Since their original description by Ramon y Cajal in 1888, the observation and analysis of dendritic spines represents a major issue in neuroscience studies. Their density and morphology are indicative of the cellular processes involved in neural plasticity which correlate with cognitive functions such as learning and memory, and are symptomatic in several neuropathologies such as mental retardation and neurodegenerative disease.
Even if the staining method found by Camillo Golgi and used by Cajal is still in use, several new techniques have been developed that allow high resolution imaging of fluorescent staining. Recently, new bioinformatical tools have become available, which perform spine detection in the three dimensions. Furthermore, those softwares permit quantitative analysis of morphological parameters. Hence, beyond the classification of spines in three distinct classes (i.e. thin, mushroom, stubby) proposed by Peters and Kaiserman-Abramof in 1970, a fine analysis is now possible to link the shape of the dendritic spine to its function.
The density of spines along the dendrite can be understood in terms of the levels of connectivity within the neuronal network as well as the integrative capabilities of the neuron. The dendritic spine is a functional integrative unit whose morphology is tightly correlated with its function. The morphological features of the single spine is indicative of the relative levels of synaptic receptors and is informative about the spatial constraints applied to electrophysiological inputs, receptor mobility and signalling pathways initiated at the synapse.
The main aims of this Research Topic in Frontiers in Neuroscience are two-fold. First, to present the state of the art methods of dendritic spine staining and imaging; the available tools for their analysis and a critical view on the qualitative and quantitative methods of morphological analysis. Second, to discuss how to interpret the different parameters from the morphological analysis and how they could help for our understanding in neuronal function, both in health and disease.
Since their original description by Ramon y Cajal in 1888, the observation and analysis of dendritic spines represents a major issue in neuroscience studies. Their density and morphology are indicative of the cellular processes involved in neural plasticity which correlate with cognitive functions such as learning and memory, and are symptomatic in several neuropathologies such as mental retardation and neurodegenerative disease.
Even if the staining method found by Camillo Golgi and used by Cajal is still in use, several new techniques have been developed that allow high resolution imaging of fluorescent staining. Recently, new bioinformatical tools have become available, which perform spine detection in the three dimensions. Furthermore, those softwares permit quantitative analysis of morphological parameters. Hence, beyond the classification of spines in three distinct classes (i.e. thin, mushroom, stubby) proposed by Peters and Kaiserman-Abramof in 1970, a fine analysis is now possible to link the shape of the dendritic spine to its function.
The density of spines along the dendrite can be understood in terms of the levels of connectivity within the neuronal network as well as the integrative capabilities of the neuron. The dendritic spine is a functional integrative unit whose morphology is tightly correlated with its function. The morphological features of the single spine is indicative of the relative levels of synaptic receptors and is informative about the spatial constraints applied to electrophysiological inputs, receptor mobility and signalling pathways initiated at the synapse.
The main aims of this Research Topic in Frontiers in Neuroscience are two-fold. First, to present the state of the art methods of dendritic spine staining and imaging; the available tools for their analysis and a critical view on the qualitative and quantitative methods of morphological analysis. Second, to discuss how to interpret the different parameters from the morphological analysis and how they could help for our understanding in neuronal function, both in health and disease.