1.  Gene-expression profiles from synovial tissue biopsies and blood monocytes were generated from patients with RA and OA.  Transcriptome analysis of synovial tissue biopsies from 10 RA and 10 OA patients revealed differential expression of more than 1500 genes. Transcriptome data indicated infiltration of various cell types like T- cells, B-cells, NK-cells, Monocytes, Granulocytes.  Transcriptome analysis of blood monocytes from 6 RA and 6 OA disclosed differential expression of more than 100 genes. Focus on single cell population from blood increased specificity of analysis by overcoming fluctuation of cellular composition that often accompanies inflammatory disorders.  These profiles were used for selection of candidate biomarkers related to inflammation in RA and that discriminated RA from OA patients.  Candidate biomarkers: Cytokines: CXL13, CCL18, CXCL9, CCL13, IL8, CXCL10, CCL2, TNFAIP6 Shedded surface molecules : SELE, CD14, CD163, PLAUR, CD44 Enzymes: SEPRINA1, MMP1, MMP3 From transcriptome to protein biomarkers in RA: joint compartment and monocytes outperform serum and whole blood Biljana Smiljanovic 1, Bruno Stuhlmüller 1, Wlodzimierz Maslinski 3, Silvia Pade 1, Marina Backhaus 1, Gerd R. Burmester 1, Andreas Radbruch 2, Andreas Grützkau 2, Thomas Häupl 1, 1 Department of Rheumatology and Clinical Immunology, Charité University Medicine Berlin, Germany 2 German Rheumatism Research Centre (DRFZ) Berlin, An Institute of the Leibniz Association, Berlin, Germany 3 Department of Pathophysiology and Immunology, Institute of Rheumatology, Warsaw, Poland Materials and Methods Background and aim of the study Results I - transcriptome Results II - proteome Conclusion Funding  Rheumatoid arthritis (RA) is a chronic disease associated with polyarticular inflammation, cartilage and/or bone destruction. Current diagnosis of RA is based on clinical criteria and measurement of non- specific markers of inflammation in the blood. A main challenge in diagnosis of RA is to establish objective criteria that provide a detailed insight into joint pathobiology and that are relevant for diagnosis and therapeutic stratification of patients.  In search for convenient biomarkers, this study focused on global approaches in dissecting inflammation in RA by analysing transcriptome data of synovial tissue biopsies and blood monocytes. Transcriptome data were used for selection of potential biomarkers that can discriminate RA from osteoarthritis (OA) when measured at the protein level in in synovial fluid and serum.  Disease-specific profiles were evident in the joint, where both synovial tissue transcripts and related SF proteins revealed clear differences between RA and OA.  The joint inflammatory signature identified in synovial fluid is diluted and neutralised in serum. Thus, joint inflammatory signatures in serum exhibit weaker discriminatory power and have more limited potential in discriminating RA from OA and/or healthy donors.  Concerning that blood is a favourable material for diagnosis this study showed that cells like monocytes are far more potent compared to serum in discriminating RA from OA. RA patientsOA patients Synovial tissue biopsiesBlood monocytes  Potential biomarkers were measured at the protein level in synovial fluid (SF) and matched serum samples from RA (n=17) and OA (n=16) patients. In addition, sera from healthy donors (ND) were used as controls (n=14).  Top biomarkers selected from synovial tissue and monocyte transcriptomes were CXCL13 and S100P, respectively. (1) Genes selected from Synovial tissue transcriptome Fold change (log2) (2) Genes selected from Monocyte transcriptome Fold change (log2)  The combination of markers measured in SF showed an obvious inflammatory pattern in RA that is neutralized and diluted in Serum.  Correlation of the top candidate markers measured in SF and Serum from RA and OA patients with clinical data  Gene-expression profiles from synovial tissues biopsies were generated by Affymetrix HG-U133A arrays. Gene-expression profiles of blood and bone marrow monocytes were generated by Affymetrix HG-U133Plus arrays. The BioRetis database was used for array data analyses (www.bioretis.de). **p=0.0011 **p=0.0035 ***p<0.0001 CXCL13 Synovial fluid (SF) Serum ***p<0.0001 (1) Correlation of SF levels of potential markers with clinical data (2) Correlation of Serum levels of potential markers with clinical data 1 0.5 0 -0.5 Corr scale Spearman corr (r values) DAS 28TJCSJCESRCrPRFANACit-Ab SF CXCL130.560.650.470.590.65 0.340.11 0.55 SF CCL18 0.33 0.58 0.450.260.410.060.010.48 SF S100P 0.52 0.56 0.37 0.540.64 0.360.050.29 SF sCD14 0.47 0.62 0.400.41 0.70 0.250.120.27 SF sCD163 0.280.450.330.320.360.260.100.33 SF OPN 0.170.300.230.180.090.180.020.08 SF sCD44 0.290.540.350.270.380.22-0.070.36 Spearman corr (r values) DAS 28TJCSJCESRCrPRFANACit-Ab CXCL130.590.61 0.32 0.510.57 0.310.270.35 CCL180.540.58 0.350.41 0.55 0.110.240.42 S100P 0.480.470.250.410.44 0.72 0.460.38 sCD14 0.34 0.48 0.070.410.430.200.190.14 sCD163 0.190.12-0.010.41-0.080.200.260.23 OPN 0.190.220.040.200.280.300.160.08 sCD44 0.150.280.080.170.01-0.07 0.53 -0.11 ****p< 0.0001 S100P CXCL13 S100P Synovial fluid (SF) Serum Protein level Clinical dataRA 1302 OA 1116OA 426 TJC168 SJC113 DAS 283.55.53.7 ESR25824 CrP (mg/mL)0.256.481.12 RF350630 ACPAn.d.600 Clinical data of misclassified RA and OA patients Clinical dataRA 1754RA 2830RA 2913RA 894 TJC135710 SJC6121 DAS 286.63.95.65.2 ESR85166433 CrP (mg/mL)3.451.722.796.1 RF8393 0 ACPA1000880 Clinical data of RA patients that exhibited common pattern Biljana Smiljanovic Department of Rheumatology and Clinical Immunology, Charité University Hospital Charitéplatz 1, D-10117 Berlin Germany E-Mail: smiljanovic@drfz.de