Machine learning programme used to predict stem cell growth

Researchers have used a computational model to learn how to manipulate stem cell arrangement, including those that may eventually be useful in generating personalised organs. According to the team, their discovery could be used to develop model organs grown from a patient’s own cells, which could ‘revolutionise’ how diseases are treated by increasing disease understanding or testing drugs. The study was conducted by a team from Gladstone Institutes, in collaboration with Boston University, both US. Induced pluripotent stem (iPS) cells, similar to the stem cells found in an embryo, have the potential to become nearly every type of cell in the body. Although researchers can prompt these cells into differentiating into specific organ cells, they cannot grow into functioning three-dimensional (3D) organs. “Despite the importance of organisation for functioning tissues, we as scientists have had difficulty creating tissues in a dish with stem cells,” said Ashley Libby, co-first author. “Instead of an organised tissue, we often get a disorganised mix of different cell types.”