Multicell-Fold：多细胞生命折叠中的几何学习
Multicell-Fold: geometric learning in folding multicellular life
AI总结：
提出几何深度学习模型Multicell-Fold，统一图结构表示多细胞数据，预测胚胎发生中的多细胞折叠与局部细胞重排，实现4维形态序列对齐，揭示细胞几何与连接网络共同调控形态发生。
摘要：
在胚胎发生等发育过程中，一组细胞如何折叠成特定结构，是生物学中定义生物体如何形成的核心问题。建立组织层面的形态学关键依赖于每个单细胞如何决定其相对于邻近细胞的位置。尽管其重要性，但在这些复杂过程中，理解和预测活体组织内每个细胞随时间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，准确捕捉细胞间高度复杂的空间相互作用。我们证明了多细胞数据可以通过统一的图数据结构，以粒状和泡沫状物理图像来表示，同时考虑细胞相互作用和细胞连接网络。我们成功使用我们的模型实现了两个重要任务：可解释的4维形态序列对齐，以及在单细胞分辨率下预测局部细胞重排发生之前的情况。此外，利用激活图和消融研究，我们证明了细胞几何形状和细胞连接网络共同调节局部细胞重排，这对胚胎形态发生至关重要。该方法为研究形态发生提供了一种新范式，强调统一的数据结构，并利用几何深度学习的力量，准确模拟发育过程中细胞的机制和行为。它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。
英文摘要：
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.
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