Automated Three-Dimensional Detection and Shape Classification of Dendritic Spines from Fluorescence Microscopy Images

By Alfredo Rodriguez, Douglas B. Ehlenberger, D. Dickstein, Patrick R. Hof, S. Wearne. PLoS ONE, 2008

552 citations recorded by the source corpus.

A fundamental challenge in understanding how dendritic spine morphology controls learning and memory has been quantifying three-dimensional (3D) spine shapes with sufficient precision to distinguish morphologic types, and sufficient throughput for robust statistical analysis. The necessity to analyze large volumetric data sets accurately, efficiently, and in true 3D has been a major bottleneck in deriving reliable relationships between altered neuronal function and changes in spine morphology. We introduce a novel system for automated detection, shape analysis and classification of dendritic spines from laser scanning microscopy (LSM) images that directly addresses these limitations. The system is more accurate, and at least an order of magnitude faster, than existing technologies. By operating fully in 3D the algorithm resolves spines that are undetectable with standard two-dimensional (2D) tools. Adaptive local thresholding, voxel clustering and Rayburst Sampling generate a profile of diameter estimates used to classify spines into morphologic types, while minimizing optical smear and quantization artifacts. The technique opens new horizons on the objective evaluation of spine changes with synaptic plasticity, normal development and aging, and with neurodegenerative disorders that impair cognitive function.

Subfields: Biophysics, Cellular and Molecular Neuroscience, Neurology

View Paper: https://www.semanticscholar.org/paper/b555667bf179caf4e89048df1a93947113cccecd

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biophysics

1. Erik H. W. Meijering
2. Oren Z. Kraus
3. Brendan J. Frey

In Cellular and Molecular Neuroscience

1. Pierre Marie Lledo
2. Erik H. W. Meijering
3. C. Greer

In Neurology

1. C. Greer
2. Pierre Marie Lledo
3. Erik H. W. Meijering

Citations in the corpus

• The Aging Cortical Synapse: Hallmarks and Implications for Cognitive Decline (2012, unknown): “To determine whether a given spine class was more vulnerable to ageing, the dendritic arbor, spine density, spine size and morphology were analysed in rodent and NHP pyramidal neurons filled with Lucifer Yello”
• Neuron tracing in perspective (2010, methodology): “Measures for spine quantification and classification encountered in the literature include (on the local level) length, head and neck diameters, orientation, moments, volume, and (on the dendrite level) position, count, and density (45, 49, 55, 60, 81, 84).”
• Fragile X Mental Retardation Protein Regulates New Neuron Differentiation in the Adult Olfactory Bulb (2011, methodology): “The first reported neuropathologic finding from FXS patients was the hyperabundance of dendritic spines in cortical neurons (Rudelli et al., 1985; Hinton et al., 1991; Wisniewski et al., 1991; Irwin et al., 2001).”