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.

Scaling microfluidic throughput with flow-balanced manifolds to simply control devices with multiple inlets and outlets.

Microfluidics can bring unique functionalities to cell processing, but the small channel dimensions often limit the throughput for cell processing that prevents scaling necessary for key applications. While processing throughput can be improved by increasing cell concentration or flow rate, an excessive number or velocity of cells can result in device failure. Designing parallel channels can linearly increase the throughput by channel number, but for microfluidic devices with multiple inlets and outlets, the design of the channel architecture with parallel channels can result in intractable numbers of inlets and outlets.