Absolute Quantification of Molecular Representation and Interaction

National Institute Of Allergy And Infectious Diseases

Investigators

Linked publications, trials & patents

Abstract

The group began in April 2009 and the projects were assigned in July 2009. I the past year, the personnel has been hired and trained. The changes to the laboratory space have been finalized at the end of August 2010. We are designing the methods for absolute quantification, developing and testing standards of tagged proteins (in collaboration with Dr. Iain Fraser) and designing peptides for AQUA method of MS-based quantification. The experimental model is the osteoclast development from macrophages. We use well characterized murine macrophage RAW 264.7 cell line. The cells fuse to form multinucleated osteoclasts when stimulated with with receptor activator of nuclear factor kappa B ligand (RANKL), but the differentiation process is inhibited by sphingosine-1 -phosphate (S1P). There are changes in protein expression connected with macrophage differentiation into osteoclast. The mRNA levels of many proteins change and we want to see if these changes are reflected in the changes of the cell proteome. Using SILAC (stable isotope labeling with amino acids in cell culture) we will compare the proteomes of untreated RAW 264.7 macrophages, differentiated osteoclasts, and macrophages treated with RANKL/S1P combination. The analysis will reveal a set of differentially expressed proteins, which will be used to design a set of standard peptides for absolute quantification by mass spectrometry. The Multiple Reaction Monitoring (MRM) analysis of a narrower set of chosen peptides differentially expressed under different experimental conditions will provide absolute numbers of molecules. These will be used for mathematical modeling of cellular pathways involved in osteoclast development. We have optimized the cell culture conditions and osteoclast enrichment. Currently, we are labeling the cells with SILAC and performing experiments to gather the quantitative proteomic datasets.