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

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 & SIM;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.

Automated summary

This study presents a scattering microscopy based method for quantifying cell motility and identifying cell death in breast cancer cells. The apoptotic and necrotic pathways can be identified by analyzing the temporal changes in morphological features of the cells. A trained neural network is able to automatically identify cellular morphological changes and classify cell death mechanisms with an accuracy of over 95%.