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Understanding Dynamic Behavior of Embryonic Stem Cells

Understanding Dynamic Behavior of Embryonic Stem Cells. Shubham Debnath University of Minnesota-Twin Cities debna002@umn.edu. Advisor: Dr. Bir Bhanu BRITE REU 2009 University of California-Riverside. Overview. Introduction to embryonic stem cells and importance

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Understanding Dynamic Behavior of Embryonic Stem Cells

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  1. Understanding Dynamic Behavior of Embryonic Stem Cells Shubham Debnath University of Minnesota-Twin Cities debna002@umn.edu Advisor: Dr. Bir Bhanu BRITE REU 2009 University of California-Riverside

  2. Overview • Introduction to embryonic stem cells and importance • Description of video processing and image segmentation methods used for study • Stem cell videos used for data and examples of use of segmentation and image analysis methods • Results and Analysis • Conclusions • Future pursuits

  3. Stem Cells and Importance • Derived from the inner cell mass of early stage embryos, known as blastocysts • Known to be pluripotent and can differentiate into a variety of cell types • Very important towards study in the future of medicine and healthcare

  4. Stem Cells and Importance • Attach to substrate to differentiate based on the environment they are placed in • For mitosis to proceed, cells must unattach themselves, divide, then reattach. • Behavior of embryonic stem cells is not fully understood • Past and continued research at Stem Cell Center at UCR • Effects of smoke and alcohol on stem cell behavior

  5. Mitosis • Process by which eukaryotic cells divide into two identical daughter cells • Consists of various phases in which the nucleus and cytoplasm divide ending with cytokinesis and cleavage into two cells • Importance for maintenance of genome set • Rate of mitosis depends on tissue renewal for stem cells; varies for different cell types

  6. Introduction to Methods • Otsu’s Algorithm for Binary Thresholding: • Inputs a grey-scale image, automatically finds a threshold value, splits the image accordingly • Threshold value is found by histogram analysis • Outputs a binary image showing regions of interest • Connected Components Analysis: • Only done on binary images • For each pixel, checks neighboring pixels and labels each region accordingly • Labeled with random pseudo colors for visual identification of each connected component

  7. Otsu’s Algorithm Threshold Value: 129

  8. Otsu’s Algorithm Original Image Segmented Image

  9. Connected Components 1 1 1 0 0 1 1 0 1 1 1 0 0 2 2 0 0 1 0 0 1 0 0 1 0 1 0 0 2 0 0 2 0 0 0 0 1 0 0 1 0 0 0 0 2 0 0 2 1 1 0 0 0 0 0 0 3 3 0 0 0 0 0 0 0 0 1 0 0 1 3 0 0 4 0 0 1 5 1 5 0 0 0 1 0 0 1 1 0 3 0 0 5 5 0 0 0 0 0 0 1 0 0 0 0 5 0 0 0 0 0 0 0 1 1 1 0 0 0 1 0 6 6 6 0 0 0 7 Binary Image Result of Connected Components Algorithm

  10. Examples

  11. Video of Stem Cells

  12. Hypotheses • Number of mitosis occurrences: approximately 6-10 • Cell count should be similar and beginning and end of video • Cells going through mitosis, attaching, apoptotic events • If recorded over a longer period of time, cell count remains essentially the same • Large jumps, spikes in data represent colonies of cells unattaching together, multiplying • Sharp drop shows colony reattaching to substrate • Low number of unattached cells should correspond to low number of pixels in “white” membranes • More pixels in darker mass of cell showing how surface area increases with attachment to substrate

  13. Plate 9 – 20X

  14. Results

  15. Results

  16. Plate 6 – 20X

  17. Results

  18. Results

  19. Analysis • Graphs complement each other – directly related • Spikes in graphs accurately show points of mitosis • Shows how surface area of cell changes with attachment and cell division • Problems with counting come with colonies of cells • Background noise, light

  20. Conclusions • Video processing can be used for the segmentation of stem cell videos for their characterization. • Mitosis is important cell differentiation in stem cells and for regulation of processes in the human body • Mitosis count between 6 and 10 divisions based on resulting graphs • Watching videos agrees with these estimations • Time: 4 to 6 frames for process of mitosis to start and complete • 8 to 12 minutes • Colonies tend to unattach and multiply together

  21. Future Research • Use of the relaxation gradient algorithm • Choosing of different thresholds • Use of a new Nikon Biostation can be used to record videos for longer times and with higher magnification and resolution. • More biomedical engineering based objective: behavior can be simulated computationally with macromolecular interactions • The free energy of cells in various states can be calculated to find a minimum at which the cell responds to diverse changes.

  22. Acknowledgements • Special thanks to my research advisor, Professor Bir Bhanu of UC-Riverside • Students at Center for Research in Intelligent System (CRIS) • Thanks to Dr. Prue Talbot and students at the Stem Cell Center at UCR for providing the data • Thanks to the BRITE REU program funded by the National Science Foundation (NSF)

  23. Questions?

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