Computational modeling of microfluidic constriction channels
Eukaryotic cells possess viscoelastic properties that may vary when the cells are subjected to diseases. These alterations cause the cell to be deformable. In fact, the invasive potential of cancerous cells, blood flow resistance, and blood pressure, have been dependent on the deformability of the affected cells1,2. Therefore, it is possible to use cell deformability to identify the diseased state of cells. Building a system to measure the cell’s mechanical properties will allow for diseased cells to be isolated from the healthy ones. One tool for single-cell analysis is microfluidic constriction channels. The mechanical properties of these cells can be quantified when pushed through a constriction channel based on its transit time through the model. Thus, microfluidic constriction channels can turn raw mechanical data into a biomarker to determine intrinsic cell properties such as cortical tension and Young’s modulus. But, regular constriction channels have several limitations. Microfluidic constriction channels have limited throughput and develop issues with clogging, hindering the ability to extract significant mechanical data. To resolve these issues, constriction channels have been modified to measure cell deformability through the micropores if it is unable to pass through the channel. Systems like parallel microfiltration and Quantitative deformability cytometry (q-DC) can increase throughput and lower the chance of clogging. Here, we apply our custom computational algorithm, referred to as VECAM, to simulate deformation of circulating cells in microfluidic constriction experiments and extract their mechanical properties such as shear elasticity, stiffness, and deformation index.