μSonic-hand: Biomedical micromanipulation driven by acoustic gas-liquid-solid interactions

Micromanipulation is crucial for operating and analyzing microobjects in advanced biomedical applications. However, safe, low-cost, multifunctional micromanipulation for operating bio-objects across scales and modalities remains inaccessible. Here, we propose a versatile micromanipulation method driven by acoustic gas-liquid-solid interactions, named μSonic-hand. The bubble contained at the end of a micropipette and the surrounding liquid form a gas-liquid multiphase system susceptible to acoustic waves. Driven by a piezoelectric transducer, the oscillating gas-liquid interface induces acoustic microstreaming, markedly increasing the mass transfer efficiency. It enables multiple liquid micromanipulations, including mixing, dispersion, enhancing cell membrane permeability, and harvesting selected cells. Furthermore, a controllable three-dimensional axisymmetric vortex in an open environment overcomes the constraints of microfluidic chip, enabling stable trapping, rapid transportation, and multidirectional rotation of HeLa cells, embryos, and other bio-objects ranging from micrometers to millimeters. A variety of applications demonstrate that the μSonic-hand, with its wide-range capabilities, inherent biocompatibility, and extremely low cost could remarkably advance biomedical science.

Design and developing a robot-assisted cell batch microinjection system for zebrafish embryo

The microinjection of Zebrafish embryos is significant to life science and biomedical research. In this article, a novel automated system is developed for cell microinjection. A sophisticated microfluidic chip is designed to transport, hold, and inject cells continuously. For the first time, a microinjector with microforce perception is proposed and integrated within the enclosed microfluidic chip to judge whether cells have been successfully punctured. The deep learning model is employed to detect the yolk center of zebrafish embryos and locate the position of the injection needle within the yolk, which enables enhancing the precision of cell injection. A prototype is fabricated to achieve automatic batch microinjection. Experimental results demonstrated that the injection efficiency is about 20 seconds per cell. Cell puncture success rate and cell survival rate are 100% and 84%, respectively. Compared to manual operation, this proposed system improves cell operation efficiency and cell survival rate. The proposed microinjection system has the potential to greatly reduce the workload of the experimenters and shorten the relevant study period.

Intelligent sensing for the autonomous manipulation of microrobots toward minimally invasive cell surgery

The physiology and pathogenesis of biological cells have drawn enormous research interest. Benefiting from the rapid development of microfabrication and microelectronics, miniaturized robots with a tool size below micrometers have widely been studied for manipulating biological cells in vitro and in vivo. Traditionally, the complex physiological environment and biological fragility require human labor interference to fulfill these tasks, resulting in high risks of irreversible structural or functional damage and even clinical risk. Intelligent sensing devices and approaches have been recently integrated within robotic systems for environment visualization and interaction force control. As a consequence, microrobots can be autonomously manipulated with visual and interaction force feedback, greatly improving accuracy, efficiency, and damage regulation for minimally invasive cell surgery. This review first explores advanced tactile sensing in the aspects of sensing principles, design methodologies, and underlying physics. It also comprehensively discusses recent progress on visual sensing, where the imaging instruments and processing methods are summarized and analyzed. It then introduces autonomous micromanipulation practices utilizing visual and tactile sensing feedback and their corresponding applications in minimally invasive surgery. Finally, this work highlights and discusses the remaining challenges of current robotic micromanipulation and their future directions in clinical trials, providing valuable references about this field.

Three-dimensional rotation of deformable cells at a bipolar electrode array using a rotating electric field

Three-dimensional rotation of cells is imperative in a variety of applications such as biology, medicine, and chemistry. We report for the first time a versatile approach for executing controllable 3D rotation of cells or particles at a bipolar electrode (BPE) array using a rotating electric field. The versatility of this method is demonstrated by 3D rotating various cells including yeast cells and K562 cells and the cells can be rotated to a desired orientation and immobilized for further operations. Our results demonstrate how electrorotation torque, induced charge electroosmosis (ICEO) flow and dielectrophoresis can be exerted on certain cells for modulating the rotation axis, speed, and direction. ICEO-based out-of-plane rotation is capable of rotating various cells in a vertical plane regardless of their shape and size. It can realize cell orientation by rotating cells toward a specific angle and enable cell rotation by steadily rotating multiple cells at a controllable speed. The rotation spectrum for in-plane rotation is further used to extract the cellular dielectric properties. This work offers a flexible method for controllable, contactless and precise rotation of different cells or particles, offering a rapid, high-throughput, and nondestructive rotation method for cell analysis and drug discovery.

A Rate-Dependent Cell Microinjection Model Based on Membrane Theory

As an effective method to deliver external materials into biological cells, microinjection has been widely applied in the biomedical field. However, the knowledge of cell mechanical property is still inadequate, which greatly limits the efficiency and success rate of injection. Thus, a new rate-dependent mechanical model based on membrane theory is proposed for the first time. In this model, an analytical equilibrium equation between the injection force and cell deformation is established by considering the speed effect of microinjection. Different from the traditional membrane-theory-based model, the elastic coefficient of the constitutive material in the proposed model is modified as a function of the injection velocity and acceleration, effectively simulating the influence of speeds on the mechanical responses and providing a more generalized and practical model. Using this model, other mechanical responses at different speeds can be also accurately predicted, including the distribution of membrane tension and stress and the deformed shape. To verify the validity of the model, numerical simulations and experiments were carried out. The results show that the proposed model can match the real mechanical responses well at different injection speeds up to 2 mm/s. The model presented in this paper will be promising in the application of automatic batch cell microinjection with high efficiency.

Automatic trajectory control of single cells using dielectrophoresis based on visual feedback

This paper deals with the automatic control of the trajectory of T-lymphocytes using dielectrophoretic (DEP) actuation. Dielectrophoresis is a physical phenomenon induced by a non-uniform electric field enabling application of a force on a dielectric object. In most of the cases, it is used in a passive way. The electric field is in a steady state and the force applied on the cells depends on the cell's characteristics and position inside the channel. These systems are limited as cells with similar characteristics will undergo the same forces. To overcome this issue, active devices where the electric field changes over time were developed. However, the voltages that should be applied to generate the desired electric field are mostly computed offline using finite element methods. Thus, there is a low number of devices using automatic approaches with dielectrophoretic actuation where the electric field is computed and updated in real time based on the current position of the cell. We propose here an experimental bench used to study the automatic trajectory control of cells by dielectrophoresis. The computation of the dielectrophoretic force is done online with a model based on the Fourier series depending on the cell's characteristics, position and electric field. This model allows the use of a controller based on visual feedback running at 120 Hz to control the position of cells inside a microfluidic chip. As cells are sensitive to the electric field, the controller limits the norm of the electric field while maximizing the gradient to maximize the DEP force. Experiments have been performed and T-lymphocytes were successfully steered along several types of trajectories at a speed of five times their size per second. The mean error along those trajectories is below 2 μm. The viability of the cells has been checked after the experiments and confirms that this active DEP actuation does not harm the cells.

Advanced tools and methods for single-cell surgery

Highly precise micromanipulation tools that can manipulate and interrogate cell organelles and components must be developed to support the rapid development of new cell-based medical therapies, thereby facilitating in-depth understanding of cell dynamics, cell component functions, and disease mechanisms. This paper presents a literature review on micro/nanomanipulation tools and their control methods for single-cell surgery. Micromanipulation methods specifically based on laser, microneedle, and untethered micro/nanotools are presented in detail. The limitations of these techniques are also discussed. The biological significance and clinical applications of single-cell surgery are also addressed in this paper.

Adaptive Sliding Mode Disturbance Observer and Deep Reinforcement Learning Based Motion Control for Micropositioners

The motion control of high-precision electromechanitcal systems, such as micropositioners, is challenging in terms of the inherent high nonlinearity, the sensitivity to external interference, and the complexity of accurate identification of the model parameters. To cope with these problems, this work investigates a disturbance observer-based deep reinforcement learning control strategy to realize high robustness and precise tracking performance. Reinforcement learning has shown great potential as optimal control scheme, however, its application in micropositioning systems is still rare. Therefore, embedded with the integral differential compensator (ID), deep deterministic policy gradient (DDPG) is utilized in this work with the ability to not only decrease the state error but also improve the transient response speed. In addition, an adaptive sliding mode disturbance observer (ASMDO) is proposed to further eliminate the collective effect caused by the lumped disturbances. The micropositioner controlled by the proposed algorithm can track the target path precisely with less than 1 μm error in simulations and actual experiments, which shows the sterling performance and the accuracy improvement of the controller.

Posture adjustment and robust microinjection of zebrafish larval heart

Unlike cells or embryos, zebrafish have a complex physiological structure, which poses challenges to posture recognition and adjustment during microinjection. Furthermore, zebrafish surface pigments exhibit strong interference with visual servo-based injection control, thus, affecting the success of microinjection and the subsequent survival rate. To address these challenges, we developed an automated microinjection system for the zebrafish heart that has advantages of high accuracy and success rate and avoids biological sample contamination. A convolutional neural networks (CNN) deep learning model is employed to determine the body axis posture. To solve the problems of blocked needle and abnormal tip positioning induced by zebrafish surface pigment during the injection process, an adaptive robust Kalman filter is proposed to suppress the abnormal values of visual feedback. Experimental results show that the success rate of body axis recognition based on the employed deep learning model exceeds 95%, and the proposed adaptive Kalman filter effectively suppresses the visual outliers, satisfying the requirements of high-precision injection for the zebrafish heart.