类器官的拓扑推断
Topological Inference for Organoids
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- University of Oxford（牛津大学）
- Ludwig Institute for Cancer Research（路德维希癌症研究所）
- Centre for Systems Biology Dresden（德累斯顿系统生物学中心）
- Max Planck Institute of Molecular Cell Biology and Genetics（马克斯·普朗克分子细胞生物学与遗传学研究所）
- Technische Universität Dresden（德累斯顿工业大学）
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中文总结 AI 辅助
本研究结合拓扑数据分析、生物物理模拟与贝叶斯推断，利用SampEuler描述符和ABC框架，从图像中准确推断胰腺类器官的渗透压与增殖率，建立非破坏性的形态-机制关联流程。
中文摘要 AI 辅助
器官形态的可重复性以及计算模型预测形态发生的程度仍然难以量化，特别是对于具有复杂流体填充腔网络结构的器官。在此，我们结合拓扑数据分析（TDA）、生物物理模拟和贝叶斯推断来研究胰腺类器官中的腔形态发生。腔的形成受难以直接测量的物理过程控制，包括细胞增殖和腔内渗透压。我们使用相场模型模拟类器官发育，并解决从延时图像或单一形态快照中推断这些参数的逆问题。由于腔结构在大小、结构和连通性上差异显著，传统的几何描述符只能部分表征其形态。因此，我们使用SampEuler（一种基于欧拉特征变换（ECT）的拓扑描述符）来表示每个类器官。我们首先证明SampEuler能够捕捉已建立的形态计量学所编码的形态信息。随后，我们使用基于近似贝叶斯计算（ABC）拒绝框架和SampEuler Wasserstein距离进行参数推断。利用具有已知真实参数的合成类器官，我们的方法能够准确恢复渗透压和增殖率，同时揭示这两个过程之间的补偿性权衡。将方法应用于来自十个胰腺类器官的实验数据，推断的后验分布与生物学预期一致。综上，这些结果建立了一条非破坏性的、基于图像的流程，用于估计控制腔形成的难以直接测量的物理参数，并凸显了拓扑表示在将复杂生物形态与机制模型联系起来方面的潜力。
英文摘要
The reproducibility of organ morphology and the extent to which computational models can predict morphogenesis remain difficult to quantify, particularly for organs with complex networks of fluid-filled lumina. Here, we combine Topological Data Analysis (TDA), biophysical simulation, and Bayesian inference to study lumen morphogenesis in pancreatic organoids. Lumen formation is governed by physical processes that are challenging to measure directly, including cell proliferation and luminal osmotic pressure. We simulate organoid development using a phase-field model and address the inverse problem of inferring these parameters from either time-lapse images or single morphological snapshots. Since lumen architectures vary substantially in size, structure, and connectivity, conventional geometric descriptors provide only a partial representation of their morphology. We therefore represent each organoid using SampEuler, a topological descriptor derived from the Euler Characteristic Transform (ECT). We first show that SampEuler captures morphological information encoded by established morphometrics. We then perform parameter inference using an approximate Bayesian computation (ABC) rejection framework with the SampEuler Wasserstein distance. Using synthetic organoids with known ground-truth parameters, our approach accurately recovers the osmotic pressure and the proliferation rate while revealing a compensatory trade-off between the two processes. Applied to experimental data from ten pancreatic organoids, the inferred posterior distributions are consistent with biological expectations. Together, these results establish a non-destructive, image-based pipeline for estimating otherwise inaccessible physical parameters governing lumen formation and highlight the potential of topological representations for linking complex biological morphology to mechanistic models.
