Single Cell Analysis Tool Identifies Cancerous Cells

Single Cell Analysis Tool Identifies Cancerous Cells

Researchers at Brown University have developed a new imaging technique that can be used to help identify cells that have undergone cancerous changes from healthy cells. The single cell analysis tool is still in the early stages of development; however, the results have been highly promising and the tool has potential to be used for cancer screening.

At present the tool is best used for screening new drugs, but Susan Leggett, lead author of the paper said the accuracy of the new tool is impressive and explained that it is the “the first of its kind to do a really extensive morphological study.”

During the healing process, human development and cancerous growth, cells undergo a change called epithelial-mesenchymal transition. The process is essential for the development of many tissue during embryogenesis and also during wound healing. The process also occurs during cancerous cell growth.

Epithelial cells and mesenchymal cells have a different shape. Epithelial cells have a cube-like form, while mesenchymal cells are elongated; however, the team showed numerous other differences in the two types of cells. The team stained the cells for various biomarkers and observed many different visual characteristics. For instance, epithelial cells were found to have higher concentrations of E-cadherin, while mesenchymal cells have higher concentrations of vimentin.

Determining the relative concentrations of each type of cell in a tissue helps clinicians determine the aggressiveness of a tumor. Since mesenchymal cells are more mobile, if tumors have higher concentrations of mesenchymal cells there is greater potential for the cells to break away and form other tumors around the body.

While many characteristics of each cell type were discovered, the team picked four of the most predictive characteristics and incorporated them into their algorithm. Using these four characteristics, the team was able to classify epithelial and mesenchymal phenotypes with 92% accuracy.

Leggett believes the technique has excellent potential for understanding and predicting cancerous growth. The single cell analysis tool could be used to assess cancerous growth and risk by machine, rather than relying on clinicians to perform an analysis by eye.

In Leggett’s words, “Our quantitative, single cell approach has the potential to screen large heterogeneous cell populations for many types of phenotypic variability, and may thus provide a predictive assay for the preclinical assessment of targeted therapeutics.”