UWM: Engineer creates software that predicts stem cell fate

MILWAUKEE — A software program created by an engineer at the University of Wisconsin–Milwaukee (UWM) can not only predict the types of specialized cells a stem cell will produce, but also foresee the outcome before the stem cell even divides.

The software, developed by Andrew Cohen, an assistant professor of electrical engineering, is 87 percent accurate in determining the specific “offspring” a stem cell will ultimately produce, and 99 percent accurate in predicting when self-renewal of these stem cells will end in specialization.

It will allow scientists to search for mechanisms that control stem cell specialization, the main obstacle in advancing the use of stem cell therapy for treatment of disease. It could also lead to new research into causes of cancer, which involves cells that continuously self-renew.

The research is published Feb. 7 in the journal Nature Methods. Co-authors are Michel Cayouette and Francisco Gomez neurobiologists at the Institut de recherches cliniques de Montreal, and Badri Roysam, a computer engineering professor at Rensselaer Polytechnic Institute.

Stem cells play a key role in human development, and also offer the potential to repair tissues or organs damaged by disease or injury. But in order to use stem cell-based therapies, biologists need to better understand the mechanisms that control stem cell differentiation.

“This is a brand-new set of tools for developmental biologists,” says Cohen, “and it supports an area where very few predictive software solutions exist.”

A hunt for markers

Scientists know little about programming of stem cell outcomes except that it is a multifaceted process.

“In many cases stem cells take their developmental cues from their environment,” says Cohen. “Part of the programming mechanism is determined by surrounding cells. But once these cells begin to develop in a particular way, their offspring continue down that path even if the environment changes. So at some point they have been programmed to their fate.”

The researchers designed the software to be used for isolating the genes and proteins that control the specialization process, which could allow researchers to identify and ultimately manipulate these programmed mechanisms.

As an example of the software’s utility, Cohen cites using stem cells to treat the eye disease macular degeneration. The stem cells would need to produce more photoreceptor neurons. “But if you simply implant the stem cells into the retina, there are other types of cells that could develop,” he says, “and that could potentially make the patient’s vision worse.”

Overcoming obstacles

Finding a solution has been hampered by the fact that there are very few markers that can predict cell division outcomes. The behaviors that characterize populations of stem cells with different fates are difficult or impossible for human observers to recognize. In addition, current methods of observing live, dynamic cellular activity produce terabytes of data, a volume that requires software to find the most relevant data.

The researchers’ software runs in real time and is able to track 40 cells at a time on a standard PC at five minutes per frame.

In designing the software, Cohen used a uniquely sensitive mathematical approach based on algorithmic information theory to manage the predictive aspects of the program. The resulting tool outperforms the human eye in detecting very subtle changes in the physical cell and its behavior over time – changes that correspond to the future development outcomes.

“The method isn’t perfect,” says Brian Link, a developmental biologist at the Medical College of Wisconsin who works with Cohen, but is not an author on the Nature Methods paper. “It doesn’t tell us about the influence of the behaviors. It tells us that a particular behavior is important, but it doesn’t tell us how.”

Still, the tool has already proven itself, says Link. In a study of stem cells from the retinas of rats, Cohen’s software independently confirmed the significance of at least one of the same cell behaviors that Link’s lab had previously identified using a gene manipulation technique.

###

(CONTACT: Andrew Cohen, 414-229-5861 or cohena@uwm.edu.)