Introduction to Proteomics

Transcription

1 Introduction to Proteomics Åsa Wheelock, Ph.D. Division of Respiratory Medicine & Karolinska Biomics Center In: Systems Biology and the Omics Cascade, Karolinska Institutet, June 9-13,

2 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 Write a short research proposal in proteomics 2

3 Proteomics publications in Pubmed First proteomics publication 3

4 Why Proteins?!? Business end of the cell Detailed information with limited efforts As compared to metabolomics Relatively robust methods available TRANSCRIPTOMICS Limited info from mrna PROTEOME Detailed info Robust technology METABOLOME Techincally challenging 4

5 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) 5

6 Gel based: 2-Dimensional Electrophoresis SEPARATION VISUALISATION Net charge Isoelectric point (pi) ph QUANTIFICATION Stoichiometric protein stain => 3rd dimension Molecular weight log Mw mobility Klose, J Humangenetic 26, O Farrel, P J. Biol.Chem, 250,

7 Image acquisition Image acquisition using fluorescent scanner 7

8 Quantitative image analysis Pixel intensity => 3rd dimension Spot volume = protein quantity 1. Detect spots 2. Match spots across gels 3. Quantify spot volumes 8

9 Protein identification Trypsin digestion Mass spectrometry (MALDI-TOF/TOF) DATABASE SEARCH => IDENTIFICATION (Swiss-prot, EnSemble) (statistical probability) 9

10 Protein Identification MS analysis Protein Trypsine digestion Peptides EXPERIMENTAL Peptide masses Protein database DTHKSEIAHRFK DLGEEHFKGLVL IAFSQYLQQCPF DEHVKLVNELTE FAKTCVADESHA GCEKSLHTLFGD ELCKVASLRET Virtual digest EXPECTED Peptide masses Statistical matching 10

11 Peptide mass mapping MS analysis=> peptide masses Statistical matching DLGEEHFK LHTLFGDR MS/MS analysis=> sequence information database search DTHKSEIAHRFK DLGEEHFKGLVL IAFSQYLQQCPF DEHVKLVNELTE FAKTCVADESHA GEKSLHTLFGRE LCKVASLRET Homology search Validate statistical hit 11

12 Shotgun vs. Gel-based proteomics Separate Quantify (2DE) Digest MS/MS extract ID Digest Separate (LC) MS/MS Quantify Adapted from Patterson and Aebersold, Nature Genetics 2003, 33: Fig. 3 12

13 Semi-quantitative proteomics Both 2DE and MS-based methods NOT quantitative by nature Co-separation: 2 samples => ratios Tags => Semi-quantitative proteomics 13

14 Shotgun isotope-tagging : Isotope coded affinity tag (ICAT) 14

15 Multiplexing in 2DE: DIGE - Differential Gel Electrophoresis Treated sample: Covalently labeled (Cy3) Control sample: Covalently labeled (Cy5) Co-separation by 2DE Protein visualization (super-imposable images) λ ex 532nm Cy3 Spot quantification λ ex 633nm Cy5 Direct ratiometric normalization Protein abundance = 532/633 signal ratio Statistical analysis 15

16 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 samples => Relative quantification 16

17 Internal Standards in 2DE: DIGE Treated Sample: Covalently labeled (Cy3) Pooled Internal standard (Cy2) Co-separation by 2DE Control Sample: Covalently labeled (Cy5) λ ex 488nm Cy2 λ ex 532nm Cy3 Protein visualization (super-imposable images) Spot quantification λ ex 633nm Cy5 Direct ratiometric normalization Protein abundance = relative to IS Statistical analysis 17

18 Proteomics in Pubmed proteomics 2DE First proteomics publication First DIGE-method published 18

19 Multiplexing in MS: itraq - isobaric Tag for Relative and Absolute Quantitation Sample A Sample B Sample C Sample D PRG Trypsin digestion (peptides) a b c d PRG PRG PRG itraq labelling ID MS/MS m/z Quantification 19

20 Differential labelling opens up new possibilities Cysteine oxidative states Identify peptides on plasma membrane surface Cellular re-localization 20

21 2D or not 2D? Gel-based methods: 2-D electrophoresis + soluble proteins + post-translational modifications 21

22 Post-translational modifications Spot trains Intact proteins 22

23 2D or not 2D? Gel-based methods: 2-D electrophoresis + soluble proteins + post-translational modifications - technical variance, time consuming MS-based (Gel-free) methods: ICAT, itraq + membrane proteins + low abundance proteins 23

24 Extremes of physiochemical properties: Peptides Charge - pi range from 3-12 Size - Mw range of 5 500,000 kda Hydrophobicity - membrane proteins 24

25 2D or not 2D? Gel-based methods: 2-D electrophoresis + soluble proteins + post-translational modifications - technical variance, time consuming MS-based (Gel-free) methods: ICAT, itraq + membrane proteins + low abundance proteins - expensive, data intense 25

26 Shotgun approcahes and gelbased approaches complementary GEL-BASED SHOTGUN No true proteomics technique yet 26

27 DYNAMIC RANGE COPIES of each PROTEIN 1012 日本 Japan Kobe 神戸 Kyoto 京都 Osaka 大阪 27

28 Post-translational Modifications (PTMs) reported PTMs Phosphorylation sites Protein copy numbers in blood (log10) 28

29 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 29

30 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 30

31 Remember that variance adds up: Multiple-step method is not your friend... 10% 40% 31

32 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 32

33 Technical variance in 2DE Biological variance Recovery? -SH modifications? Solubilization Rehydration Isoelectric focusing Inhomogenous electric field? Gel-to-gel variations? Staining artifacts? Load IPG strip Recovery? SDS-PAGE Background fluorescence? 33 Protein visualization

34 Tools to reduce variance Technical variance Internal standard: DIGE Software algorithms: Background subtraction Normalization 34

35 Dynamic range of scanner 16 bit pixel resolution ( ,000 ~ 10 5 ) Make sure you are using the entire range! 35

36 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 36

37 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 37

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40 Global Background Subtraction PDQuest: : Floating/Rolling Ball 40

41 Software induced variance PDQuest PG PD PD Quest; Quest; Power Power mean mean Expression; Expression; No No background background subtraction subtraction CV CV (%) (%) n=5 n= CV CV (%) (%) n=5 n= Average CV=10% Average CV=4.6% Software variance up to 30% of technical variance 41

42 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 Hypothesis generation: avoid local maxima Systems Biology 42

43 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 43

44 Proteomics in the NEAR future... Focus on INTERPRETING data, not on ACQUIRING data. Pathway Analysis Integrate data from omics cascade Integrate heatmap with biological pathways 44

45 Preview of coming attractions KEGG & KegArray 45

46 Take home messages......keep your variance down and your dynamic range up!...keep your false positives down, and your power up! 46