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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

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
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


Stem cells and importance
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


Stem cells and importance1
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


Mitosis
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


Introduction to methods
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


Otsu s algorithm
Otsu’s Algorithm

Threshold Value: 129


Otsu s algorithm1
Otsu’s Algorithm

Original Image

Segmented Image


Connected components
Connected Components

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Binary Image

Result of Connected Components Algorithm




Hypotheses
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








Analysis
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


Conclusions
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


Future research
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.


Acknowledgements
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)