Skin aging - Identification of proteins secreted by human dermal fibroblasts using a quantitative proteome approach BMFZ Results Daniel M. Waldera-Lupa - daniel.waldera-lupa@hhu.de - Molecular Proteomics Laboratory Biologisch-Medizinisches Forschungszentrum - Heinrich-Heine Universität Düsseldorf Building 23.12.02.64 - Universitätsstr. 1 - 40225 Düsseldorf Dermal fibroblasts of 15 female donors of three different age groups (20-26, 40-49, 60-67) were cultivated under standardized conditions. Confluent cells were cultivated for two days in serum-free medium and supernatants were collected. For LC-MS analysis the cells and the supernatants were prepared by standard cell lysis or concentrated and desalted, respectively. Subsequently, the proteins were digested with trypsin using in-gel digestion. For LC-MS/MS analysis a setup comprising high-resolution ESI-MS (Orbitrap Elite) and nano-HPLC (RSLCnano) were used [4]. For protein identification MASCOT search engine and Swiss-Prot database were applied. Quantification was carried out using Progenesis [4] and MaxQuant [5]. Statistical analysis was performed using ANOVA analysis and Pearson correlation (both p ≤ 0.05 and ratio ≥1.3). Network analysis of differentially regulated proteins was performed using ClueGO Cytoscape plugin [6]. Conclusions Introduction Daniel M. Waldera-Lupa 1 , Julia Tigges 2 , Faiza Khalfallah 3 , Gereon Poschmann 1 , Jean Krutmann 2 , Fritz Boege 3 , Kai Stühler 1 1 Molecular Proteomics Laboratory, Biologisch-Medizinisches Forschungszentrum, Heinrich-Heine Universität Düsseldorf, Düsseldorf, Germany 2 Leibniz-Institut für umweltmedizinische Forschung, Düsseldorf, Germany 3 Zentralinstitut für Klinische Chemie und Labordiagnostik, Universitätsklinikum Düsseldorf, Düsseldorf, Germany Methods References Aging is a multi-factorial process, in which endogenous changes and exogenous factors are discussed to play relevant role. It has been observed that exposing aged stem cells to a young environment leads to rejuvenation of these cells [1]. This suggests that the aging process of an organ is influenced by its stroma, with fibroblasts as the dominate cell type [2]. This indicates that the driving force for skin ageing is not the constantly self-renewing epidermis, but the much more static dermis with its interplay of fibroblasts and their matrix. Proteins are the key players in cell biology and involved in relevant aging processes like e.g. proteostasis, detoxification or protein degradation. Furthermore, secreted proteins have a particular impact on the surrounding cells in the tissue [3]. Therefore, an exhaustive analysis of aged fibroblasts’ secretome has been performed to identify key proteins in stromal aging.  1331 unique protein groups were identified by 15 LC-MS/MS analyses in the secretome of in situ aged fibroblasts.  Bioinformatics approach revealed 977 proteins with high likelihood to be released by human dermal fibroblasts (Fig. 1).  Cluster analysis revealed high biological variance and no global age-related alteration (Fig. 2).  Statistical analysis revealed 63 proteins with a significant age-associated alteration.  Network analysis revealed the extracellular matrix organization, hemostasis and cell-cell communication as the most dominant biological processes altered with ageing (Fig. 3). [1] Rando TA (2006) Stem cells, ageing and the quest for immortality. Nature 441:1080-1086. [2] Boukamp P (2005) Skin aging: a role for telomerase and telomere dynamics? Curr Mol Med. 5:171- 177. [3] Beltrami AP, Cesselli D, Beltrami CA (2011) At the stem of youth and health. Pharmacol Ther. 129 (1):3-20. [4] Sitek B, Waldera-Lupa DM, Poschmann G, Meyer HE, Stühler K (2012) Application of label-free proteomics for differential analysis of lung carcinoma cell line A549. Methods Mol Biol. 893:241-248. [5] Cox J and Mann M (2008) MaxQuant enables high peptide identification rates, individualized p.p.b.- range mass accuracies and proteome-wide protein quantification. Nat Biotechnol. 26, 1367-1372. [6] Bindea G, Mlecnik B, Hackl H, Charoentong P, Tosolini M, Kirilovsky A, Fridman WH, Pagès F, Trajanoski Z, Galon J (2009) ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics. 25(8):1091-1093. Contact Figure 2 Unsupervised hierarchical clustering of aged fibroblasts’ secretome by Euclidean similarity measures and centroid linkage. A total of 977 secreted proteins (SignalP, SecretomeP, extracellular and membrane) were used for generating the hierarchical cluster. Figure 1 Categorization of aged fibroblasts’ secretome by SignalP, SecretomeP, ExoCarta and UniProt database. Secretome proteins were grouped into five categories: proteins with signal peptide, classically secreted proteins, extracellular and membrane localized proteins, proteins identified in exosomes and contaminants. Figure 3 Detailed network analysis of significantly altered proteins using biological ontology terms of Reactome database. A total of 63 proteins were used for generating the network of biological processes. About 41 proteins were successfully mapped to 151 biological processes.  The label-free proteomics approach allowed the detection of novel aging-related changes in protein expression in dermal fibroblasts involved in process of stromal aging.  Using high-resolution ESI-MS in combination with HPLC it was possible to identify 977 proteins in the secretome of fibroblasts.  Applying computational prediction identified fibroblasts’ secretome was rectified of contaminant proteins.  Age-related alterations of in situ aged fibroblasts’ secretome were only found on single protein level.  Applying detailed network analysis it was possible to identify biological processes related to in situ ageing already described in the literature. 1 2 3