A Neuron-Glial Perspective for Computational Neuroscience

International audienceThere is growing excitement around glial cells, as compelling evidence point to new, previously unimaginable roles for these cells in information processing of the brain, with the potential to affect behavior and higher cognitive functions. Among their many possible functions, glial cells could be involved in practically every aspect of the brain physiology in health and disease. As a result, many investigators in the field welcome the notion of a Neuron-Glial paradigm of brain function, as opposed to Ramon y Cayal's more classical neuronal doctrine which identifies neurons as the prominent, if not the only, cells capable of a signaling role in the brain. The demonstration of a brain-wide Neuron-Glial paradigm however remains elusive and so does the notion of what neuron-glial interactions could be functionally relevant for the brain computational tasks. In this perspective, we present a selection of arguments inspired by available experimental and modeling studies with the aim to provide a biophysical and conceptual platform to computational neuroscience no longer as a mere prerogative of neuronal signaling but rather as the outcome of a complex interaction between neurons and glial cells

3-Dimensional Immunostaining and Automated Deep-Learning Based Analysis of Nerve Degeneration

Multiple sclerosis (MS) is an autoimmune and neurodegenerative disease driven by inflammation and demyelination in the brain, spinal cord, and optic nerve. Optic neuritis, characterized by inflammation and demyelination of the optic nerve, is a symptom in many patients with MS. The optic nerve is the highway for visual information transmitted from the retina to the brain. It contains axons from the retinal ganglion cells (RGCs) that reside in the retina, myelin forming oligodendrocytes and resident microglia and astrocytes. Inflammation, demyelination, and axonal degeneration are also present in the optic nerve of mice subjected to experimental autoimmune encephalomyelitis (EAE), a preclinical mouse model of MS. Monitoring the optic nerve in EAE is a useful strategy to study the presentation and progression of pathology in the visual system; however, current approaches have relied on sectioning, staining and manual quantification. Further, information regarding the spatial load of lesions and inflammation is dependent on the area of sectioning. To better characterize cellular pathology in the EAE model, we employed a tissue clearing and 3D immunolabelling and imaging protocol to observe patterns of immune cell infiltration and activation throughout the optic nerve. Increased density of TOPRO staining for nuclei captured immune cell infiltration and Iba1 immunostaining was employed to monitor microglia and macrophages. Axonal degeneration was monitored by neurofilament immunolabelling to reveal axonal swellings throughout the optic nerve. In parallel, we developed a convolutional neural network with a UNet architecture (CNN-UNet) called BlebNet for automated identification and quantification of axonal swellings in whole mount optic nerves. Together this constitutes a toolkit for 3-dimensional immunostaining to monitor general optic nerve pathology and fast automated quantification of axonal defects that could also be adapted to monitor axonal degeneration and inflammation in other neurodegenerative disease models