Name: video bioinformatics analysis of human embryonic stem cell colony growth
Uploaded: 2010-05-20T17:00:00
Description: Watch this Scientific Journal Video about Video Bioinformatics Analysis of Human Embryonic Stem Cell Colony Growth at JoVE.com

<p class="jove_content">The development of this method was made possible by funding from the California Tobacco-Related Disease Research Program, the California Institute for Regenerative Medicine, and NSF IGERT grant on Video Bioinformatics (#0903667) at UCR (<a href="http://www.cris.ucr.edu/IGERT/index.php">http://www.cris.ucr.edu/IGERT/index.php</a>). Sabrina Lin is supported by a Dissertation Fellowship from the Graduate Division, Shawn Forteno is supported by an NIH MARC Fellowship, and Shruthi Satish is supported by a Graduate Division Fellowship. We are grateful to Anna Trtchounian for her help preparing the figures. We also thank Sam Alworth and Ned Jastromb for teaching us how to use the CL-Quant software.</p>

<p class="jove_title">Part 1: Experimental Procedure</p>
<p class="jove_content">Video data contain an abundance of information. However, this information is often difficult to extract and when done manually by humans and may require many hours of personnel time to complete.  Manual analysis by humans is also subject to variation in interpretation and error.  Video bioinformatics involves the use of computer software to mine specific data from video images. This method of analysis is rapid and can eliminate error that occurs when analysis is d...

<p class="jove_content">Video bioinformatics tools are powerful for rapidly extracting data from video images. Our protocol for quantifying hESC colony growth demonstrates one application of video bioinformatics to a biological problem. This method is quantitative and has the interesting feature of revealing data from individual hESC colonies. Video bioinformatics recipes can be developed to monitor other cellular processes such as proliferation, migration, apoptosis, and cell attachment to the substrate, and cell attachment to adjacent cells. ...

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		<div>
		<table border="1">
		<thead>
		<tr>
		<th>Material Name</th>
		<th>Type</th>
		<th>Company</th>
		<th>Catalogue Number</th>
		<th>Comment</th>
		</tr>
		</thead>
		<tbody>
		
<tr>
<td>mTeSR1 Human Embryonic Stem Cell Maintenance Medium</td>
<td>&nbsp;</td>
<td>Stem Cell Technologies</td>
<td>05850</td>
<td>or any suitable medium for hESC culture.</td>
</tr>
<tr>
<td>BD Matrigel</td>
<td>&nbsp;</td>
<td>BD Bioscience</td>
<td>356234</td>
<td>or other suitable substrate.</td>
</tr>
<tr>
<td>DMEM/F12 Basal Medium</td>
<td>&nbsp;</td>
<td>Invitrogen</td>
<td>11330-032</td>
<td>&nbsp;</td>
<tr>
<td>Phosphate Buffered Saline without Ca<sup>2+</sup> and Mg<sup>2+</sup></td>
<td>&nbsp;</td>
<td>&nbsp;</td>
<td>&nbsp;</td>
<td>&nbsp;</td>
<tr>
<td>Accutase Enzyme Cell Detachment Medium</td>
<td>&nbsp;</td>
<td>eBioscience</td>
<td>00-4555-56</td>
<td>or other suitable detachment enzyme.</td>
</tr>
<tr>
<td>3mm Glass beads</td>
<td>&nbsp;</td>
<td>Fisher Scientific</td>
<td>11-312A</td>
<td>optional.</td>
</tr>
<tr>
<td>12-well Tissue Culture Plates</td>
<td>&nbsp;</td>
<td>BD Falcon</td>
<td>353043</td>
<td>or any other plate format.</td>
</tr>
<tr>
<td>Nikon BioStation CT/IM</td>
<td>&nbsp;</td>
<td>&nbsp;</td>
<td>&nbsp;</td>
<td>or other incubator/microscope suitable for collecting video data.</td>
</tr>
<tr>
<td>CL-Quant software (Nikon) and/or Photoshop (Adobe).</td>
<td>&nbsp;</td>
<td>&nbsp;</td>
<td>&nbsp;</td>
<td>&nbsp;</td>		</tbody>
		</table>
		</div>
	

video bioinformatics analysis of human embryonic stem cell colony growth

Because video data are complex and are comprised of many images, mining information from video material is difficult to do without the aid of computer software.  Video bioinformatics is a powerful quantitative approach for extracting spatio-temporal data from video images using computer software to perform dating mining and analysis. In this article, we introduce a video bioinformatics method for quantifying the growth of human embryonic stem cells (hESC) by analyzing time-lapse videos collected in a Nikon BioStation CT incubator equipped with a camera for video imaging. In our experiments, hESC colonies that were attached to Matrigel were filmed for 48 hours in the BioStation CT.  To determine the rate of growth of these colonies, recipes were developed using CL-Quant software which enables users to extract various types of data from video images.  To accurately evaluate colony growth, three recipes were created. The first segmented the image into the colony and background, the second enhanced the image to define colonies throughout the video sequence accurately, and the third measured the number of pixels in the colony over time.  The three recipes were run in sequence on video data collected in a BioStation CT to analyze the rate of growth of individual hESC colonies over 48 hours. To verify the truthfulness of the CL-Quant recipes, the same data were analyzed manually using Adobe Photoshop software. When the data obtained using the CL-Quant recipes and Photoshop were compared, results were virtually identical, indicating the CL-Quant recipes were truthful. The method described here could be applied to any video data to measure growth rates of hESC or other cells that grow in colonies. In addition, other video bioinformatics recipes can be developed in the future for other cell processes such as migration, apoptosis, and cell adhesion.  

Watch this Scientific Journal Video about Video Bioinformatics Analysis of Human Embryonic Stem Cell Colony Growth at JoVE.com

Video Bioinformatics Analysis of Human Embryonic Stem Cell Colony Growth (Video) | JoVE

<p class="jove_content">Video bioinformatics is the automated processing, analysis, understanding, and data mining of biological spatio-temporal data extracted from microscopic videos.  The purpose of this article is to demonstrate a method for measuring human embryonic stem cell colony growth using a video bioinformatics method.  </p>

Video Bioinformatics Analysis of Human Embryonic Stem Cell Colony Growth

Because video data are complex and are comprised of many images, mining information from video material is difficult to do without the aid of computer ...

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