Automated Transportation of Biological Cells for Multiple Processing Steps in Cell Surgery

Hao Yang ; Xiangpeng Li ; Yunhui Liu ; Dong Sun

Most studies on automated cell transportation are single-task oriented. Results from these investigations hardly meet the increasing demand for emerging cell surgery operations that usually require a series of manipulation tasks with multiple processing steps. In this paper, automated cell transportation to accomplish a multistep process in cell surgery was investigated. A novel control system that can manipulate grouped cells to move into different task regions sequentially and continuously without interruption was developed based on a robot-aided optical tweezers manipulation system. A potential field-based controller was designed to achieve multistep processing control, where the new concepts of contractive coalition and switching region were incorporated into tweezers-cell coalition. The success of this controller lies in simultaneously controlling the positions of the optical tweezers, trapping multiple cells effectively, and avoiding collisions in a unified manner. Simulations and experiments of transferring a group of cells to a number of task regions were performed to demonstrate the effectiveness of the proposed approach.

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Automated Translational and Rotational Control of Biological Cells With a Robot-Aided Optical Tweezers Manipulation System

Xie, M. ; Mills, J.K. ; Wang, Y. ; Mahmoodi, M. ; Sun, D.

Research and biomedical applications in cell surgery require transportation and rotation of biological cells. In these cell manipulation tasks, the cell of interest must be translated and oriented properly such that the desired component, such as the polar body or other organelles, can be imaged with optical microscopy. This paper presents a holographic optical tweezers (HOT) based system to carry out automated translational control in the plane, and rotational control about one rotational axes of a suspended cell. Based on the proposed general equations of motion of the cell, held in an optical trap, two controllers, one for cell translational and one for rotational control, are developed to translate and orient the cells to the desired position and orientation in a sequential manner. Experiments are performed to demonstrate the effectiveness of the proposed approach.

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Rapidly Exploring Random Tree Algorithm-Based Path Planning for Robot-Aided Optical Manipulation of Biological Cells

Tao Ju; Shuang Liu; Jie Yang; Dong Sun

In numerous cellular applications, cells are transported to specific positions or extracted from complex cell solutions. Therefore, an efficient cell transportation path planner for these applications is important for avoiding collisions with other cells or obstacles. In this paper, a path planning approach to transporting cells using a robot-aided optical manipulation system is presented. Optical tweezers functions as a special end-effector in transporting a target cell to the desired position along the generated path. The path planner is designed based on the rapidly exploring random trees (RRT) algorithm for calculating a collision-free path for cell transportation. Both static and dynamic path planners are developed. For the dynamic path planner, an online monitoring strategy is employed to dynamically avoid collisions with randomly appeared obstacles caused by environmental influence such as the Brownian movement of microparticles. Experiments of transporting yeast cells are performed to demonstrate the effectiveness of the proposed approach. Note to Practitioners - Manipulations of cells and other microparticles represent an essential process for most cell-based bioengineering applications, such as cytopathology, cell sociology, and cytotaxonomy. Cell transportation, which is treated as a typical cell manipulation task, has recently received considerable attention because of its wide applications. This paper presents a novel approach to applying RRT-based path planner to cell transportation with a robot-aided optical manipulation system. The research outcome provides a unique solution to achieving cell transportation automatically and efficiently.

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Automated Manipulation of Biological Cells Using Gripper Formations Controlled By Optical Tweezers

Sagar Chowdhury, Atul Thakur, Petr Švec, Chenlu Wang, Wolfgang Losert, and Satyandra K. Gupta
The capability of noninvasive and precise micromanipulation of sensitive, living cells is necessary for understanding their underlying biological processes. Optical tweezers (OT) is an effective tool that uses highly focused laser beams for accurate manipulation of cells and dielectric beads at microscale. However, direct exposure of the laser beams on the cells can negatively influence their behavior or even cause a photo-damage. In this paper, we introduce a control and planning approach for automated, indirect manipulation of cells using silica beads arranged into gripper formations. The developed approach employs path planning and feedback control for efficient, collision-free transport of a cell between two specified locations. The planning component of the approach computes a path that explicitly respects the nonholonomic constraints of the gripper formations. The feedback control component ensures stable tracking of the path by manipulating the cell using a set of predefined maneuvers. We demonstrate the effectiveness of the approach by transporting a yeast cell using four different types of gripper formations along collision-free paths on our OT setup. We analyzed the performance of the proposed gripper formations with respect to their maximum transport speeds and the laser intensity experienced by the cell that depends on the laser power used.
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Automated Cell Transport in Optical Tweezers-Assisted Microfluidic Chambers

Svec, P. ; Wang, C. ; Seale, K. T. ; Wikswo, J. P. ; Losert, W.; Gupta, S. K.

In this paper, we present a physics-aware, planning approach for automated transport of cells in an optical tweezers-assisted microfluidic chamber. The approach can be used for making a uniform distribution of cells inside the chamber to allow the study of a variety of biological processes, including cell signaling. Fluid forces inside the chamber, modeled using computational fluid dynamics, are incorporated into the widely used Langevin equation to simulate the motion of cells. The developed simulator was used for building a map that contains probabilities of a cell successfully reaching one of the outlets of the chamber from different locations under the influence of the fluid flow. The developed planner not only generates collision-free paths that exploit the fluid flow inside the chamber but also utilizes the offline generated simulation data to decide suitable locations for releasing the cells. This ensures fast and robust cell transport, while minimizing the required laser power and operational time. The planner is based on the heuristic D* Lite algorithm that employs a specific cost function for searching over a novel state-action space representation. The effectiveness of the planning algorithm is demonstrated using both simulation and physical experiments in a microfluidics-optical tweezers hybrid manipulation setup.

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Real-Time Path Planning for Coordinated Transport of Multiple Particles Using Optical Tweezers

Banerjee, A.G., Chowdhury, S., Losert, W., Gupta, S.K.

Automated transport of multiple particles using optical tweezers requires real-time path planning to move them in coordination by avoiding collisions among themselves and with randomly moving obstacles. This paper develops a decoupled and prioritized path planning approach by sequentially applying a partially observable Markov decision process algorithm on every particle that needs to be transported. We use an iterative version of a maximum bipartite graph matching algorithm to assign given goal locations to such particles. We then employ a three-step method consisting of clustering, classification, and branch and bound optimization to determine the final collision-free paths. We demonstrate the effectiveness of the developed approach via experiments using silica beads in a holographic tweezers setup. We also discuss the applicability of our approach and challenges in manipulating biological cells indirectly by using the transported particles as grippers.

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Developing a Stochastic Dynamic Programming Framework for Optical Tweezer-Based Automated Particle Transport Operations

Banerjee, A. G.; Pomerance, A.; Losert, W.; Gupta, S. K.;

Automated particle transport using optical tweezers requires the use of motion planning to move the particle while avoiding collisions with randomly moving obstacles. This paper describes a stochastic dynamic programming based motion planning framework developed by modifying the discrete version of an infinite-horizon partially observable Markov decision process algorithm. Sample trajectories generated by this algorithm are presented to highlight effectiveness in crowded scenes and flexibility. The algorithm is tested using silica beads in a holographic tweezer set-up and data obtained from the physical experiments are reported to validate various aspects of the planning simulation framework. This framework is then used to evaluate the performance of the algorithm under a variety of operating conditions.

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