Automated detection of apoptotic bodies and cells in label-free time-lapse high-throughput video microscopy using deep convolutional neural networks

Motivation: Reliable label-free methods are needed for detecting and profiling apoptotic events in time-lapse cell-cell interaction assays. Prior studies relied on fluorescent markers of apoptosis, e.g. Annexin-V, that provide an inconsistent and late indication of apoptotic onset for human melanoma cells. Our motivation is to improve the detection of apoptosis by directly detecting apoptotic bodies in a label-free manner.Results: Our trained ResNet50 network identified nanowells containing apoptotic bodies with 92% accuracy and predicted the onset of apoptosis with an error of one frame (5 min/frame). Our apoptotic body segmentation yielded an IoU accuracy of 75%, allowing associative identification of apoptotic cells. Our method detected apoptosis events, 70% of which were not detected by Annexin-V staining.

Automated summary

This study aims to improve the detection of apoptosis using label-free methods. By training a ResNet50 network, nanowells containing apoptotic bodies were successfully identified and the onset of apoptosis was accurately predicted. The method achieved good results in apoptotic body segmentation, enabling the identification of apoptotic cells. Compared to traditional Annexin-V staining, this method detected a higher number of apoptosis events.