Optimized proteomic analysis of rat liver microsomes using dual enzyme digestion with 2D-LC-MS/MS

A systematic approach was developed to optimize the analysis of rat liver microsomes combining ion exchange fractionation with reverse-phase chromatography coupled to high resolution quadrupole-time-of-flight mass spectrometry. A comparison was performed with several conditions to select the most efficient solubilization and proteolysis protocol to achieve highest proteome coverage. Optimal trypsin digestion conditions were achieved with SDS and heat to increase solubilization of microsomal samples, with an increase from 621 to 686 identified proteins when SDS and heat were applied. Pepsin digestion yielded complementary results, especially in terms of hydrophobic environments, thus allowing sequence coverage to be increased substantially. Several dual digestion strategies were tested, with trypsin and pepsin combined in series or in parallel. A parallel tryptic-peptic dual digestion, combining mass spectral data of single enzyme digestions, yielded the best results in terms of number of identified proteins, increasing by 29% from the best single enzyme procedure, and sequence coverage improved by 5% on average for all proteins identified. Using our complete set of data, a total of 1095 proteins were identified with less than 1% FDR, out of which 213 proteins (19.5%) were integral membrane proteins. Proteomics data have been deposited to the ProteomeXchange Consortium with dataset identifier PXD000128.

A systematic approach to optimize the proteomic analysis of rat liver microsomes by 2D-LC-MS/MS using several different digestion conditions was performed in order to increase our knowledge of the rat liver proteome, especially important to drug metabolism and toxicology. A parallel (combined) tryptic-peptic digestion yielded best overall performance, when mass spectral data were acquired separately and combined prior to database searching. This large-scale data set will be available publicly on the ProteomeXchange server and will therefore be accessible to a large scientific community interested in using this data for their own studies. One of the main goals of this study is to identify a comprehensive list of proteins for follow-up protein covalent binding studies related to drug toxicity.