Washington, May 13 : A software toolbox developed by researchers at Carnegie Mellon University may enable scientists to characterize protein patterns in human tissues.
Doctoral student Justin Y. Newberg says that the automated protein pattern recognition tool, and its underlying methods may be helpful in identifying such biomarkers as may be crucial to cancer diagnosis and therapy.
"Distribution of proteins in a cell or group of cells can be used to identify the state of surrounding tissue, whether it is healthy or diseased," said Newberg, the newsletter editor for Carnegie Mellon's Graduate Biomedical Engineering Society.
"So, our tools can be used to develop novel approaches to screen tissue, which could have an immense benefit in such things as cancer diagnosis," he added.
In a research article in the Journal of Proteome Research, the researchers say that scientists are increasingly collecting large numbers of images due to the availability of automated microscopes, and these images provide an excellent opportunity for improving the understanding of biological processes.
However, the availability of such microscopic images has also created a need for automated bio-image analysis tools, say the researchers.
Newberg describes the Human Protein Atlas as an excellent example of a large-scale dataset ripe for automated analysis.
According to the researcher, the atlas consists of more than 3,000 proteins imaged in 45 normal and 20 cancerous human tissues.
Newberg has revealed that he and his colleagues have already applied their tool to analyse images of eight major sub-cellular location patterns with a high degree of accuracy.
He says that the team's work is a strong indication that automated analysis of the whole atlas is feasible.
The research team is now planning to continue studying and characterizing all of the proteins in the atlas.
"Knowing the exact location of thousands of proteins in human cells will enable a much better understanding of how these cells work and could ultimately advance the detection and diagnosis of serious diseases," Robert F. Murphy, who jointly conducted the research with Newberg.