Solid-supported phospholipid bilayers are versatile model structures for mimicking the biological cell membrane, and are increasingly utilized as functional interface components of biosensors and other bio-micro- and nanofluidic devices. In the context of biosensor development, membrane-embedded peptides have gained importance as bio-recognition elements, targeting diverse analytes including proteins, nucleic acids, bacteria, metal ions, enzymes and antibodies. For example, the neuropeptide oxytocin has a key role during labor and lactation as well as in the development of social behavior. Divalent cations such as Zn2+ and Cu2+ vitally affect the activity of oxytocin upon binding. Deviations in the quantity and whereby binding of such ions to oxytocin are associated with diseases; e.g., multiple sclerosis, Alzheimer, and autism spectrum disorder (ASD). We developed an immunofluorescence assay to verify and quantify lipid bilayer membrane-integration of oxytocin-cholesterol conjugate, which was designed and synthesized as membrane-associated recognition element for a surface acoustic resonance (SAR) sensor. In our study, a microfluidic open-volume superfusion device, the Biopen, was used to deposit small unilamellar vesicles, prepared from 1-palmitoyl-2-oleoyl phosphatidylcholine (POPC) and oxytocin-conjugated cholesterol, onto a glass surface, where they transformed into extended patches of planar surface-supported bilayer. Thereafter, oxytocin endogenous carrier protein neurophysin-1 (primary antibody) and a fluorescently tagged secondary antibody, were sequentially delivered to the membrane. An antibody binding dependence on oxytocin-concentration was determined by means of fluorescence microscopy, and an optimal concentration for sensor applications was established. The fluorescence assay can be directly transferred to the SAR sensor, where a supported bilayer is established as sensing layer in order to quantify interactions between oxytocin and molecules of interest in a quantitative manner with high sensitivity, fundamentally supporting the development of new diagnostic and therapeutic options for the early detection of neurological and neurodegenerative conditions.
The detection of ionic variation patterns could be a significant marker for the diagnosis of neurological and other diseases. This paper introduces a novel idea for training chemical sensors to recognise patterns of ionic variations. By using an external voltage signal, a sensor can be trained to output distinct time-series signals depending on the state of the ionic solution. Those sequences can be analysed by a relatively simple readout layer for diagnostic purposes. The idea is demonstrated on a chemical sensor that is sensitive to zinc ions with a simple goal of classifying zinc ionic variations as either stable or varying. The study features both theoretical and experimental results. By extensive numerical simulations, it has been shown that the proposed method works successfully in silico. Distinct time-series signals are found which occur with a high probability under only one class of ionic variations. The related experimental results point in the right direction.
Oxytocin is a peptide hormone with high affinity to both Zn2+ and Cu2+ ions compared to other metal ions. This affinity makes oxytocin an attractive recognition layer for monitoring the levels of these essential ions in biofluids. Native oxytocin cannot differentiate between Cu2+ and Zn2+ ions and hence it is not useful for sensing Zn2+ in the presence of Cu2+. We elucidated the effect of the terminal amine group of oxytocin on the affinity toward Cu2+ using theoretical calculations. We designed a new Zn2+ selective oxytocin-based biosensor that utilizes the terminal amine for surface anchoring, also preventing the response to Cu2+. The biosensor shows exceptional selectivity and very high sensitivity to Zn2+ in impedimetric biosensing. This study shows for the first time an oxytocin derived sensor that can be used directly for sensing Zn2+ in the presence of Cu2+.