Correlating mechanical and gene expression data on the single cell level to investigate metastatic phenotypes
Correlating mechanical and gene expression data on the single cell level to investigate metastatic phenotypes
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DOI：
10.1016/j.isci.2023.106393
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发表时间：
2023-03-23
期刊：
影响因子：
5.8
通讯作者：
Sulchek，Todd
中科院分区：
文献类型：
--
作者：
Young，Katherine M.;Xu，Congmin;Sulchek，Todd
已经观察到许多癌细胞类型的硬度随着其转移潜力的增加而降低。虽然细胞力学和转移潜能是相关的，但与这些表型相关的潜在分子因素仍然未知。因此，我们开发了一个工作流程来测量用于生成大型链接数据集的单细胞的机械特性和基因表达。该过程结合了原子力显微镜来测量单个细胞的力学，并对相同的单个细胞进行多重RT-qPCR基因表达分析。令人惊讶的是，与机械性能最密切相关的基因不是细胞骨架，而是细胞外基质重塑，上皮-间充质转化，细胞粘附和癌症干性的标志物。此外，降维分析表明，通过结合机械和基因表达数据类型，细胞聚类得到改善。单细胞基因力学方法展示了单细胞研究如何识别可能影响转移基础生物物理过程的分子驱动因素。
Stiffness has been observed to decrease for many cancer cell types as their metastatic potential increases. Although cell mechanics and metastatic potential are related, the underlying molecular factors associated with these phenotypes remain unknown. Therefore, we have developed a workflow to measure the mechanical properties and gene expression of single cells that is used to generate large linked-datasets. The process combines atomic force microscopy to measure the mechanics of individual cells with multiplexed RT-qPCR gene expression analysis on the same single cells. Surprisingly, the genes that most strongly correlated with mechanical properties were not cytoskeletal, but rather were markers of extracellular matrix remodeling, epithelial-to-mesenchymal transition, cell adhesion, and cancer stemness. In addition, dimensionality reduction analysis showed that cell clustering was improved by combining mechanical and gene expression data types. The single cell genomechanics method demonstrates how single cell studies can identify molecular drivers that could affect the biophysical processes underpinning metastasis.