Institut Pasteur, Université Paris Cité, Physical microfluidics and Bioengineering, Paris, F-75015, France.
LadHyX, CNRS, Ecole Polytechnique, Institut Polytechnique de Paris, Palaiseau, 91120, France.
BMC Biol. 2022 Aug 11;20(1):178. doi: 10.1186/s12915-022-01376-2.
Microscopy techniques and image segmentation algorithms have improved dramatically this decade, leading to an ever increasing amount of biological images and a greater reliance on imaging to investigate biological questions. This has created a need for methods to extract the relevant information on the behaviors of cells and their interactions, while reducing the amount of computing power required to organize this information.
This task can be performed by using a network representation in which the cells and their properties are encoded in the nodes, while the neighborhood interactions are encoded by the links. Here, we introduce Griottes, an open-source tool to build the "network twin" of 2D and 3D tissues from segmented microscopy images. We show how the library can provide a wide range of biologically relevant metrics on individual cells and their neighborhoods, with the objective of providing multi-scale biological insights. The library's capacities are demonstrated on different image and data types.
This library is provided as an open-source tool that can be integrated into common image analysis workflows to increase their capacities.
显微镜技术和图像分割算法在过去十年中得到了显著的改进，导致生物图像的数量不断增加，并且对成像技术的依赖程度也越来越高，以研究生物学问题。这就需要开发方法来提取有关细胞行为及其相互作用的相关信息，同时减少组织这些信息所需的计算能力。
可以通过使用网络表示来执行此任务，其中将细胞及其属性编码为节点，而将邻域交互编码为链接。在这里，我们介绍了 Griottes，这是一个开源工具，用于从分割的显微镜图像构建 2D 和 3D 组织的“网络孪生体”。我们展示了该库如何为单个细胞及其邻居提供广泛的生物学相关指标，旨在提供多尺度的生物学见解。该库的功能已在不同的图像和数据类型上得到了验证。
该库作为一个开源工具提供，可以集成到常见的图像分析工作流程中，以提高其能力。
