Systematic assessment of organelle architectures in cells, known as the organellome, could provide valuable insights into cellular states and disease pathologies but remains largely uncharted. Here, we devised a novel pipeline combining self-supervised deep learning and transfer learning to generate a Neuronal Organellomics Vision Atlas (NOVA). Analyzing over 1.5 million confocal images of 24 distinct membrane-bound and membrane-less organelles in human neurons, we enable a simultaneous evaluation of all organelles. We show that organellomics allows the study of cellular phenotypes by quantifying the localization and morphological properties embodied in multiple different organelles, using a unified score. We further developed a strategy to superimpose all organelles, which represents a new realization of cellular state. The value of our approach is demonstrated by characterizing specific organellar responses of human neurons to stress, cytoplasmic mislocalization of TDP-43, or disease[1]associated variations in ALS genes. Therefore, organellomics offers a novel approach to study the neuro-cellular biology of diseases.