Robotic Fluorescent Lighting Method Based on Dynamic Fluorescence Imaging Modeling for In Vivo Cell Manipulation

In vivo cell manipulation is crucial for understanding organ functions and dysfunctions at the cellular level. Due to its low optical transparency, the in vivo environment needs to be lighted up first to visualize the targets for manipulation. At present, operators usually carefully blow a micropipette filled with fluorescent dye solution to create a fluorescence field around the micropipette opening, lighting up the in vivo environment. This manual dynamic control of the fluorescence field is usually a low-accuracy, labor-intensive, and high-skill requirement task, becoming even more challenging when the micropipette moves to locate or approach target cells. A large number of in vivo lighting-control tests may improve manual control efficiency of the fluorescent field, but they usually cost many precise animal samples, making them costly and often unaffordable. To enhance the dynamic control efficiency of the fluorescent field while reducing experimental costs, a robotic fluorescent lighting control method is proposed based on an in vivo dynamic fluorescent imaging simulator. First, a dynamic fluorescence imaging model composing an injection model, a diffusion model, and a luminescence model of fluorescent molecules is established to simulate the fluorescence field in vivo. The fluorescent intensity distribution obtained through the model is highly in accordance with experimental results. Based on this model, an adaptive sliding mode controller is employed to achieve the desired fluorescent intensity in the brain tissue. Both the simulation results and the experimental results demonstrate significant advantages of the proposed controller in terms of control accuracy and stability compared to the traditional PID controller and manual operation. Note to Practitioners—Forming a stable fluorescent light field with a specified fluorescent intensity is of vital importance operation for in vivo micromanipulations. At present, manual adjustment of the fluorescent field by blowing the micropipette with mouth is time-consuming and energy-draining. In this paper, we first propose a dynamic fluorescence imaging simulator to address the economic and time costs of live experiments. We then design an adaptive sliding mode controller to dynamically control the fluorescent intensity at a specified position in the fluorescent field. Simulations based on the established model, along with experiments on brain tissues, demonstrate the controller’s effectiveness. Our system is expected to reduce human involvement and enable high-precision lighting for in vivo cell manipulation in the future.