Researchers develop new tools to study traditional cell cycle phases

The human body contains approximately 200 different kinds of cells, including many kinds of stem cells that have the potential to turn into a variety of specialized cells. Probing into the life cycle states or phases of these cells is important to understanding and treating diseases like dementia and cancer.

Christopher Plaisier, an assistant professor of biomedical engineering in the Ira A. Fulton Schools of Engineering at Arizona State University, and Samantha O’Connor, a biomedical engineering doctoral student in the Plaisier Lab, are leading research into a new state of the stem cell life cycle that could be the key to unlocking new methods of brain cancer treatment. Their work was recently published in the research journal Molecular Systems Biology.

Arizona State University biomedical engineering researchers developed a new cell classifier tool that takes a higher-resolution look at the life cycle of neuroepithelial stem cells, which led to the discovery and exploration of a new resting phase called Neural G0. This knowledge could help scientists to better understand glioma brain tumors and develop new methods of treatment. 

“The cell cycle is such a well-studied thing, and yet here we are looking at it again for the umpteenth time and a new phase pops out at us,” Plaisier said. “Biology always has new insights to show us; you just have to look.”

Taking a closer look

The spark for this discovery came through a collaboration with Patrick Paddison, an associate professor at the Fred Hutchinson Cancer Research Center in Seattle, and Dr. Anoop Patel, an assistant professor of neurological surgery at the University of Washington who is also involved in the Fred Hutchinson Cancer Research Center. Paddison’s team was looking at genes that play a role in stem cells’ growth in the brain as a potential path to a treatment for neurodegeneration.

“The primary feature of any cancer is that the cells are proliferating,” Plaisier said. “If we could get in there and figure out what the mechanisms are, that might be a place to slow them down.

“In cases where you have neurodegeneration, having stem cells that proliferate is potentially beneficial. The one problem with that is it’s also similar to what happens when cancer cells increase rapidly. It’s two sides of the same coin.”

Paddison’s team called upon Plaisier to help analyze their brain stem cell data characterized through a process called single-cell RNA sequencing.

“That data turned out to be pretty amazing,” Plaisier said. “It mapped out into this beautiful circular pattern that we identified as all of the different phases of the cell cycle.”

By taking a closer look at the data, they found a new cell phase that had never been observed before in neuroepithelial stem cells, which they call Neural G0. This discovery sparked a six-year research project to study the new phase and determine what it means for the progression of diseases, particularly glioma brain tumors.

Developing new tools

Researchers use tools called classifiers to assign a cell cycle phase to individual cells based on the state of RNA messenger substances inside the cell.

Many existing cell cycle classifiers pick out only the major steps in a cell’s life cycle, like a map of the world that depicts only outlines of the continents. However, just as we know there are far more details that can be mapped on a continent, it turns out there is much more to see within the individual stages of the cell cycle.

“We were able to pick out phases that are glommed together in the other cell cycle classifiers,” Plaisier said. “That potentially has several different uses.”

The tool O’Connor developed — called ccAF, or cell cycle ASU/Fred Hutchinson to represent the collaboration between the two institutions — takes a closer, “high-resolution” look at what’s happening within the growth cycles of stem cells and identifies genes that can be used to track progress through the cell cycle.

“Our classifier gets deeper into the cell cycle because there could be pieces we’re capturing that have important implications for disease,” O’Connor said.

While it is possible to pick out the cell phases using complicated techniques such as applying fluorescent markers on actual cells, Plaisier and O’Connor found the Neural G0 state purely through looking at data, a much simpler process.

Plaisier and O’Connor are making the ccAF classifier tool open source and available in a variety of formats for anyone studying single-cell RNA sequencing data to ease into the process of studying cell cycles.

The tool could also be used to study other types of stem cells that potentially have yet-to-be-discovered G0 states similar to or completely different from the Neural G0 state of neuroepithelial stem cells.

Source – Arizona State University

Availability – The the ccAF classifier tool open source and available at: https://github.com/plaisier-lab/ccAF