From Proteomics to Systems Biology Outline and learning objectives “Omics” science provides global analysis tools to study entire systems • How to obtain omics - data • What can we learn? Limitations? • Integration of omics - data • In-class practice: Omics-data visualization Integration of “omics”- information Omics - data provide systems-level information Omics - data provide systems-level information Whole-genome sequencing Microarrays 2D-electrophoresis, mass spectrometry Joyce & Palsson, 2006 Joyce & Palsson, 2006 Transcriptomics (indirectly) tells about Proteomics aims to detect and quantify RNA-transcript abundances a system’s entire protein content ! primary genomic readout Strengths: - very good genome-wide coverage - variety of commercial products Drawback: No protein-level info!! -> RNA degradation Strengths: -> Post-translational modifications -> info about post-translational modifications => validation by e.g. RT-PCR -> high throughput possible due to increasing quality and cycle speed of mass spec instrumentation Limitations: - coverage dependent on sample, preparation & separation method - bias towards most highly abundant proteins Omics - data provide systems-level information Metabolomics and Lipidomics Detector Metabolites GC-MS extracted NMR from cell lysate Glycan arrays, Lipids Glyco-gene chips, mass spec / NMR of carbohydrates ESI-MS/MS Joyce & Palsson, 2006 Metabolomics and Lipidomics Metabolomics and Lipidomics Metabolomics: Metabolomics: Large-scale measurement of cellular metabolites Large-scale measurement of cellular metabolites and their levels and their levels -> combined with proteome:Detector -> combined with proteome:Detector functional readout of a cellular state functional readout of a cellular state Metabolites Metabolites Limitations / challenges: Limitations / challenges: extracted - often low reproducibility of sample preparations extracted - often low reproducibility of sample preparations from cell lysate - highly divers set of metabolites from cell lysate - highly divers set of metabolites Lipids - large dynamic range Lipids - large dynamic range Lipidomics aim: To identify & classify cellular inventory of lipids and lipid interacting factors -> pathobiological impact Limitations: - low to medium throughput - reproducibility difficult Glycomics identifies cellular glycan components Omics - data provide systems-level information and glycan-interacting factors Glycan-array -> glycan-recognition proteins Impact: Glycan mass - antigen recognition spec - cell adhesion - cancer biology Glyco-gene chip Limitations: In silico predictions, Methods still under development organelle proteomics histocytomics, http://www.functionalglycomics.org Joyce & Palsson, 2006 ‘Localizomics’ tells about sub-cellular locations ‘Localizomics’ tells about sub-cellular locations Bioinformatics Bioinformatics Organellar TargetP TargetP proteomics http://www.cbs.dtu.dk/services http://www.cbs.dtu.dk/services /TargetP/ /TargetP/ Preparation / digestion PSORT PSORT http://www.psort.org/ http://www.psort.org/ -> 2DE & MS Expasy Expasy -> Topology Prediction -> Topology Prediction http://www.expasy.ch/tools http://www.expasy.ch/tools Histocytomics /#proteome /#proteome e.g. LSC (Laser Microscopy Scanning Cytometry) techniques or LES (Layered -> tagging (GFP) Expression Screening) Eukaryotic protein sorting signals -> antibody detection Emanuelsson 2002 Spalding et al., 2010 Coulton, 2004 Omics - data provide systems-level information Interactomics Protein-DNA Protein-Protein ChIP-chips interactions interactions -> e.g. ChIP on chips -> e.g. Antibody-based protein arrays Yeast-2Hybrid, Protein-chips Amplification and analysis Wingren_Borrebaeck_2009 Joyce & Palsson, 2006 www.avivasysbio.com Interactomics All interactomics Omics - data provide systems-level information data need to be Protein-Protein validated!!! interactions => often false -> Yeast two-hybrid screens positives!!! Isotopic tracing Joyce & Palsson, 2006 Fluxomics looks at global and dynamic Fluxomics looks at global and dynamic changes of metabolite levels over time changes of metabolite levels over time metabolite identification ? ? A ? C ? ? ? ? ? E’ E v v v A 1 B 2 C 3 D v4 v6 pathway reconstruction v5 v E 7 -> integration of omics-data from other sources metabolic flux analysis But: If 14C-label: Method suffers from same scintillation shortcomings as metabolomics: counting -> sample prep reproducibility -> wide variety of metabolites http://www.nat.vu.nl/~ivo/flux/ Van Beek, Van Stokkum -> large dynamic range http://www.nat.vu.nl/~ivo/flux/ Van Beek, Van Stokkum Omics - data provide systems-level information Phenomics High-throughput approaches to determine cellular fitness or viability in response to genetic / environmental manipulation Some commonly used experimental approaches: ! Phenotyping microarrays Phenotype arrays, RNAi-screens http://www.biolog.com/pmTechDesOver.html Joyce & Palsson, 2006 Phenomics Integration of omics-data => RNAi screens The Challenge: How to integrate extreme abundances of heterogeneous data from very divers sources? Interactomics Phenomics Metabolomics Genomics Transcriptomics Proteomics http://systemsbiology.ucsd.edu Integration of omics-data: Integration of omics-data: Network reconstruction Network reconstruction Cell Literature Interactomics Cell Literature Interactomics Physiology Physiology Annotated Fluxomics Annotated Fluxomics & Enzymatic & Enzymatic genome Known & genome Known & Localizomics biochemical complexes, Localizomics biochemical complexes, Metabolomics Metabolomics data regulatory/ data regulatory/ Homologies signaling Homologies signaling networks Proteomics networks Proteomics Phenomics & Phenomics & Network Transcriptomics Network Transcriptomics Functional reconstruction Pathways, Functional reconstruction Pathways, genomics capacities, genomics capacities, inferred inferred reactions Metabolic reactions model http://systemsbiology.ucsd.edu http://systemsbiology.ucsd.edu Integration of omics-data: Model testing and validation The holy grail of systems biology: Cell Literature Interactomics Automatically updated, genome-scale, Physiology Annotated Fluxomics comprehensive network reconstructions & genome Known Enzymatic & for any system of interest Localizomics biochemical complexes, Metabolomics data regulatory/ => Advanced projects for some model Homologies signalling organisms (Human, mouse, yeast, E. coli) networks Proteomics Phenomics & Increasing Transcriptomics Metabolic Metabolic medical Toxico- impact engineering Functional model Pathways, genomics genomics capacities, Biochemical & inferred Persona- genetic methods reactions lized drug Metabolic Meta- Nutri- disorders Cancer design Revised Omics New etc. genomics biology assignments & predictions model functions.