Cell morphology and mechanosensing can be decoupled in fibrous microenvironments and identified using artificial neural networks.
Cell morphology and mechanosensing can be decoupled in fibrous microenvironments and identified using artificial neural networks.
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DOI：
10.1038/s41598-021-85276-5
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发表时间：
2021-03-15
影响因子：
4.6
通讯作者：
Mauck RL
中科院分区：
文献类型：
--
作者：
Bonnevie ED;Ashinsky BG;Dekky B;Volk SW;Smith HE;Mauck RL
细胞根据纤维微环境的机制和组织以及细胞的表型状态，解读来自纤维微环境的信号并与之相互作用。这反过来调节机械活性途径，如机械敏感因子的定位。在这里，我们利用工程纤维微环境固有的微尺度异质性来产生跨多种细胞类型的大型形态学数据集，同时在单细胞水平上测量机械生物学反应（YAP/TAZ核定位）。该数据集描述了细胞形态和反应的大动态范围，并结合了机器学习方法来预测来自多个谱系的单个细胞的机械生物学状态。我们还注意到，某些细胞（如侵袭性癌细胞）或生化扰动（如调节收缩性）可以限制细胞在普遍情况下的可预测性。利用这一发现，我们开发了进一步的模型，将生化线索纳入单细胞预测或识别不遵循既定规则的单个细胞。这里开发的模型提供了一种连接细胞形态和信号传导的工具，将生化线索纳入预测模型，并在单细胞水平上识别异常细胞行为。
Cells interpret cues from and interact with fibrous microenvironments through the body based on the mechanics and organization of these environments and the phenotypic state of the cell. This in turn regulates mechanoactive pathways, such as the localization of mechanosensitive factors. Here, we leverage the microscale heterogeneity inherent to engineered fiber microenvironments to produce a large morphologic data set, across multiple cells types, while simultaneously measuring mechanobiological response (YAP/TAZ nuclear localization) at the single cell level. This dataset describing a large dynamic range of cell morphologies and responses was coupled with a machine learning approach to predict the mechanobiological state of individual cells from multiple lineages. We also noted that certain cells (e.g., invasive cancer cells) or biochemical perturbations (e.g., modulating contractility) can limit the predictability of cells in a universal context. Leveraging this finding, we developed further models that incorporate biochemical cues for single cell prediction or identify individual cells that do not follow the established rules. The models developed here provide a tool for connecting cell morphology and signaling, incorporating biochemical cues in predictive models, and identifying aberrant cell behavior at the single cell level.
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影响因子：
2.4
作者：
Michalek, A. J.;Gardner-Morse, M. G.;Iatridis, J. C.
通讯作者：
Iatridis, J. C.
影响因子：
2.4
作者：
Billiar, KL;Sacks, MS
通讯作者：
Sacks, MS
影响因子：
2.4
作者：
KRUMMEL, TM;NELSON, JM;COHEN, IK
通讯作者：
COHEN, IK
细胞介导的纤维募集驱动工程原纤维微环境中的细胞外基质机械感应。
影响因子：
41.2
作者：
Baker BM;Trappmann B;Wang WY;Sakar MS;Kim IL;Shenoy VB;Burdick JA;Chen CS
通讯作者：
Chen CS
影响因子：
14
作者：
Chen, Desu;Sarkar, Sumona;Losert, Wolfgang
通讯作者：
Losert, Wolfgang