Integration of Optical Manipulation, Microfluidic and Deep Learning for Experimental Mechanobiology

In recent years, the field of mechanobiology has seen significant advancements through the application of microfluidics and optical trapping techniques. Microfluidics enables precise control of chamber and channel content on a micrometer scale, while optical tweezers allow for trapping microscopic objects within an ‘optical trap.’ Combining these disciplines under the name 'optofluidics' offers new opportunities for studying biological phenomena with reduced scale experiments. However, challenges remain in coordinating microfluidics with optical tweezers within confined spaces. To address these limitations, an integrated approach is proposed, using 3D optical manipulation setup, microfluidics and deep learning image recognition to estimate forces experienced by optically trapped objects. By analyzing the bead's displacement within the flow, forces are quantified using a deep learning algorithm. Experimental results demonstrate force variations based on the position within the chip, revealing the potential for improved understanding of biological mechanisms. This study facilitates the establishment of a dialogue between microfluidics and optical trapping manipulations, paving the way for future explorations in mechanobiology.