This paper proposes a practical micropipette-image 2-D pixel and 3-D spatial coordination) calibration method indispensable to somatic cell microinjection, leveraging advancements in modern motorized micromanipulators. The method determines the depth information of the micropipette in the microscope field by assessing the contact between the micropipette tip and the bottom of the culture dish. It uses recoverable deformation upon contact as a criterion for precise positioning, ensuring the tip is on the dish’s bottom surface and in the microscope’s focus plane. Additionally, the paper introduces an on-the-spot method for breaking a micropipette tip and a preprocessing technique for somatic cells. The proposed micropipette tip-breaking method, using a low-cost acrylic ring, overcomes previous drawbacks and proves quick and user-friendly. The preprocessing technique converts fully adherent somatic cells into semi-adherent cells, increasing cell thickness for easier puncturing. Combining these techniques, the study validated the approaches through over 900 injections on human dermal fibroblast (HDF) cells, achieving a success rate of 53.3% and a survival rate of 95.8%. Note to Practitioners–This study was initiated to address the challenge of establishing accurate mapping relationships between observed 2-D coordinates of a micropipette tip in microscope vision and 3-D positional data obtained from an external controller of an XYZ micromanipulator. However, its relevance extends beyond somatic cell microinjection to other micromanipulation applications like micro-assembly. Common calibration methods involve motorized objectives or stereo microscopic vision, whereas our paper proposes a practical approach utilizing recoverable deformation upon tip contact with the dish bottom for precise positioning. The paper mathematically characterizes relationships between the pixel coordinate system and spatial coordinate systems, demonstrating problem-solving by ensuring the tip aligns with the dish’s bottom surface and the microscope’s focus plane. Although experiments affirm feasibility, refinement and testing in other micromanipulation applications are pending. Future research aims to enhance system modeling and calibration accuracy.

Despite the rapid development in providing precise delivery of extraneous samples to the vast majority of cells, robotic microinjection is still hindered by cumbersome operations and low throughput in practice. This study presents a new automated microinjection system equipped with two micromanipulators and a deep learning algorithm for cell identification. The introduction of two coordinated micromanipulators based on the same cell handling platform results in a large increase in injection throughput. A deep convolutional neural network, Mask R-CNN, is used to detect and segment stain-free adherent cells, leading to a considerable increase in operational efficiency and subsequent throughput. In the three independent experiments, over 10,000 MC3T3 mouse fibroblast cells are injected to evaluate the injection speed, success rate, and survival rate. Experimental results confirm that our system can inject around 4,000 cells in 1 h with an approximately 60.3% success rate and an 82.0% survival rate. This research’s success will make robotic microinjection a competitive tool in many biomedical applications, such as plasmid DNA transfection. Note to Practitioners—The motivation of this study is to improve the throughput of cell microinjection, which has become a bottleneck problem hindering the clinical application of microinjection technology. Existing cell microinjection systems typically use only one micromanipulator and rely on manual identification of cells, resulting in a limited ability to process cells for one experimental cycle. This study proposes a novel method that identifies cells through deep learning automatically and uses two micromanipulators simultaneously to improve cell processing capabilities. The problem of manipulating two micromanipulators for nearby cells is formulated as a route optimization problem, which is also suitable for three or more micromanipulators. A deep learning algorithm is used to identify and select cells to increase the processing speed. Experiments are performed to demonstrate that the proposed method can greatly increase the throughput while maintaining satisfactory performance in microinjection.

In this paper, we present a precise robot-aided single-cell surgery system to perform single-cell biopsy for cells <25 μm in diameter. A microfluidic chip is designed to arrange upto 100 individual cells in an array. A micropipette mounted onto a 3-DOF micromanipulator and a computer mouse-operated high-precision XY stage is developed to perform high-precision and high-throughput single-cell biopsy. The system is evaluated experimentally by extracting two organelles from adherent cells patterned in a microfluidic chip. The fluorescent-labeled nucleus and mitochondria of human foreskin fibroblast cells are biopsied to demonstrate the capability of the proposed system. The survival rate of the semi-automated biopsy is 73% and 45% for mitochondrial and nucleus biopsies, respectively.

As the rapid development of precision medicine, in-vivo manipulation of micro/nano-scaled particles has attracted increasing attention in recent years. The collision is one of the main reasons that falls the in-vivo particle transportation fail. In this paper, we develop an in-vivo cell transportation control approach using a robotically controlled optical tweezers manipulation system, where a so-called wide-area image gradient algorithm is used to avoid collision of the transported cell with other obstacles. The controller exhibits advantages of the reduced online calculation for collision avoidance, fast response, high accuracy, as well as an ability to compensate the environmental disturbance caused by blood flow. Simulation and experimental results are presented to demonstrate the effectiveness of the proposed controller.

In vivo manipulation of biological cells has attracted considerable attention in recent years. This process is particularly useful for precision medicine, such as cancer target therapy. Robotics technology is becoming necessary to stably and effectively manipulate and control single target cells in a complex in vivo environment. This paper presents a robot-aided optical tweezers-based manipulation technology that serves a function in the transport of single biological cells in vivo. An enhanced disturbance compensation controller is developed to minimize the effect of fluids (e.g., blood flow) on the cell. The method has exhibited advantages of flexibility in adjusting cell tracking trajectory online and the capability to minimize steady-state error and eliminate overshoot. Simulations and experiments of tracking single target cells in living zebrafish embryos have demonstrated the effectiveness of the proposed approach in a dynamic in vivo environment.

As rapid development of precision medicine, in vivo manipulation of micro/nano-scaled particles have attracted increasing attention in recent years. To accommodate complex in-vivo environment, robot-aided automated manipulation technology is highly demanded in trapping and controlling micro/nano-particles stably and effectively. This paper presents an in-vivo cell manipulation system, where a disturbance compensation controller is utilized to minimize the effect of fluid (e.g., blood flow) on the cell. The controller has exhibited advantages in adjusting cell tracking trajectory online, minimizing the steady-state error, and eliminating overshoot. Simulation and experimental results verify the performance of the controller.

Few of the current injection technologies can be applied to those human cells whose diameters are ranged about 10-25 (im only. This paper reports our most recent effort in developing a robot-aided microinjection system to solve the challenging problem of automated injection on human cells. A unique microfluidic cell holding chip is designed and fabricated to trap the single cells in the predefined docking area. Imaging processing technique is used to recognize automatically the target cells to be injected. A microrobot system equipped with a micropipette is used to perform the injection tasks on these target cells. Injection experiments on human embryonic stem cells (hESCs) (ranged about 17-25μm) are performed to demonstrate the effectiveness of the proposed microinjection system.

Engineering induced cell fusion is becoming a promising tool in novel therapeutic studies for treating various diseases. The majority of current in vitro cell fusion methods are based on random cell pairing with loose contact, which also needs large amounts of cells. In this paper, we present a robotically controlled laser-induced cell fusion approach based on the microwell array, which exhibits advantages of high selectivity and controllability. Optical tweezers and optical scissors are employed to achieve cell pairing and fusion, respectively. The specific cells are characterized and preselected with an on-chip isolation method prior to pairing. The paired cells are then transported, deposited, fused, and released at a predefined location with high spatiotemporal resolution. Experiments of fusion on individual pairs of human embryonic stem cells are performed to evaluate the performance of the proposed cell fusion tool.

Isolation and deposition of specific live cells with the high spatio-temporal resolution from the heterogeneous mixtures are of critical importance to a wide range of biomedical applications. In this paper, we report a robot-assisted cell manipulation tool with optical tweezers based on a uniquely designed microwell array. The whole automatic manipulation includes the target cell recognition, isolation, transportation and deposition. The microwell array is designed based on microfluidics technology, which allows the passive hydrodynamic docking of cells. Image processing technique is used to recognize the target cells based on the cell size or fluorescence label. After recognition, the target cells can be levitated from the microwell, and then assembled by multiple optical traps in parallel. The optically trapped target cells are then transported and deposited to the desired location precisely. Experiments are performed to demonstrate the effectiveness of the proposed cell manipulation approach.

This paper presents a robot-assisted methodology that integrates optical tweezer and microfluidic chip technologies to realize automatic cell sorting from small sample population. The microfluidic chip used for cell sorter is designed and fabricated, and the flow environment within the microfluidic channel is investigated with simulation. Two image processing methods, depending on size and fluorescence label respectively, are used to recognize the target cells. With robotic manipulation of optical tweezers, the target cells can be moved to the desired area. Motions of optical tweezers are further analyzed for improvement of transportation efficiency. The relationship between the laser power and the cell maximum moving velocity is analyzed such that the robust cell sorting with as low power as possible can be achieved. Experiments on sorting yeast cells are performed to demonstrate the effectiveness of the proposed cell sorting approach.