Real-time high-throughput characterisation of the surface elasticity of suspended cells

Abstract

The intrinsic elasticity of the cell membrane cortex complex, i.e., cell surface, is a promising biomarker for cell status and disease, and has widespread biological and biomedical applications. However, measuring cell surface elasticity in real-time with high throughput has not been achieved so far. Here we develop a system and demonstrate that it can characterise the intrinsic surface elasticity of up to 411 cells per second, with a low latency of less than 1 millisecond per cell from an image to predicted elasticity. Our key innovation is to integrate a multi-layer perception (MLP) based machine learning algorithm, which infers the surface elasticity of cells from their camera-recorded steady-deformation profiles in a microchannel, with a high-fidelity mechanistic model, which resolves the cell surface, cytoplasm and nucleus and can accurately predict the flow-induced cell deformation. Applied to human prostate cancer PC-3 and leukaemia K-562 cell lines, the system enables measuring tens of thousands of cells within minutes, to explore the cell mechano-heterogeneity, the relation between surface elasticity and cell size, and the possibility of using surface elasticity and cell size for cell classification. We show that the measured cell surface elasticity is little affected by flow condition, when doubling the flow speed or suspension fluid viscosity. The system is also sensitive enough to detect a reduction of cell surface elasticity as a result of the cytochalasin D-induced actin disassembly. By enabling real-time high-throughput characterisation of the surface elasticity of cells, the present method may inspire new applications.