Ruimin Li

In this article, a narrow-necked micropipette (NNM) was designed, fabricated, and utilized for robotic fast cell positioning in cell transportation. First, key geometrical parameters of NNM were determined through the aspiration model and computational microfluidics model. Then, a heating-and-rotation method was developed to fabricate a narrow neck structure inside the micropipette based on a heating model. Further, the dynamic models of the cell before and after being held by the narrow neck were conducted. Based on that, a sliding mode motion controller and a deformation controller were designed for fast positioning of the cell at the narrow neck position and reducing the cell deformation, respectively. Robotic transportation results of 15 sheep oocytes demonstrate that our system can position the cell with a 100% success rate at doubled throughput in comparison to the traditional methods. The average positioning error and cell deformation degree are significantly reduced to 1.89 pixels and 2.59%, respectively. In addition, experiments on HeLa cells showed an average position time of 2.1 s with 100% success rate. Besides, limited harm to the cell survival rate is found through culturing experiments. With the above advantages, our robotic system is expected to improve cell transportation efficiency in the future.

Double patch clamp technique, using two micropipette electrodes to patch and measure electrophysiological signals of two neurons, is essential for investigating the functional connections between neurons in brain. However, the interactive mechanical disturbances from the dual-micropipette motions inside viscoelastic brain tissue cause dynamic drifts of neurons, making double patching low efficiency and challenging. In this paper, an interactive mechanical modeling of two micropipettes approaching two cells in elastic environment was established to estimate the dynamic drift of the cells. Based on that, a synchronous descent strategy, an appropriate relative position of two cells, and an online trajectory plan of two micropipettes were determined to improve double patch clamp efficiency. Finally, a robotic double patch clamp operation process was established for functional connectivity measurement between neurons in brain slice. The effectiveness of the proposed work is validated through both finite element modeling and experiments. The double patch clamp experiments on neurons in visual cortex demonstrate that our method achieves a 40% improvement in success rate and a 38% improvement in speed in comparison to the traditional manual method. With the above advantages, diverse functional connectivity activities between neurons were found using our system, paving a solid ground for further research.

The patch clamp technique has been widely utilized to measure extremely weak ion channel signals. In patch clamp technique, the measurement accuracy and duration rely on the tightness and stability of the seal between the aspirated cell membrane and the recording micropipette. The current patch clamp operations utilize a pressure-driven seal formation method that can only provide a 2D driving force on a small part of the aspirated cell membrane, which easily causes looseness or even failures of the seal, significantly limiting the measurement quality of ion channel signals. Addressing this, a robotic precise patch clamp measurement method was proposed in this paper. In this method, a 3D-driven seal formation method is developed to improve the measurement accuracy and duration of ion channel signals by providing a tight and stable gigaohmscale seal (gigaseal). First, the forces applied by aspiration pressure and voltage on the aspirated cell membrane inside the micropipette are analyzed, respectively. Then, a dynamic model of the gigaseal formation process driven by pressure and voltage is established to provide a 3D driving force on the target aspirated cell membrane. Further, a dual-input adaptive sliding mode controller is developed to form a tight and stable gigaseal to improve the measurement accuracy and duration in patch clamp operation. Finally, a robotic precise patch clamp measurement process was established based on the above work. Experimental results on CA1 pyramidal neurons of mouse brain slices demonstrate that, in comparison to the current 2D-driven method, the proposed patch clamp measurement method has a 30% higher gigaseal formation success rate, 52% higher gigaseal resistance, and a fourfold increase in the success rate of 1-hour duration recordings, demonstrating the formation of a tighter and more stable gigaseal using our method. With the above advantages, a 3 dB-higher signal-to-noise ratio (SNR) was achieved, and long-term glycine-induced dynamic increases in amplitude and frequency of spontaneous excitatory postsynaptic currents (sEPSCs) were detected using the proposed method. With aforementioned higher measurement success rate, higher SNR and longer recording time, our patch clamp recording is expected to be applied in brain science and nervous system disease research in the future.

Retinal vein cannulation (RVC) is a surgical procedure utilized in the treatment of retinal vein occlusion. During RVC, the micropuncture process encounters challenges due to unstable contact between the bent needle and the retinal vein, potentially resulting in micropuncture failure. To enhance the success rate, a robotic micropuncture procedure with preload strategy is proposed, which can increase the tissue stiffness and improve stability. First, a nonlinear viscoelastic (NV) model is developed to characterize the nonlinearity and relaxation behavior of the preload force. Subsequently, an NV model based adaptive integral terminal sliding mode controller (NAITSMC) is designed to ensure precise force tracking under tissue uncertainty and respiratory motion. The effectiveness of the robotic micropuncture is validated through in vitro experiments conducted on open-sky porcine eyes. The experimental results indicate that the NV model is more suitable for modeling the force applied to the retinal vein under various inner pressure cases. Under simulated respiratory motion, the NAITSMC demonstrates statistically reduction in error compared to the advanced sliding mode force controller. The preload strategy significantly improves the success rate, achieving a maximum rate of 90%. Furthermore, a preload force of 40 μN is recommended, demonstrating a higher success rate while maintaining acceptable safety considerations.

For the first time, an improved robotic micropipette aspiration (MA) method considering the effects of the intra-cellular pressure, friction between cell and micropipette, and the micropipette opening size was proposed for improving the measurement accuracy of the cell elasticity properties in this paper. First, an elastic energy model of the aspirated cell was established to analyze the effects of intracellular pressure on MA results. Then a force analysis of MA process was conducted to evaluate the influences of the friction force between the cell surface and micropipette opening as well as the inner diameter of the micropipette opening to MA results. Based on the above work, an improved robotic MA process was developed for reducing the cell elasticity measurement errors through automated release of intracellular pressure, micropipette opening polishing and inner diameter control of the micropipette. The effectiveness of the above work was validated by both finite elemental model (FEM) and MA experiments. The MA results on porcine oocytes demonstrate that our proposed MA method can rectify the 20.11%, 35.08% and 6.80% measure errors of cell Young’s modulus caused by the above three factors, respectively. Our research may provide a more objective evaluation of cell mechanical properties in the related research.

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. 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.

The patch clamp technique has become the gold standard for neuron electrophysiology research in brain science. Brain slices have been widely utilized as the targets of the patch clamp technique due to their higher optical transparency compared to a live brain and their intercellular connectivity in comparison to cultured single neurons. However, the narrow working space, small scope, and depth of the field of view make the positioning of the operation’s micropipette to the target neuron a time-consuming task reliant on a high level of experience, significantly slowing down operation of the patch clamp technique in brain slices. Further, the current poor controllability in gigaseal formation, which is the key to electrophysiology signal recording, significantly lowers the patch clamp success rate. In this paper, a stepwise navigation of the micropipette is conducted to accelerate the positioning process of the micropipette tip to the target neuron in the brain slice. Then, a fuzzy proportional–integral–derivative controller is designed to control the gigaseal formation process along a designed resistance curve. The experimental results demonstrate an almost doubled patch clamp technique speed, with a 25% improvement in the success rate compared to the conventional manual method. The above advantages may promote the application of our method in brain science research based on brain slice platforms.