As there are significant variations of cell elasticity among individual cells, measuring the elasticity of batch cells is required for obtaining statistical results of cell elasticity. At present, the micropipette aspiration (MA) technique is the most widely used cell elasticity measurement method. Due to a lack of effective cell storage and delivery methods, the existing manual and robotic MA methods are only capable of measuring a single cell at a time, making the MA of batch cells low efficiency. To address this problem, we developed a robotic MA system capable of storing multiple cells with a feeder micropipette (FM), picking up cells one-by-one to measure their elasticity with a measurement micropipette (MM). This system involved the following key techniques: Maximum permissible tilt angle of MM and FM determination, automated cell adhesion detection and cell adhesion break, and automated cell aspiration. The experimental results demonstrated that our system was able to continuously measure more than 20 cells with a manipulation speed quadrupled in comparison to existing methods. With the batch cell measurement ability, cell elasticity of pig ovum cultured in different environmental conditions was measured to find optimized culturing protocols for oocyte maturation.

Cell rotation has great significance for cell manipulation, which is applied to intracytoplasmic sperm injection, preimplantation genetic screening and diagnosis, somatic cell nuclear transfer, etc. In this letter, an automatic cell rotation method is proposed based on real-time detection and tracking of the polar body. Image segmentation and visual tracking are combined to detect the polar body accurately and robustly during cell rotation. Both out-of-plane and in-plane rotation are realized by utilizing a pair of standard glass micropipettes. In experiments, two kinds of oocytes, which are quite different in appearance, are used to validate the system. For mouse oocytes, the robotic system achieved a 98% success rate of out-of-plane rotation and a 93% success rate of in-plane rotation. For porcine oocytes, the robotic system achieved an 80% success rate of out-of-plane rotation and a 93% success rate of in-plane rotation. The average time of cell rotation is approximately 9.1s for mouse oocytes and 12.8s for porcine oocytes. The experiment results demonstrate that the proposed method is effective, stable and high speed for automatic cell rotation in 3D.

Objective: The invisibility of domestic oocyte nucleus in bright field currently forces operators to blindly aspirate nucleus out in oocyte enucleation, usually causing large cytoplasm losses and poor developmental competences of cloned embryos. Although fluorescent labeling of nucleus allows for nucleus localization, the involved photobleaching problems and barriers to the execution of enucleation process limit its online-application in oocyte enucleation. This paper reports a novel label-free oocyte enucleation method for precise removal of the nucleus with less cytoplasm loss. Methods: The relative positions between the injection pipette and nucleus for complete removal of nucleus with less cytoplasm loss were determined through a finite element modeling of nucleus aspiration. To position injection pipette to the above positions relative to nucleus, the appropriate oocyte orientation and trajectory of injection pipette inside oocyte were derived according to the offline-calibrated 3-D distribution of nucleus and the simulated dynamic drift of nucleus that occurs as injection pipette is maneuvered inside oocyte. Finally, a robotic label-free precise enucleation procedure was established. Results: The experimental results on more than 1000 porcine oocytes proved that this system is capable of reducing cytoplasm loss by 60% at the same level of enucleation success rate and almost doubling the cleavage rate of clone embryos in comparison to blind aspiration method. Conclusions: The results prove that our method significantly improves the developmental competence of cloned embryos in comparison to manual enucleation method. Significance: Our method is expected to improve the extremely low success rate of animal cloning in the future.

The formation of the gigaseal, a giga Omega-scale seal between the micropipette electrode and cell surface, is the keystone to measure the extremely weak electric signals of cell ion channels in patch clamp technique. To determine a point on the cell surface facilitating gigaseal formation, 3-D cell morphology information is required. However, the current 3-D cell morphology measurement methods relying on special devices or easily causing clogging issues of the micropipette electrode due to contact on cells are usually not applicable in the traditional patch clamp system. Addressing this, a noninvasive 3-D cell morphology measurement method was developed in this article for robotic patch clamp with a higher success rate of gigaseal formation. First, the measured bath impedance of the micropipette electrode was modeled and then utilized to measure the cell surface height noninvasively. Using the measured cell surface heights at key positions, the 3-D cell morphology was fitted to determine a contact position on the cell surface to facilitate gigaseal formation. Finally, a robotic whole-patch clamp process was conducted at the determined contact position. Experimental results demonstrated that 100% and 90% success rates of noninvasive 3-D cell morphology measurements were achieved on the cultured human embryonic kindey (HEK)-293 cells and pyramidal neurons in mouse brain slices, respectively, with an average measurement error of cell surface height less than $0.15~\mu \text{m}$ . At the determined contact position, significant improvements in success rates of gigaseal formation and whole-cell patch clamp operation were achieved in comparison to the results at the positions recommended in related work. Our research may spike inspiration to improve the success rates of gigaseal formation and patch clamp operation based on 3-D cell morphologies.

Measuring the mechanical force applied to a cell during penetration has long been studied. However, existing sensor methods can disturb or even prevent a planned cell surgery because of their physical units. We propose a contactless mechanical force estimation method based on an improved point-load model and cell contour detection. We extended the point-load model to large cell deformations and obtained a relationship between the mechanical force and the morphology of the deformed cell. We designed two cell contour detection methods for visually detecting the deformation of different cell morphologies to obtain the geometric parameters required by the model. Finally, we carried out penetration experiments on porcine oocytes and zebrafish embryos. The contour detection methods showed a consistency of up to 99.29% compared with manual marking, and the force estimation accuracy was 1.05 μN. We compared the estimation results of our method with measurements taken in real experiments and confirmed that our method accurately estimated the mechanical force applied to the cell without contact. Thus, it may be applied to estimating the applied force during cell surgery in real time.