A New Strategy for Quantitative Proteomics Using Isotope-Coded Protein Labels

Transcription

1 ICPL TM -Isotope Coded Label A New Strategy for Quantitative Proteomics Using Isotope-Coded Labels Alexander Schmidt Department for Max-Planck-Institute of Biochemistry Spotfire User Conference 2004, Cologne

2 ICPL TM -Isotope Coded Label Why Proteomics? Principle of ICPL Analysis of apoptotic human cells Data Analysis

3 ICPL TM -Isotope Coded Label Why Proteomics? Principle of ICPL Analysis of apoptotic human cells Data Analysis

4 The Facts One Genome One Genome (Orgyia antiqua, L.) Different Proteomes

5 Why Proteomics? DNA mrna Preprotein Processed Complex / Interaction Amount? Dynamic? Localization? Active Form? Associated s? Proteomics

6 The Challenge genes splicing modifications degradation > protein species / organism ~ protein species / cell highly dynamic large variability in amount no amplification possible heterogeneous molecules

7 State of the Art Number of proteins/cell Copies/cell Sequence coverage % S e q u e n c e C o v e r a g e - 40% - 60% - 80% % Covered Uncovered

8 ICPL TM -Isotope Coded Label Why Proteomics? Principle of ICPL Analysis of apoptotic human cells Data Analysis

9 ICPL TM -Isotope Coded Label Mouse with specific tumor One organ Tumor Tissue Healthy Tissue Lyse cells solubilize proteins Tumor Proteome Healthy Proteome

10 Differential Labeling Tumor Proteome Healthy Proteome Labeling of all proteins with ICPL Freeze status on the protein level Combine

11 Reduction of Complexity Reduction of complexity on protein level 2D LC FFE 1D Cleave proteins into peptides Reduction of complexity on peptide level

12 Quantification and Identification m MS-scan - Detect labeled peptide pairs % Intensity m/z Select differentially expressed peptide pairs Tumor Healthy Analyze sequence by MS/MS % Intensity A I V K* E m/z identification by database search

13 ICPL TM -Isotope Coded Label Why Proteomics? Principle of ICPL Analysis of apoptotic human cells Data Analysis

14 Analysis of Human Cell Extracts Healthy (125µg) Apoptosis (125µg) Label with light tags Label with heavy tags Combine & digest (Glu-C) 20 Fractions SCX-LC 20 x 192 = 3840 MALDI-SPOTS RP-LC

15 SCX-LC Separation 20 Fractions a 2 min Gradient: In 25 min from 2-50% B, then in 10 min to 100% B A: 25% ACN, 10mM KH 2 PO 4, ph = 3 B: 25% ACN, 10mM KH 2 PO 4, ph = 3, 500mM KCl

16 RP-LC Separation

17 Spotting MALDI-Targets Tray for six 192 MALDI-targets, in total 1152 Spots

18 Quantification and Identification MALDI-TOF/TOF

19 MS- and Data Analysis MS-Data Precursor T2 ORACLE MSMS-Data Mass Spectrometer T2 System - TOF TOF DB ORACLE Spotfire Server MASCOT Server

20 Data Overview I

21 Data Overview II

22 Data Analysis Spotfire DecisionSite 1) Elimination of false positive identified peptides 2) Deletion of incorrect quantified peptides 3) Detection of post-translational modified peptides 4) Data visualisation 5) Data exchange

23 Data Analysis Spotfire DecisionSite 1) Elimination of false positive identified peptides 2) Deletion of incorrect quantified peptides 3) Detection of post-translational modified peptides 4) Data visualisation 5) Data exchange

24 Elimination of False Positive Identifications 2522 peptides with identification probability >80%

25 Elimination of False Positive Identifications N-terminal labelled 1037 peptides with identification probability >98%

26 Data Analysis Spotfire DecisionSite 1) Elimination of false positive identified peptides 2) Deletion of incorrect quantified peptides 3) Detection of post-translational modified peptides 4) Data visualisation 5) Data exchange

27 Deletion of Incorrect Quantified Peptides 1037 peptides with identification probability >98%

28 Deletion of Incorrect Quantified Peptides 950 peptides correctly quantified and identified

29 Data Analysis Spotfire DecisionSite 1) Elimination of false positive identified peptides 2) Deletion of incorrect quantified peptides 3) Detection of post-translational modified peptides 4) Data visualisation 5) Data exchange

30 Analysis of PTMs The dataset was searched for the following post-translational modifications (PTMs) Acetylation (K and protein n-terminus) Formylation (protein n-terminus) Methylation (K and R) Phosphorylation (S, T, Y)

31 Detection of Post-translational translational Modifications 63 peptides are post-translational modified

32 Data Analysis Spotfire Decision Site 1) Elimination of false positive identified peptides 2) Deletion of incorrect quantified peptides 3) Detection of post-translational modified peptides 4) Data visualisation 5) Data exchange

33 Scatter-Plot of all Peptides

34 Zoomed Scatter-Plot

35 Generation of Average Ratios is not regulated

36 Generation of Average of all Ratios Important for normalisation of the protein regulation

37 Generation of Summary Table

38 Generation of Summary Table Number of unique proteins

39 Generation of Summary Table

40 Summary Table Unfortunately only available as web page and has to added manually into DecisionSite

41 Importing Summary Table Data

42 Importing Summary Table Data

43 Normalisation of Mean Ratio

44 Normalised Bar Chart

45 Final Visualisation of Regulation

46 Final Visualisation of Regulation

47 Up-regulated s 2 Ratios

48 List of all Up-regulated s

49 Data Analysis Spotfire DecisionSite 1) Elimination of false positive identified peptides 2) Deletion of incorrect quantified peptides 3) Detection of post-translational modified peptides 4) Data visualisation 5) Data exchange

50 Data Exchange with Spotfire Posters

51 Results In total 3840 MS-spectra (13.3h) and pairs for MS/MS-analysis (37.4h) were acquired This corresponds to 413 unique proteins (average = 2.3 peptides/protein) 31 proteins were found to be up- and 33 down-regulated during apoptosis 25 acetylation, 2 methylation and 2 phosphorylation sites could be identified

52 Acknowledgements Dr. Lottspeich Dr. Kellermann Dr. Ciosto Monika Zobawa Friedrich Förstner Applied Biosystems Jakob Petersson Phil Langley