Label-free classification of cell death pathways via holotomography-based deep learning framework

Abstract

Accurate classification of cell death pathways is critical in understanding disease mechanisms and evaluating therapeutic responses, as dysregulated cell death underlies a wide range of pathological conditions including cancer and therapy resistance. Conventional imaging methods such as fluorescence and bright-field microscopy, or 2D phase imaging, often suffer from phototoxicity, labeling artifacts, or limited morphological contrast. Here, we present a real-time, label-free platform for classifying cell death phenotypes--apoptosis, necroptosis, and necrosis--by combining three-dimensional holotomography with deep learning. Our convolutional neural network, trained on refractive index (RI)-based features from HeLa cells, achieved high classification accuracy (97.2 {+/-} 2.8%) under varying cell densities. Notably, the model identified early RI changes during necroptosis several hours prior to fluorescence-based markers. These findings demonstrate the potential of holotomography-based AI for high-resolution, label-free cell death profiling.