R esearchers created a machine learning algorithm using computational topology that profiles shapes and spatial patterns in embryos to study how these cells organize themselves into tissue-like architectures. In a new study, they take that system to the next level, opening a path to studying how multiple types of cells assemble themselves.
The fact that humans and other living organisms can develop and grow from a single cell relies on a process called embryonic development. For healthy tissue to form, cells in the embryo have to organize themselves in the right way in the right place at the right time. When this process doesn't go right, it can result in birth defects, impaired tissue regeneration or cancer.
For example, in an animal embryo, the outer layer of cells goes on to form skin, the middle layer forms muscle and bone, while the innermost layer forms the liver or lungs. Cells within each layer will preferentially adhere to each other, sorting apart from cells in other layers that go on to form other parts of the body.
The hiccup with the original system was that it is a slow and labor-intensive process. The algorithm painstakingly compared these topological features one-by-one against those in other sets of cell positions to determine how topologically different or similar they are.
The researchers say the goal is to work backward and infer the rules that describe how different cell types arrange themselves based on the final pattern. For instance, if they tinker with how certain cells are more adhesive or less adhesive, the researchers can identify how and when dramatic alterations occur in tissue architecture.
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