This video shows experiments with subsequent analysis of protein-protein interactions by the use of micro-patterned surfaces. The approach offers the possibility to detect protein interactions in living cells and combines high throughput capabilities with the possibility to extract quantitative information.
The accompanying video demonstrates a method for detecting protein-protein interactions in the plasma-membrane of living cells (Schwarzenbacher et al., 2008; Brameshuber et al., 2009; Weghuber et al., in press). In principle, any TIRF-based microscopy platform can be used as readout system. Only when high sensitivity is desired (e.g. for the detection of single molecules), advanced microscopes will be required. To achieve best results the following critical points during the preparation process require special attention:
The micro-patterning technique offers several possibilities for the analysis of protein-protein interactions. Firstly, along with the quantification of local, spatially resolved protein-protein interactions also the detection of weak or indirect interactions is possible without the disadvantage of giving a high number of false positives or negatives. Secondly it enables the researcher to analyze protein-protein interactions in the plasma-membrane of live cells, which is difficult to achieve by biochemical approaches like the 2-hybrid screen. Thirdly the approach allows for the detection of bait-prey interactions that are modulated by environmental changes like the temperature, the presence of different proteins or other molecules or post-translational modifications. Thus the assay allows for screening modulators of a given interaction pair in the context of live cells. Furthermore, by reducing the surface density of the capture ligand or the use of monovalent ligands, the analysis of the resting state becomes possible. Finally, if adequate scanning platforms are used, the number of analyzed cells is high enough to match high throughput demands of pharmaceutical companies for drug screening (Ramm, 2005).
The authors have nothing to disclose.
We would like to thank Quentina Beatty, University of Linz, Austria, for her kind help, Katharina Strub, University of Geneva, Switzerland, for the hCD71-GFP construct, and Daniel Legler, University of Konstanz, Switzerland, for the GPI-GFP-DAF construct. This work was supported by the Austrian Science Fund (FWF; project Y250-B03) and the GEN-AU project of the Austrian Federal Ministry for Science and Research.