US Research Centre uses 2D Gel Image Analysis Software To Rapidly Assess the Effects of Vitamin E on Prostate Cancer

10 Jun 2008

Syngene, a world-leading manufacturer of image analysis solutions, today announced that scientists at East Tennessee State University (ETSU) have detected proteins associated with cytotoxic effects of Vitamin E on prostate cancer cells, using Dymension, Syngene’s innovative 2D gel image analysis software.

Researchers in the Departments of Pediatrics/Chemistry at ETSU are using Dymension to rapidly analyse 2D gel images of silver stained proteins derived from a prostate cancer (LNCaP) cell line treated with delta-tocotrienol, (a form of Vitamin E). From the analysis, they have isolated a number of proteins that are significantly up or down-regulated, which when identified, could provide critical information for the design of more effective drugs for the treatment and prevention of prostate cancer.

Mr Christian Mbangha Muenyi, Research Assistant in the Pediatrics/Chemistry Departments at ETSU explained: ”In many studies, it has been shown that Vitamin E is cytotoxic to some prostate cancer cell lines, so we want to find out what is happening at the molecular level during this induced cell death. We have been using a proteomics approach with 2D gels for several years but found with our previous analysis software it was difficult and time consuming to manipulate gel images to obtain meaningful data.”

Mr Muenyi continued: “This is why we switched to using Dymension two years ago. Since then analysis has been more straightforward and this coupled with excellent technical support we have had from Syngene has helped us with rapidly detecting a number of interesting proteins. In fact, we are so pleased with Dymension we are going to upgrade the software’s capability to allow us to perform DIGE analysis.”

Paula Maia, Vice President of Sales, Syngene US stated: "We are delighted to see our Dymension software’s performance exceeds ETSU scientists’ expectations. The software’s use in such critical pre-clinical studies demonstrates it can provide 2D protein gel analysis of exceptional speed and accuracy, making Dymension ideally suited to any gel-based proteomics cancer research project.”

DYMENSION 3

Syngene

Quick and Easy Analysis of all 2D Gel Applications The exciting DYMENSION 3 software has all the functionality of DYMENSION 2, so can be used to analyse multiple gel sets. In addition, DYMENSION 3 is capable of analysing multi-stained fluorescent gel images stained with up to three contrasting fluorescent labels, for example, Cy™2, Cy™3 and Cy™5. This feature allows users to analyse multiple sets of any type of 2D protein gel, regardless of how it was stained, using only one software package.

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DYMENSION 1

Syngene

Revolutionary 2DGE Software Analysis of single 2D gels - Dymension 1 is ideal for users looking for software to analyse spots between samples containing one 2D gel only. It offers a range of automatic image correction features and methods for resolving and analysing spots on single gels. The software can be easily upgraded to Dymension 2 or 3 if the proteomics needs of the laboratory change.

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DYMENSION 2

Syngene

Rapid Analysis of Multiple 2D Gel Sets DYMENSION 2 has all the features of DYMENSION 1 but has the added ability to detect and compare protein spot maps in multiple gel sets. The software offers many background correction and noise filtering features including a SYPRO® Ruby filter, which can detect and automatically remove spots caused by SYPRO® Ruby crystals adhering to the gel, thus saving users valuable time editing their image. DYMENSION 2 is suitable for users that need to determine the amount of protein present before and after drug or protein induction treatments. The software automatically detects and assigns statistical confidence to each and every difference in spot normalised volume, thus accurately highlighting proteins of specific interest. The results of the analysis are displayed as a dynamically linked table, 3D spot profile, bar chart and scatter plot, which make it easy to compare many expression profiles simultaneously and therefore accurately pick proteins suitable for further analysis.

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