Cell death pathways in multicellular organisms play an important role in maintaining homeostasis and any irregularities can cause diseases like cancer. Studying cell death mechanism will aid in understanding its role in different diseases and their translational implications. It is particularly important for screening new drugs. Here we present a high-throughput and label-free method of identifying the two most common forms of cells death, programmed (apoptosis) and unprogrammed (necrosis), using lensless holographic microscopy. Breast cancer cells BT-20 were treated with a series of drugs, that induce different types of cell death, and imaged continuously during the incubation step. The cells were imaged by illuminating them with partially coherent light and the in-line holograms of the cells, resulting from the interference between the transmitted wave and scattered wave, were recorded in a CMOS imaging chip. The holograms were digitally backpropagated to reconstruct the phase and amplitude images of the cells. The absence of lenses enables imaging at unit magnification over an area <10 mm2, which includes over a thousand mammalian cells. The temporal changes in cell morphology, such as membrane blebbing, shrinking, swelling, and membrane rupture, which are reflected in the phase image was used to identify the cell death mechanism. This process was further automated using deep learning, which enabled the classification of the cell death process with <93% accuracy. This label-free approach of identifying cell death mechanisms enables high-throughput toxicity studies unlike the conventional biochemical assays, e.g., western blot, and can be useful for a variety of biomedical applications.
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