A new microfluidic platform that integrates droplet and digital microfluidics to automate a variety of fluidic operations. The platform was applied to culturing and to selecting yeast mutant cells in ionic liquid.

Gene-editing techniques such as RNA-guided endonuclease systems are becoming increasingly popular for phenotypic screening. Such screens are normally conducted in arrayed or pooled formats. There has been considerable interest in recent years to find new technological methods for conducting these gene-editing assays. We report here the first digital microfluidic method that can automate arrayed gene-editing in mammalian cells. Specifically, this method was useful in culturing lung cancer cells for up to six days, as well as implementing automated gene transfection and knockout procedures. In addition, a standardized imaging pipeline to analyse fluorescently labelled cells was also designed and implemented during these procedures. A gene editing assay for interrogating the MAPK/ERK pathway was performed to show the utility of our platform and to determine the effects of knocking out the RAF1 gene in lung cancer cells. In addition to gene knockout, we also treated the cells with an inhibitor, Sorafenib Tosylate, to determine the effects of enzymatic inhibition. The combination of enzymatic inhibition and guide targeting on device resulted in lower drug concentrations for achieving half-inhibitory effects (IC50) compared to cells treated only with the inhibitor, confirming that lung cancer cells are being successfully edited on the device. We propose that this system will be useful for other types of gene-editing assays and applications related to personalized medicine.

The expression of a recombinant gene in a host organism through induction can be an extensively manual and labor-intensive procedure. Several methods have been developed to simplify the protocol, but none has fully replaced the traditional IPTG-based induction. To simplify this process, we describe the development of an autoinduction platform based on digital microfluidics. This system consists of a 600 nm LED and a light sensor to enable the real-time monitoring of  the optical density (OD) samples coordinated with the semicontinuous mixing of a bacterial culture. A hand-held device was designed as a microbioreactor to culture cells and to measure the OD of the bacterial culture. In addition, it serves as a platform for the analysis of regulated protein expression in E. coli without the requirement of standardized well-plates or pipetting-based platforms. Here, we report for the first time, a system that offers great convenience without the user to physically monitor the culture or to manually add inducer at specific times. We characterized our system by looking at several parameters (electrode designs, gap height, and growth rates) required for an autoinducible system. As a first step, we carried out an automated induction optimization assay using a RFP reporter gene to identify conditions suitable for our system. Next, we used our system to identify active thermophilic β-glucosidase enzymes that may be suitable candidates for biomass hydrolysis. Overall, we believe that this platform may be useful for synthetic biology applications that require regulating and analyzing expression of heterologous genes for strain optimization.

Digital microfluidics (DMF) is a technology that provides a means of manipulating nL-μL volumes of liquids on an array of electrodes. By applying an electric potential to an electrode, these discrete droplets can be controlled in parallel which can be transported, mixed, reacted, and analyzed. Typically, an automation system is interfaced with a DMF device that uses a standard set of basic instructions written by the user to execute droplet operations. Here, we present the first feedback method for DMF that relies on imaging techniques that will allow online detection of droplets without the need to reactivate all destination electrodes. Our system consists of integrating open-source electronics with a CMOS camera and a zoom lens for acquisition of the images that will be used to detect droplets on the device. We also created an algorithm that uses a Hough transform to detect a variety of droplet sizes and to detect singular droplet dispensing and movement failures on the device. As a first test, we applied this feedback system to test droplet movement for a variety of liquids used in cell-based assays and to optimize different feedback actuation schemes to improve droplet movement fidelity. We also applied our system to a colorimetric enzymatic assay to show that our system is capable of biological analysis. Overall, we believe that using our approach of integrating imaging and feedback for DMF can provide a platform for automating biological assays with analysis.