Introduction to Proteomics

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Introduction to Proteomics

Åsa Wheelock, Ph.D.

Division of Respiratory Medicine &

Karolinska Biomics Center [email protected]

In: Systems Biology and the Omics Cascade, Karolinska Institutet, June 9-13, 2008

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Focus of course: Tools for data analysis Your analysis is no better than data you have collected...

The goals of this proteomics overview:

• Understand possibilities & limitations

• Pros and cons of different method

• Sources of variance in proteomics

• Take advantage of proteomics core facilities

• Perform proteomics collaborations

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Proteomics publications in Pubmed

0 500 1000 1500 2000 2500 3000

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

First ”proteomics”

publication

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Why Proteins?!?

• Business end of the cell

• Detailed information with limited efforts

– As compared to metabolomics

TRANSCRIPTOMICS

PROTEOME

Limited info from mRNA

Detailed info

Robust technology

• Relatively robust methods available

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Proteomics Methodology

• No ”protein PCR”

– 4 nucleotides vs 20+ amino acids

– Post-translational modifications (PTM) 3 MAIN PROTEOMICS PLATFORMS

• Gel based methods

• Shotgun methods (mass spec-based)

”chromatography-based”, ”gel-free”

• Array based (antibody based)

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Gel based: 2-Dimensional Electrophoresis

Isoelectric point (pI)

Net charge

3 4 5 6 7 8 9 10 11 pH +2

+1 0 -1 -2

log Mw

Molecular weight

SEPARATION VISUALISATION

QUANTIFICATION Stoichiometric protein stain => 3rd dimension

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Image acquisition

Image acquisition using fluorescent scanner

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Quantitative image analysis

1. Detect spots

2. Match spots across gels 3. Quantify spot volumes

Pixel intensity => 3rd dimension Spot volume = protein quantity

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Protein identification

⇒ Mass spectrometry

(MALDI-TOF/TOF) Trypsin digestion

⇒ DATABASE SEARCH => IDENTIFICATION

(Swiss-prot, EnSemble) (statistical probability)

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Protein Identification

Protein Trypsine digestion Peptides EXPERIMENTAL Peptide masses

DTHKSEIAHRFK DLGEEHFKGLVL IAFSQYLQQCPF DEHVKLVNELTE FAKTCVADESHA GCEKSLHTLFGD

MS analysis

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Peptide mass mapping

LHTLFGDR

MS/MS analysis=>

sequence information MS analysis=>

peptide masses

Statistical matching

DLGEEHFK database

search

DTHKSEIAHRFK DLGEEHFKGLVL IAFSQYLQQCPF DEHVKLVNELTE FAKTCVADESHA GEKSLHTLFGRE LCKVASLRET

Homology search Validate statistical hit

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Shotgun vs. Gel-based proteomics

Adapted from Patterson and Aebersold, Nature Genetics 2003, 33:311-23. Fig. 3 extract

Separate Quantify

Separate (LC) Digest

Digest

MS/MS

MS/MS (2DE)

ID

Quantify

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Semi-quantitative proteomics

Both 2DE and MS-based methods NOT quantitative by nature

Co-separation: 2 samples => ratios

Tags => Semi-quantitative proteomics

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Shotgun isotope-tagging :

Isotope coded affinity tag (ICAT)

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Multiplexing in 2DE: DIGE Multiplexing in 2DE: DIGE

-- Differential Gel ElectrophoresisDifferential Gel Electrophoresis

Protein abundance = 532/633 signal ratio Co-separation by 2DE

Direct ratiometric normalization Spot quantification

Statistical analysis Protein visualization (super-imposable images)

λ_ex 633nm ⇒ Cy5 λ_ex 532nm ⇒ Cy3

Treated sample:

Covalently labeled (Cy3)

Control sample:

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Semi-quantitative proteomics

Both 2DE and MS-based methods NOT quantitative by nature

Co-separation: 2 samples => ratios

Tags => Semi-quantitative proteomics

Pooled internal standard + 2-3 samples =>

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Internal Standards in 2DE: DIGE Internal Standards in 2DE: DIGE

Protein abundance = relative to IS Co-separation by 2DE

Direct ratiometric normalization Spot quantification

Statistical analysis Protein visualization (super-imposable images)

λ_ex 633nm ⇒ Cy5 λ_ex 532nm ⇒ Cy3

Treated Sample:

Control Sample:

λ_ex 488nm ⇒ Cy2

Pooled Internal standard (Cy2)

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Proteomics in Pubmed

0 500 1000 1500 2000 2500 3000

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

proteomics 2DE

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Sample A

Trypsin digestion (peptides)

31 114

PRG

30 115

PRG

29 116

PRG

28 117

PRG

Multiplexing in MS: iTRAQ

- isobaric Tag for Relative and Absolute Quantitation

Sample B Sample C Sample D

a b c d

iTRAQ labelling

MS/MS m/z

ID

Quantification

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Differential labelling opens up new possibilities

• Cysteine oxidative states

• Identify peptides on plasma

membrane surface

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2D or not 2D?

Gel-based methods: 2-D electrophoresis + soluble proteins

+ post-translational modifications

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Post-translational modifications

”Spot trains”

Intact proteins

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2D or not 2D?

- technical variance, time consuming

MS-based (Gel-free) methods: ICAT, iTRAQ + membrane proteins

+ low abundance proteins

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Extremes of physiochemical properties: Peptides

• Charge

- pI range from 3-12

• Size

- Mw range of 5 – 500,000 kDa

• Hydrophobicity

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2D or not 2D?

- technical variance, time consuming

MS-based (Gel-free) methods: ICAT, iTRAQ + membrane proteins

+ low abundance proteins - expensive, data intense

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Shotgun approcahes and gel-

based approaches complementary

SHOTGUN GEL-BASED

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日本 Japan

10³

10⁰ 10¹ 10² 10³ 10⁴ 10⁵ 10⁶ 10⁷ 10⁸ 10⁹ 10¹⁰ 10¹¹ 10¹²

COPIES of each PROTEIN

Osaka 大阪

Kyoto 京都

Kobe 神戸

10⁴

10¹² DYNAMIC RANGE

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- 400 reported PTMs

0 1 2 3 4 5 6 7 8 9 10 11 12

Phosphorylation sites

7 6 5 4 3 2 1

Post-translational Modifications (PTMs)

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Variance in 2DE

• BIOLOGICAL VARIANCE

• Experimental variance

– Pre-fractionation, isolation & labelling of proteins – Protein staining

• Technical variance

– Gel-to-gel variation in 2DE – Image acquisition (scanner)

• Post-experimental variance

– Software-induced variance – User dependant variance

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Variance in 2DE

•• Experimental varianceExperimental variance

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Remember that variance adds up:

Multiple-step method is not your friend...

10% 40%

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Variance in 2DE

• Technical variance

– Gel-to-gel variation in 2DE – Image acquisition (scanner)

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Technical variance in 2DE

Solubilization Rehydration Isoelectric focusing

SDS-PAGE

Load IPG strip Protein visualization

Recovery?

Inhomogenous electric field?

Gel-to-gel variations?

-SH modifications?

Staining artifacts?

Background fluorescence?

Biological variance

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Tools to reduce variance

Technical variance

• Internal standard:

– DIGE

• Software algorithms:

– Background subtraction – Normalization

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Dynamic range of scanner

16 bit pixel resolution (2¹⁶ ∼ 65,000 ~ 10⁵) Make sure you are using the entire range!

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Variance in 2DE

• Post-experimental variance

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2DE analysis software

• Main purpose: match and quantify spots

• Normalization: reduce gel-to-gel variation

• Background subtraction:

– Reduce background noise – Increase signal/noise ratio – Increase sensitivity

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Global Background Subtraction Global Background Subtraction

PDQuest

PDQuest: Floating/Rolling Ball: Floating/Rolling Ball

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Software induced variance Software induced variance

Expression; No background subtraction

0 25 50 75 100

1 51 101 151 201 251 301 351 401 451

CV (%) n=5

Expression; No background subtraction

0 25 50 75 100

1 51 101 151 201 251 301 351 401 451

CV (%) n=5

Average CV=4.6%

Average CV=10%

PDQuest PG200

PD Quest; Power mean

0 25 50 75 100

1 51 101 151 201 251 301 351

CV (%) n=5

PD Quest; Power mean

0 25 50 75 100

1 51 101 151 201 251 301 351

CV (%) n=5

Software variance up to 30% of technical variance

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Applications of proteomics Applications of proteomics

BIOMARKER DISCOVERY

• Biomarker of disease & susceptibility

CLINICAL APPLICATIONS

• Pharmaceutical target identification

• Improved diagnostics

MECHANISTIC STUDIES

• Protein-protein interactions

• Protein adduction /Altered protein expression

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Proteomics in the future

• Improved sensitivity

– Currently: scratching the surface – laser capture microdissection

• Protein microarrays

– Antibody arrays (e.g. for cytokines)

– Tissue microarrays (Peter Nilsson, Friday)

• In vivo subcellular localization assays

• Protein amplicifation method?

– i.e. ”protein-PCR”

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Focus on INTERPRETING data,INTERPRETING not on ACQUIRING data.ACQUIRING

Pathway Analysis

• Integrate data from omics cascade

• Integrate heatmap with biological pathways

Proteomics in the NEAR future...

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Preview of coming attractions

Preview of coming attractions … … KEGG &

KEGG & KegArray KegArray

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Introduction to Proteomics