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Contribution of Human Fibroblasts and Endothelial Cells to the Hallmarks of Inflammation as Determined by Proteome Profiling

2016; Elsevier BV; Volume: 15; Issue: 6 Linguagem: Inglês

10.1074/mcp.m116.058099

1535-9484

Astrid Slany, Andrea Bileck, Dominique Kreutz, Rupert L. Mayer, Besnik Muqaku, Christopher Gerner,

Advanced Proteomics Techniques and Applications

In order to systematically analyze proteins fulfilling effector functionalities during inflammation, here we present a comprehensive proteome study of inflammatory activated primary human endothelial cells and fibroblasts. Cells were stimulated with interleukin 1-β and fractionated in order to obtain secreted, cytoplasmic and nuclear protein fractions. Proteins were submitted to a data-dependent bottom up analytical platform using a QExactive orbitrap and the MaxQuant software for protein identification and label-free quantification. Results were further combined with similarly generated data previously obtained from the analysis of inflammatory activated peripheral blood mononuclear cells. Applying a false discovery rate of less than 0.01 at both, peptide and protein level, a total of 8370 protein groups assembled from 117,599 peptides was identified; mass spectrometry data have been made fully accessible via ProteomeXchange with identifier PXD003406 to PXD003417.Comparative proteome analysis allowed us to determine common and cell type-specific inflammation signatures comprising novel candidate marker molecules and related expression patterns of transcription factors. Cardinal features of inflammation such as interleukin 1-β processing and the interferon response differed substantially between the investigated cells. Furthermore, cells also exerted similar inflammation-related tasks; however, by making use of different sets of proteins. Hallmarks of inflammation thus emerged, including angiogenesis, extracellular matrix reorganization, adaptive and innate immune responses, oxidative stress response, cell proliferation and differentiation, cell adhesion and migration in addition to monosaccharide metabolic processes, representing both, common and cell type-specific responsibilities of cells during inflammation. In order to systematically analyze proteins fulfilling effector functionalities during inflammation, here we present a comprehensive proteome study of inflammatory activated primary human endothelial cells and fibroblasts. Cells were stimulated with interleukin 1-β and fractionated in order to obtain secreted, cytoplasmic and nuclear protein fractions. Proteins were submitted to a data-dependent bottom up analytical platform using a QExactive orbitrap and the MaxQuant software for protein identification and label-free quantification. Results were further combined with similarly generated data previously obtained from the analysis of inflammatory activated peripheral blood mononuclear cells. Applying a false discovery rate of less than 0.01 at both, peptide and protein level, a total of 8370 protein groups assembled from 117,599 peptides was identified; mass spectrometry data have been made fully accessible via ProteomeXchange with identifier PXD003406 to PXD003417.Comparative proteome analysis allowed us to determine common and cell type-specific inflammation signatures comprising novel candidate marker molecules and related expression patterns of transcription factors. Cardinal features of inflammation such as interleukin 1-β processing and the interferon response differed substantially between the investigated cells. Furthermore, cells also exerted similar inflammation-related tasks; however, by making use of different sets of proteins. Hallmarks of inflammation thus emerged, including angiogenesis, extracellular matrix reorganization, adaptive and innate immune responses, oxidative stress response, cell proliferation and differentiation, cell adhesion and migration in addition to monosaccharide metabolic processes, representing both, common and cell type-specific responsibilities of cells during inflammation. Inflammation is a complex process, which plays, especially in its chronic form, an important role in many diseases of modern civilization such as cardiovascular and neurological disorders and diverse cancers (1.Fischer R. Maier O. Interrelation of oxidative stress and inflammation in neurodegenerative disease: role of TNF.Oxid. Med. Cell. Longev. 2015; 2015: 610813-610830Crossref PubMed Scopus (444) Google Scholar, 2.Crusz S.M. Balkwill F.R. Inflammation and cancer: advances and new agents.Nat. Rev. Clin. Oncol. 2015; 12: 584-596Crossref PubMed Scopus (705) Google Scholar, 3.Siti H.N. Kamisah Y. Kamsiah J. The role of oxidative stress, antioxidants and vascular inflammation in cardiovascular disease (a review).Vascular Pharmacol. 2015; 71: 40-56Crossref PubMed Scopus (618) Google Scholar). Although it is possible to cure acute inflammation, chronic inflammation still represents a great challenge and often responds in an unsatisfying fashion to sustained treatment. In acute inflammation, the relations between cause and effects may be rather straight, so that it may be sufficient to block a single activity, for example that of COX-2, in order to achieve relieve of symptoms and subsequent healing. In chronic inflammation, these relations seem to be more complex and a simple treatment may not be successful. Actually, several different cell types are involved in inflammation, contributing to the complex signaling network necessary for the appropriate exertion and completion of this process. Chronic inflammation may occur when specific regulation mechanisms that are necessary to resolve the inflammatory process fail, resulting in an uncontrolled escalation of the ongoing processes (4.Perez D.A. Vago J.P. Athayde R.M. Reis A.C. Teixeira M.M. Sousa L.P. Pinho V. Switching off key signaling survival molecules to switch on the resolution of inflammation.Mediators Inflamm. 2014; 2014 (Article ID 829851)Crossref Scopus (21) Google Scholar). Accumulation of pro-inflammatory signaling molecules and effector cells at the site of inflammation (5.Buckley C.D. Pilling D. Lord J.M. Akbar A.N. Scheel-Toellner D. Salmon M. Fibroblasts regulate the switch from acute resolving to chronic persistent inflammation.Trends Immunol. 2001; 22: 199-204Abstract Full Text Full Text PDF PubMed Scopus (479) Google Scholar), the production of new blood vessels enabling the incessant recruitment of inflammatory cells (6.Kim Y.W. West X.Z. Byzova T.V. Inflammation and oxidative stress in angiogenesis and vascular disease.J. Mol. Med. 2013; 91: 323-328Crossref PubMed Scopus (160) Google Scholar), or the excess deposition of extracellular matrix components resulting from an uncontrolled inflammation-related wound healing process (7.Wallach-Dayan S.B. Golan-Gerstl R. Breuer R. Evasion of myofibroblasts from immune surveillance: a mechanism for tissue fibrosis.Proc. Natl. Acad. Sci. U. S. A. 2007; 104: 20460-20465Crossref PubMed Scopus (59) Google Scholar) can be some of the consequences. Different cell types may fulfill different functionalities during inflammation. Obviously, each cell type has its repertoire of specific regulatory factors and may contribute to the regulation of inflammation in a specific manner. In this way, all cell types may be cooperating to achieve the fine tuning of the complex process of inflammation. Main players of inflammation, and main targets for anti-inflammatory treatments, are leukocytes, including neutrophils and monocytes as part of the innate immune response, as well as B- and T lymphocytes, activated in the course of an inflammation-related adaptive immune response. Under normal conditions, when they have fulfilled their tasks, these cells are rapidly neutralized by induction of apoptosis (8.Ortega-Gomez A. Perretti M. Soehnlein O. Resolution of inflammation: an integrated view.EMBO Mol. Med. 2013; 5: 661-674Crossref PubMed Scopus (489) Google Scholar). Stromal cells such as fibroblasts and endothelial cells are involved in the process of inflammation as well, and these cells are capable of surviving for a longer time and may stay in their functionally activated state when the inflammatory process should be completed, thus possibly contributing to the development of chronic inflammation (9.Naylor A.J. Filer A. Buckley C.D. The role of stromal cells in the persistence of chronic inflammation.Clin. Exp. Immunol. 2013; 171: 30-35Crossref PubMed Scopus (55) Google Scholar). Although the most important players of inflammation have been well described, a systematic analysis of the proteins fulfilling the effector functionalities during inflammation has not yet been undertaken. This would, however, contribute to a better understanding of the ongoing complex processes and may thus support the development of new therapeutic strategies to combat chronic inflammation and related diseases (10.Van Dyke T.E. Kornman K.S. Inflammation and factors that may regulate inflammatory response.J. Periodontol. 2008; 79: 1503-1507Crossref PubMed Scopus (51) Google Scholar). Here we present a systematic proteome study of inflammatory activated primary human dermal fibroblasts (NHDF) 1The abbreviations used are:NHDFnormal human dermal fibroblastsCOX-2prostaglandin G/H synthase 2ECendothelial cellsECMextracellular matrixEndMTendothelial to mesenchymal transitionFAformic acidFDRfalse discovery rateGBPsguanylate-binding proteinsHUVEChuman umbilical vein endothelial cellsIAAiodoacetamideIFITsinterferon-induced proteins with tetratricopeptide repeatsIFNinterferonIL-1RAinterleukin-1 receptor antagonist proteinLAMC2laminin subunit gamma-2LFQlabel-free quantificationLIFleukemia inhibition factorMEKmitogen-activated protein kinase kinaseMMPsmatrix metalloproteinasesMx1interferon-induced GTP-binding protein Mx1NRCAMneuronal cell adhesion moleculePBMCsperipheral blood mononuclear cellsPRRX1paired mesoderm homeobox protein 1PRRX2paired mesoderm homeobox protein 2TAL1T-cell acute lymphocytic leukemia protein 1TGFβ2transforming growth factor β-2TNCtenascinTSG6tumor necrosis factor-inducible gene 6 proteinTSP1thrombospondin-1TSP2thrombospondin-2VEGFvascular endothelial growth factor. and human umbilical vein endothelial cells (HUVEC). These cells have been analyzed by us previously (11.Slany A. Meshcheryakova A. Beer A. Ankersmit H.J. Paulitschke V. Gerner C. Plasticity of fibroblasts demonstrated by tissue-specific and function-related proteome profiling.Clin. Proteomics. 2014; 11: 41Crossref PubMed Scopus (23) Google Scholar, 12.Slany A. Paulitschke V. Haudek-Prinz V. Meshcheryakova A. Gerner C. Determination of cell type-specific proteome signatures of primary human leukocytes, endothelial cells, keratinocytes, hepatocytes, fibroblasts and melanocytes by comparative proteome profiling.Electrophoresis. 2014; 35: 1428-1438Crossref PubMed Scopus (12) Google Scholar) demonstrating that they display all relevant cell type characteristics of stromal fibroblasts and endothelial cells, and thus represent suitable model systems. A standardized approach has been applied to semi-quantitatively determine and compare the relevant regulatory factors that were up- and downregulated by fibroblasts and endothelial cells upon inflammatory activation. To this end, NHDF and HUVEC were stimulated in vitro with the canonical inflammation mediator interleukin-1β (IL-1β) (13.Dowling J.K. O'Neill L.A. Biochemical regulation of the inflammasome.Critical Rev. Biochem. Mol. Biol. 2012; 47: 424-443Crossref PubMed Scopus (102) Google Scholar). Secreted, cytoplasmic and nuclear proteins were extracted from the cells and analyzed separately by shotgun proteomics using a QExactive orbitrap mass spectrometer. Results were further combined with data obtained from previous investigations on inflammatory activated peripheral blood mononuclear cells (PBMCs) (14.Bileck A. Kreutz D. Muqaku B. Slany A. Gerner C. Comprehensive assessment of proteins regulated by dexamethasone reveals novel effects in primary human peripheral blood mononuclear cells.J. Proteome Res. 2014; 13: 5989-6000Crossref PubMed Scopus (40) Google Scholar). In this way, cell type-specific inflammation-related functionalities were determined, as well as inflammatory signatures and marker molecules that may be indicative for the inflammatory processes occurring in vivo. This motivated us to define hallmarks of inflammation - in the style of the hallmarks of cancer (15.Hanahan D. Weinberg R.A. The hallmarks of cancer.Cell. 2000; 100: 57-70Abstract Full Text Full Text PDF PubMed Scopus (22350) Google Scholar) - representing the biological processes essential for the successful resolution of inflammation and to specify responsibilities of fibroblasts, endothelial cells and leukocytes therein. normal human dermal fibroblasts prostaglandin G/H synthase 2 endothelial cells extracellular matrix endothelial to mesenchymal transition formic acid false discovery rate guanylate-binding proteins human umbilical vein endothelial cells iodoacetamide interferon-induced proteins with tetratricopeptide repeats interferon interleukin-1 receptor antagonist protein laminin subunit gamma-2 label-free quantification leukemia inhibition factor mitogen-activated protein kinase kinase matrix metalloproteinases interferon-induced GTP-binding protein Mx1 neuronal cell adhesion molecule peripheral blood mononuclear cells paired mesoderm homeobox protein 1 paired mesoderm homeobox protein 2 T-cell acute lymphocytic leukemia protein 1 transforming growth factor β-2 tenascin tumor necrosis factor-inducible gene 6 protein thrombospondin-1 thrombospondin-2 vascular endothelial growth factor. Primary HUVEC were purchased from (Lonza Walkersville Inc., Walkersville, MD). HUVEC were cultured in endothelial basal medium supplemented with the EGM-2 SingleQuot Kit (both Lonza), 10% FCS and 100 U/ml penicillin/streptomycin (both ATCC/LGC Standards, London, UK), according to the instructions of the manufacturer. Normal human dermal fibroblasts (NHDF) were kindly provided by Verena Paulitschke from the General Hospital of Vienna. NHDF were cultured in RPMI 1640 (Thermo Fisher Scientific, Life Technologies, Loughborough, UK) supplemented with 10% FCS and 100U/ml penicillin/streptomycin (both ATCC) at 37 °C and 5% CO2. Cells were used up to passage 7 and 22 for HUVEC and NHDF, respectively. Experiments were performed in 75 cm2-culture flasks, using ∼5 × 106 cells per flask. Cell numbers, as well as cell viability that was consistently better than 98%, were determined using a Moxi Z cell counter (ORFLO Technologies, Carlsbad, CA). Inflammatory stimulation with 10 ng/ml of IL-1β (Sigma-Aldrich, Vienna, Austria) was carried out for 24 h, as applied in previous studies (11.Slany A. Meshcheryakova A. Beer A. Ankersmit H.J. Paulitschke V. Gerner C. Plasticity of fibroblasts demonstrated by tissue-specific and function-related proteome profiling.Clin. Proteomics. 2014; 11: 41Crossref PubMed Scopus (23) Google Scholar, 16.Groessl M. Slany A. Bileck A. Gloessmann K. Kreutz D. Jaeger W. Pfeiler G. Gerner C. Proteome profiling of breast cancer biopsies reveals a wound healing signature of cancer-associated fibroblasts.J. Proteome Res. 2014; 13: 4773-4782Crossref PubMed Scopus (28) Google Scholar, 17.Muqaku B. Slany A. Bileck A. Kreutz D. Gerner C. Quantification of cytokines secreted by primary human cells using multiple reaction monitoring: evaluation of analytical parameters.Anal. Bioanal. Chem. 2015; 407: 6525-6536Crossref PubMed Scopus (15) Google Scholar). Control cells were incubated in parallel without IL-1β. After that, cells were washed with PBS and further cultured for 6 h in 6 ml of serum-free medium. Biological replicates were prepared for each cell type to allow statistical analyses of the resulting data. Supernatants of control and inflammatory activated cells were filtered through 0.2 μm filters (GE Healthcare, Freiburg, Germany) and proteins therein were precipitated overnight with ethanol at −20 °C. To obtain the cytoplasmic protein fractions as well as the nuclear protein extracts, we proceeded as previously described (14.Bileck A. Kreutz D. Muqaku B. Slany A. Gerner C. Comprehensive assessment of proteins regulated by dexamethasone reveals novel effects in primary human peripheral blood mononuclear cells.J. Proteome Res. 2014; 13: 5989-6000Crossref PubMed Scopus (40) Google Scholar). In short, cells were lysed in isotonic lysis buffer supplemented with protease inhibitors by applying mechanical shear stress. Cytoplasmic proteins were separated from nuclei by centrifugation and precipitated overnight with ethanol at −20 °C. Nuclear proteins were extracted by incubating the nuclei in 500 mm NaCl and solubilizing the proteins in Nonidet P-40 buffer supplemented with protease inhibitors. The extracted proteins were separated from resting cell materials by centrifugation and precipitation of the resulting supernatant with ethanol at −20 °C overnight. After precipitation, all samples were dissolved in sample buffer (7.5 m urea, 1.5 m thiourea, 4% 3-[(3-cholamidopropyl)dimethylammonio]-1-propanesulfonate (CHAPS), 0.05% SDS, 100 mm dithiothreitol (DTT)) and the protein concentrations were determined by means of a Bradford assay (Bio-Rad Laboratories, Munich, Germany). For proteomics analyses, we prepared in-solution digests from all three subcellular protein fractions of NHDF and HUVEC, as previously described (14.Bileck A. Kreutz D. Muqaku B. Slany A. Gerner C. Comprehensive assessment of proteins regulated by dexamethasone reveals novel effects in primary human peripheral blood mononuclear cells.J. Proteome Res. 2014; 13: 5989-6000Crossref PubMed Scopus (40) Google Scholar). In short, 20 μg of each protein sample was concentrated onto a 3kD MWCO filter (Pall Austria Filter GmbH, Vienna, Austria) pre-washed with LC-MS grade water (Merck Chemicals and Life Science GesmbH, Vienna, Austria); proteins were reduced with DTT and alkylated with iodoacetamide (IAA). After centrifugation at 14,000 × g for 10min, proteins on top of the filter were washed with 50 mm ammonium bicarbonate buffer. Trypsin (Roche Austria GmbH, Vienna, Austria) was then added and incubation was performed at 37 °C for 18 h. After trypsin digestion, peptide samples were cleaned up with C-18 spin columns (Thermo Fisher Scientific) and eluted two times with 50% acetonitrile (ACN), 0.1% trifluoroacetic acid (TFA) and once with 80% ACN, 0.1% TFA. Samples were finally dried in a speedvac and stored at −20 °C until further MS analyses. For cytoplasmic proteins and nuclear extracts, in addition to in-solution digests, in-gel digests were prepared, for one biological replicate in case of NHDF and two in case of HUVEC. This was done as previously described (18.Slany A. Haudek V.J. Gundacker N.C. Griss J. Mohr T. Wimmer H. Eisenbauer M. Elbling L. Gerner C. Introducing a new parameter for quality control of proteome profiles: consideration of commonly expressed proteins.Electrophoresis. 2009; 30: 1306-1328Crossref PubMed Scopus (29) Google Scholar). In short, 20 μg of each sample was loaded on an SDS-PAGE and allowed to separate for 1.5 cm, after what proteins in the gel were stained by an MS-compatible silver staining procedure. Afterward, each protein band was cut into 4 slices that were again decolored. Upon reduction with DTT and alkylation with IAA, the proteins were digested for 18 h at 37 °C using trypsin (Roche Diagnostics, Austria GmbH, Germany). The digested peptides were eluted, once with 50 mm ammonium bicarbonate, and twice with 5% FA/50% ACN. The eluted peptide samples were dried and then stored at −20 °C until further MS analyses. Dried samples were solubilized in 5 μl 30% formic acid (FA) containing 10 fmol each of four synthetic standard peptides (allowing us to verify the quality of the chromatographic separation) and diluted with 40 μl mobile phase A (98% H2O, 2% ACN, 0.1% FA). Of this solution, 10 μl were injected into the nanoUPLC-system UtiMate 3000 RSLCnano (Thermo Fisher Scientific). Peptides were first concentrated on a 2 cm x75 μm C18 Pepmap100 pre-column (Thermo Fisher Scientific) at a flow rate of 10 μl/min using mobile phase A. Separation of the peptides was achieved by eluting them from the precolumn to a 50 cm × 75 μm Pepmap100 analytical column (Thermo Fisher Scientific) applying a flow rate of 300 nl/min and using a gradient of 8% to 40% mobile phase B (80% ACN, 20% H2O, 0.1% FA), over 235 min for the analysis of cytoplasmic samples and nuclear fractions, and over 95 min in case of secretome analysis. The mass spectrometric analysis was performed on a QExactive orbitrap mass spectrometer, equipped with a nanospray ion source (Thermo Fisher Scientific), coupled to the nano HPLC system. For detection, MS scans were performed in the range from m/z 400–1400 at a resolution of 70,000 (at m/z = 200). MS/MS scans were performed choosing a top 12 method for cytoplasmic samples and nuclear fractions and a top 8 method for secretome analysis; HCD fragmentation was applied at 30% normalized collision energy and analysis in the orbitrap at a resolution of 17,500 (at m/z = 200). For the investigation of functional signatures, pairs of treated and untreated cells were compared. Technical replicates provided a measure for the coefficient of variation introduced by the applied methodology. In addition, independent cell experiments, here designated as biological replicates, were performed. Furthermore, to assess potential effects of different donors, in case of HUVEC, three individual donors were investigated. Two independent cell experiments each of donor 1 and 2, and three independent cell experiments of donor 3 were performed, thus resulting in seven biological replicates. Although quantitative differences concerning the extent of regulation of individual proteins between the donors were evident, the actually regulated proteins were the same in all donors. Consequently, in case of NHDF, the investigation of three biological replicates derived from one donor was considered as adequate to assess regulatory effects in these cells. Combining biological and technical replicates finally resulted in a total of fourteen individual LC-MS/MS measurements in case of HUVEC and six in case of NHDF. All replicates were used for statistical analyses. The positive identification of a large number of known inflammation players strongly supports the present strategy. Before statistical evaluation, identified proteins were filtered for reversed sequences, common contaminants and a minimum of three independent experimental identifications in at least one cell type in a given functional state. Identification of proteins as well as label-free quantification (LFQ) and statistical analyses were performed using the MaxQuant 1.5.2.8 software including the Andromeda search engine and the Perseus statistical analysis package (19.Cox J. Mann M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.Nat. Biotechnol. 2008; 26: 1367-1372Crossref PubMed Scopus (9214) Google Scholar, 20.Cox J. Mann M. 1D and 2D annotation enrichment: a statistical method integrating quantitative proteomics with complementary high-throughput data.BMC Bioinformatics. 2012; 13: S12Crossref PubMed Scopus (413) Google Scholar), a commonly used workflow for processing and statistical assessment of shotgun proteomics data. For statistical analysis, data obtained from both biological and technical replicates were used. Furthermore, the obtained data from the current study were combined with data obtained from previous investigations on inflammatory activated PBMCs. Proteins were identified using the UniProt database for human proteins (version 102014 with 20,195 entries, restricted to reviewed entries only), a peptide mass tolerance of 25 ppm, an MS/MS match tolerance of 20 ppm and a maximum of two missed cleavages with trypsin as protease. Search criteria further included carbamidomethylation of cysteines as fixed modification, methionine oxidation as well as N-terminal protein acetylation as variable modifications, and a minimum of two peptide identifications per protein, at least one of them unique. Furthermore, match between runs was performed using a 5 min match time window and a 15 min alignment time window. For both, peptides and proteins, a false discovery rate (FDR) of less than 0.01 was applied; the FDR was determined by the target-decoy approach. No additional filtering concerning the Andromeda score for accepting MS/MS identifications was recommended by the MaxQuant software when applying a strict FDR. The mass spectrometry-based proteomics data (including raw files, result files and peak list files, peptide sequences, precursor charges, mass to charge ratios, amino acid modifications, peptide identification scores, protein accession numbers, number of distinct peptides assigned for each identified protein, percent coverage of each identified protein in each individual experiment and annotated MS2 spectra for each peptide spectrum match) have been deposited to the ProteomeXchange Consortium (21.Vizcaino J.A. Deutsch E.W. Wang R. Csordas A. Reisinger F. Rios D. Dianes J.A. Sun Z. Farrah T. Bandeira N. Binz P.A. Xenarios I. Eisenacher M. Mayer G. Gatto L. Campos A. Chalkley R.J. Kraus H.J. Albar J.P. Martinez-Bartolome S. Apweiler R. Omenn G.S. Martens L. Jones A.R. Hermjakob H. ProteomeXchange provides globally coordinated proteomics data submission and dissemination.Nat. Biotechnol. 2014; 32: 223-226Crossref PubMed Scopus (2076) Google Scholar) via the PRIDE partner repository with the data set identifier PXD003406 to PXD003417 (supplemental Table S9), accessible via www.proteomeexchange.org. As MaxQuant-derived data are not yet supported for complete submissions, here we used Proteome Discoverer 1.4 running Mascot 2.5 and Uniprot for human proteins (version 112015 with 20,193 entries, restricted to reviewed entries only) as search engine. Actually all proteins found to be regulated via MaxQuant were positively identified by Proteome Discoverer as well. Finally, for selected proteins, heat maps representing corresponding LFQ values determined in the respective cell type and cell state, were generated. In case of NHDF, average LFQ-values of the technical replicates were used. In case of HUVEC, the average LFQ-values per donor were used, in this case averaging both technical and biological replicates. Heat maps were generated using an R script (22..R Development Core Team (2010) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria,Google Scholar) based on the raw data obtained from MaxQuant without any further data manipulation. Label-free quantification as described in the former paragraph resulted in LFQ values for each individual protein and was used for quantitative assessment of protein regulation. LFQ values were obtained for all proteins from all experiments (supplemental Tables S6, S7, and S8) and subjected to a comparative analysis; the same initial protein amount of 20 μg used in all experiments served for normalization. Isoforms of individual proteins were summarized into protein groups by the Andromeda software and were not further considered here. Mutual comparisons between untreated and inflammatory activated cells were performed to determine protein groups that were significantly up- or downregulated upon inflammatory activation in each cell type. To this end, using the Perseus statistical analysis package, differences of LFQ values were calculated. Changes in protein abundance values between untreated and stimulated cells were determined by a two-sided t test with p < 0.05 and a minimum of a twofold abundance difference. All proteins meeting these criteria were considered in the present study as potentially contributing to the regulatory effects taking place during inflammation. In addition, to emphasize the most robust regulatory effects observed within one kind of cell, we determined significantly regulated proteins with a global FDR<0.05 (indicated in Table I, Table II, Table III, Table IV and supplemental Tables S1–S5) as determined by a permutation-based method, referring to Cox et al. and Tusher et al. (23.Cox J. Hein M.Y. Luber C.A. Paron I. Nagaraj N. Mann M. Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ.Mol. Cell. Proteomics. 2014; 13: 2513-2526Abstract Full Text Full Text PDF PubMed Scopus (2710) Google Scholar, 24.Tusher V.G. Tibshirani R. Chu G. Significance analysis of microarrays applied to the ionizing radiation response.Proc. Natl. Acad. Sci. U.S.A. 2001; 98: 5116-5121Crossref PubMed Scopus (9768) Google Scholar).Table IProteins regulated in a common as well as a cell type-specific way in HUVEC, NHDF and PBMCs. Proteins are listed which were at least twofold up- or downregulated (p < 0.05) in all three kinds of cells upon inflammatory activation (A); proteins which were at least twofold up- or downregulated (p < 0.05) in inflammatory activated stromal cells, but not in activated PBMCs (B); proteins which were at least twofold up- or downregulated (p < 0.05) in inflammatory activated HUVEC only (C); and proteins which were at least twofold up- or downregulated (p < 0.05) in inflammatory activated NHDF only (D). Acc.Nr., UniProt accession number. Significant regulation with FDR < 0.05 in one (*), two (**) or three kinds of cells (***)Table IICandidate marker proteins for inflammatory activated fibroblasts and endothelial cells. Blood-borne markers i.e. secreted proteins, as well as membrane-bound proteins from the cell surface and intracellular proteins were determined for endothelial cells (ECs) and fibroblasts. Differences of LFQ values between control and activated cells (Δ_act vs con; logarithmic scale to the base of two) with corresponding p values are indicated for each subcellular fraction, cytoplasm (cyt), nuclear extract (ne) and supernatant (sn). Acc.Nr., UniProt accession numberTable IICandidate marker proteins for inflammatory activated fibroblasts and endothelial cells. Blood-borne markers i.e. secreted proteins, as well as membrane-bound proteins from the cell surface an