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

Abstract

The detection of cell death and identification of the mechanism underpins many of the biological and medical sciences. A scattering-based microscopy-based method is presented here for identifying, measuring, and quantifying cell death in breast cancer cells using a label-free approach. We identify apoptosis and necrosis 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 different chemotherapeutic drugs, which were not used for training, and it correctly identified cell death mechanisms almost 100% of the time. 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 ELISAs. As a result, such studies will be faster and cost effective.

Advances in Lensless Fluorescence Microscopy Design

“…LLMF has been used for various biological applications, including for imaging cells, tissues, and pathogens. Cell imaging is a cornerstone of biological and medical research, offering the real-time visualization of cellular processes like division, migration, and interaction to understand how cells function and respond to stimuli (e.g., drugs), as well as to diagnose diseases through the identification of morphological changes [58][59][60]. LLFM has been used to image different kinds of cells, such as the multicolor imaging of double-stained human breast cancer SK-BR-3, revealing detailed cellular structures such as membranes and nuclei [51].…”