In an initial study, we carried out the profiling of mouse neonatal kidney, using two-dimensional micro-high pressure liquid chromatography coupled to tandem mass spectrometric (MS/MS) analysis. The results identified 9,208 peptides representing 3,855 proteins, and indicated that further pre-fractionation of protein would increase the sensitivity of the approach to detect low abundant proteins. A more extensive study of mouse embryonic (13.5 days post coitum) placenta and neonatal kidney therefore implemented gel electrophoretic fractionation to separate groups of proteins prior to Mass Spectrometric analysis. For the kidney, the latest experimental results were combined with the previous analysis resulting in 15,600 peptide signatures mapped to 7,000 proteins. When we searched EST database with these proteins 3,081 of these proteins had EST (Expressed sequence tags) matches from a collection of 11,348 ESTs. Thus, 3,919 proteins were independently identified based on the Mass Spectrometric analysis. For placenta, the approach facilitated the identification of 21,781 peptide signatures, 13,409 of which were unique and could be assigned to 6,415 proteins. As with kidney proteomics, proteins were recovered from a wide range of intracellular compartments and represented the full range of isoelectric charge; thus the fractionation method showed no apparent bias. The representation of proteins in placenta varied greatly, with dynein cytoplasmic heavy chain (90 peptides) one of the most abundant proteins. For 2,809 proteins, matching ESTs from placental source were found from a set of 8,387 ESTs in the NCBI EST database. Mass spectrometric results provide direct evidence for expression of the remaining 3,606 proteins. Of particular interest, several proteins that had been predicted solely on the basis of sequence analysis have now been substantiated as true products of translation from transcribed genes. Further comparisons with mRNA species and their abundance are in progress using expression profiling of placental RNA on DNA microarrays. In an ongoing collaborative work with Ravi Sirdeshmukh from Center for Cellular and Molecular Biology at Hyderabad, India, we are analyzing the proteomics data generated for R1-9 ES cells. Using our computing resources, we have curated the NCBI mouse protein NR database collate protein IDs more used analysis including DTA select, X!Tandem, Scaffold and Trans Proteomics pipeline, to identify peptide signatures for 5,017 proteins in ES cells.