Cell surface capturing technologies for the surfaceome discovery of hepatocytes

Abstract

Proteins expressed at the cell surface define how cells can functionally interact with their microenvironment in time and space. The cell surface subproteome, or surfaceome, represents a cellular information gateway not only enabling the processing of environmental molecular cues but also limiting cellular interaction capacities. Therefore, the array of antibody-detectable cell surface proteins is widely used to phenotype and categorize cells. Quantitative differences in surfaceome markers can not only indicate different developmental cellular stages but also serve as markers of disease. In fact, cell surface proteins are promising biomarker candidates, since they are often, apart from their plasma membrane expression, secreted, shed, or released otherwise from the tissue into the bloodstream. From minute amounts of blood these informative proteins can be detected and quantified by ELISA or highly sensitive state-of-the art targeted mass spectrometric techniques. However, the identification of the complete surfaceome and its constituents is hampered by a lack of suitable technologies to detect these proteins at the cell surface location. Antibodies for the detection of cell surface proteins are only available for a subset of the potentially expressed surfaceome members. The mass spectrometry-based cell surface capturing (CSC) technology and recently developed variants overcome these limitations by selectively enriching and identifying cell surface proteins that are either N-glycosylated (Glyco-CSC, Cys-Glyco-CSC), or have an extracellularly exposed and conformationally available lysine (Lys-CSC). Here, we outline the CSC technology and its variants in a detailed step-by-step protocol for soluble and adherent cells. Representative results from the application of the CSC technologies to the hepatocyte cell line Hepa1-6 illustrate the complementary nature of the CSC technologies, which enables a systems biology view of the surfaceome. © 2012 Springer Science+Business Media New York.