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

Introduction:: The metastatic cascade is a multi-step process that allows tumor cells to undergo epithelial-to-mesenchymal transition (EMT), dissociate from the tumor, degrade the extracellular matrix (ECM), migrate to neighboring tissues and undergo mesenchymal-to-epithelial transition (MET) to form a new metastatic tumor. For cancer cells to invade and migrate, their mechanical properties often change. The processes of EMT and MET reorganize the cytoskeleton and allow for cell motility¹. Cancer cell softening has been observed in many cancer types; more specifically, as metastatic potential increases, cell stiffness decreases^2–4. While cell mechanics and metastatic potential are related, the underlying molecular factors associated with these behaviors remain unknown. Developing hypotheses to understand how molecular signaling networks are related to cellular phenotype and mechanotype is needed to help elucidate how metastasis and mechanics are coupled. Therefore, we have developed a workflow to measure the mechanical properties and gene expression of single cells that can be used to generate large linked-datasets and develop new hypotheses concerning molecular networks and their relationship with cell mechanics.

Materials and Methods:: To explore the relationship between cellular stiffness and metastatic cell behaviors, we developed and validated a novel single cell genomechanical methodology using OVCAR3, HEY and HEY A8 ovarian cancer cell lines of increasing metastatic potential. The process combines atomic force microscopy (AFM) and optical microscopy to measure the mechanics and morphology of individual cells, followed by multiplexed RT-qPCR gene expression analysis on the same single cells (Figure 1A). In more detail, cells were grown on gridded coverslips, which allowed for cells to be identified by their spatial position. Next, cell mechanical properties, including stiffness and viscosity, were ascertained using AFM force curves. Each cell was then aspirated using a micropipette and isolated into individual PCR tubes for loading along with specific primers of interest into a Fluidigm Integrated Fluidic Circuit microfluidic device, which allows for the multiplexed RT-qPCR of up to 96 samples and 96 genes. The large linked dataset was analyzed to identify correlations between cell stiffness, cell gene expression, and cell morphological factors as well as observing the effect of using multiple data types with principal components analysis for clustering cells by cell type and metastatic potential.


Results, Conclusions, and Discussions:: After collecting and testing over 300 single cells, we identified 43 genes that were significantly correlated with cell stiffness (Figure 1B). Surprisingly, the genes that most strongly correlated with mechanical properties were not cytoskeletal, but rather were markers of ECM remodeling, EMT, cell adhesion, and cancer stemness. Of particular interest, cell stiffness was negatively correlated with transglutaminase 2, a crosslinking protein involved with ECM remodeling and EMT in ovarian cancer, the intermediate filament protein vimentin, an important mesenchymal marker and mediator of cytoskeletal organization, and ZEB1, a transcription factor implicated in the EMT and metastatic process. Of note from the positive correlations were the metastasis suppressor and metalloproteinase inhibitor TIMP3, nuclear structure protein lamin A, which protects against DNA damage and provides a major barrier to cell invasion, and E-cadherin, an adhesion protein associated with an epithelial phenotype. These genes of interest support the association of a softer phenotype with a more migratory, metastatic phenotype while also providing more hypotheses to explore the molecular mechanisms and gene networks that govern this cell softening phenomena.

Additionally, dimensionality reduction analysis showed that the clustering of cells by differential metastatic potential could be improved by combining mechanical and gene expression data types. The use of single cell gene expression data alone was insufficient to effectively separate by cell type and metastatic potential (Figure 1C). This was primarily due to a low inter-cluster variability between the HEY and HEY A8 cell populations, which were derived from the same cell line with different migratory potential. However, clustering was improved by combining multiple data types collected for each single cell, allowing for an observed distinction between the HEY and HEY A8 cell populations (Figure 1D).


The single cell genomechanics method demonstrates how single cells can be studied to identify molecular drivers that disable the biophysical processes underpinning metastasis. With this new genomechanical method we have explored important connections between a cell’s mechanotype, genotype, and phenotype. With this technique, we have produced an extremely rich dataset that can be explored further to understand the various gene networks that may be governing a single cell’s metastatic potential.


Acknowledgements (Optional): : The authors would like to thank Ilya Kolb, Corey Landry, and Mighten Yip for help with micropipette aspiration; Nicholas Stone for help with code written to analyze AFM force curves; and Dalia Arafat and the Institute for Bioengineering and Biosciences Core Facilities for help with gene expression analysis. This research project was supported by funding through the National Science Foundation, award #1538161 and award #2225476, and through the National Institutes of Health: National Cancer Institute, grant #1F31CA243345-01.


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