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Analysis of Neurite Outgrowth for a Laser Patterned Neuronal Culture S. K. Guduru1, S. V. Narasimhan2, S. T. Birchfield1, and B. Z. Gao2 Department of 1Electrical and Computer Engineering and 2Bioengineering, Clemson University Clemson, SC 29634 USA INTRODUCTION Manipulating individual neurons while maintaining their normal physiological functions is a crucial part of constructing a biological neural network with specific design synapse connections. Such networks are important for studying neurite outgrowth, synapse formation, and neural control in normal and pathological conditions. In order to coerce individual cells in these networks we have developed a laser cell micropatterning system that is capable of positioning individual cells at submicron accuracy. In this paper we concentrate on the development of a neurite tracing algorithm that automatically assesses the patterned cell culture based on that of Al-Kohafi et al [1]. OBJECTIVE Using our current technique, individual cells are placed at predetermined positions with submicron accuracy, enabling detailed study of neurite outgrowths regulated by various cell contact interactions under phase contrast microscopes combined with digital image processing techniques. In this paper, the images of the cells are analyzed automatically by an algorithm to detect the soma and neurite outgrowth. Images of embryonic chick forebrain neurons are used to demonstrate the effectiveness of the technique. A Laser Beam B Suspended Cells Evenly Coated Substrate Axial Force Radial Force Pattern of Multiple Cell Types Fig. 1 A: Schematic depicting the principle of laser guidance, B: Patterned neurons with intercellular spacing of 40 microns created ) using the system. (20 microns A B Fig. 3 Soma and neurites detected by the algorithm RESULTS Fig. 3 shows the traced neurites along with the detected somas. Fig. 2 shows the image of the neurites followed by the soma detected image and the reliable seed points initialized images. As shown in Fig 2.D and 2.E, the second step removes a large number of false positives that arise in the first step due to the gradual intensity gradient in the upperleft corner of the image. CONCLUSIONS C D PRINCIPLE & PROCEDURE Optical forces generated by a weakly focused laser beam are used to create a radial trap of individual cells in the focal region of the beam. The trapped cells are then guided forward along the beam axis. Placement of a movable substrate perpendicular to the beam axis allows for the deposition of individual cells at specific positions on the substrate. Movement of the substrate, as the cells are trapped and guided by the beam, allows for patterning cells in a process known as laser guidance. After pattern formation, the images are processed using code written in Matlab. The soma detection is based upon gray-level morphological processing, while the neurite outgrowth tracing is based upon the work of AlKohafi et al. [1], which is modified to improve the computational performance. Connected components are applied to the morphologically closed binary image to yield to the individual soma. High density of seed points are initialized. A two-step procedure is adopted to select reliable seed points. Then the neurites are recursively traced using normalized Gaussian template. Each reliable seed point is traversed in the forward and backward directions by computing the score of the template at different orientations. This is repeated until one of the stopping criteria is met. E Fig. 2 A: Image of neurons, B: Closed image - soma detected, C: Seed points initialized, D: Reliable seed points detected using normalized Gaussian kernels & E: Final reliable seed points We have described a system to pattern neuronal cultures using a weakly focused laser beam. The system is capable of positioning individual cells at submicron accuracy, thus opening the door for more precise studying of neurite outgrowth, synapse formation, and neural control in normal and pathological conditions. We have also described an algorithm for automatically analyzing the images of the cells captured by our system. Future work should be aimed at running the algorithm on a video sequence to measure the outgrowth of the neurites automatically. REFERENCES [1] K. Al-Kofahi et al., "Rapid Automated three-dimensional tracing of neurons from confocal image stacks," IEEE Trans. on Information Technology in Biomedicine, Vol. 6, pp. 171-187, 2002. ACKNOWLEDGEMENTS South Carolina Spinal Cord Injury Research Board; SC BRIN; Mr. Daniel Bakken, Department of Bioengineering, Clemson University.