New Nanotechnology Technique Can Differentiate Cancer Cells from Normal Cells

A University of California at Los Angeles (UCLA) team of scientists and researchers were able to differentiate cancer cells from normal cells using a new nanotechnology technique that "feels" the "softness" (or "stiffness") of the cells. Details of this new technique and its potential for cancer detection were published recently in the advance online edition of the leading-edge scientific journal Nature Nanotechnology.

According to the new paper, this is the first time that scientist were able to take living cells from cancer patients and, using nanotechnology, analyze them and screen which were cancerous and which were not. The new nanotechnology technique may have interesting applications for detecting cancer and may aid in giving patients more personalizing treatments protocols.

The new technique is based on measuring a rather elusive concept in cell biology: the "flexibility" of the cell. Cancerous cells must travel long distances through the body to infect other organs. These methastasic cells should become more "flexible" if they will be able to overcome all sort of physical obstacles in the blood stream and within tiny and complex cell matrices of cell tissue. The new technology can detect, at the nano level, this softness or flexibility of the cancerous cells.

Researchers at the UCLA's Jonsson Cancer Center, collected fluid samples of lung, breast and pancreatic cancer patients. Under a regular microscope cancer and normal cells looked very similar so there was no way to differentiate one cell from the other. In addition, conventional tests had 70% success rate in detecting cancerous cells (which is normal for these tests).

Rao and colleagues used a new leading edge technology known as Atomic Force Microscope (AFM) to measure cell softness. The Atomic Force Microscope utilizes a very tiny and sharp tip that pushes and feels the cell surface and determines the degree of softness. It is like a physical examination at the cell level.