A multistage-dialysis microdevice for extraction of cryoprotectants

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.

Rapid and continuous on-chip loading of trehalose into erythrocytes

Freeze-drying is a promising approach for the long-term storage of erythrocytes at room temperature. Studies have shown that trehalose loaded into erythrocytes plays an important role in protecting erythrocytes against freeze-drying damage. Due to the impermeability of the erythrocyte membrane to trehalose, many methods have been developed to load trehalose into erythrocytes. However, these methods usually require multistep manual manipulation and long processing time; the adopted protocols are also diverse and not standardized. Thus, we develop an osmotically-based trehalose-loading microdevice (TLM) to rapidly, continuously, and automatically produce erythrocytes with loaded trehalose. In the TLM, trehalose is loaded through the erythrocyte membrane pores induced by hypotonic shock; then, the trehalose-loaded erythrocytes are rinsed to remove hemoglobin molecules and cell fragments, and the extracellular solution is restored to the isotonic state by integrating a rinsing-recovering design. First, the mixing function and the rinsing-recovering function were confirmed using a fluorescent solution. Then, the performance of the TLM was evaluated under various operating conditions with respect to the loading efficiency of trehalose, the hemolysis rate of erythrocytes (ϕ), the recovery rate of hemoglobin in erythrocytes (φ), and the separation efficiency of the TLM. Finally, the preliminary study of the freeze-drying of erythrocytes with loaded trehalose was accomplished using the TLM. The results showed that under the designated operating conditions, the loading efficiency for human erythrocytes reached ~21 mM in ~2 min with a ϕ value of ~17% and a φ value of ~74%. This study provides insights into the design of the on-chip loading of trehalose into erythrocytes and promotes the automation of life science studies on biochips.