Department of Bioengineering, University of Pennsylvania; Philadelphia, Pennsylvania, United States of America.
Department of Bioengineering, Rice University, Houston, Texas, United States of America.
PLoS Comput Biol. 2022 Jun 13;18(6):e1009846. doi: 10.1371/journal.pcbi.1009846. eCollection 2022 Jun.
We introduce cytoNet, a cloud-based tool to characterize cell populations from microscopy images. cytoNet quantifies spatial topology and functional relationships in cell communities using principles of network science. Capturing multicellular dynamics through graph features, cytoNet also evaluates the effect of cell-cell interactions on individual cell phenotypes. We demonstrate cytoNet's capabilities in four case studies: 1) characterizing the temporal dynamics of neural progenitor cell communities during neural differentiation, 2) identifying communities of pain-sensing neurons in vivo, 3) capturing the effect of cell community on endothelial cell morphology, and 4) investigating the effect of laminin α4 on perivascular niches in adipose tissue. The analytical framework introduced here can be used to study the dynamics of complex cell communities in a quantitative manner, leading to a deeper understanding of environmental effects on cellular behavior. The versatile, cloud-based format of cytoNet makes the image analysis framework accessible to researchers across domains.
我们介绍了 cytoNet，这是一个基于云的工具，用于从显微镜图像中描述细胞群体。cytoNet 使用网络科学原理来量化细胞群落的空间拓扑和功能关系。通过图特征捕获多细胞动力学，cytoNet 还评估了细胞间相互作用对单个细胞表型的影响。我们在四个案例研究中展示了 cytoNet 的功能：1）描述神经祖细胞群体在神经分化过程中的时空动态，2）在体内识别疼痛感受神经元群落，3）捕捉细胞群落对内皮细胞形态的影响，4）研究层粘连蛋白α4 对脂肪组织血管周龛的影响。这里介绍的分析框架可用于以定量方式研究复杂细胞群落的动态，从而更深入地了解环境对细胞行为的影响。cytoNet 灵活的基于云的格式使图像分析框架可供跨领域的研究人员使用。
