A Cell-type-resolved Liver Proteome
2016; Elsevier BV; Volume: 15; Issue: 10 Linguagem: Inglês
10.1074/mcp.m116.060145
ISSN1535-9484
AutoresChen Ding, Yanyan Li, Feifei Guo, Ying Jiang, Wantao Ying, Dong Li, Dong Yang, Xia Xia, Wanlin Liu, Yan Zhao, Yangzhige He, Xianyu Li, Wei Sun, Qiongming Liu, Lei Song, Bei Zhen, Pumin Zhang, Xiaohong Qian, Jun Qin, Fuchu He,
Tópico(s)Glycosylation and Glycoproteins Research
ResumoParenchymatous organs consist of multiple cell types, primarily defined as parenchymal cells (PCs) and nonparenchymal cells (NPCs). The cellular characteristics of these organs are not well understood. Proteomic studies facilitate the resolution of the molecular details of different cell types in organs. These studies have significantly extended our knowledge about organogenesis and organ cellular composition. Here, we present an atlas of the cell-type-resolved liver proteome. In-depth proteomics identified 6000 to 8000 gene products (GPs) for each cell type and a total of 10,075 GPs for four cell types. This data set revealed features of the cellular composition of the liver: (1) hepatocytes (PCs) express the least GPs, have a unique but highly homogenous proteome pattern, and execute fundamental liver functions; (2) the division of labor among PCs and NPCs follows a model in which PCs make the main components of pathways, but NPCs trigger the pathways; and (3) crosstalk among NPCs and PCs maintains the PC phenotype. This study presents the liver proteome at cell resolution, serving as a research model for dissecting the cell type constitution and organ features at the molecular level. Parenchymatous organs consist of multiple cell types, primarily defined as parenchymal cells (PCs) and nonparenchymal cells (NPCs). The cellular characteristics of these organs are not well understood. Proteomic studies facilitate the resolution of the molecular details of different cell types in organs. These studies have significantly extended our knowledge about organogenesis and organ cellular composition. Here, we present an atlas of the cell-type-resolved liver proteome. In-depth proteomics identified 6000 to 8000 gene products (GPs) for each cell type and a total of 10,075 GPs for four cell types. This data set revealed features of the cellular composition of the liver: (1) hepatocytes (PCs) express the least GPs, have a unique but highly homogenous proteome pattern, and execute fundamental liver functions; (2) the division of labor among PCs and NPCs follows a model in which PCs make the main components of pathways, but NPCs trigger the pathways; and (3) crosstalk among NPCs and PCs maintains the PC phenotype. This study presents the liver proteome at cell resolution, serving as a research model for dissecting the cell type constitution and organ features at the molecular level. Organs consist of multiple cell types that are arranged with a high level of organization. The architecture and interactions between the different cell types define the identity and microenvironment of the organ. Generally, parenchymal cells (PCs) 1The abbreviations used are: PCparenchymal cellAGCautomatic gain controlAIHautoimmune hepatitisDMEMDulbecco's modified Eagle's mediumFAformic acidFACSfluorescence activated cell sortingFDRfalse discovery rateFPKMfragments per kilobase of exon per million fragments mappedGOgene ontologyGPgene productHChepatocyteHCDhigher-energy collision dissociationHPLChigh-performance liquid chromatographyHSChepatic stellate celliBAQintensity based absolute protein quantificationKCKupffer cellLC-MSliquid chromatograph-mass spectrometerLSECliver sinusoidal endothelial cellMACSmagnetic-activated cell sortingPRMparallel reaction monitoringMSmass spectrometryNASHnonalcoholic steatohepatitisNPCnonparenchymal cellRPLCreversed-phase liquid chromatographyTFtranscription factorTGtarget gene. and many different types of nonparenchymal cells (NPCs) play significant roles in the organ. PCs are the most abundant cell type, performing the dominant roles of the organ. NPCs usually account for a minor portion of the cellular population, regulating the functions and microenvironment of the organ. The material exchanges, ligand-receptor recognition, signal transduction, and pathway crosstalk among cell types, especially between PCs and NPCs, are critical for performing organ functions and maintenance. In this process, the patterns of protein expression in different cell types undertake fundamental tasks. Thus, a proteome map of an organ with cell type resolution would enable us to dissect the basic features of the cellular composition of the organ. However, despite extensive studies focused on function and regulation between different cell types, because of the lack of a global view at the "-omics" scale, the features and mechanisms of the cellular composition of organs are still unknown. parenchymal cell automatic gain control autoimmune hepatitis Dulbecco's modified Eagle's medium formic acid fluorescence activated cell sorting false discovery rate fragments per kilobase of exon per million fragments mapped gene ontology gene product hepatocyte higher-energy collision dissociation high-performance liquid chromatography hepatic stellate cell intensity based absolute protein quantification Kupffer cell liquid chromatograph-mass spectrometer liver sinusoidal endothelial cell magnetic-activated cell sorting parallel reaction monitoring mass spectrometry nonalcoholic steatohepatitis nonparenchymal cell reversed-phase liquid chromatography transcription factor target gene. As the largest solid organ in the body, the liver consists of multiple cell types that are responsible for the organism-level functions of metabolism, detoxification, coagulation, and immune response. Four major liver cell types—hepatocytes (HCs), hepatic stellate cells (HSCs), Kupffer cells (KCs), and liver sinusoidal endothelial cells (LSECs)—spatiotemporally cooperate to shape and maintain liver functions. HCs constitute ∼70% of the total liver cell population. The remaining population is composed of the NPCs, namely LSECs, KCs and HSCs (1.Racanelli V. Rehermann B. The liver as an immunological organ.Hepatology. 2006; 43: S54-62Crossref PubMed Scopus (890) Google Scholar). As the parenchymal portion of the liver, HCs are primarily engaged in the basic functions of the liver, including lipid metabolism, drug metabolism, and the secretion of coagulation and complement factors (2.Jenne C.N. Kubes P. Immune surveillance by the liver.Nat. 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Structure and function of sinusoidal lining cells in the liver.Toxicol. Pathol. 1996; 24: 100-111Crossref PubMed Scopus (233) Google Scholar). The distinct cell types of the liver are arranged in a highly organized architectural pattern with individual cells in communication with each other (7.Ishibashi H. Nakamura M. Komori A. Migita K. Shimoda S. Liver architecture, cell function, and disease.Semin. Immunopathol. 2009; 31: 399-409Crossref PubMed Scopus (107) Google Scholar). Correlation and crosstalk between the different cell types are common (8.Malik R. Selden C. Hodgson H. The role of non-parenchymal cells in liver growth.Sem. Cell Develop. Biol. 2002; 13: 425-431Crossref PubMed Scopus (122) Google Scholar). It has been increasingly recognized that under both physiological and pathological conditions, HCs are regulated by factors released from neighboring NPCs (9.Kmiec Z. Cooperation of liver cells in health and disease.Advances in anatomy, embryology, and cell biology. 2001; 161: 1-151Crossref Google Scholar). KCs, in response to pathogenic agents, produce inflammatory cytokines, growth factors, and reactive oxygen species (ROS) that induce hepatic injury (10.Dixon L.J. Barnes M. Tang H. Pritchard M.T. Nagy L.E. Kupffer cells in the liver.Comprehensive Physiol. 2013; 3: 785-797PubMed Google Scholar). Acute damage activates the transformation of hepatic stellate cells into myofibroblast-like cells that play a key role in the development of liver fibrosis (11.Seki E. Schwabe R.F. Hepatic inflammation and fibrosis: functional links and key pathways.Hepatology. 2015; 61: 1066-1079Crossref PubMed Scopus (562) Google Scholar). LSECs contribute to liver regeneration after liver injury (12.DeLeve L.D. Liver sinusoidal endothelial cells and liver regeneration.J. Clin. Investig. 2013; 123: 1861-1866Crossref PubMed Scopus (144) Google Scholar). Although the cooperative pathways between several types of liver cells, including IL6-Jak-STAT (13.Kishimoto T. Interleukin-6: from basic science to medicine–40 years in immunology.Ann. Rev. Immunol. 2005; 23: 1-21Crossref PubMed Scopus (785) Google Scholar), and TGFβ-SMAD (14.Yang L. Roh Y.S. Song J. Zhang B. Liu C. Loomba R. Seki E. Transforming growth factor beta signaling in hepatocytes participates in steatohepatitis through regulation of cell death and lipid metabolism in mice.Hepatology. 2014; 59: 483-495Crossref PubMed Scopus (174) Google Scholar), have been studied, the global network of the different cell types has not been previously reported. Therefore, the liver is an ideal model organ for studying the features and mechanisms of the cellular composition of organs. Moreover, the liver is composed of obvious PC and NPC types, which allows us to investigate the cooperation and crosstalk between these cell types. Mass spectrometry (MS)-based proteomics is a powerful tool that provides insights into the spatiotemporal patterns of protein expression (15.Aebersold R. Mann M. Mass spectrometry-based proteomics.Nature. 2003; 422: 198-207Crossref PubMed Scopus (5585) Google Scholar). The liver is the first organ whose proteome was investigated at the organ level (16.Sun A. Jiang Y. Wang X. Liu Q. Zhong F. He Q. Guan W. Li H. Sun Y. Shi L. Yu H. Yang D. Xu Y. Song Y. Tong W. Li D. Lin C. Hao Y. Geng C. Yun D. Zhang X. Yuan X. Chen P. Zhu Y. Li Y. Liang S. Zhao X. Liu S. He F. Liverbase: a comprehensive view of human liver biology.J. Proteome Res. 2010; 9: 50-58Crossref PubMed Scopus (37) Google Scholar), both at fetal (17.Ying W. Jiang Y. Guo L. Hao Y. Zhang Y. Wu S. Zhong F. Wang J. Shi R. Li D. Wan P. Li X. Wei H. Li J. Wang Z. Xue X. Cai Y. Zhu Y. Qian X. He F. A dataset of human fetal liver proteome identified by subcellular fractionation and multiple protein separation and identification technology.Mol. Cell. Proteomics. 2006; 5: 1703-1707Abstract Full Text Full Text PDF PubMed Scopus (49) Google Scholar) and adult stages (18.Chinese Human Liver Proteome Profiling Consortium First insight into the human liver proteome from PROTEOME(SKY)-LIVER(Hu) 1.0, a publicly available database.J. Proteome Res. 2010; 9: 79-94Crossref PubMed Scopus (36) Google Scholar). In recent years, considerable progress in MS techniques has made the precise characterization of the proteome possible. S. Babak Azimifar et al. reported cell type resolution liver proteome data (19.Azimifar S.B. Nagaraj N. Cox J. Mann M. Cell-type-resolved quantitative proteomics of murine liver.Cell Metabolism. 2014; 20: 1076-1087Abstract Full Text Full Text PDF PubMed Scopus (111) Google Scholar), providing quantitative proteome patterns of individual cell types of the mammalian organ. In addition, this work highlighted the importance of cell type resolution proteomics in understanding liver function. However, the researchers employed a less accurate identification approach to increase the proteome coverage, which could cause confusion in data analysis and minimize the value of the cell type resolution data set. Thus, despite improvements in liver proteomics, previous studies have presented data sets that have provided little comprehensive insight into liver biology. The proteomic mechanisms involved in the division of labor and the collaboration and crosstalk between cell types have been masked and have not yet been characterized. In this study, we chose the liver as a model organ to investigate the features and mechanisms of the cellular composition of organs by screening the cell-type-resolved liver proteome and secretome. We isolated four liver cell types with high purity and viability and employed cutting-edge MS approaches to profile the proteomes of these cell types. Comprehensive bioinformatics analysis revealed the basic features of cellular composition and liver biology associated with the different cell types, including pathway complementarity, maintenance, and crosstalk between cell types. In contrast to traditional proteomics works that merely described and presented broad-scale data, our study provides a substantial amount of novel knowledge in cellular composition of the organ based on an integrated "-omics" analysis and progressive logic. We used three male C57BL/6J mice as a group for liver cell isolation each time, with three biological replicates. We isolated HCs, HSCs, KCs, and LSECs from livers simultaneously, with high purity and viability. RNA for each cell was extracted for Transcriptome after quality control and whole cell protein was extracted separately, followed by digestion in solution and RP-HPLC for peptide separation and LC-MS/MS for protein identification and quantification to profile the proteomes of these cell types. Comprehensive bioinformatics analysis revealed the basic features of cellular composition and liver biology associated with the different cell types, including pathway complementarity, maintenance, and crosstalk between cell types. Mann-Whitney U test was applied to test whether two population means are equal; two populations includes shortest lengths of Specific TFs/Nonspecific TFs, functional category entropy of four liver cell types and so on. The enrichment of specific ontology terms (TFs, GO and KEGG) was tested using a Hypergeometric Test. For Multiple tests, Bonferroni multiple testing correction was used to control the FDR. Difference with p value smaller than 0.05 was considered statistically significant. The following reagents were used: collagenase type IV (Invitrogen, Carlsbad, CA), trypsin inhibitor (Amresco, Cochran Solon, OH), DNase I (AppliChem, Gatersleben, Saxony-Anhalt, Germany), bovine serum albumin (BSA, Sigma-Aldrich, Merck KGaA, Darmstadt, Germany), DMEM (Dulbecco's modified Eagle's medium, Sigma-Aldrich, Merck KGaA, Darmstadt, Germany), fetal bovine serum (FBS, Hyclone, South Logan, UT), OptiprepTM density gradient liquid (Axis-shield, Rodelokka, N-0504 Oslo, Norway), ASGPR1 (Santa Cruz Biotechnology, Dallas, TX), goat anti-mouse lgG-PE (Santa Cruz Biotechnology), F4/80 (eBioscience, Santa Clara, California), CD146 (Miltenyi Biotec, Bergisch Gladbach, Germany), CD45 (Miltenyi Biotec, Bergisch Gladbach, Germany), APC Rat IgG2b k isotype (BD Pharmingen, San Jose, CA), fluorescein isothiocyanate (FITC) Rat IgG2b k isotype (BD Pharmingen), phycoerythrin (PE) Rat IgG2b k isotype (eBioscience), IC fixation buffer (eBioscience), and permeabilization buffer (eBioscience). Normal male C57BL/6J mice (8 weeks old, 25–28 g) were used for liver cell type isolation. Two-step liver perfusion digestion in situ was performed with collagenase IV and DNase I using a previously described protocol with some modifications (20.Ding C. Wei H. Sun R. Zhang J. Tian Z. Hepatocytes proteomic alteration and seroproteome analysis of HBV-transgenic mice.Proteomics. 2009; 9: 87-105Crossref PubMed Scopus (17) Google Scholar). We isolated HCs, HSCs, KCs, and LSECs simultaneously using a combination of modified collagenase-based density gradient centrifugation and fluorescence-activated cell sorting (FACS) with high purity, viability, and yield. Cell purity was assessed by cytological microscopy, electron microscopy, immunocytochemistry, and flow cytometry. Cell viability was determined by 7-aminoactinomycin D (7-AAD)-stained flow cytometry, and cell yield was determined by cell count. Below, the methods for isolating and assessing each cell type are described separately. HCs were isolated by modified in situ perfusion followed by natural sedimentation after differential centrifugation to enrich the HCs and then PE-conjugated ASGPR1-marked FACS to purify and sort the HCs. The sorted cells were then labeled with FITC-conjugated CD146 to evaluate cell purity. For KCs and LSECs, cells between 11.2 and 17% in the OptiprepTM density gradient working solution were carefully collected. The collected cells were primarily a mixture of KCs and LSECs. We then labeled the cell mixture with phenotypic markers and purified specific cell populations by FACS. Specifically, PE-conjugated F4/80 and FITC-conjugated CD146 were used to label KCs and LSECs, respectively. The corresponding isotype antibodies were also used as negative controls to measure the nonspecific binding of the specific antibodies. After sorting, these two cell types were back-tested to determine the purity. HSCs, which were suspended in a less than 8.2% OptiprepTM density gradient working solution, were removed and labeled with PE-conjugated F4/80 and FITC-conjugated CD146 for FACS analysis. Forward and side scatter gates were set to exclude debris and to include all viable cells. Negative cells without positive markers of F4/80 and CD146 were sorted and back-tested to confirm the purity of HSCs. All data were acquired with a BD FACS Aria II instrument and were analyzed with Diva 6.1.2 (BD Biosciences, Franklin Lakes, NJ). Primary HCs, HSCs, KCs, and LSECs were cultured in DMEM supplemented with 20% FBS, penicillin/streptomycin (100 U/ml), and 2 mm glutamine at 37 °C and 5% CO2 in collagen-coated plates. Cells were cultured in six-well plates at a density of 5 × 105 cells/ml. The state of cell culture growth was recorded in real time with inverted phase contrast microscopy. A total of 1 × 106 cells of the isolated primary cell types were collected for RNA or protein extraction. Total RNA was isolated from primary cell types using a Qiagen reagent kit according to the manufacturer's protocol. Proteins were extracted with 8 m urea. After protein extraction from each cell type, gel electrophoresis of the whole cell extract was performed with a 12% separating gel and a 5% stacking gel at 80 V for 20 min, followed by 120 V for 60 min. Coomassie brilliant blue staining was used to mark the protein bands in all samples. The protein sample was reduced with dithiothreitol and alkylated with iodoacetamide in the dark and then was finally digested using sequencing grade trypsin at an enzyme/protein mass ratio of 1:50 overnight at 37 °C. The reaction was stopped by the addition of 0.1% formic acid (FA). HCs and KCs were isolated and purified as described above and then plated in DMEM/1640 supplemented with 10% FBS, penicillin/streptomycin (100 U/ml) and 2 mm glutamine at 37 °C and 5% CO2. After the cells attached, they were washed with serum-free DMEM/1640 three times to remove FBS and cell debris and cultured with serum-free DMEM/1640 for an additional 24 h. For secretome studies, we collected the cell supernatant in a clear centrifuge tube and centrifuged at 100,000 × g and 4 °C for 20 min to remove cells and debris. We then transferred the supernatant to fresh centrifuge tubes, added trichloroacetic acid (TCA) to a final concentration of 12%, and incubated at 4 °C overnight to precipitate the secretory proteins. Afterward, protein precipitations were collected by centrifugation at 24,000 × g for 10 min. The protein pellet was resuspended and washed carefully with 1 ml of cold acetone at −20 °C twice. We then added 10 μl of 8 m urea to resolve the protein pellet and took 0.5 μl of the protein solution to measure the protein concentration. We took 30 μg of protein for the proteome analysis. The secreted proteins were digested with trypsin (1:50) overnight at 37 °C. The digestion process was ended by the addition of 0.1% FA. The tryptic peptides were separated and identified by RP-HPLC (reversed-phase high-performance liquid chromatography) and liquid chromatography tandem mass spectrometry (LC-MS/MS) as described by Ding et al. (21.Ding C. Jiang J. Wei J. Liu W. Zhang W. Liu M. Fu T. Lu T. Song L. Ying W. Chang C. Zhang Y. Ma J. Wei L. Malovannaya A. Jia L. Zhen B. Wang Y. He F. Qian X. Qin J. A fast workflow for identification and quantification of proteomes.Mol. Cell. Proteomics. 2013; 12: 2370-2380Abstract Full Text Full Text PDF PubMed Scopus (89) Google Scholar). The secretomes of HCs and KCs were analyzed independently in three biological replicates. To perform an in-depth proteome screening, dual short-gradient two-dimensional reversed-phase liquid chromatography mass spectrometry (2D-RPLC-MS) (21.Ding C. Jiang J. Wei J. Liu W. Zhang W. Liu M. Fu T. Lu T. Song L. Ying W. Chang C. Zhang Y. Ma J. Wei L. Malovannaya A. Jia L. Zhen B. Wang Y. He F. Qian X. Qin J. A fast workflow for identification and quantification of proteomes.Mol. Cell. Proteomics. 2013; 12: 2370-2380Abstract Full Text Full Text PDF PubMed Scopus (89) Google Scholar) was performed for the four liver cell types. Briefly, 200 μg of total tryptic peptides was separated into 24 fractions with high-pH RPLC (Durashell RP column 5 μm, 150 Å, 250 mm × 4.6 mm i.d., Agela; mobile phase A (2% acetonitrile, pH = 10.0) and B (98% acetonitrile, pH = 10.0)). The eluent samples were dried and reconstituted in HPLC loading buffer (0.1% (v/v) FA, 2% (v/v) acetonitrile in water), and 24 fractions were submitted to low-pH RPLC-MS (C18 column, 3 μm C18) for identification. Mobile phase A consisted of 0.1% FA in water, and mobile phase B consisted of 0.1% FA in acetonitrile. The Orbitrap Q-Exactive source MS was operated at 1.8 kV. For full MS survey scans, the automatic gain control (AGC) target was 3e6 and the scan range was from 300 to 1400 m/z, with a resolution of 70,000. The 75 most intense peaks with charge states of 2 or above were selected for fragmentation via higher-energy collision dissociation (HCD) with a normalized collision energy of 27%. The dynamic exclusion time for MS/MS was set as 18 s. The MS2 spectra were acquired with a resolution of 17,500. HCs and NPCs from three wild-type C57BL/6J mice livers were prepared separately via the gravity centrifugation method (HC, centrifugation at 50 × g; NPC, centrifugation at 600 × g). Cell pellets of six samples, HC1/NPC1 (mouse 1), HC2/NPC2 (mouse 2) and HC3/NPC3 (mouse 3) were suspended in lysis buffer (8 m urea containing 1% phenylmethylsulfonyl fluoride (PMSF)) and sonicated using twenty 0.2-s pulses with 1-s intervals for cooling between each pulse. The extracted proteins were reduced at 37 °C for 4 h and alkylated at room temperature in the dark for 45 min by the addition of dithiothreitol (at a final concentration of 10 mm) and iodoacetamide (at a final concentration of 25 mm). Sequencing grade trypsin (Promega, Madison, WI) was added to each sample at a 1:50 enzyme/substrate ratio, and the reactions were incubated overnight at 37 °C. The digestion mixtures were separated on 4.6 × 250 mm XBridge BEH300 C18 column (Waters) at a flow rate of 0.7 ml/min using the following linear gradient: 5–35% phase B for 30 min (phase A: 2% acetonitrile (ACN) in ammonium hydroxide solution, pH 10; phase B: 98% ACN in ammonium hydroxide solution, pH 10; column temperature, 45 °C), 35–95% phase B for 2 min, 95% phase B for 5 min, 95–5% phase B for 2 min, 5% phase B for 6 min. The eluate was collected each minute into vials starting at the sixth minute. Vials 6, 18, and 30 were pooled, with a total of 12 fractions prepared by the leaping pooling strategy. Proteopeptides of target gene products (GPs) were identified and focused in the exact RT windows and fractions by data dependent acquisition (DDA) scan. The parent ions in the table were monitored in the different fractions of 6 samples on an Easy nLC system (Thermo Fisher Scientific) coupled with Fusion (Thermo Fisher Scientific). Peptides were separated on a homemade reverse-phase capillary column (75 μm × 150 mm, New Objective) packed with C18 media (Agela, 3 μm, China) using the following gradient: 5–8% phase B (98% ACN in 0.1% formic acid) for 8 min, 8–22% phase B for 50 min, 22–32% phase B for 12 min, 32–90% phase B for 1 min, and 90% phase B for 7 min at a flow rate of 350 nL/min. The peptides were analyzed using full scan plus PRM modes. The full mass within the range of 300 to 1400 m/z was collected. The MS1 resolution was set at 30,000. For PRM spectra acquisition, the resolution parameter was 30,000, the HCD collision energy was 32%, the AGC target value was 1.0e5 and the maximum IT time was 64 ms. All of the raw files were processed using Skyline 3.1. The intensities of three fragment ions were summed for peptide quantification. The intensities of up to three peptides were summed and used for GP quantitative comparison. Raw files from Orbitrap Q-Exactive were searched with the MASCOT 2.3 search engine with percolator against the mouse RefSeq protein database (29,764 proteins, updated on 07–01-2013) in the Proteome Discoverer (Version 1.4). A target-decoy-based strategy was applied to control both peptide- and protein-level false discovery rates (FDRs) lower than 1% (22.Elias J.E. Gygi S.P. Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry.Nat. Methods. 2007; 4: 207-214Crossref PubMed Scopus (2827) Google Scholar). 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Both the GO biological process and KEGG pathway categories were used to calculate the functional category entropy. The proteomap and transcriptomap were plotted using the web service http://bionic-vis.biologie.uni-greifswald.de/. The ligand-receptor interactions were downloaded from DLRP (24.Graeber T.G. Eisenberg D. Bioinformatic identification of potential autocrine signaling loops in cancers from gene expression profiles.Nat. Genetics. 2001; 29: 295-300Crossref PubMed Scopus (93) Google Scholar), IUPHAR (25.Pawson A.J. Sharman J.L. Benson H.E. Faccenda E. Alexander S.P. Buneman O.P. Davenport A.P. McGrath J.C. Peters J.A. Southan C. Spedding M. Yu W. Harmar A.J. Nc I. The IUPHAR/BPS Guide to PHARMACOLOGY: an expert-driven knowledgebase of drug targets and their ligands.Nucleic Acids Res. 2014; 42: D1098-1106Crossref PubMed Scopus (776) Google Scholar), and the literature. 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