Abstract
Simultaneous topography and recognition imaging (TREC) allows for the investigation of receptor distributions on natural biological surfaces under physiological conditions. Based on atomic force microscopy (AFM) in combination with a cantilever tip carrying a ligand molecule, it enables us to sense topography and recognition of receptor molecules simultaneously with nanometre accuracy. In this study we introduce optimized handling conditions and investigate the physical properties of the cantilever-tip-sample ensemble, which is essential for the interpretation of the experimental data gained from this technique. In contrast to conventional AFM methods, TREC is based on a more sophisticated feedback loop, which enables us to discriminate topographical contributions from recognition events in the AFM cantilever motion. The features of this feedback loop were investigated through a detailed analysis of the topography and recognition data obtained on a model protein system. Single avidin molecules immobilized on a mica substrate were imaged with an AFM tip functionalized with a biotinylated IgG. A simple procedure for adjusting the optimal amplitude for TREC imaging is described by exploiting the sharp localization of the TREC signal within a small range of oscillation amplitudes. This procedure can also be used for proving the specificity of the detected receptor-ligand interactions. For understanding and eliminating topographical crosstalk in the recognition images we developed a simple theoretical model, which nicely explains its origin and its dependence on the excitation frequency.
Original language | English |
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Article number | 215103 |
Pages (from-to) | 215103 |
Number of pages | 9 |
Journal | Nanotechnology |
Volume | 20 |
Issue number | 21 |
DOIs | |
Publication status | Published - 2009 |
Fields of science
- 210006 Nanotechnology
- 103 Physics, Astronomy
- 106006 Biophysics
- 106023 Molecular biology
- 301114 Cell biology
JKU Focus areas
- Nano-, Bio- and Polymer-Systems: From Structure to Function
- Engineering and Natural Sciences (in general)