A Deep Learning-Based Model Approach for Quantitative Analysis of Cell Chemotaxis in a Microfluidic Chip.

The rapid and accurate quantitative analysis of cell chemotaxis, which is essential in biology, medicine, and drug development, enables the evaluation of the directional migration capability of cells and the simulation of in vivo cell chemotaxis. However, traditional methods for studying cell chemotaxis often depend on complex experimental procedures, which are not only time-consuming and labor-intensive but also prone to human error. Recently, the rapid advancement of microfluidic technology and deep learning has provided a new way for evaluation of cell chemotaxis. In this study, a chemotaxis evaluation method based on microfluidics and deep learning is proposed. A microfluidic device was designed to simulate cell chemotaxis, allowing for the controlled assessment of cell chemotaxis by generating chemical gradients within microchannels and shear stress. Concurrently, deep learning technology was introduced to identify the migrated and non-migrated states of cell images, thereby enabling the automatic counting and analysis of chemotactic cells. Compared with traditional manual assays, this method not only reduced time and labor costs but also achieved higher accuracy and reproducibility. This innovative approach, which integrates microfluidics and deep learning, provides a novel perspective and tool for cell chemotaxis research. This method not only offers a fresh perspective on cell migration analysis but also has the potential to significantly advance the field of biomedical research, particularly in biosensor development related to drug discovery and disease diagnosis.