High-throughput adjustable deformability cytometry utilizing elasto-inertial focusing and virtual fluidic channel

Abstract

Cell mechanical properties provide a label-free marker for indicating cell states and disease processes. Although microfluidic deformability cytometry has demonstrated great potential and successes in mechanical phenotyping in recent years, its universal applicability for characterizing multiple sizes of cells using a single device has not been realized. Herein, we propose high-throughput adjustable deformability cytometry integrated with three-dimensional (3D) elasto-inertial focusing and a virtual fluidic channel. By properly adjusting the flow ratio of the sample and sheath, the virtual fluidic channel in a wide solid channel can generate a strong shear force in the normal direction of the flow velocity and simultaneously squeeze cells from both sides to induce significant cell deformation. The combination of elasto-inertial focusing and a virtual fluidic channel provides a great hydrodynamic symmetrical force for inducing significant and homogeneous cell deformation. In addition, our deformability cytometry system not only achieves rapid and precise cell deformation, but also allows the adjustable detection of multiple sizes of cells at a high throughput of up to 3000 cells per second. The mini-bilateral segmentation network (mini-BiSeNet) was developed to identify cells and extract features quickly. The classification of different cell populations (A549, MCF-7, MDA-MB-231, and WBCs) was carried out based on the cell size and deformation. By applying deep learning to cell classification, a high accuracy reaching approximately 90% was achieved. We also revealed the potential of our deformability cytometry for characterizing pleural effusions. The flexibility of our deformability cytometry holds promise for the mechanical phenotyping and detection of various biological samples.