用流模型追踪组织微环境演变，揭示细胞群体协同变化规律。
Modeling Microenvironment Trajectories on Spatial Transcriptomics with NicheFlow
- 将细胞邻域建模为点云，结合最优传输与变分流匹配建模时空演化
- 在胚胎到脑发育数据中同时恢复全局空间结构与局部微环境组成
- 适合研究组织发育、疾病进展中的细胞协作机制
理解时空数据中细胞微环境的演变对解析组织发育和疾病进展至关重要。尽管空间转录组学技术现已实现高分辨率的组织空间与时间映射，但现有细胞演化建模方法仍局限于单细胞层面，忽略了组织内细胞状态的协同发展。我们提出 NicheFlow，一种基于流的生成模型，用于推断多时相空间切片中细胞微环境的时序轨迹。通过将局部细胞邻域表示为点云，NicheFlow 利用最优传输与变分流匹配联合建模细胞状态与空间坐标的演化。该方法在多种时空数据集（包括胚胎发育至脑发育）中成功恢复了全局空间架构与局部微环境组成。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。