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Introduction to Proteomics Phil Charles CCMP. Overview of Talk Overview of proteomics as a concept Techniques discussion 2D Gels and experimental design.

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Presentation on theme: "Introduction to Proteomics Phil Charles CCMP. Overview of Talk Overview of proteomics as a concept Techniques discussion 2D Gels and experimental design."— Presentation transcript:

1 Introduction to Proteomics Phil Charles CCMP

2 Overview of Talk Overview of proteomics as a concept Techniques discussion 2D Gels and experimental design paradigms Proteomics mass spectrometry Identification Quantitation

3 Proteomics is the study of the overall state of an organisms temporal protein composition The biological state of the proteome is encoded in The relative abundance of currently expressed proteins (and their isoform) Their localisation relative to cellular (or extracellular) structures Their interaction partner molecules and substrates Their current post-translational modification state Their folded structures …

4 A Different View on Life Different levels of biological complexity More layers of regulation and control Increased heterogeneity of samples Genome Transcriptome Proteome Phenotype

5 Why consider Proteomics? Orthogonal verification of gene activity. Observe biological state after more levels of regulation and control – closer to phenotypic outcome. Observe proteomes of extracellular locations – blood plasma/serum, urine etc.

6 Proteomics Classical biochemistry Two-dimensional gels (2DGE) Mass spectrometry Computational analysis

7 Methods in Proteomics Separation –Gels –Immunochemistry –Chromatography Identification –Immunochemistry –Mass spectrometry Quantitation –All of the above

8 Identification vs Quantitation Whats there? How much of it is there? How sure are you about the ID? How sure are you about the abundance? Not there versus not detectable

9 2DGE Separate proteins by isoelectric point, then by mass Visualise with silver staining or coomassie Use CyDyes to label samples so they can be run together on the same gel Appl Microbiol Biotechnol October; 76(6): 1223–1243.

10 Quantitation Experimental Paradigm - Labelling Label samples in such a way as to not affect subsequent processing but allow differentiation in final analysis. Examples: –Fluorescent dyes (2DGE) –SILAC amino acid labels (MS) –Isobaric mass tags (MS/ MS) Process multiple samples simultaneously, differentiate only in final analysis on basis of label. –Avoid some proportion of technical variance –Best to worst (for avoiding technical variance): Labelling in vivo Labelling protein mixture Labelling peptide digestion mixture

11 Aline Chrétien, Edouard Delaive, Marc Dieu, Catherine Demazy, Noëlle Ninane, Martine Raes, Olivier Toussaint Upregulation of annexin A2 in H2O2-induced premature senescence as evidenced by 2D- DIGE proteome analysis Experimental Gerontology, Volume 43, Issue 4, April 2008, Pages 353–359

12 Quantitation Experimental Paradigm – Normalising to standard Combine each sample (labelled with one label) with a representative standard (labelled with another label). Perform analysis For each protein in each run, normalise observed abundance in labelled sample to observed abundance in labelled standard.

13 Normalised Abundance Statistical Analysis

14 Mass Spectrometry Mass Spectrometry is a technique for the detection and resolution of a sample of ions by their mass-to-charge ratio - represented by m/z where m is the mass in Daltons and z is the charge.

15 Proteomic Mass Spectrometry Classical biochemistry techniques and 2DGE are, in general, top- down proteomics – identify and quantify whole proteins. Most modern proteomic MS is bottom-up

16 Shotgun/bottom-up proteomics ProteinsPeptides LNDLEEALQQAKEDLAR NKLNDLEEALQQAK NVQDAIADAEQR SKEEAEALYHSK SLVGLGGTK TAAENDFVTLK TAAENDFVTLKK TSQNSELNNMQDLVEDYK TSQNSELNNMQDLVEDYKK VDLLNQEIEFLK YEELQVTVGR YLDGLTAER ADLEMQIESLTEELAYLK ADLEMQIESLTEELAYLKK AETECQNTEYQQLLDIK Peptide IDs + Quantitation IPI:IPI IPI:IPI IPI:IPI IPI:IPI IPI:IPI IPI:IPI IPI:IPI IPI:IPI IPI:IPI IPI:IPI IPI:IPI IPI:IPI IPI:IPI IPI:IPI IPI:IPI Observed Proteins + Quantitation Analysis MS-MS/ Tandem MS Separation SCX High pH RP LC Low pH RP LC Separation SDS-PAGE Antibody-based approaches LNDL EEAL QQAC EDLA R N KLND LEEAL QQAK Digestion

17 Tandem Mass Spectrometry Mass Analyser + Detector Sample Intensity m/z Mass Analyser + Detector m/z Intensity Mass Spectrum Tandem Mass Spectrum MS/MS spectrum

18 Identification by MS/MS Search fragment spectrum against a database of protein sequences. For each sequence, digest into peptides, generate an expected fragment ion spectrum, and match to observed spectrum m/z Intensity m/z Intensity IITHPNFNGNTLDNDIMLIK ? Mass Analyser + Detector

19 Identification by MS/MS There are multiple commonly used MS/MS fragment spectra search engines, including: –Mascot –Sequest –OMSSA –X!Tandem –MS Amanda –Andromeda –ProteinPilot

20 A brief overview of Mass Spectrometric quantitation Please feel free to stop me and ask questions!

21 Tandem Mass Spectrometry Mass Analyser + Detector Sample Intensity m/z Mass Analyser + Detector m/z Intensity Mass Spectrum Tandem Mass Spectrum MS/MS spectrum

22 Low pH Reverse Phase LC Survey Scan/ MS1/ MS Scan Select Peptide Ions Fragmentation CID Also ETD, PQD,HCD Fragment Ions Scan/ MS2/ MS/MS Scan time Data-Dependent Acquisition (DDA)

23 Intensity Retention Time m/z

24 Intensity Retention Time m/z Intensity m/z

25 Intensity m/z 1/charge (z) Peptide Isotopomer Distribution This is all 1 peptide Think of it as a frequency distribution based on a probability function. The relative intensity of each peak is the relative chance of a single peptide molecule having that m/z

26 Intensity Retention Time m/z Intensity m/z

27 Intensity Retention Time m/z Intensity IITHPNFNGNTLDNDIMLIK Intensity m/z

28 Quantitation Labelling Strategies MS-based strategies –In-vivo labelling (compare peak pairs) SILAC, 15N, 18O, 2H MS/MS-based strategies –Isobaric Tags iTRAQ, TMT

29 Intensity Retention Time m/z Intensity m/z Intensity

30 Retention Time m/z Intensity m/z Intensity

31 Isobaric Tag Labels e.g. iTRAQ, TMT

32 Intensity Retention Time m/z Intensity IITHPNFNGNTLDNDIMLIK Intensity m/z

33 Intensity Retention Time m/z Intensity m/z Intensity

34 Retention Time m/z

35 Intensity Retention Time m/z MS quantitation - peak pair comparison

36 Intensity Retention Time m/z

37 Intensity Retention Time m/z

38 Intensity Retention Time m/z ID

39 Identification vs Quantitation Whats there? How much of it is there? How sure are you about the ID? How sure are you about the abundance? Not there versus not detectable

40 Quantitation Software MaxQuant Progenesis LC-MS ABI Peaks Thermo ProteomeDiscoverer + bespoke and specific tools

41 The Oxford Central Proteomics Facility CCMP/CPF – Kessler Lab – WTCHG CPF - Ben Thomas – Dunn School Computational Biology Research group - WIMM

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44 Thank you for your attention Please feel free to ask questions


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