Transcript 48x36 poster template - Bourns College of Engineering

UNDERSTANDING DYNAMIC BEHAVIOR OF EMBRYONIC
STEM CELL MITOSIS
Shubham
1
Debnath ,
Bir
METHODS
Embryonic stem cells are derived from the inner cell mass of early stage
embryos, known as blastocysts. They are known to be pluripotent and can
differentiate into a variety of different cell types, making them valuable for
research for applications in medicine and healthcare. The Stem Cell Center
at the University of California-Riverside has done previous and continuing
research on the effects of cigarette smoke and alcohol on human and mouse
embryonic stem cells. Video recordings taken over thirty-six hours show
actions of cells under different conditions. The research aims to apply video
processing techniques to characterize the processes of mitosis and preattachment behavior. Otsu’s algorithm is for binary thresholding and, when
used, maximizes the variance between regions in a grey image. It can be
applied in an iterative program to observe variations in cell to quantify
several phenomenon. Software tools such as Matlab and ImageJ are used in
this research. This presentation will include an overview of stem cells,
different types and uses, and the importance in studying and understanding
how stem cells interact with substrate and each other. A description of
mitosis and attachment behaviors will precede an explanation of the
approach of research, along with results, images, and normalized graphs.
The graphs will display ways to quantify cellular process, including the
change in number of cells over time and change in area over time.
Browse and select video to be
segmented
• Stem cells are very important towards study in the future of
regenerative medicine and tissue replacement in the human body.
The behavior of embryonic stem cells is not fully understood and
studied by various stem cell centers.
• Currently, work is done by biologists by hand and manual
observation. Video processing and image segmentation can be
implemented with Matlab programming to better understand the
dynamics of cells as go through processes of attachment and mitosis.
Find threshold automatically
Split image using threshold
Above and below are the results from two different videos. The left figures show
the number of unattached cells and times of mitosis; the right graphs show the
total number of pixels in whitespace with frame number. The graphs are directly
related, and the results show that.
BINARY IMAGE
For each pixel, check 8
neighboring pixels
Label as connected component
based on adjacent labeling
Label 0
Add pseudo-color for each label
CONNECTED
COMPONENTS
INTRODUCTION and BACKGROUND
Open first frame & crop section
of interest
RESULTS
OTSU’S ALGORITHM
ABSTRACT
2
Bhanu
Fill and smooth image
Make video from array of
frames
Analysis of results
• Stem cells attach to substrate to differentiate based on the
environment they are placed in. In order for mitosis to proceed, cells
must unattach themselves, divide into daughter cells, then reattach.
• Videos of this behavior are recorded by a Nikon® Biostation and
analyzed using Matlab, following Otsu’s algorithm and connected
components analysis.
• Otsu’s algorithm for binary thresholding inputs a grey-scale image,
automatically finds a threshold value, and splits the image accordingly.
This threshold value is found by histogram analysis, separating
regions with a maximum mean and variance. It outputs a binary
image, only black and white, showing regions of interest. A flowchart
describing the process and resulting images are shown to the right.
• This video processing is an improvement on any manual counting
and analysis previously done.
• Video processing can be used for the segmentation of stem cell videos
for their characterization. Various algorithms can be applied to ascertain
visual differences between cells in different states, thereby able to
determine how cells react under changing conditions.
• Future pursuits may include the use of the relaxation gradient algorithm,
choosing of different thresholds, and using connected components
similarly.
• Use of a new Nikon Biostation can be used to record videos for longer
times and with higher magnification and resolution.
• As a more biomedical engineering based objective, the 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
Special thanks to my research advisor, Professor Bir Bhanu of UCRiverside, as well as all the students at the Center for Research in
Intelligent System (CRIS) at UCR. Also, thanks to Dr. Prue Talbot and the
students at the Stem Cell Center at UCR for providing the data and videos
for this study. Thanks to the BRITE REU program funded by the National
Science Foundation (NSF) for the opportunity to do this research.
• After forming a binary image, each region is labeled by connected
components. The flowchart also describes this process, along with
resulting images. The regions are labeled with random pseudo colors
for visual identification of each connected component.
• The pixel areas of regions are calculated. Depending on the size of
these areas, the regions are labeled as unattached cells and clusters.
CONCLUSIONS and FUTURE RESEARCH
Shown above are two frames from a single video of a stem cell undergoing mitosis. The time
between frames is approximately eight minutes. From left to right, the images represent the
original image, application of Otsu’s algorithm to produce a binary image, and the use of connected
components to determine different regions of interest. The top row all show the same frame, as
does the bottom row.
1. University of Minnesota-Twin Cities
2. Center for Research in Intelligent Systems, University of California-Riverside