The development of compartments in eukaryotic cells and the distribution of nuclear-encoded proteins underlies the specialization of cellular functions. The exploration of the proteome of the cell in terms of the collection of its subcompartments is therefore both a practical approach and also a function led necessity that recognizes that proper interpretation of proteomic data requires information about compartmentation of protein machinery.
Subcellular proteomics decreases the complexity of proteomie discovery. With the typical compartment representing 500-4000 proteins, its analysis by gel based and MS based systems approach the resolution of the analytical techniques. In contrast, whole cell proteomes of 12, 000-40, 000 proteins extend well beyond the ability of proteomic tools to resolve them, leaving whole cell proteome studies being “tip of the iceberg” activities. Subcellular proteomics stands on the shoulders of decades of biochemical research that has developed methods for isolation of subcellular compartments. Extensive laboratory work involving the tinkering with density, size, and charge separation techniques has enabled incremental limitation of contamination in isolation methods from a range of subcellular structures.
Based on the powerful combination of subcellular fractionation and protein identification by LC-MS/MS, we are able to develop the subcellular proteomics approach which is currently the most effective approach to characterize subcellular compartments.
Recent success stories demonstrated that the combination of subcellular prefractionation methods with proteomic analysis is a very potent approach to simplify complex protein extracts from cells or tissues and to detect low abundance proteins. It also made clear that sophisticated strategies encompassing sample preparation, analytics and validation steps were required to fully exploit the potential of subcellular proteomics.
Creative Proteomics also provide the following bioinformatics services in Subcellular Proteomics:
Functional annotation and enrichment analysis Clustering analysis Network analysis Statistical analysis
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