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

Abstract

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 micro-scale. 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 speed that depends on the laser power used.Note to Practitioners -This paper presents computational tools necessary for automated cell micromanipulation using Optical Tweezers. Autonomy in optical manipulation enables us to realize a method for fast and accurate placement of micro-particles. The method presented in the paper is especially useful for accurately placing cells in an array for biological experiments.

The interplay of cell–cell and cell–substrate adhesion in collective cell migration

“…For further comparison, we sketch a simplified cartoon of the shape dynamics in figure 4d. To further investigate the response of suspended cells to contact with another object, we use controlled indirect optical-gripping of cells, a technique that we had previously developed and refined [22][23][24][25]. In our sample applications, we demonstrated that assistant tools, such as glass beads, could be used to indirectly bring two cells in controlled contact, without directly exposing cells to the potential photo-damage from the laser.…”

Optical Tweezers: Autonomous Robots for the Manipulation of Biological Cells

“…Researchers at the University of Maryland have been successful in bringing about autonomy for indirect manipulation of yeast cells using silica as the gripper finger material [13]. The sequence of operations during manipulation are as follows: (1) target gripper fingers are trapped and transported to the cell to form the gripper, (2) the cell is transported using the gripper to the desired goal location, (3) the gripper fingers are moved away from the cell to release the cell at the goal location, and (4) the manipulation is completed by removing the fingers by switching off the laser beams.…”

“…New strategies for autonomous cell manipulation have been developed [13], [14] recently that limit the exposure of laser light to the cells. Thus, these new strategies enable experiments with cells that are sensitive to laser exposure.…”

An Accurate Perception Method for Low Contrast Bright Field Microscopy in Heterogeneous Microenvironments

“…Representative examples of dynamics modeling include the use of Langevin equation to simulate the behavior of an optically-trapped microsphere [6] and a stochastic perturbation model to analyze the trapping stability of microparticles during motion [7]. For path or trajectory planning, representative examples include a partially observable Markov decision process algorithm for single object [8] and multiple object [9] transport, a rapidly-exploring random tree method for cell transport [10], and an A* algorithm for indirect manipulation of cells using optically-trapped microspheres [11]. With respect to motion control, recent representative works include a vision-based observer method for multiple cell transport [12], a proportional-derivative (PD) control strategy for single cell transport [13], a saturation controller for swarming motions of cells [14], a combined translational and rotational controller for cells [15], and a motorized stage-optical tweezers cooperative controller for multi-cell manipulation [16].…”