Understanding the evolution of cellular microenvironments in spatiotemporal data is essential for deciphering tissue development and disease progression. While experimental techniques like spatial transcriptomics now enable high-resolution mapping of tissue organization across space and time, current methods that model cellular evolution operate at the single-cell level, overlooking the coordinated development of cellular states in a tissue. We introduce NicheFlow, a flow-based generative model that infers the temporal trajectory of cellular microenvironments across sequential spatial slides. By representing local cell neighborhoods as point clouds, NicheFlow jointly models the evolution of cell states and spatial coordinates using optimal transport and Variational Flow Matching. Our approach successfully recovers both global spatial architecture and local microenvironment composition across diverse spatiotemporal datasets, from embryonic to brain development.

翻译：理解时空数据中细胞微环境的演变对于解析组织发育和疾病进程至关重要。尽管空间转录组学等实验技术现已能够实现跨时空组织结构的高分辨率图谱绘制，但当前模拟细胞演化的方法均基于单细胞层面，忽略了组织中细胞状态的协同发展。我们提出NicheFlow——一种基于流的生成模型，能够通过连续空间切片推断细胞微环境的时序轨迹。该方法将局部细胞邻域表示为点云，并利用最优传输和变分流匹配技术联合建模细胞状态与空间坐标的演化过程。我们的方法在从胚胎发育到大脑发育的多种时空数据集中，成功重建了全局空间结构与局部微环境组成。