Super-resolution microscopy and deep learning methods: what can they bring to Neuroscience: from neuron to 3D spine segmentation

Paul Nazac^1,2Shengyan Xu^1,2Victor Breton^2,3David Boulet^4,5Lydia Danglot^2,5*

• ¹Université Paris Cité, INSERM Public Health, Paris, France
• ²Membrane Traffic in healthy & diseased brain team, Institute of Psychiatry and Neuroscience of Paris, Inserm 1266., Paris, France
• ³Institut Curie Research Center, CNRS UMR3666, INSERM U1339, Membrane Mechanics and Dynamics of Intracellular Signaling, Paris, France
• ⁴Institute of Psychiatry and Neuroscience of Paris, Inserm 1266, Membrane Traffic in healthy & diseased brain team, Paris, France
• ⁵Inserm, Université Paris Cité, Paris, France

In recent years, advances in microscopy and the development of novel fluorescent probes have significantly improved neuronal imaging. Many neuropsychiatric disorders are characterized by alterations in neuronal arborization, neuronal loss—as seen in Parkinson’s disease—or synaptic loss, as in Alzheimer’s disease. Neurodevelopmental disorders can also impact dendritic spine morphogenesis, as observed in autism spectrum disorders and schizophrenia. In this review, we provide an overview of the various labeling and microscopy techniques available to visualize neuronal structure, including dendritic spines and synapses. Particular attention is given to available fluorescent probes, recent technological advances in super-resolution microscopy (SIM, STED, STORM, MINFLUX), and segmentation methods. Aimed at biologists, this review presents both classical segmentation approaches and recent tools based on deep learning methods, with the goal of remaining accessible to readers without programming expertise.