  Cell lineage decisions occur in three-dimensional spatial patterns that are
difficult to identify by eye. There is an ongoing effort to replicate such
patterns using mathematical modeling. One approach uses long ranging cell-cell
communication to replicate common spatial arrangements like checkerboard and
engulfing patterns. In this model, the cell-cell communication has been
implemented as a signal that disperses throughout the tissue. On the other
hand, machine learning models have been developed for pattern recognition and
pattern reconstruction tasks. We combined synthetic data generated by the
mathematical model with deep learning algorithms to recognize and reconstruct
spatial cell fate patterns in organoids of mouse embryonic stem cells. A graph
neural network was developed and trained on synthetic data from the model.
Application to in vitro data predicted a low signal dispersion value. To test
this result, we implemented a multilayer perceptron for the prediction of a
given cell fate based on the fates of the neighboring cells. The results show a
70% accuracy of cell fate reconstruction based on the nine nearest neighbors of
a cell. Overall, our approach combines deep learning with mathematical modeling
to link cell fate patterns with potential underlying mechanisms.
