Lab-on-a-chip electrical multiplexing techniques for cellular and molecular biomarker detection

Rapid, multiplexed detection of biomolecules using electrically distinct hydrogel beads

Rapid, low-cost, and multiplexed biomolecule detection is an important goal in the development of effective molecular diagnostics. Our recent work has demonstrated a microfluidic biochip device that can electrically quantitate a protein target with high sensitivity. This platform detects and quantifies a target analyte by counting and capturing micron-sized beads in response to an immunoassay on the bead surface. Existing microparticles limit the technique to the detection of a single protein target and lack the magnetic properties required for separation of the microparticles for direct measurements from whole blood. Here, we report new precisely engineered microparticles that achieve electrical multiplexing and adapt this platform for low-cost and label-free multiplexed electrical detection of biomolecules. Droplet microfluidic synthesis yielded highly-monodisperse populations of magnetic hydrogel beads (MHBs) with the necessary properties for multiplexing the electrical Coulter counting on chip. Each bead population was designed to contain a different amount of the hydrogel material, resulting in a unique electrical impedance signature during Coulter counting, thereby enabling unique identification of each bead. These monodisperse bead populations span a narrow range of sizes ensuring that all can be captured sensitively and selectively under simultaneously flow. Incorporating these newly synthesized beads, we demonstrate versatile and multiplexed biomolecule detection of proteins or DNA targets. This development of multiplexed beads for the electrical detection of biomolecules, provides a critical advancement towards multiplexing the Coulter counting approach and the development of a low cost point-of-care diagnostic sensor.

Impedance Spectroscopy as a Tool for Monitoring Performance in 3D Models of Epithelial Tissues

In contrast to traditional 2D cell cultures, both 3D models and organ-on-a-chip devices allow the study of the physiological responses of human cells. These models reconstruct human tissues in conditions closely resembling the body. Translation of these techniques into practice is hindered by associated labor costs, a need which may be remedied by automation. Impedance spectroscopy (IS) is a promising, automation-compatible label-free technology allowing to carry out a wide range of measurements both in real-time and as endpoints. IS has been applied to both the barrier cultures and the 3D constructs. Here we provide an overview of the impedance-based analysis in different setups and discuss its utility for organ-on-a-chip devices. Most attractive features of impedance-based assays are their compatibility with high-throughput format and supports for the measurements in real time with high temporal resolution, which allow tracing of the kinetics. As of now, IS-based techniques are not free of limitations, including imperfect understanding of the parameters that have their effects on the impedance, especially in 3D cell models, and relatively high cost of the consumables. Moreover, as the theory of IS stems from electromagnetic theory and is quite complex, work on popularization and explanation of the method for experimental biologists is required. It is expected that overcoming these limitations will lead to eventual establishing IS based systems as a standard for automated management of cell-based experiments in both academic and industry environments.

Processing code-multiplexed Coulter signals via deep convolutional neural networks

Beyond their conventional use of counting and sizing particles, Coulter sensors can be used to spatially track suspended particles, with multiple sensors distributed over a microfluidic chip. Code-multiplexing of Coulter sensors allows such integration to be implemented with simple hardware but requires advanced signal processing to extract multi-dimensional information from the output waveform. In this work, we couple deep learning-based signal analysis with microfluidic code-multiplexed Coulter sensor networks. Specifically, we train convolutional neural networks to analyze Coulter waveforms not only to recognize certain sensor waveform patterns but also to resolve interferences among them. Our technology predicts the size, speed, and location of each detected particle. We show that the algorithm yields a >90% pattern recognition accuracy for distinguishing non-correlated waveform patterns at a processing speed that can potentially enable real-time microfluidic assays. Furthermore, once trained, the algorithm can readily be applied for processing electrical data from other microfluidic devices integrated with the same Coulter sensor network.