Organellar Proteomics: Turning Inventories into Insights Jens S. Anderson and Matthias Mann

Human Genome and Proteome ~ 23,000 Genes in Humans Active proteins far outnumber genes: Alternative Splicing Post Translational Modifications

Proteomics MicroArray Experiments: Gives information on expression No location information

Organellar Proteomics Traditional Techniques: Separation and Enrichment Microscopy

Fluorescence Based Microscopy Powerful localization technique Use of antibodies raises issues Candidate based approach

Challenges Traditional techniques in mammals: Fusion proteins are usually over expressed Tagging is difficult and can lead to artifacts

Mass Spectrometry

Validation

Subtractive Proteomics Compares the identified constituents of the complex of interest to a related background complex

Subtractive Proteomics Limitations: In large proteomes not all possible peptides will be sequenced Successive runs of the same sample will not overlap

Question How does one know if the proteins found in the nucleus are nuclear or just being transcribed there at the time of the experiment?

Stable Isotope Labeling in Cells SILAC Stable Isotope Labeling in Cells

Protein Correlation Profiling

Protein Correlation Profiling Substantial increase in quantification accuracy Large scale PCP has suggested error rates in published data sets between 3 and 64% owing to co purifying proteins

Cataloguing Proteins Bioinformatic Methods Most useful for membrane bound organelles Subnuclear domains, such as the nucleolus cannot be predicted accurately

Cataloguing Proteins Web based organellar databases Gene Ontology Max Planck Unified Proteome Database

Organelle Dynamics Organelles have both resident and transient proteins Microscopy is limited in full classification due to its candidate based approach

Current State Mass Spectroscopy Technology is no longer the limiting step Main challenges now lie in purifying organelles and removing background proteins