What's Happening?
MIT engineers have developed a geometric deep learning method called MultiCell, which predicts how individual cells will fold, divide, and rearrange during the early development of fruit fly embryos. The
model records and tracks properties such as a cell's position and its interactions with neighboring cells. Applied to videos of developing fruit fly embryos, the model achieved 90% accuracy in predicting cell behavior during the first hour of development. This phase, known as gastrulation, involves significant cellular rearrangement, and the model's predictions could help uncover how local cell interactions give rise to global tissues and organisms.
Why It's Important?
The ability to predict cell behavior with high accuracy has significant implications for developmental biology and medicine. The model could be applied to predict the development of more complex tissues, organs, and organisms, potentially identifying cell patterns corresponding to early-onset diseases like asthma and cancer. This method sets the stage for data-driven quantitative studies of dynamic multicellular developmental processes at single-cell precision, offering a pathway toward a unified morphodynamic atlas. The research highlights the potential for deep learning to advance our understanding of biological development and disease.
What's Next?
The researchers aim to apply the model to predict cell-by-cell development in other species, such as zebrafish and mice, to identify common patterns across species. They also envision using the method to discern early patterns of disease, such as asthma, by capturing subtle dynamical differences in tissue behavior. The limiting factor is the availability of high-quality video data, and future work could focus on increasing data availability through standardized pipelines to create large datasets. The model is ready for application, but the real bottleneck is obtaining good quality data.
