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For precision medicine, it is necessary to quantify the expression levels of marker proteins as well as their micro-regional densities. Many techniques are available for evaluating protein expression levels on tissue sections of patients in order to assess the malignancy of cancer and predict drug efficacy. These technologies, however, have not achieved sufficiently accurate clinical outcome predictions. Here we developed a new method for evaluating the microregional density of antigens. In this method, the surface of an AFM cantilever was modified with approximately 149-nm diameter nanoparticles. The nanoparticles were densely coated with streptavidin (SA) via polyethylene glycol chains. When the adhesion force between SA on nanoparticles and a biotin moiety immobilized on a self-assembled monolayer was measured by AFM, the adhesion force histogram corresponding to the one-to-one bonds between biotin and SA was obtained. Paraffin-embedded sections of three cancer cell lines, SKOV3, AU565, and MCF7, expressing different levels of human epidermal growth factor receptor type 2 (HER2) and type 3 (HER3) were then prepared. The adhesion force between the SA and biotinylated antibodies bound to target proteins (HER2 and HER3) on cancer cells was measured by AFM. Adhesion force histograms of each sample showed unique distributions, reflecting differences in the microregional density of HER2 and HER3. These results indicated that higher adhesion forces represented multi-point bindings of SAs on nanoparticles to biotinylated antibodies; thus this method could detect differences in microregional (<30 nm) antigen densities. • Phospho-integrated dots (PIDs) have high target recognition ability and form multi-point bindings at the microregion level. • Multi-point bindings at the AFM cantilever tip are reflected in the magnitude of the adhesion force. • PIDs pre-modified with high-density streptavidin (SA) were bound to the AFM cantilever tip. • The microregional density of HER2 and HER3 on cell samples were successfully evaluated based on adhesion force histograms.
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Kitamura et al. (2025) studied this question.
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