Bioinspired detection sensor based on functional nanostructures of S-proteins to target the folate receptors in breast cancer cells

Development of biosensors requires a well-organized template to enhance the sensing specificity. Biological molecules that have the ability to self-assemble into ordered structures can be exploited to construct nanostructures and nanodevices. In this regard, bacterial surface layer proteins (SbpA) have been used as an underlying matrix to bind molecules on the nanometer scale. The present study provides evidence that a gold substrate functionalized with folate-modified SbpA can be used as a sensor for breast cancer cells with superior efficacy over traditional sensors. The developed sensor uses folate to recognize folate receptors, which are highly expressed on the cell membranes of some cancer cells, such as breast cancer cells. To examine the specificity of the developed sensor, folate was used as a binding partner for folate receptors on breast cancer cells (MCF-7), and liver cancer cells (HepG2) were used as the control because they lack folate receptors. The fabricated biosensor comprised a gold surface and a recrystallized S-layer protein lattice functionalized with folate and addressed acoustically and electro-chemically. Acoustic studies performed using quartz crystal microbalance with dissipation monitoring (QCM-D) showed the efficiency of the developed biosensor in distinguishing between MCF-7 and HepG2 cells. Despite the ability of QCM-D to recognize cells in situ and in real-time, this biosensor had a limited detection range (1 x 10(5) cells/mL). To confirm the functionality of the developed platform, electrochemical measurements were performed and the obtained results were in agreement with the acoustic sensor and proving the efficiency of the designed sensing layer. The S-layer lattice-folate modified biosensor provides a promising strategy for designing efficient sensing platforms that can be used to diagnose early stage cancers with highly expressed folate receptors. (C) 2018 Elsevier B.V. All rights reserved.