A schematic illustration to describe the experimental process of building the models. First, live-cell imaging with an Opera Phenix High-Content Screening System (PerkinElmer); cells are loaded with live-cell imaging dyes. Representative images for the three channels: Hoechst 33342 (nucleic labelling within 387/11 nm excitation and 417447 nm emission); TMRM (mitochondrial labelling within 505 nm excitation and 515 nm emission); and LysoTracker deep red (lysosomal labelling within 614 nm excitation and 647 nm emission). Second, a Columbus Image Data Storage and Analysis System (PerkinElmer) was used to extract 56 morphological features (Extended Data Figs. 1a and 2a ) and whole images. Third, models are trained on tabular data extracted from cell profiling features or images uniformly gridded by 8 8 segmented cropped images and categorically labelled and fed into the neural network. Fourth, the learned model enables the prediction of the healthy group or the four disease subtypes.
