Scientists from Massachusetts Institute of Technology (Cambridge, MA), Pennsylvania State University (University Park, PA), and Carnegie Mellon University (Pittsburgh, PA) have collectively published a paper on cell separation using sound waves. The study initially focused on separating tiny plastic beads varying from 7.3 to 9.9 microns in diameter, resulting in 97% sorting accuracy. They then moved on to separating white blood cells (20 microns) and MCF-breast cancer tumour cells (12 microns), achieving 71% accuracy. The potential for this tool holds great significance in the medical field as finding tumour cells in the blood indicates the spreading of a tumour. When a tumour is about to spread, some cells migrate into the blood via a process called extravasation and travel to another site in the body.
The tool works by taking a sample of cells, making them all flow in the same direction and then exposing them to sound waves. The sound waves deflect the cells slightly, and depending on properties such as density and size, they will be sorted into different cell types. The development of a similar tool has been attempted before, however in previous studies the sound waves could never generate enough deflection to separate the cells completely. The change the scientists made to differ from earlier attempts was tilting the sound waves so that they passed through the cells at an angle instead of head on.
The development of this tool crucially means no chemical or mechanical damage to cells when being tested. Dr. Ming Dao, a leading research scientist at Massachusetts Institute of Technology said, “Acoustic pressure is very mild and much smaller in terms of forces and disturbance to the cell. This is a most gentle way to separate cells, and there’s no artificial labelling necessary.”
The team are now hoping to test the tool in clinical trials, using blood samples from cancer patients. If these trials prove to be a success, sound waves may be a simple solution to telling whether a tumour is about to spread or not.
To read the full paper, click [here]
Illustration: Christine Daniloff/MIT