Science/Tech

Researchers Exploit Bioelectric Signals to Detect, Manipulate Early Cancer Cells

By Affirunisa Kankudti | Update Date: Feb 01, 2013 05:10 AM EST

Researchers have now found a way to use bioelectricity to detect early cancer cells and even manipulate the bioelectric signals of these cells to lower the risk of cancer occurrence. 

Bioelectric signals control the functions of the cell, telling it when to grow and multiply. Researchers from Tufts University School of Arts and Sciences investigated how the bioelectric signals prompt a cell to become tumorous in Xenopus laevis frog embryos.

"The news here is that we've established a bioelectric basis for the early detection of cancer," said Brook Chernet, doctoral student and the first author of a newly published research.

For the study, researchers first induced cancer in the embryos of frog. This they did by injecting the embryos with genes that are associated with human cancers like melanoma, leukemia and lung cancer.

Next, they checked the bioelectric activity of the tumor cells using membrane voltage-sensitive dye and fluorescence microscopy. They found that these tumor cells had a unique bioelectric signature when compared to normal cells.

"The tumor sites had unique depolarized membrane voltage relative to surrounding tissue. They could be recognized by this distinctive bioelectric signal," said Chernet.

Researchers have even found a way to manipulate these bioelectric signals to lower the incidence of the tumor cells in the body. For this, they injected the tumor cells with mRNA (messenger RNA) that code for specific ion channels or proteins that control movement of ions across the membrane of the cell.

"We've shown that electric events tell the cells what to do. The voltage changes are not merely a sign of cancer. They control and direct whether the cancer occurs or not," said Michael Levin, Ph.D., professor of biology and director of the Center for Regenerative and Developmental Biology, a co-author of the study.

The study is published in the Disease Models and Mechanisms.     

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