Quantitative cellular morphology in neurodegenerative and neuropsychiatric diseases: insights into disease mechanisms and therapeutic discovery

Abstract Background Neurodegenerative and neuropsychiatric diseases are characterised by complex cellular pathology involving coordinated alterations across multiple subcellular components. Advances in high-content imaging, multiplexed fluorescent labelling, and quantitative image analysis now allow systematic measurement of cellular morphology, closely linked to cellular function, at single-cell resolution in human-derived models. These developments have enabled the emergence of cell morphomics , a quantitative framework that captures changes in cellular and subcellular morphology across disease and drug treatment response contexts. Main body Here, we systematically review studies published over the past five years applying high-content imaging with quantitative morphometric analysis in human cellular models of neurological disease. Searches of PubMed, Embase, and Scopus identified 33 eligible studies spanning Parkinson’s disease, Alzheimer’s disease, amyotrophic lateral sclerosis, hereditary spastic paraplegia, Huntington’s disease, dominant optic atrophy, and neurodevelopmental and neuropsychiatric conditions including, schizophrenia and autism spectrum disorder. Across genetic, sporadic, and environmental contexts, these studies reveal coordinated morphological alterations across multiple subcellular compartments including, mitochondria, lysosomes, nucleus, cytoskeleton, and endoplasmic reticulum, with both disease-specific and partially convergent subcellular phenotypes emerging within and between diseases. Within idiopathic cohorts, morphomic analyses reveal marked intra-group variability in cellular and subcellular morphology, indicating underlying mechanistic heterogeneity. Cellular context further shapes disease-associated morphology, as neuronal and non-neuronal models from the same disease differ in the magnitude and integration of structural alterations. Machine learning approaches integrate high-dimensional morphomic features for patient group classification and prediction, identify the subcellular components driving group differences, and characterise changes following therapeutic perturbation. In multiple studies, these morphological alterations correspond with independent functional measures, supporting quantitative cellular morphology as a biologically relevant intermediate phenotype. Conclusion Together, these findings position cell morphomics as a scalable and human-relevant intermediate phenotype linking molecular alterations with functional outcome. As patient-derived cellular models increasingly drive translational neuroscience and early-phase drug discovery, quantitative cellular morphology provides an analytical approach for understanding disease mechanisms and evaluating therapeutic response across neurological disorders.