Determination of cell type‐specific proteome signatures of primary human leukocytes, endothelial cells, keratinocytes, hepatocytes, fibroblasts and melanocytes by comparative proteome profiling. Issue 10 (20th March 2014) - Slany, Astrid; Paulitschke, Verena; Haudek‐Prinz, Verena; Meshcheryakova, Anastasia; Gerner, Christopher
<x xml:space="preserve">Abstract</x> Cells gain their functional specialization by different protein synthesis. A lot of knowledge with respect to cell type‐specific proteins has been collected during the last thirty years. This knowledge was built mainly by using antibodies. Nowadays, modern MS, which supports comprehensive proteome analyses of biological samples, may render possible the search for cell type‐specific proteins as well. However, a therefore necessary systematic MS study comprising many different cell types has not been performed until now. Here we present a proteome analysis strategy supporting the automated and meaningful comparison of any biological samples. We have presently applied this strategy to six different primary human cell types, namely leukocytes, endothelial cells, keratinocytes, hepatocytes, fibroblasts, and melanocytes. Comparative analysis of the resulting proteome profiles allowed us to select proteins specifically identified in one of the six cell types and not in any of the five others. Based on these results, we designated cell type‐specific proteome signatures consisting each of six such characteristic proteins. These signatures independently reproduced well‐known marker proteins already established for FACS analyses in addition to novel candidate marker proteins. We applied these signatures for the interpretation of proteome profiles obtained from the analyses of hepatocellular carcinoma‐associated tissue homogenates and normal liver tissue homogenates. The identification of members of the above described signatures gave us an indication of the presence of characteristic cells in the diseased tissues and thus supported the interpretation of the proteomics data of these complex biological samples.