Prediction of Cellular Identities from Trajectory and Cell Fate Information
This work addresses the challenge of cell identification for developmental biology, offering a faster alternative to complex cell tracking methods, though it is incremental as it applies existing methods to a specific domain.
The study tackled the problem of identifying cells in imaging sequences during early C. elegans embryogenesis by using machine learning models, achieving over 91% accuracy in cell classification with limited data.
Determining cell identities in imaging sequences is an important yet challenging task. The conventional method for cell identification is via cell tracking, which is complex and can be time-consuming. In this study, we propose an innovative approach to cell identification during early $\textit{C. elegans}$ embryogenesis using machine learning. Cell identification during $\textit{C. elegans}$ embryogenesis would provide insights into neural development with implications for higher organisms including humans. We employed random forest, MLP, and LSTM models, and tested cell classification accuracy on 3D time-lapse confocal datasets spanning the first 4 hours of embryogenesis. By leveraging a small number of spatial-temporal features of individual cells, including cell trajectory and cell fate information, our models achieve an accuracy of over 91%, even with limited data. We also determine the most important feature contributions and can interpret these features in the context of biological knowledge. Our research demonstrates the success of predicting cell identities in time-lapse imaging sequences directly from simple spatio-temporal features.