Understanding and Quantifying the Dancing Behavior of Stem Cells Before Attachment Clinton Y. Jung 1 and Dr. Bir Bhanu 2, Department of Electrical Engineering 1. Biomedical Engineering, Johns Hopkins University, MD 21218, USA 2. Center for Research in Intelligent Systems, University of California, Riverside, CA 92521, USA Stem cells have proven themselves to be a promising frontier of biomedical research. They have an ability to develop into different cell types in the body especially when the human body is still in its earliest stages of life and growth. As the body ages, stem cells continue to serve in different tissues and divide to replenish other cells. What is incredible about stem cells is the fact that they can become other types of cells in order to perform a specialized function. As we continue to observe stem cells and try to understand their behaviors in order to more fully tap into their potential, learning about the different states of the stem cells is of significant importance. In this project, stem cells are hypothesized to be in one of five states—attached, non-attached, dancing, death, and mitosis. We focus on investigating the dancing, or pre-attachment, behavior of stem cells. This is a new, previously unobserved phenomenon. In this state, the cell loses its defined border and instead has several small bulbs attached to it. INTRODUCTION TECHNICAL METHODS DATA /IMAGE SEGMENTATION PROCESS ANALYSIS CONCLUSIONS Unattached Cell Attached Cell Pre-attachment Behavior Connected Components Image -First, apply Otsu’s Method to separate the image into two groups of pixels such that the intra-class variance is minimal and inter-class variance maximal -Next, apply connected components algorithm, another image segmentation method. -Expand connected components program to count the number of pixels in a cell undergoing pre- attachment behavior and plot this value over time -Also expand program to compute average grayscale value of a dancing cell over time and examine the averages and standard deviations of these values over time per frame. -Compare calculations for different stem cell stages Example of a single frame of video OBJECTIVES -Crop images for the cells undergoing dancing and play as video for each cell -Perform segmentation techniques -Compute features -Quantify the dancing phenomenon and change in shape Binary Image Histogram Grayscale Image Grayscale Value Number of Pixels Grayscale Value Number of Pixels Step 1: Input Original Image and convert to grayscale Step 2: Find threshold automatically using histogram Step 3: Split image into two classes (binary) based on the threshold value. This final image will have pixel values of either 0 or 255. Otsu’s Method Connected Components Algorithm -We are supplied videos captured in time-lapse fashion. The videos are in.avi format and cover 3.5 hours over the course of 106 still images -All programming and image segmentation techniques are carried out in MATLAB code Step 1: Input Original Image which must be a binary image Step 2: For each pixel, examine surrounding 8 pixels If the pixel is neighboring, assign label 1 If pixel is not neighboring, assign label 0 Step 3: Continue to check each pixel line by line until entire image is checked, resulting in a matrix of 1’s and 0’s Step 4: Convert image back to rgb in order to display components in colors. Time (Video Frame Number) Average Grayscale Value Average Grayscale Value vs. Time Total Number of Pixels vs. Time Time (Video Frame Number) Total Number of Pixels -The graphical analysis shows that the cells in the dancing, attached, and unattached states have very distinct average grayscale values per frame over the course of the video and can potentially be automatically differentiated using this property -However, the standard deviations of these values for the different stem cell states have a more inconsistent pattern which results in interference with each other -A preliminary calculation of the changes in pixel sizes for a cell undergoing pre-attachment behavior displays no periodic behavior Acknowledgements Time (Video Frame Number) Grayscale Standard Deviation Grayscale Standard Deviation vs. Time Jun Wang, Benjamin Guan, Dr. Prue Talbot, Shawn Fonteno, Sabrina Lin, Giovanni Denina, National Science Foundation Calculated Threshold Value: 162 Calculated Threshold Value: 155 Calculated Threshold Value: 138