Proteome analysis Cellular proteomics Plasma proteomics
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1 Proteome analysis Cellular proteomics Plasma proteomics Janne Lehtiö Maria Pernemalm Karolinska Biomics Center Karolinska Institutet / Karolinska University Hospital Janne Lehtio
2 KBC Proteomics Janne Lehtio
3 Outline What can proteomics do? Example on genotoxic stress effects on proteome Plasma proteomics methods Janne Lehtio
4 omics Cellular processes leave a biochemical trace Genes Transcriptomics Proteins Proteomics Metabolites Metabolomics Janne Lehtio
5 DNA damage induced stress signaling Janne Lehtio
6 P53 network simplified version Janne Lehtio
7 Why Study Proteins by Mass Spectrometry? MS can unambiguously identify proteins - Gel separated proteins - Proteins in mixtures - Protein:Protein interactions - DNA:Protein interactions MS can identify the precise site of post translational modifications - Phosphorylation -N-or C-terminal modifications - Glycosylation - Many other modifications MS can perform the profiling of protein expression Janne Lehtio
8 LCQ-Deca Mass Spectometers Q-Star LTQ-FT LTQ-Orbitrap Janne Lehtio
9 Recent technical development UPLC Ultra Performance Liquid Chromatography Separation Very high resolution Reproducible (retention time) Fast sample troughput New label free quantification algorithms ProSpectPLS i =1 6 K195 K058 4 K058 K047 K151 K167 K033 K046 K033 K088 K038 2 K203 K203 K150 K151 K159 K159 K186 K045 K047 K037K037 K043 K038 K043 K150 K119 K198 K107 K167 K014 K014 K045 K039 K176 K040 K040 K176 K088 K046 K042 K119 K111 K042 K168 0 K107 K002 K198 K060 K155 K155 K186 Analysis K039 K034 K020 K015 K104 K060 K106 K103 K103 K017 K104 K036 K156 K165 K020 K036 K165-2 K044K044 K112 K022 K015 K021 K022 K002 K034 K106 K008 K007K118 K156 K017 K166 K008 K112 K105 K166 K021 K181 K118-4 K001 K007 K005 K105 K181 K K K086 K086 Fast and sensitive Mass Spectrometers Detection Janne Lehtio
10 Mitogen-Activated Protein Kinases (MAPKs) activation courtesy of Mattias Mann Janne Lehtio
11 Dynamic Phosphorylation Profiles Mann, Cell 2007 Janne Lehtio
12 Technology Platform at KBC Discovery Q-TOF Proteomics Metabolomics Discovery/ Structural I.D. Multiple Ionization/ Ultra High Resolution Mass Spec (Orbitrap) Proteomics Metabolomics Validation Triple Quadrupole Proteomics Metabolomics Janne Lehtio
13 Cancer therapy Prediction Response to genotoxic therapy IR Proteomic profiling to detect molecular changes related to tumor cell response to IR and doxorubicin Janne Lehtio
14 S100A6 is upregulated and modified in response to IR Orre et al MCP Janne Lehtio
15 What is S100A6? Member of the S100 family of small calcium binding proteins The functions of S100 proteins are largely unknown S100 proteins are closely connected to different diseases: Neurodegenerative diseases, Inflammatory diseases, Cancer S100A6 has been shown overexpressed in many different types of cancer: Pancreatic, colorectal, thyroid, gastric, breast cancer and melanoma. Cellular functions of S100A6 unknown Janne Lehtio
16 S100A4 in lung cancer cell lines Janne Lehtio
17 Tissue Micro Arrya clinical validation 1,8 Cum. Survival,6,4 F,2 M Time Janne Lehtio
18 Tissue microarray-ihc Stage I NSCLC 103 stage I NSCLC cases S100A6 higher in: Well differentiated tumors Non-squamous tumors S100A4 positive tumors p53 negative tumors Janne Lehtio
19 Tissue microarray-ihc Stage I NSCLC De Petris el al 2008 Janne Lehtio
20 Plasma and Pleural effusion proteomics in lung cancer Analytical challenges in proteomics Maria Pernemalm Janne Lehtio
21 Why plasma proteomics? Plasma as source of biomarkers Early diagnosis Response to treatment Minimally invasive Repetitive sampling possible Standard in the clinic Janne Lehtio
22 Plasma proteome 1 g/ml 1 mg/ml 1 µg/ml 1 ng/ml 1 pg/ml Janne Lehtio
23 Analytical challenges High dynamic range of concentrations on the protein level, interesting proteins low abundant No mass spectrometer that can handle this Result Only high abundant proteins are detected Divide and conquer! Janne Lehtio
24 Primary effects of fractionation Coagulation pathway 70 proteins in total 150 proteins in total Janne Lehtio
25 How to reach low abundant proteins in plasma? Targeted fractionation? Enrich for interesting proteins Extensive fractionation? Shot gun proteomics New mass spectrometry technology needed? Janne Lehtio
26 Meta analysis Accepted in Proteomics Janne Lehtio
27 Conclusion Large number >500 proteins needs to be identified to reach low abundant proteins Other samples? Pleural effusion BAL Tumor tissue New mass spectrometry technology? Janne Lehtio
28 Pleural effusion Close to the tumor Potentially higher concentration of tumor derived markers Both plasma and pleural effusion from same patient for comparison Extensive fractionation using narrow range isoelectric focusing (IEF) Janne Lehtio
29 Narrow range IEF A Theoretical digest and predicted peptide pi value distribution of the complete human proteome B Experimental setup peptide IEF proteomics C Experimental data showing predicted pi values of identified peptides Number of peptides pi IEF ph % of the peptides will focus between ph Simplification of the sample at thepeptide level 96% of the proteins have peptides focusing between ph Retention of information at the protein level Complex Sample Peptide IEFfractionation ph strip Digestion labelling simplified sample full proteomecoverage MS based proteomic analysis on resulting fractions Predicted pi value of identified peptides <99% >99% Confident identification Peptide number pi value >4.5 99% conf.peptides focusing between ph but with a predicted pi value over 4.5 have been modified resulting in an actual pi value that is lower than the predicted pi value. pi value The vast majority of 99% conf.peptidesfocusing between ph have a predicted pi value in this ph area proteins identified from lung cancer cell line Janne Lehtio
30 MS/MS summary Number of identified proteins disappointing! Janne Lehtio
31 Plasma and Pleural effusion No major enrichment of tissue derived proteins! Janne Lehtio
32 Cell line vs Plasma Janne Lehtio
33 Conclusion Large number >500 proteins needs to be identified to reach low abundant proteins More extensive fractionation? Other samples? Pleural effusion BAL Tumor tissue New mass spectrometry technology Janne Lehtio
34 Acknowledgements Rolf Lewensohn Lukas Orre Lyris Godoy Luigi DePetris Birgitta Sundelin Johan Lengqvist Hanna Eriksson Per-Johan Jakobsson Hirsh Koyi Eva Branden Janne Lehtio
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