Label-free identification of cell death mechanism using scattering-based microscopy and deep learning

Abstract

Abstract The detection of cell death and identification of its mechanism underpins many of the biological and medical sciences. A scattering microscopy based method is presented here for quantifying cell motility and identifying cell death in breast cancer cells using a label-free approach. We identify apoptotic and necrotic pathways by analyzing the temporal changes in morphological features of the cells. Moreover, a neural network was trained to identify the cellular morphological changes and classify cell death mechanisms automatically, with an accuracy of over 95%. A pre-trained network was tested on images of cancer cells treated with a different chemotherapeutic drug, which was not used for training, and it correctly identified cell death mechanism with ∼100% accuracy. This automated method will allow for quantification during the incubation steps without the need for additional steps, typically associated with conventional technique like fluorescence microscopy, western blot and ELISA. As a result, this technique will be faster and cost effective.