2(All cysteines labeled (All cysteines labeled

with heavy ICAT)with heavy ICAT)

Rel

ativ

e A

bund

ance

Rel

ativ

e A

bund

ance

00

100100

Prote

in A

Prote

in A

Prote

in B

Prote

in B

Prote

in C

Prote

in C

Prote

in D

Prote

in D

Prote

in E

Prote

in E

Prote

in F

Prote

in F

. . . .

200200 400400 600600 800800200200 400400 600600 800800

m/zm/z

00

100100

NHNH22--EACDPLREACDPLR--COOHCOOH=Protein A=Protein A

Rel

ativ

e A

bund

ance

Rel

ativ

e A

bund

ance

TimeTime

Quantitate relative protein levels by measuring peak ratios

Identify proteins by sequence information (MS/MS scan)

CombineCombine

Optional fractionationOptional fractionation

Affinity separationAffinity separation

Analyze by LCAnalyze by LC--MS/MSMS/MS

ProteolyzeProteolyze

Thiol-specific group = binds to Cysteins

ICAT

Thiol-specific group = binds to Cysteins

Quantitation at MS1 level

m/z

Inte

nsity

Double sample complexity, i.e. instrument have more “features”to identify, i.e. decrease in identification rate

iTRAQ (isobaric Tag for Relative and Absolute Quantitation)

Sample prep

Total mass of label= 145 Da ALWAYS

RecognizesArg or Lys

iTRAQ

iTRAQ

Multiplexing

Metabolic VS Chemical Labeling

• Metabolic labeling

- 15N labeling

- SILAC

Living cells

Efficient labeling

Simple!

• Chemical methods

- many… but ICAT is prototype

Isolated protein sample

Depends on chemistry

Multi-step protocols

Require optimization

Summary

Kolkman A et al., 2005

Label-free

Mobile phase

A

A = 5% organic solvent in waterB = 95% organic solvent in water

B

C18 column, 25cm long

Time

20 s

Label-free

Strassberger V et al., 2010

Summary

Summary

Take home message

1. Quantitation can be done gel-free 2. Labeling can be performed at protein or peptide level,

during normal cell growth or in vitro

3. Quantitation can be achieved at MS1 or MS2 level

4. Method choice depends on experimental design, costs, expertise etc

5. In my PERSONAL OPINION, chemical label should be avoided at all costs unless heavy multiplexing is required

Applications

State A State B

Light Isotope Heavy Isotope

Mix 1:1

Optional Protein Fractionation

Digest with Trypsin

Protein Identification and Quantitation by LC-MS

Upregulated protein - Peptide ratio >1

Arg-12C6

Arg-13C6

m/z

Arg-12C6

Arg-13C6

m/z

Control vs Tumor Cell?

Control vs drug treated cell?

Control vs knock-out cell?

Applications – Cell Biology

Geiger T et al., 2012

Applications – Cell Biology

Applications – Immunology

Meissner et al, Science 2013

Clinical Proteomics

A. Amyloid tissue stained in Congo Red; B. After LMD.

Wisniewski JR et al., 2012

Interactomics

Schulze and Mann, 2004Schulze WX et al., 2005

Signaling Pathways

Take home message

1. Anything is possible!

SILAC

October 2013

Gustavo de SouzaIMM, OUS

SILAC

*

m/zm/z

Passage cells to allow incorporation of labelled AA

By 5 cell doublings cells have incorporated

*

m/zm/z

Grow SILAC labelled cells to desired number of cells for experiment

*

m/zm/z

Start SILAC labelling by growing cells in labelling media

(labelled AA / dialized serum)m/zm/z

Media with Normal AA ()

Media with Labelled AA (*)

X 3 X 3

Cells in normal culture media

Ong SE et al., 2002

Importance of Dialyzed Serum

• non-dialzed serum contains free (unlabeled) amino acids!

No alterations to cell phenotype

C2C12 myoblast cell line

Labeled cells behaved as expected under differentiation protocols

Why SILAC is convenient?

Why SILAC is convenient?

• Convenient - no extra step introduced to experiment, just special medium • Labeling is guaranteed close to 99%. All identified proteins in

principle are quantifiable

• Quantitation of proteins affected by different stimuli, disruption of genes, etc.

• Quantitation of post-translational modifications (phosphorylation, etc.)

• Identification and quantitation of interaction partners

Catch 22

- SILAC custom formulation media (without Lys and/or Arg) $$$$$$

- Labeled amino acids – Lys4, Lys6, Lys8, Arg6, Arg10. Use formulation accordingly to media formula (RPMI Lys, 40mg/L)

***** When doing Arg labeling, attention to Proline conversion!

(50% of tryptic peptides in a random mixture predicted to contain 1 Pro)

Proline Conversion!

Typical SILAC experiment workflow

State A State B

Light Isotope Heavy Isotope

Mix 1:1

Optional Protein Fractionation

Digest with Trypsin

Protein Identification and Quantitation by LC-MS

Upregulated protein - Peptide ratio >1

Background protein - Peptide ratio 1:1

Arg-12C6

Arg-13C6

m/z

Arg-12C6

Arg-13C6

m/z

m/z

Arg-12C6

Arg-13C6

m/z

Arg-12C6

Arg-13C6

Additional validation criteria

* Never use labelled Arg or Lys with same mass difference (Lys6/Arg6)

Triple SILAC

Triple Encoding SILAC allows:

Monitoring of three cellular states simultaneously

Study of the dynamics of signal transduction cascades even in short time scales

m/z

Inte

nsity

32

Blagoev B et al., 2004

Five time-point “multiplexing” profile

Blagoev B et al., 2004

Quantitative phosphoproteomics in EGFR signaling

Blagoev B et al., 2004

SILAC-HeLa cells

0’ EGF

1’ EGF

5’ EGF

5’ EGF

10’ EGF

20’ EGF

0-5-10 min.Cytoplasmic ext.Nuclear extract

Lysis andFractionationAnf digestion

1-5-20 min.Cytoplasmic ext.Nuclear extract

SCX / TiO2

SCX / TiO2

SCX / TiO2

SCX / TiO2

Phospho-peptide

enrichment

44 LC-MS runs

4x (10 SCX-frac-tions +FT)

ID and quantitation

8x

8x

8x

8x

8x

8x

MAP kinases activation

40

EGF (minutes)1 5 10 15 20

10

2

EGFr-pY1110ShcA-pY427ERK1-pY204ERK2-pY187EMS1-pS405

Rela

tive r

ati

os

Signal progression

Spatial distribution of phosphorylation dynamics

Cytosolic STAT5 translocates to the nucleus upon phosphorylation

Interactomics

Schulze and Mann, 2004Schulze WX et al., 2005

Limitations

- Expensive

- Quantitation at MS1 level increased sample complexity

- Cells has to grow in culture. Not a choice for primary cells,tissues or body fluids.

- Cell lines have to be dyalized serum-friendly.

SILAC-labeled organism

Sury MD et al., 2010

Super-SILAC

Geiger T et al., 2010

Spike-In SILAC

Geiger T et al., 2013

Take home message

1. Arguably the best labeling strategies: easy to handle, no chemical steps, >98% incorporation low variability

2. Successfully used in the most diverse applications

3. Cells must be stable and growing in the media

4. There are decent alternative strategies for primary cells or organisms.

Label-free

October 2013

Gustavo de SouzaIMM, OUS

Label-free

Label-free

Strassberger V et al., 2010

Time

10 s

Time

500 fmol peptide

100 fmol peptide

Label-free

Kiyonami R. et al, Thermo-Finnigan application note 500, 2010.

Label-free

Replicates

xx x

xx x

Ideal (low std)

Replicates

x

x

x

x

x

x

Reality (late 90’s)

Label-free

Strassberger V et al., 2010

Label-free

Neilson et al., Proteomics 2011

Spectral Count

899.013

899.013

899.013

MS1 (or MS)

MS2 (or MS/MS)

Spectral Count

Time

20 s

Time

Depending on how complex the sample is at a specificretention time, the machine might be busy (i.e., doing many MS2)or idle (i.e., few or none MS2)

Limitation in Spectral Count

Time

Time

MS scan

MS2 scan2 counts

2 counts

Area Under Curve measurement

Retention Time

AUC

Area Under Curve measurement

MS2 scan

Ion intensityin one MS1

Retention Time

Importance of Resolution for label-free

RT

m/z

RT

m/z

2+ 2+

3+ 3+

Cox and Mann, Nature Biotechnol 26, 2008.

-Label-free became reliable (*)

Importance of Resolution for label-free

1. Retention time2. Peak intensity3. Monoisotopic mass accuracy

1

2

3

080711_Gustavo_Mtub_07 #1001 RT: 24.80 AV: 1 NL: 3.43E6T: FTMS + p NSI Full ms [300.00-2000.00]

791 792 793 794 795 796 797 798 799 800 801 802 803 804 805m/z

0

5

10

15

20

25

30

35

40

45

50

55

60

65

70

75

80

85

90

95

100

Rel

ativ

e A

bun

danc

e

798.32

798.83

799.33

799.83800.32 802.13

803.40797.73793.82790.90 796.31 802.72805.84792.68 795.17

x

Cox and Mann, Nature Biotechnol 26, 2008.

Area Under Curve measurement

Regarding Label-free…

- Calculate individual peptide “Intensity”. Protein Intensity = mean of peptides intensities

- LFQ normalization

Data without Normalization

-7422 proteins identified

- 7105 proteins quantified(95.72%)

How this was demonstrated?

Yeast model

Ghaemmagami S. et al., Nature 425, 2003

Huh WK. et al., Nature 425, 2003

How this was demonstrated?

Ghaemmagami S. et al., Nature 425, 2003

MaxQuant and Yeast

De Godoy LM. et al, 2008.-Label-free became reliable AND showed good correlation with a well-established model

Label-free in primary cells

Higher CD4+Higher CD8+

Pattern Recognition Receptors Pathway

Infection with Sendai virus(activate RIG-I PRR)

RIG-I knockout

Label-free in primary cells

Take home message

1. “Labe-free” represents a myriad of ANY method that does not use any labeling

2. Area Under Curve calculations are the most

appropriate

3. Reliability is heavily dependent in good instrumentation and good bioinformatics (MaxQuant)

4. Currently, almost as good as SILAC (yet slightly less accurate)

SRM / MRM

October 2013

Gustavo de SouzaIMM, OUS

A little history…

So far, ID everything we can

Mobile phase

A B

C18 column, 25cm long

Time

20 s

Targeted analysis

In some cases, the researcher don’t want the MSinstrument to waste time trying to sequence as much as possible, but just to “search” and sequence pre-determined peptides.

-Biomarker research-Tracking specific metabolic pathways-Tracking low abundant proteins in challenging sample (f.ex., in serum)

Plasma dynamic range

Schiess R et al., 2009

Improving detection through tergeting

Michalski A et al., 2011

Biomarker

Discovery phase

Screening the sample gives you the following info:

-For protein X most intense peptides (not all peptides from same protein have the same intensity)

- most common m/z format (+2, +3, PTM?)- their Retention times- their fragmentation profiles (does the +2fragments well?)

Biomarker

Shorter gradient = More complex MS1

As you decreaseseparation resolution,you increase the chance that two or more peptides withdifferent sequencesBUT very close m/zelutes at the sametime.

SRM (Selected Reaction Monitoring)

Different transitions from same peptide

Performance with synthetic peptides

Shorter gradient = More complex MS1

As you decreaseseparation resolution,you increase the chance that two or more peptides withdifferent sequencesBUT very close m/zelutes at the sametime.

Number of biomarkers discovered so far by MS

0

Spiking sinthetic labeled peptide for absolute quantitation

Applying SRM to a proper model

Bacterial genomic structure

- 700-6000 genes- No alternative splicing- Limited PTM presence

Discovery Phase

Validation on metabolic network

Validation on metabolic network

- It open possibilities to studymolecular function implicationsat metabolic level.

- Generate knockout, discoveryphase to visualize pahways possibly altered by the KO,targeted the candidate pathwaysfor in-depth quantitation.

Take home message

- 1st step is to make the regular analysis to collect acquisitionfeatures for as many peptides as possible.

- Relevant in Biomarker research

- Very challenging for complex samples, very powerful for simpler organisms and for pure biology projects.

- Targeted analysis: ignore whole sample and focus in fewprotein.