Robotic Label-Free Precise Oocyte Enucleation for Improving Developmental Competence of Cloned Embryos

Modeling and Evaluation of Penetration Process Based on 3D Mechanical Simulation

In biological micromanipulation, cell penetration is a typical procedure that precedes cell injection or oocyte enucleation. During this procedure, cells usually undergo significant deformation, which leads to cell damage. In this paper, we focus on modeling and evaluating the cell penetration process to reduce cell deformation and stress, thereby reducing cell damage. Initially, a finite element model (FEM) is established to simulate the cell penetration process. The effectiveness of the model is then verified through visual detection and comparison of cell deformation with experimental data. Next, various mechanical responses are analyzed, considering the influence of parameters, such as the radius and shape of the injection micropipettes, material properties, and size of the cells. Finally, the relationship between the intracellular stress and the cell penetration depth of biological cells is obtained. The evaluation results will be applied to develop optimized operation plans, enhancing the efficiency and safety of the cell penetration process.

An FPGA-Implemented Antinoise Fuzzy Recurrent Neural Network for Motion Planning of Redundant Robot Manipulators

When a robot completes end-effector tasks, internal error noises always exist. To resist internal error noises of robots, a novel fuzzy recurrent neural network (FRNN) is proposed, designed, and implemented on field-programmable gated array (FPGA). The implementation is pipeline-based, which guarantees the order of overall operations. The data processing is based on across-clock domain, which is beneficial for computing units' acceleration. Compared with traditional gradient-based neural networks (NNs) and zeroing neural networks (ZNNs), the proposed FRNN has faster convergence rate and higher correctness. Practical experiments on a 3 degree-of-freedom (DOs) planar robot manipulator show that the proposed fuzzy RNN coprocessor needs 496 lookup table random access memories (LUTRAMs), 205.5 block random access memories (BRAMs), 41384 lookup tables (LUTs), and 16743 flip-flops (FFs) of the Xilinx XCZU9EG chip.

Robotic Intracellular Pressure Measurement Using Micropipette Electrode

Intracellular pressure, a key physical parameter of the intracellular environment, has been found to regulate multiple cell physiological activities and impact cell micromanipulation results. The intracellular pressure may reveal the mechanism of these cells' physiological activities or improve the micro-manipulation accuracy for cells. The involvement of specialized and expensive devices and the significant damage to cell viability that the current intracellular pressure measurement methods cause significantly limit their wide applications. This paper proposes a robotic intracellular pressure measurement method using a traditional micropipette electrode system setup. First, the measured resistance of the micropipette inside the culture medium is modeled to analyze its variation trend when the pressure inside the micropipette increases. Then, the concentration of KCl solution filled inside the micropipette electrode that is suitable for intracellular pressure measurement is determined according to the tested electrode resistance-pressure relationship; 1 mol/L KCl solution is our final choice. Further, the measurement resistance of the micropipette electrode inside the cell is modeled to measure the intracellular pressure through the difference in key pressure before and after the release of the intracellular pressure. Based on the above work, a robotic measurement procedure of the intracellular pressure is established based on a traditional micropipette electrode system. The experimental results on porcine oocytes demonstrate that the proposed method can operate on cells at an average speed of 20~40 cells/day with measurement efficiency comparable to the related work. The average repeated error of the relationship between the measured electrode resistance and the pressure inside the micropipette electrode is less than 5%, and no observable intracellular pressure leakage was found during the measurement process, both guaranteeing the measurement accuracy of intracellular pressure. The measured results of the porcine oocytes are in accordance with those reported in related work. Moreover, a 90% survival rate of operated oocytes was obtained after measurement, proving limited damage to cell viability. Our method does not rely on expensive instruments and is conducive to promotion in daily laboratories.

Strategies to Improve the Efficiency of Somatic Cell Nuclear Transfer

Mammalian oocytes can reprogram differentiated somatic cells into a totipotent state through somatic cell nuclear transfer (SCNT), which is known as cloning. Although many mammalian species have been successfully cloned, the majority of cloned embryos failed to develop to term, resulting in the overall cloning efficiency being still low. There are many factors contributing to the cloning success. Aberrant epigenetic reprogramming is a major cause for the developmental failure of cloned embryos and abnormalities in the cloned offspring. Numerous research groups attempted multiple strategies to technically improve each step of the SCNT procedure and rescue abnormal epigenetic reprogramming by modulating DNA methylation and histone modifications, overexpression or repression of embryonic-related genes, etc. Here, we review the recent approaches for technical SCNT improvement and ameliorating epigenetic modifications in donor cells, oocytes, and cloned embryos in order to enhance cloning efficiency.

Robotic Precisely Oocyte Blind Enucleation Method

Oocyte enucleation is a critical procedure for somatic cell nuclear transfer. Yet, the main steps of oocyte enucleation are still manually operated, which presents several drawbacks such as low precision, high repetition error, and long training time for operators. For improving the operation efficiency and success rate, a robotic precise oocyte blind enucleation method is presented in this paper. The proposed method involves the following key techniques: oocyte translation control, oocyte immobilization and penetration control, and enucleation volume control based on the adaptive slide mode. Compared with the manual blind enucleation method, the proposed robotic blind enucleation method reduced the operation time by 44.5% (manual method: 62 s vs. proposed method: 34.4 s), increased the accuracy of enucleation by 83.1% (manual method: 30.7 vs. proposed method: 5.2), increased the success rate from 80% to 93.3%, and increased the cleavage rate from 41.7% to 63.3%.