Modeling adult skeletal stem cell response to laser-machined topographies through deep learning
This reduces experimental cell culture needs and accelerates prediction of novel surface effects for tissue engineering applications.
The researchers tackled the problem of predicting adult human bone marrow stem cell responses to laser-machined surface topographies using a deep neural network, achieving statistically significant predictions with P < 0.001 and enabling determination of minimum line separation for cell alignment.
The response of adult human bone marrow stromal stem cells to surface topographies generated through femtosecond laser machining can be predicted by a deep neural network. The network is capable of predicting cell response to a statistically significant level, including positioning predictions with a probability P < 0.001, and therefore can be used as a model to determine the minimum line separation required for cell alignment, with implications for tissue structure development and tissue engineering. The application of a deep neural network, as a model, reduces the amount of experimental cell culture required to develop an enhanced understanding of cell behavior to topographical cues and, critically, provides rapid prediction of the effects of novel surface structures on tissue fabrication and cell signaling.