Abstract
Detection of analytes using streaming current has previously been explored using both experimental approaches and theoretical analyses of such data. However, further developments are needed for establishing a viable microchip that can be exploited to deliver a sensitive, robust, and scalable biosensor device. In this study, we demonstrated the fabrication of such a device on silicon wafer using a scalable silicon microfabrication technology followed by characterization and optimization of this sensor for detection of small extracellular vesicles (sEVs) with sizes in the range of 30 to 200 nm, as determined by nanoparticle tracking analyses. We showed that the sensitivity of the devices, assessed by a common protein-ligand pair and sEVs, significantly outperforms previous approaches using the same principle. Two versions of the microchips, denoted as enclosed and removable-top microchips, were developed and compared, aiming to discern the importance of high-pressure measurement versus easier and better surface preparation capacity. A custom-built chip manifold allowing easy interfacing with standard microfluidic connections was also constructed. By investigating different electrical, fluidic, morphological, and fluorescence measurements, we show that while the enclosed microchip with its robust glass-silicon bonding can withstand higher pressure and thus generate higher streaming current, the removable-top configuration offers several practical benefits, including easy surface preparation, uniform probe conjugation, and improvement in the limit of detection (LoD). We further compared two common surface functionalization strategies and showed that the developed microchip can achieve both high sensitivity for membrane protein profiling and low LoD for detection of sEV detection. At the optimum working condition, we demonstrated that the microchip could detect sEVs reaching an LoD of 104 sEVs/mL (when captured by membrane-sensing peptide (MSP) probes), which is among the lowest in the so far reported microchip-based methods.