Abstract
In recent years, the importance of the investigation of regulated cell death (RCD) has significantly increased and different methods are proposed for the detection of RCD including biochemical as well as fluorescence assays. Researchers have shown that early stages of cell death could be detected by using AFM. Although AFM offers a high single-cell resolution and sensitivity, the throughput (<100 cells/h) limits a broad range of biomedical applications of this technique. Here, a microfluidics-based mechanobiology technique, named shear flow deformability cytometry (sDC), is used to investigate and distinguish dying cells from viable cells purely based on their mechanical properties. Three different RCD modalities (i.e., apoptosis, necroptosis, and ferroptosis) are induced in L929sAhFas cells and analysed using sDC. Using machine learning on the extracted parameters, it was possible to predict the dead or viable state with 92% validation accuracy. A significant decrease in elasticity can be noticed for each of these RCD modalities by analysing the deformation of the dying cells. Analysis of morphological characteristics such as cell size and membrane irregularities also indicated significant differences in the RCD induced cells versus control cells. These results highlight the importance of mechanical properties during RCD and the significance of label-free techniques, such as sDC, which can be used to detect regulated cell death and can be further linked with sorting of live and dead cells.