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

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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.

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Acknowledgments

This work was supported by Centre National pour la Recherche Scientifique (CNRS), University Pierre and Marie Curie (UPMC), the hereditary disease foundation (HDF) and l’ “Agence Nationale pour la Recherche” (ANR-08-BLAN). We wish to thank Dr. Virginie Georget, Dr; Susanne Bolte and Richard Schwartzmann from the Cellular Imaging facility of the IFR 83 (Institut Fédératif de Recherche). We thank Dr. Anina Moritz, Dr. Sophie Scotto and Emma Cahill for critical reading and helpful comments on the manuscript. All authors report no biomedical financial interests or potential conflicts of interest.

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  1. Laboratoire de Physiopathologie des Maladies du Système Nerveux central, CNRS UMR 7224, INSERM U952, Université Pierre et Marie Curie (UPMC), Paris 06, 9 Quai St Bernard, 75005, Paris, France

    Nicolas Heck, Sandrine Betuing, Peter Vanhoutte & Jocelyne Caboche

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Heck, N., Betuing, S., Vanhoutte, P. et al. A deconvolution method to improve automated 3D-analysis of dendritic spines: application to a mouse model of Huntington’s disease. Brain Struct Funct 217, 421–434 (2012). https://doi.org/10.1007/s00429-011-0340-y

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