During developmental processes such as embryogenesis, how a group of cells fold into specific structures, is a central question in biology that defines how living organisms form. Establishing tissue-level morphology critically relies on how every single cell decides to position itself relative to its neighboring cells. Despite its importance, it remains a major challenge to understand and predict the behavior of every cell within the living tissue over time during such intricate processes. To tackle this question, we propose a geometric deep learning model that can predict multicellular folding and embryogenesis, accurately capturing the highly convoluted spatial interactions among cells. We demonstrate that multicellular data can be represented with both granular and foam-like physical pictures through a unified graph data structure, considering both cellular interactions and cell junction networks. We successfully use our model to achieve two important tasks, interpretable 4-D morphological sequence alignment, and predicting local cell rearrangements before they occur at single-cell resolution. Furthermore, using an activation map and ablation studies, we demonstrate that cell geometries and cell junction networks together regulate local cell rearrangement which is critical for embryo morphogenesis. This approach provides a novel paradigm to study morphogenesis, highlighting a unified data structure and harnessing the power of geometric deep learning to accurately model the mechanisms and behaviors of cells during development. It offers a pathway toward creating a unified dynamic morphological atlas for a variety of developmental processes such as embryogenesis.

我们提出了一个几何深度学习模型，可以预测多细胞折叠和胚胎发生过程，准确捕捉细胞之间高度复杂的空间相互作用。通过统一的图数据结构，考虑细胞相互作用和细胞连接网络，我们成功地完成了可解释的4D形态序列对齐和在单细胞分辨率上预测局部细胞重新排列的两个重要任务。此方法提供了研究形态发生的新范式，突出了统一的数据结构，并利用几何深度学习的能力来准确建模发育过程中细胞的机制和行为，为创建各种发育过程（如胚胎发生）的统一动态形态图谱提供了途径。

多细胞折叠：多细胞生命中的几何学学习