Gene Activity In Thousands Of Cells Visualized For The First Time, Important For Understanding Cancer Tumors

For the first time, scientists will be able to visualize gene activity in thousands of cells all at once.

Biologists at the University of Zurich developed a new method using robots, an automated fluorescence microscope and a supercomputer where 1,000 human genes can be studied in 10,000 single cells. Details surrounding the new technique, published in the journal Nature Methods, may impact how cancer is treated.

"Our method will be of importance to basic research and the understanding of cancer tumors because it allows us to map the activity of genes within single tumor cells," Professor Lucas Pelkmans from the University of Zurich, said in a statement.

The technique revolves around transcript molecules -- particles that are created whenever cells activate genes. Transcript molecules in effect make the function of the gene available to the cell. While present methods allow scientists to determine gene activity by measuring the amount of transcript molecules, they are not able to measure thousands of them simultaneously.

"When genes become active, specific transcript molecules are produced. We can stain them with the help of a robot," Thomas Stoeger of the University of Zurich said. Once stained, fluorescence microscope images of glowing transcript molecules are generated and then analyzed by the supercomputer. Not only can 1,000 human genes be studied in 10,000 single cells but, for the first time, the spatial organization of the transcript molecules of many genes can be observed.

The technique yielded a few surprises. It showed that individual cells distinguish themselves from the activity in their genes. Plus, scientists were shocked to discover inconsistences in the spatial organization of transcript molecules within single cells and between multiple single cells.

"We realized that genes with a similar function also have a similar variability in the transcript patterns," Nico Battich said. "This similarity exceeds the variability in the amount of transcript molecules, and allows us to predict the function of individual genes."