Strain-dependent elastography of cancer cells reveals heterogeneity and stiffening due to attachment.

Because cells vary in thickness and in biomechanical properties, the use of a constant force trigger during atomic force microscopy (AFM) stiffness mapping produces a varied nominal strain that can obfuscate the comparison of local material properties. In this study, we measured the biomechanical spatial heterogeneity of ovarian and breast cancer cells by using an indentation-dependent pointwise Hertzian method. Force curves and surface topography were used together to determine cell stiffness as a function of nominal strain. By recording stiffness at a particular strain, it may be possible to improve comparison of the material properties of cells and produce higher contrast representations of cell mechanical properties. Defining a linear region of elasticity that corresponds to a modest nominal strain, we were able to clearly distinguish the mechanics of the perinuclear region of cells. We observed that, relative to the lamelopodial stiffness, the perinuclear region was softer for metastatic cancer cells than their nonmetastatic counterparts. Moreover, contrast in the strain-dependent elastography in comparison to conventional force mapping with Hertzian model analysis revealed a significant stiffening phenomenon in the thin lamellipodial region in which the modulus scales inversely and exponentially with cell thickness. The observed exponential stiffening is not affected by relaxation of cytoskeletal tension, but finite element modeling indicates it is affected by substrate adhesion. The novel cell mapping technique explores cancer cell mechanical nonlinearity that results from regional heterogeneity, which could help explain how metastatic cancer cells can show soft phenotypes while simultaneously increasing force generation and invasiveness.

Correlating mechanical and gene expression data on the single cell level to investigate metastatic phenotypes.

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.