Proteomic Profiling Of Mouse Liver Sinusoidal Endothelial Cells

As"omics"research model, proteomics is different from previous"fishing"model, which studies single protein. It uses large-scale, high-throughput, high sensitivity techniques, through the study of all the proteins expressed by the whole genome in different time to reveal the nature of life activities in entirety. Human Proteome Organization (HUPO) was founded in 2001, and Human Proteome Project (HPP) was raised then, including the Human Plasma Proteome Project led by USA and the Human Liver Proteome Project (HLPP) led by China. The proteomic profiling of liver sinusoidal endothelial cells was part of the HLPP.Liver is the largest parenchymal organ in the organism, constituted by 300 billion cells on average. There are more than 2 000 enzymes in these cells, which participate in various life activities as the medium of biochemical reaction. So liver expresses important functions in metabolic, detoxify, immune, bile acid secretion, blood store et al. Biological research has entered into cellular and molecular level today. Liver is constituted of various types of cells, and is complicate in structure and function, while cellular standard research is simple, convenient and speedy, in vitro cellular level study of the liver can speed up the understood of the liver.The sample preparation for proteomic profiling is very important as the purity and viability of LSEC influences the reliability of the proteomic data. So, great efforts were made on the purification and identification of LSEC. Firstly, high specificity magnetic activated cell sorting was used to sort LSEC based on in situ collagenase perfusion. Secondly, the purity of LSEC obtained from magnetic activated cell sorting was evaluated strictly, 3 methods including flow cytometry, immunofluorescence and Ac-LDL endocytosis were used. Thirdly, to demonstrate the cells we obtained were LSEC, scan electron microscope and transmission electron microscope was used to observe the fenestrae of LSEC, fenestrae was the particular structure of LSEC. Ultimately, the obtained cells were demonstrated to be LSEC, and the purity reached to 90%. At the same time, we devoted to optimize the sorting procedure to guarantee high viability (94%) and high yield (2.6*106cell/liver). Based on this, a standard operation procedure (SOP) to sort hepatocytes, LSECs, Kupffers and hepatic stellate cells with high purity and yield was consructed, which made a foundation for the separation of human liver cells.Highly purified LSECs from mouse liver were used to compile a protein expression profile with strategy that combined SDS-PAGE separation with LC-MS/MS. Sequest software was used to search IPI mouse v3.62 database. Mixed database was used to evaluate the confidence level, the false positive rate was calculated by the rate of inverted sequence in all peptide sequence. At 95P2 standard 3 852 proteins was identified. LSEC protein database was constructed. LSEC IPI protein list was annotated by Gene ID, Gene Symbol, Swiss Prot accession number, sublocation in Swiss-Prot, biological process in GeneOncology (GO) and absolute protein expression measurements (APEX) quantitation information. By all 3 852 proteins 3 824 proteins got corresponding Gene ID, Gene Symbol; 3 059 proteins got corresponding Swiss-Prot accessing number, of which 2 405 proteins got sublocation information; 3 401 proteins got GO biological process annotation, in which 3 116 annotation were detailed; APEX was applied to quantify MS data, and 1 850 proteins obtained relative quantition data.Proteomic profiling of hepatocytes and Kupffer cells was constructed by the same method and 7842 and 4 309 proteins were obtained respectively. The proteomic data of LSEC and hepatocyte and Kupffer cells were compared, 1952 proteins were in all the three data, and there are 736, 4 526 and 899 specific proteins respectively. Proteins identified from 3 cells were corresponded to 9 077 genes, which enhanced 3 225 genes, amounting to 37%, based on data obtained from liver tissue lysate and liver tissue organelle by this lab, which demonstrate the strategy by sorting different types of liver cells to enrich low abundance proteins was successful.Liver is the most important metabolic organ. Protein list of LSEC was matched to Kyoto Encyclopedia of Genes and Genomes (KEGG). There are 83 metabolic pathways in all, while LSEC covered 73 pathways, in which 7 pathways were 100% covered. LSEC were proved possessing particular standing out metabolic capability in carbohydrate, lipid and amino acides. GeneGo was a mainstream commercial software to analyze omics data and drug development.There are 690 mouse signal transmission and metabolic pathway maps in GeneGo. Protein data obtained from 3 liver cells were matched to these maps and compared. In the top 10 most abundant GeneGo pathways 3 were more abundant in LSEC data, which were Cytoskeletonremodeling_Role of PKA in cytoskeleton reorganisation, Cell adhesion_Histamine H1 receptor signaling in the interruption of cell barrier integrity, Ubiquinone metabolism. Besides, 3 new protein-protein interaction networks were obtained from LSECs and hepatocytes, LSECs and Kupffer cells respectiely.The consistancy of proteome and transcriptome is a controversial problem, no final conclusion has been reached till now. Proteomic data of LSEC obtained in this article was compared to transcriptome data from literature. 62% of the proteomic data was found in transcriptomic data.Based on function annotation in Swiss Prot and biological process and molecular function annotation in GeneOncology, 322 new proteins were discovered for the first time. The functions of these proteins were awaited for further exploring.In conclusion, we provided a strategy that could separate hepatocytes, LSECs, Kupffer cells and hepatic stellate cells from the same mouse liver, and an SOP procedure was constructed, which lay a foundation for the proteomic profiling of human liver cells. The proteomic profiling of mouse LSECs were constructed for the first time and GO, KEGG and GeneGo software were used to analyze the proteomic data. The proteomic data from 3 liver cells added 37% data to the liver proteomic database based on data obtained from liver tissue lysate and liver tissue organelles in this lab, which demonstrated the strategy by sorting different types of liver cells to enhance protein identification coverage was successful.