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1 quantification using ms based proteomics lennart martens Computational Omics and Systems Biology Group Department of Medical Protein Research, VIB Department of Biochemistry, Ghent University Ghent, Belgium

2 INTRODUCTION: TWO OVERVIEW DIAGRAMS

3 Protein quantification by MS in one slide Quantification methods Isotope labeling SRM/MRM Label free MS2 Multiplexing MS1 empai TIC itra Q TMT In vivo In vitro Replicate Average SILAC other 18O ICPL ICAT AQUA See: Vaudel et al., Proteomics, 2010

4 Data processing introduces uncertainty Raw data Peaklists Peptide sequences ambiguity Protein accession numbers data size See: Martens and Hermjakob, Molecular BioSystems, 2007

5 QUANTITATIVE PROTEOMICS ANALYSIS METHODS

6 The primary principles in quantitation Make each sample distinguishable fluorescent markers with different excitation wavelengths (1) introduce mass differences between the samples (2) perform distinct experimental runs for each sample (3) Measure the intensity of the signal for each analyte in each sample Statistically process the accumulated information 1/2 1/1 2/1

7 Techniques: overview SILAC (2), cell cultures, relative 2D PAGE spot intensity (1), proteins, relative ICAT (2), proteins, relative ICPL (2), proteins, relative LC peak area (3), peptides, relative, absolute Trypsin-mediated 18 O incorporation (1), peptides, relative itraq (2), peptides, relative Spiked peptides (eg. AQUA) (2), peptides, absolute Label-free approaches (3), peptides, peptide fragments, relative, absolute MRM (2, 3), peptide fragments, relative

8 SILAC Mix in 1:1 ratio and analyse with mass spec Adapted from: Andersen JS & Mann M, EMBO Reports, 2006

9 16 O 18 O labelling O Arg C OH + HO Tryp. H 2 O O Arg C O Tryp. H 2 O O Arg C O Tryp. H 2 O O Arg C OH H 2 O H 2 O HO Tryp. + Arg C O Tryp. O Arg C OH O H 2 O H 2 O = 16 O water 18 O = H 2 O Arg C OH O See: Staes A et al., Journal of Proteome Research, 2004

10 itraq neutral balancer precursors 1+ reporter fragmentation Reporter fragments From: Applied Biosystems, Product Bulletin itraq Reagents

11 AQUA Aimed at absolute quantitation sample unlabelled peptides unknown abundance Mix in desired ratio internal standard labelled, synthesized peptides known abundance MS analysis Compare signal intensities Derive absolute quantitation

12 Moment of labelling matters From: CEBI, University of Southern Denmark, 2003

13 RAW DATA PROCESSING: THE BLACK BOX THAT LL GET YOU

14 Errors on the order of 10% are common Mass spectrometer specific processing required Sets the dynamic range lower limit (S/N) 10-15% error in the final ratios with the instrument vendor peak-picker 100,00% Normalized intensity 80,00% 60,00% 40,00% Intensity ,00% ,00% 117,09 117, ,1 117, ,11 117, ,12 117, ,13 m/z 0 503,73 503,74 503,75 503,76 503,77 m/z Black: 0,02 Da Blue: 0,04 Da Red: 0,08 Da Non-adapted shape -> +10% error See: Vaudel et al., Proteomics, 2010

15 Different options for peak detection Decon2LS See: Vaudel et al., Proteomics, 2010

16 But there s more to a peak than m/z OpenMS TOPPView See: Vaudel et al., Proteomics, 2010

17 SRM: peak transmission vs.purity See: Vaudel et al., Proteomics, 2010

18 Finally, a word on detector response See: Gevaert et al., Proteomics, 2007 See: Vaudel et al., Proteomics, 2010

19 DIFFERENT ALGORITHMS DIFFERENT OPINIONS?

20 A comparison of quantification algorithms Peptides Proteins See: Colaert et al, Methods in Molecular Biology, 2010

21 Peptide-level correlation graphs Forward experiment See: Colaert et al, Methods in Molecular Biology, 2010

22 Protein-level correlation graphs See: Colaert et al, Methods in Molecular Biology, 2010

23 QUANTIFICATION VALIDATION: ROVER

24 Rover in a nutshell Rover is a quantification post-processing application Various data formats can be read in, and the quantification results are processed inside Rover Rover supports various quantification methods (itraq, SILAC) Rover performs both peptide-level and protein-level statistics The statistics can be based on standard statistical estimators (mean and standard deviation) or robust estimators (median and Huber scale) Advanced peptide-level filtering allows the user to only take into account peptides that fulfil customizable quality criteria See: Colaert et al, Proteomics, 2010

25 Rover screenshots (i) See: Colaert et al, Proteomics, 2010

26 Rover screenshots (ii) See: Colaert et al, Proteomics, 2010

27 Thank you! Questions?

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