High-content live-cell time-lapse imaging predicts cells about to die via apoptosis

Abstract

Cell death is a dynamic process that unfolds through time. Live-cell time-lapse imaging captures these dynamics in a way thats impossible for static snapshots. High-content imaging (HCI), which has been developed for static microscopy, applied to time-lapse imaging can quantify how single-cell states change through time. Here we show the ability of high-content live-cell time-lapse imaging (HCLTI) to quantify the onset and progression of one form of cell death called apoptosis. We apply the Live Cell Painting assay called ChromaLIVETM and develop an HCLTI analysis pipeline. We show that HCLTI can discern the morphology dynamics of cells undergoing apoptosis, and demonstrate that machine learning can predict apoptosis as early as 100 minutes after exposing HeLa cells to the apoptosis inducer Staurosporine. This technical advancement paves the way for future studies to better understand the dynamics of other forms of cell death. Understanding cell death dynamics is one piece of solving larger biomedical puzzles like understanding how cells resist death (e.g., therapeutic resistance of cancer cells) and how cells die too soon (e.g., neurodegeneration).