Centre for Integrative Systems Biology and Bioinformatics, Imperial College London, London, SW7 2BU, UK.
Syngenta Crop Protection AG, Schaffhauserstrasse 101, 4332, Stein, Switzerland.
Nat Commun. 2021 Nov 5;12(1):6424. doi: 10.1038/s41467-021-26577-1.
Medicines and agricultural biocides are often discovered using large phenotypic screens across hundreds of compounds, where visible effects of whole organisms are compared to gauge efficacy and possible modes of action. However, such analysis is often limited to human-defined and static features. Here, we introduce a novel framework that can characterize shape changes (morphodynamics) for cell-drug interactions directly from images, and use it to interpret perturbed development of Phakopsora pachyrhizi, the Asian soybean rust crop pathogen. We describe population development over a 2D space of shapes (morphospace) using two models with condition-dependent parameters: a top-down Fokker-Planck model of diffusive development over Waddington-type landscapes, and a bottom-up model of tip growth. We discover a variety of landscapes, describing phenotype transitions during growth, and identify possible perturbations in the tip growth machinery that cause this variation. This demonstrates a widely-applicable integration of unsupervised learning and biophysical modeling.
药品和农用生物杀灭剂通常通过数百种化合物的大型表型筛选来发现，在这种筛选中，比较整个生物体的可见效应来评估疗效和可能的作用模式。然而，这种分析通常仅限于人为定义的和静态的特征。在这里，我们引入了一个新的框架，可以直接从图像中描述细胞-药物相互作用的形状变化（形态动力学），并使用它来解释亚洲大豆锈病作物病原体 Phakopsora pachyrhizi 被干扰的发育。我们使用两个具有条件相关参数的模型来描述形状（形态空间）上的种群发展：一个是基于扩散的 Waddington 型景观的自上而下的福克-普朗克模型，另一个是顶端生长的自下而上的模型。我们发现了各种各样的景观，描述了生长过程中的表型转变，并确定了可能导致这种变化的顶端生长机制的干扰。这证明了无监督学习和生物物理建模的广泛应用。
