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.

翻译：在胚胎发生等发育过程中，细胞群如何折叠成特定结构是生物学的一个核心问题，它定义了生命体如何形成。组织层面形态的建立关键取决于每个单细胞如何决定自身相对于邻近细胞的位置。尽管这一问题至关重要，但在此类复杂过程中理解和预测活体组织内每个细胞随时间变化的行为仍然是一个重大挑战。为解决这一问题，我们提出了一种几何深度学习模型，能够预测多细胞折叠和胚胎发生过程，精确捕捉细胞间高度复杂的空间相互作用。我们证明，通过统一的图数据结构（同时考虑细胞相互作用和细胞连接网络），多细胞数据可以用颗粒状和泡沫状两种物理图像进行表征。我们成功运用该模型完成了两项重要任务：可解释的四维形态序列比对，以及在单细胞分辨率下预测尚未发生的局部细胞重排。此外，通过激活图谱和消融实验，我们证实细胞几何形态与细胞连接网络共同调控对胚胎形态发生至关重要的局部细胞重排。该方法为研究形态发生提供了新范式，通过统一的数据结构并利用几何深度学习的力量，精确模拟发育过程中细胞的机制和行为。这为构建涵盖胚胎发生等多种发育过程的统一动态形态图谱开辟了道路。