Divison of Biomedical Engineering, School of Engineering, University of Glasgow, Glasgow, UK.
School of Computing Science, University of Glasgow, Glasgow, UK.
Nat Commun. 2020 Mar 13;11(1):1384. doi: 10.1038/s41467-020-15114-1.
Cells respond in complex ways to their environment, making it challenging to predict a direct relationship between the two. A key problem is the lack of informative representations of parameters that translate directly into biological function. Here we present a platform to relate the effects of cell morphology to gene expression induced by nanotopography. This platform utilizes the 'morphome', a multivariate dataset of cell morphology parameters. We create a Bayesian linear regression model that uses the morphome to robustly predict changes in bone, cartilage, muscle and fibrous gene expression induced by nanotopography. Furthermore, through this model we effectively predict nanotopography-induced gene expression from a complex co-culture microenvironment. The information from the morphome uncovers previously unknown effects of nanotopography on altering cell-cell interaction and osteogenic gene expression at the single cell level. The predictive relationship between morphology and gene expression arising from cell-material interaction shows promise for exploration of new topographies.
细胞以复杂的方式对其环境作出反应，因此很难预测两者之间的直接关系。一个关键问题是缺乏能够直接转化为生物学功能的参数的信息表示。在这里，我们提出了一个将细胞形态的影响与纳米形貌诱导的基因表达联系起来的平台。该平台利用“形态组学”，即细胞形态参数的多元数据集。我们创建了一个贝叶斯线性回归模型，该模型使用形态组学来稳健地预测纳米形貌诱导的骨、软骨、肌肉和纤维基因表达的变化。此外，通过该模型，我们可以有效地从复杂的共培养微环境中预测纳米形貌诱导的基因表达。形态组学中的信息揭示了纳米形貌在单细胞水平上改变细胞间相互作用和成骨基因表达的先前未知的影响。细胞-材料相互作用产生的形态和基因表达之间的预测关系为探索新的形貌提供了希望。
