No abstract

The identification of specific protein markers for breast cancer would provide the basis for early diagnosis. Particularly, membrane and membrane-associated proteins are rich in targets for antibodies that may constitute suitable biomarkers of carcinogenesis. However, membrane proteins separation using 2-DE remains difficult. In this work, the breast cancer cell line MCF7 was used as source of proteins for the screening of potential cell membrane-associated antigens recognized by autoantibodies in patients with breast cancer and healthy volunteers. The protein extract obtained using trifluoroethanol (TFE) as cosolvent was compared to a total cell lysate protein extract prepared by a current technique. After 2-DE separation of the two extracts, their protein patterns clearly differed. About 63% of the proteins identified in the TFE-extract were predicted to possess at least one transmembrane domain. 2-D blots probed with sera from cancer patients or from healthy volunteers showed that, as expected, additional antigens were provided in the TFE-extract. Thus, the method described here appeared well suited for proteomic investigation of potential biomarkers undetected by current techniques.

The use of robots has major effects on maximizing the proteomic workflow required in an increasing number of high-throughput projects and on increasing the quality of the data. In peptide mass finger printing (PMF), automation of steps downstream of two-dimensional gel electrophoresis is essential. To achieve this goal, the workflow must be fluid. We have developed tools using macros written in Microsoft Excel and Word to complete the automation of our platform. Additionally, because sample preparation is crucial for identification of proteins by matrix-assisted laser desorption/ionization (MALDI) mass spectrometry, we optimized a sandwich method usable by any robot for spotting digests on a MALDI target. This procedure enables further efficient automated washing steps directly on the MALDI target. The success rate of PMF identification was evaluated for the automated sandwich method, and for the dried-droplet method implemented on the robot as recommended by the manufacturer. Of the two methods, the sandwich method achieved the highest identification success rate and sequence coverage of proteins.