NDLI: A deconvolution method to improve automated 3D-analysis of dendritic spines: application to a mouse model of Huntington’s disease

Content Provider	Springer Nature Link
Author	Heck, Nicolas Betuing, Sandrine Vanhoutte, Peter Caboche, Jocelyne
Copyright Year	2011
Abstract	Dendritic spines are postsynaptic structures the morphology of which correlates with the strength of synaptic efficacy. Measurements of spine density and spine morphology are achievable using recent imaging and bioinformatics tools. The three-dimensional automated analysis requires optimization of image acquisition and treatment. Here, we studied the critical steps for optimal confocal microscopy imaging of dendritic spines. We characterize the deconvolution process and show that it improves spine morphology analysis. With this method, images of dendritic spines from medium spiny neurons are automatically detected by the software Neuronstudio, which retrieves spine density as well as spine diameter and volume. This approach is illustrated with three-dimensional analysis of dendritic spines in a mouse model of Huntington’s disease: the transgenic R6/2 mice. In symptomatic mutant mice, we confirm the decrease in spine density, and the method brings further information and show a decrease in spine volume and dendrite diameter. Moreover, we show a significant decrease in spine density at presymptomatic age which so far has gone unnoticed.
Starting Page	421
Ending Page	434
Page Count	14
File Format	PDF
ISSN	18632653
Journal	Anatomy and Embryology
Volume Number	217
Issue Number	2
e-ISSN	18632661
Language	English
Publisher	Springer-Verlag
Publisher Date	2011-08-06
Publisher Place	Berlin/Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Dendritic spine Confocal microscopy Deconvolution Huntington’s disease Cell Biology Neurology Neurosciences
Content Type	Text
Resource Type	Article
Subject	Neuroscience Anatomy Histology

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