No abstract

While astrocytes have been traditionally described as passive supportive cells, studies during the last decade have shown they are active players in many aspects of cnS physiology and function both in normal and disease states. However, the precise mechanisms regulating astrocytes function and interactions within the cnS are still poorly understood. this knowledge gap is due in large part to the limitations of current image analysis tools that cannot process astrocyte images efficiently and to the lack of methods capable of quantifying their complex morphological characteristics. to provide an unbiased and accurate framework for the quantitative analysis of fluorescent images of astrocytes, we introduce a new automated image processing pipeline whose main novelties include an innovative module for cell detection based on multiscale directional filters and a segmentation routine that leverages deep learning and sparse representations to reduce the need of training data and improve performance. extensive numerical tests show that our method performs very competitively with respect to state-of-the-art methods also in challenging images where astrocytes are clustered together. Our code is released open source and freely available to the scientific community.The human brain is a complex network of over 2 × 10 11 neural cells comprising neurons and glial cells. Neuroglia comprise astrocytes, oligodendrocytes, NG2 glia, microglia and all peripheral glia 1 . Astrocytes, a subtype of glial cells with a complex star-shaped morphology, are the most abundant cells in most part of the human brain 2 . Although they were long characterized as supportive cells only involved in maintaining neuron homeostasis and survival, a number of studies during the last decade have revealed that astrocytes play an active role in fundamental brain processes underlying neuronal development and function. Several studies have implicated astrocytes in controlling the development of the nervous system through axon guidance and synaptogenesis. During neural circuit development, astrocytes are responsible for the regularization of the neuronal network by pruning abnormal or dysfunctioning synapses 3 . In addition, together with the pre-and post-synaptic parts of two neuronal synapses, astrocytes can form a so-called "tripartite" synapse that helps modulate synaptic transmission via the release of neurotransmitters such as glutamate and ATP 4,5 . The critical roles of astrocytes in neuronal development and connectivity make them among the most promising targets for innovative therapies designed to treat a range of brain disorders or neurological injuries 6 . As a result, the attention on astrocytes has dramatically increased in recent years.Astrocytes have been shown to reflect their diverse abilities and functions on their special structural design, and alterations in astrocyte morphology are known to correlate to traumatic brain injury, infection, ischemia, autoimmune responses, and neurodegenerative diseases 5,7,8 . For instance, their intricate ar...

Fluorescence confocal microscopy has become increasingly more important in neuroscience due to its applications in image-based screening and profiling of neurons. Multispectral confocal imaging is useful to simultaneously probe for distribution of multiple analytes over networks of neurons. However, current automated image analysis algorithms are not designed to extract single-neuron arbors in images where neurons are not separated, hampering the ability map fluorescence signals at the single cell level. To overcome this limitation, we introduce NeuroTreeTracer – a novel image processing framework aimed at automatically extracting and sorting single-neuron traces in fluorescent images of multicellular neuronal networks. This method applies directional multiscale filters for automated segmentation of neurons and soma detection, and includes a novel tracing routine that sorts neuronal trees in the image by resolving network connectivity even when neurites appear to intersect. By extracting each neuronal tree, NeuroTreetracer enables to automatically quantify the spatial distribution of analytes of interest in the subcellular compartments of individual neurons. This software is released open-source and freely available with the goal to facilitate applications in neuron screening and profiling.

The axon initial segment (AIS) is a highly regulated subcellular domain required for neuronal firing. Changes in the AIS protein composition and distribution are a form of structural plasticity, which powerfully regulates neuronal activity and may underlie several neuropsychiatric and neurodegenerative disorders. Despite its physiological and pathophysiological relevance, the signaling pathways mediating AIS protein distribution are still poorly studied. Here, we used confocal imaging and whole-cell patch clamp electrophysiology in primary hippocampal neurons to study how AIS protein composition and neuronal firing varied in response to selected kinase inhibitors targeting the AKT/GSK3 pathway, which has previously been shown to phosphorylate AIS proteins. Image-based features representing the cellular pattern distribution of the voltage-gated Na+ (Nav) channel, ankyrin G, βIV spectrin, and the cell-adhesion molecule neurofascin were analyzed, revealing βIV spectrin as the most sensitive AIS protein to AKT/GSK3 pathway inhibition. Within this pathway, inhibition of AKT by triciribine has the greatest effect on βIV spectrin localization to the AIS and its subcellular distribution within neurons, a phenotype that Support Vector Machine classification was able to accurately distinguish from control. Treatment with triciribine also resulted in increased excitability in primary hippocampal neurons. Thus, perturbations to signaling mechanisms within the AKT pathway contribute to changes in βIV spectrin distribution and neuronal firing that may be associated with neuropsychiatric and neurodegenerative disorders.