Automation technologies can be used to support research in biological cell manipulation and characterization. Physical characterization of cells can be performed by indenting a cell membrane a fixed amount and recording the corresponding force data. The manipulation system at the MSRAL can be outfitted for these types of studies. This research requires the attachment of a pipette with suction capabilities to the manual manipulator along with the integration of a force sensing device to the active manipulator. A study has been conducted on golden whitefish egg cells, which have an outside diameter of 2-3 mm and manipulation and puncture forces on the order of milli-Newtons. Therefore, an off-the-shelf 10g load cell is used for force sensing. Cell indentations of various amounts were produced on the cell by actuating the active manipulator and corresponding forces recorded. This type of force-deflection data can be used to determine stiffness metrics for a particular cell and is useful when comparing different types of cells. Cell relaxation has been observed from examining the force values over time that the cell exerts on the manipulation tool while the cell is indented. This relaxation is modeled with a spring-damper system to further characterize the behavior of the cell. In addition, the effects of salinity and diameter of the stiffness of the cell have also been studied.
Experimental setup for cell characterization studies.
Image from microscope field of view (FOV) during cell indentation test.
Viscoelastic modeling of the cell using spring-mass-dampers to characterize cell relaxation behavior.
Experimental cell relaxation data plotted against fitted viscoelastic cell model.
This project is on automating the phototransfection process on neuron and fibroblast cells. Phototransfection is presently done manually in a very tedious manner: First, the cell of interest needs to be identified in the microscope FOV and location recorded. Then an appropriate location of where to administer a laser beam on the cell body needs to be determined. Applying the laser to the wrong location can damage the cell. The laser beam creates a hole in the cell membrane where donor mRNA can be inserted to the cell from a pipette, completing the process. A framework for fully automating this procedure has been designed and proof-of-concept implementation achieved. Computer vision techniques are used to identify the cell of interest in the FOV and determine target locations for the laser beam. A control program takes this information and coordinates movements of the computer controlled XY stage, translating the coordinates of the laser target location to a predefined, fixed, laser firing location. A 20X improvement is possible with this implementation with room for improvement to greater than 80X.
Process flow for AutoPT. (A) Instrumented microscope with fixed-position petri dish with coverslips of cells to be phototransfected. (B) Local mapping of individual coverslips for image processing and data retrieval. (C) Image segmentation to identify cell features and morphology. Morphological data are stored in a database for each cell in the FOV. (D) Laser target calculation is based on feature extraction and morphology. (E) Coordinated microscope stage movements and laser firing to administer laser at designated targets. (F) mRNA release into porated cells by pipette mounted on computer-controlled micromanipulator. The process loop then restarts on next FOV on coverslip. Once all FOVs on coverslip have been phototransfected, the process continues on next coverslip in the petri dish.
Images of the phototransfected cell are observed before and after the process to assess morphological changes in the cell as way to characterize them. The problem in comparing two different images of the same cell before and after phototransfection is that the changes in the cell are hard to discern because of changes in illumination, camera viewpoint and background in both images. Image segmentation algorithms are used to segment the cell from the background in order to compare both images of the cell without ambiguities. From a properly segmented image, the morphology is quantified by computing measures such as cell area, asymmetry, perimeter, and eccentricity.
Schematic of a cell before (left) and after (right) phototransfection and associated morphological measures quantifying cell changes.
Images of four fibroblast cells before and after the phototransfection process, with the segmented areas from the graph-cuts method overlaid (red) on the original images. The images in the top row are before the process, whereas those in the bottom row are after the process has been completed
J-Y Sul, C. K. Wu, F. Zeng, J. Jochems, M. T. Lee, T. K. Kim, T. Peritz, P. Buckley, D. Cappelleri, M. Maronski, M. Kim, V. Kumar, D. Meaney, J. Kim and J. Eberwine, “Transcriptome Transfer Produces a Predictable Cellular Phenotype”, Proceedings of the National Academy of Science, 2009: May 5;106(18):7624-9. Epub 2009 Apr 20. PMCID: PMC2670883.
D. Cappelleri, A. Halasz, J-Y. Sul, T. Kim, J. Eberwine, and V. Kumar, “Towards Fully Automated Phototransfection”, Proceedings of the IEEE Conference on Robotics, Automation, and Science (CASE), Bangalore, India, August 22-25, 2009.*
D. Cappelleri, A. Halasz, J-Y. Sul, T. Kim, J. Eberwine, and V. Kumar, “Towards Fully Automated Phototransfection”, Lab Automation 2010, Palm Springs, CA, January 23-27, 2010.
D. Cappelleri, A. Halasz, J-Y. Sul, T. Kim, J. Eberwine, and V. Kumar, “Towards A Fully Automated High-Throughput Phototransfection System”, Journal of the Association for Laboratory Automation (JALA), vol. 15, pp. 329-341, August 2010, DOI information: 10.1016/j.jala.2010.03.003
*Winner of the Association for Laboratory Automation (ALA) Young Scientist award for the best paper and presentation in laboratory automation by a junior faculty member or graduate student.
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