Name: analyzing dendritic morphology in columns and layers
Uploaded: 2017-03-23T15:00:00
Description: Watch this Scientific Journal Video about Analyzing Dendritic Morphology in Columns and Layers at JoVE.com

This work was supported by the Intramural Research Program of the National Institutes of Health, the Eunice Kennedy Shriver National Institute of Child Health and Human Development (grant HD008913 to C.-H.L.), and the Center for Information Technology (P.G.M., N.P., E.S.M., and M.M.).

Note: The protocol contains three sections: dual-view imaging (sections 1 - 3), dendritic tracing and registration (sections 4 - 6), and dendritic analysis (sections 7 - 9) (Figure 1). The codes and example files are provided in Table of Materials/Equipment.
1. Dual-image Acquisition
NOTE: This step is designed to acquire two image stacks of the neuron of interest in two orthogonal (horizontal and frontal) orientations.
<ol>
	<li>...

<ol>
	<li>Kaas, J. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Topographic+maps+are+fundamental+to+sensory+processing.">Topographic maps are fundamental to sensory processing.</a> Brain Res Bull. 44 (2), 107-112 (1997).</li><li>Sanes, J. R., Zipursky, S. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Design+principles+of+insect+and+vertebrate+visual+systems.">Design principles of insect and vertebrate visual systems.</a> Neuron. 66 (1), 15-36 (2010).</li><li>Huberman, A. D., Clandinin, T. R., Baier, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Molecular+and+cellular+mechanisms+of+lamina-specific+axon+targeting.">Molecular and cellular mechanisms of lamina-specific axon targeting.</a> CSH Perspect Biol. 2 (3), a001743 (2010).</li><li>Clandinin, T. R., Feldheim, D. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Making+a+visual+map:+mechanisms+and+molecules.">Making a visual map: mechanisms and molecules.</a> Curr Opin Neurobiol. 19 (2), 174-180 (2009).</li><li>Luo, J., McQueen, P. G., Shi, B., Lee, C. H., Ting, C. Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Wiring+dendrites+in+layers+and+columns.">Wiring dendrites in layers and columns.</a> J Neurogenet. 30 (2), 69-79 (2016).</li><li>Meinertzhagen, I. A., Hanson, T. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> The development of the optic lobe. In The Development of Drosophila melanogaster. , 1363-1491 (1993).</li><li>Takemura, S. Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+visual+motion+detection+circuit+suggested+by+Drosophila+connectomics.">A visual motion detection circuit suggested by Drosophila connectomics.</a> Nature. 500 (7461), 175-181 (2013).</li><li>Takemura, S. Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Synaptic+circuits+and+their+variations+within+different+columns+in+the+visual+system+of+Drosophila.">Synaptic circuits and their variations within different columns in the visual system of Drosophila.</a> Proc Natl Acad Sci U S A. 112 (44), 13711-13716 (2015).</li><li>Gao, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+neural+substrate+of+spectral+preference+in+Drosophila.">The neural substrate of spectral preference in Drosophila.</a> Neuron. 60 (2), 328-342 (2008).</li><li>Karuppudurai, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+hard-wired+glutamatergic+circuit+pools+and+relays+UV+signals+to+mediate+spectral+preference+in+Drosophila.">A hard-wired glutamatergic circuit pools and relays UV signals to mediate spectral preference in Drosophila.</a> Neuron. 81 (3), 603-615 (2014).</li><li>Strother, J. A., Nern, A., Reiser, M. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Direct+observation+of+ON+and+OFF+pathways+in+the+Drosophila+visual+system.">Direct observation of ON and OFF pathways in the Drosophila visual system.</a> Curr Biol. 24 (9), 976-983 (2014).</li><li>Ting, C. Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Photoreceptor-derived+activin+promotes+dendritic+termination+and+restricts+the+receptive+fields+of+first-order+interneurons+in+Drosophila.">Photoreceptor-derived activin promotes dendritic termination and restricts the receptive fields of first-order interneurons in Drosophila.</a> Neuron. 81 (4), 830-846 (2014).</li><li>Ting, C. Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Tiling+of+R7+axons+in+the+Drosophila+visual+system+is+mediated+both+by+transduction+of+an+activin+signal+to+the+nucleus+and+by+mutual+repulsion.">Tiling of R7 axons in the Drosophila visual system is mediated both by transduction of an activin signal to the nucleus and by mutual repulsion.</a> Neuron. 56 (5), 793-806 (2007).</li><li>Peng, H., Ruan, Z., Long, F., Simpson, J. H., Myers, E. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=V3D+enables+real-time+3D+visualization+and+quantitative+analysis+of+large-scale+biological+image+data+sets.">V3D enables real-time 3D visualization and quantitative analysis of large-scale biological image data sets.</a> Nat Biotechnol. 28 (4), 348-353 (2010).</li><li>Takemura, S. Y., Lu, Z., Meinertzhagen, I. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Synaptic+circuits+of+the+Drosophila+optic+lobe:+the+input+terminals+to+the+medulla.">Synaptic circuits of the Drosophila optic lobe: the input terminals to the medulla.</a> J Comp Neurol. 509 (5), 493-513 (2008).</li><li>Takemura, S. Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cholinergic+circuits+integrate+neighboring+visual+signals+in+a+Drosophila+motion+detection+pathway.">Cholinergic circuits integrate neighboring visual signals in a Drosophila motion detection pathway.</a> Curr Biol. 21 (24), 2077-2084 (2011).</li><li>Keller, P. J., Schmidt, A. D., Wittbrodt, J., Stelzer, E. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Reconstruction+of+zebrafish+early+embryonic+development+by+scanned+light+sheet+microscopy.">Reconstruction of zebrafish early embryonic development by scanned light sheet microscopy.</a> Science. 322 (5904), 1065-1069 (2008).</li><li>Wu, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Spatially+isotropic+four-dimensional+imaging+with+dual-view+plane+illumination+microscopy.">Spatially isotropic four-dimensional imaging with dual-view plane illumination microscopy.</a> Nat Biotechnol. 31 (11), 1032-1038 (2013).</li><li>Popko, J., Fernandes, A., Brites, D., Lanier, L. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automated+analysis+of+NeuronJ+tracing+data.">Automated analysis of NeuronJ tracing data.</a> Cytometry A. 75 (4), 371-376 (2009).</li><li>Meijering, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Design+and+validation+of+a+tool+for+neurite+tracing+and+analysis+in+fluorescence+microscopy+images.">Design and validation of a tool for neurite tracing and analysis in fluorescence microscopy images.</a> Cytometry A. 58 (2), 167-176 (2004).</li><li>Pool, M., Thiemann, J., Bar-Or, A., Fournier, A. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=NeuriteTracer:+a+novel+ImageJ+plugin+for+automated+quantification+of+neurite+outgrowth.">NeuriteTracer: a novel ImageJ plugin for automated quantification of neurite outgrowth.</a> J Neurosci Methods. 168 (1), 134-139 (2008).</li><li>Scorcioni, R., Polavaram, S., Ascoli, G. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=L-Measure:+a+web-accessible+tool+for+the+analysis,+comparison+and+search+of+digital+reconstructions+of+neuronal+morphologies.">L-Measure: a web-accessible tool for the analysis, comparison and search of digital reconstructions of neuronal morphologies.</a> Nat Protoc. 3 (5), 866-876 (2008).</li><li>Kaplan, E. L., Meier, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Nonparametric+Estimation+from+Incomplete+Observations.">Nonparametric Estimation from Incomplete Observations.</a> JASA. 53, 457-481 (1958).</li></ol>

Here, we show how to image and analyze dendritic arbors of Drosophila medulla neurons. The first section, dual-view imaging, describes the deconvolution and combination of two image stacks into a high-resolution image stack. The second section, dendrite tracing and registration, describes the tracing and registration of dendrites of medulla neurons to the reference column array. The third section, dendritic analysis, describes the use of custom scripts to analyze dendritic patterns. Together these protocols prov...

During development, neurons elaborate dendrites in complex but stereotyped branched patterns to form synapses with their presynaptic partners. Dendritic branching patterns correlate with neuronal identity and functions. The locations of dendritic arbors determine the type of presynaptic inputs they receive, while the dendritic branching complexity and field sizes govern the input number. Thus, dendritic morphological properties are critical determinants for synaptic connectivity and neuronal computation. In many regions of complex brains, such as the fly optic lobes and the vertebrate retina, synaptic circuits are organized in columns and layers to facilitate information processing1,2. In such a column and layer organization, presynaptic neurons of a distinct modality project axons to terminate at a specific layer (so-called layer-specific targeting) and to form an orderly two-dimensional array (so-called topographic map), while postsynaptic neurons extend dendrites of appropriate sizes in specific layers to receive presynaptic inputs of the correct types and numbers. While axonal targeting to layers and columns has been well studied3,4, much less is known about how dendrites are routed to specific layers and expand appropriately sized receptive fields to form synaptic connections with the correct presynaptic partners5. The difficulty of imaging and quantifying dendritic targeting to layers and columns has hindered the study of dendritic development in columnar and laminated brain structures.
Drosophila medulla neurons are an ideal model for studying dendritic routing and circuit assembly in columns and layers. The fly medulla neuropil is organized as a 3D lattice of 10 layers and approximately 750 columns. Each column is innervated by a set of afferents, including photoreceptors R7/R8 and lamina neurons L1 - L5, whose axonal terminals form topographic maps in a layer-specific fashion6. About 38 types of medulla neurons are present in every medulla column and elaborate dendrites in specific layers and with appropriate field sizes to receive inputs from these afferents7. The synaptic circuits in the medulla have been reconstructed at the electron microscopic level; thus, the synaptic partnerships are well established7,8. Furthermore, genetic tools for labeling various types of medulla neurons are available9,10,11. By examining three types of transmedulla (Tm) neurons (Tm2, Tm9 and Tm20), we have previously identified two cell-type-specific dendritic attributes: (i) Tm neurons project dendrites in either the anterior or posterior direction (planar projection direction), depending on the cell types and (ii) dendrites of medulla neurons terminate in specific medulla layers in a cell-type-specific fashion (layer-specific termination)12. Planar projection direction and layer-specific termination are sufficient to differentiate these three types of Tm neurons, while mutations that disrupt Tm responses to layer and column cues affect distinct aspects of these attributes.
Here, we present a complete workflow for examining the dendritic patterning of Drosophila medulla neurons in columns and layers (Figure 1). First, we show a dual-view imaging method, which uses customized software to combine two confocal image stacks to generate high-quality isotropic images. This method requires only conventional confocal microscopy to generate high-quality images that allow for the reliable tracing of dendritic branches, without resorting to super-resolution microscopy, such as STED (Stimulated Emission Depletion) or structural illumination. Second, we present a method for tracing dendritic arbors and for registering the resulting neurite traces to a reference column array. Third, we show the computational methods for extracting information on the planar projection direction and layer-specific termination of dendrites, as well as for deriving estimates for dendritic branching and termination frequencies. Together, these methods allow for the characterization of dendritic patterns in 3D, the classification of cell types based on dendritic morphologies, and the identification of potential defects in mutants.

<table border="0" cellpadding="0" cellspacing="0" width="1632">
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:464px;">Software</td>
			<td style="width:301px;"></td>
			<td style="width:227px;"></td>
			<td style="width:640px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Huygens Professional&#160;</td>
			<td>Scientific Volume Imaging</td>
			<td>version 16.05</td>
			<td>for image deconvolution (https://svi.nl).&#160; commercial software</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">MIPAV</td>
			<td></td>
			<td>version 7.3.0</td>
			<td>for image recombination and registration (http://mipav.cit.nih.gov/.).&#160; freeware</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">MIPAV plugin: PlugInDrosophilaRetinalRegistration.class</td>
			<td></td>
			<td></td>
			<td>freeware</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">MIPAV plugin: PlugInDrosophilaStandardColumnRegistration.class</td>
			<td></td>
			<td></td>
			<td>freeware</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Imaris</td>
			<td>Bitplane</td>
			<td></td>
			<td>for tracing neurites and assigning reference points for image registration (http://www.bitplane.com). commercial software</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Vaa3D</td>
			<td></td>
			<td></td>
			<td>for visualizing swc files (https://github.com/Vaa3D/release/releases/).&#160; freeware</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Matlab</td>
			<td>Mathworks</td>
			<td>R2014b</td>
			<td>for morphometric analysis of dendrites (http://www.mathworks.com).&#160; commercial software</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Matlab toolbox: TREES1.14</td>
			<td></td>
			<td>v1.14</td>
			<td>for analyzing dendritic morphometric parameters (http://www.treestoolbox.org/download.html).&#160; freeware</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Matlab toolbox: Dendritic_Tree_Toolbox</td>
			<td></td>
			<td>v1.0</td>
			<td>for calculating morphometric parameters (https://science.nichd.nih.gov/confluence/display/snc/Data+collections+for+imagines+combination+and+standardize+column+registration). Freeware</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Name</td>
			<td>Company</td>
			<td>Catalog number</td>
			<td>Comments</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Sample files</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">SWC file definition</td>
			<td></td>
			<td></td>
			<td>http://www.neuronland.org/NLMorphologyConverter/MorphologyFormats/SWC/Spec.html</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">The codes and sample files for image combination and registration</td>
			<td></td>
			<td></td>
			<td>https://science.nichd.nih.gov/confluence/display/snc/Data+collections+for+imagines+combination+and+standardize+column+registration</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Reference point example&#160;</td>
			<td></td>
			<td></td>
			<td>https://science.nichd.nih.gov/confluence/download/attachments/117216914/points.csv?version=1&#38;modificationDate=1471880596000&#38;api=v2</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Name</td>
			<td>Company</td>
			<td>Catalog number</td>
			<td>Comments</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Computer system</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">MS Windows Windows 7 x64 or Macintosh OS X 10.7 or later</td>
			<td></td>
			<td></td>
			<td>3GHz 64-bit quad-core processor, 16G RAM (minimal)</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Optional: Quadro4000&#160; (or above) graphic card</td>
			<td>Nvidia</td>
			<td></td>
			<td>for stereographic visualization of dendrites.</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Optional: NVIDIA 3D vision2</td>
			<td>Nvidia</td>
			<td></td>
			<td>http://www.nvidia.com/object/3d-vision-main.html</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Optional: 120 Hz LCD display for NVIDIA 3D vision2</td>
			<td></td>
			<td></td>
			<td>http://www.nvidia.com/object/3d-vision-system-requirements.html</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Name</td>
			<td>Company</td>
			<td>Catalog number</td>
			<td>Comments</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Reagents for imaging</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">24B10 antibody</td>
			<td>The Developmental Studies Hybridoma Bank</td>
			<td>24B10</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">GFP Tag Antibody</td>
			<td>Thermofisher Scientific</td>
			<td>G10362</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Goat anti-Rabbit (H+L), Alexa Fluor 488</td>
			<td>Thermofisher Scientific</td>
			<td>A11034</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Goat anti-Mouse (H+L), Alexa Fluor 568</td>
			<td>Thermofisher Scientific</td>
			<td>A21124</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">VECTASHIELD Antifade Mounting Medium</td>
			<td>Vector Laboratories</td>
			<td>H-1000</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Mounting&#160;Clay&#160;</td>
			<td>Fisher</td>
			<td>S04179</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">70% glycerol in 1X PBS</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Cover glasses, high performance, D=0.17mm</td>
			<td>Zeiss</td>
			<td>474030-9000-000</td>
			<td></td>
		</tr>
	</tbody>
</table>

analyzing dendritic morphology in columns and layers

In many regions of the central nervous systems, such as the fly optic lobes and the vertebrate cortex, synaptic circuits are organized in layers and columns to facilitate brain wiring during development and information processing in developed animals. Postsynaptic neurons elaborate dendrites in type-specific patterns in specific layers to synapse with appropriate presynaptic terminals. The fly medulla neuropil is composed of 10 layers and about 750 columns; each column is innervated by dendrites of over 38 types of medulla neurons, which match with the axonal terminals of some 7 types of afferents in a type-specific fashion. This report details the procedures to image and analyze dendrites of medulla neurons. The workflow includes three sections: (i) the dual-view imaging section combines two confocal image stacks collected at orthogonal orientations into a high-resolution 3D image of dendrites; (ii) the dendrite tracing and registration section traces dendritic arbors in 3D and registers dendritic traces to the reference column array; (iii) the dendritic analysis section analyzes dendritic patterns with respect to columns and layers, including layer-specific termination and planar projection direction of dendritic arbors, and derives estimates of dendritic branching and termination frequencies. The protocols utilize custom plugins built on the open-source MIPAV (Medical Imaging Processing, Analysis, and Visualization) platform and custom toolboxes in the matrix laboratory language. Together, these protocols provide a complete workflow to analyze the dendritic routing of Drosophila medulla neurons in layers and columns, to identify cell types, and to determine defects in mutants.

Using the dual-view imaging procedure presented here, a fly brain containing sparsely labeled Tm20 neurons was imaged in two orthogonal directions. Prior to imaging, the brain was stained with appropriate primary and secondary antibodies for visualizing membrane-tethered GFP and photoreceptor axons. For imaging, the brain was first mounted in the horizontal orientation (Figure 2A,&#160;B). A GFP-labeled Tm20 neuron and the surrounding photoreceptor axons were imaged using...

Watch this Scientific Journal Video about Analyzing Dendritic Morphology in Columns and Layers at JoVE.com

Analyzing Dendritic Morphology in Columns and Layers

Here, we show how to analyze dendritic routing of Drosophila medulla neurons in columns and layers. The workflow includes a dual-view imaging technique to improve the image quality and computational tools for tracing, registering dendritic arbors to the reference column array and for analyzing the dendritic structures in 3D space.

In many regions of the central nervous systems, such as the fly optic lobes and the vertebrate cortex, synaptic circuits are organized in layers and ...

The overall goal of this protocol is to analyze dendric routing of adult drosophila medulla neurons in columns and layers to characterize dendric patterns, determine cell types based on morphology, and identify mutants. This method can help answer key questions in the field of neuroscience, including mapping the brain circuitry, and understanding the wiring mechanism of the dendrite. The main advantage of this technique is that it passes down the dendritic properties in three dimensional space, thus improving the understanding of dendritic wiring logic and functions.The goal of this procedure is to acquire two image stacks of the neurons of interest in two orthogonal, horizontal, and frontal orientations. Using the standard techniques, stain dissected fly brains with rabbit anti-GFP and an antibody that labels photoreceptor axons primary antibodies. Then label the primary antibodies with fluorescent secondary antibodies and clear the brain in 70%glycerol and 1X PBS.To mount the brain in the horizontal orientation, transfer a glycerol cleared brain into a 20 microliter drop of antifade mounting medium deposited centrally on the microscope slide. Then on a coverslip, attach small patches of clay at the four corners to prevent the coverslip from crushing the brain. Now under a dissecting microscope, position the brains in the ventral up position.Use the convex dorsal surface of the brain as a landmark to identify the orientation of the brain sample. This will provide a horizontal view of the neuron. Then attach the coverslip.Using a confocal microscope, ideally equipped with GaAsP detectors, obtain the horizontal view image stack with a high numerical aperture objective and digital zoom of 2.5. Acquire at least 180 optical sections, have 512 square pixels, and a step size of 2 microns. After taking the first image stack, remove the coverslip from the slide and reposition the brain in the anterior up position which provides a frontal view.Then using the same technique, make a new image stack of the frontal view of the same neurons imaged in the horizontal view. Be sure to also record the location of the neuron of interest with respect to the medulla neuropal. Identifying the same neurons is feasible under lower magnification.However, if signal is lost because the tissue is deep, just image the ventral half of your brain. Also check if the sample moved while acquiring images by examining the image stacks. Image de convolution and image combining are detailed in the text protocol.The goal of this procedure is to trace neurites and to assign reference points for registration. First open the recombined image file. Then go to edit.Then show display adjustment and turn off the photoreceptor channel, which is red. Next visualize the image in the surpass mode. If the computer is equipped with a stereograph system, turn on stereo and use the quad buffer mode to visualize 3D images.Add new filaments, go to surpass, then filaments, and click on the tab labeled skip automatic creation, edit manually. Then click on the draw tab and select AutoDepth. Next select settings.Toggle the line option and input the appropriate pixel number for better visualization. Then toggle show dendrites, beginning point, and branching point, and set the render quality to 100%Now select the draw tab, and start tracing neurites. Start with the axon, then move to the dendrites.The axon and dendrites of the trans medulla neurons should be easy to differentiate. After tracing the neurites, go back to settings, and de toggle both beginning point and branching point. Then go to surpass, then export.Select object, and save the filament as an inventor file. The next step is to assign reference points. To begin, select show display adjustment and turn on both imaging channels.Then go to surpass, then measurement. And under the edit tab, toggle specific channel, and select the photoreceptor channel in this case. Now assign reference points for the top layer.Select surpass, then measurement points, and mark the beginning of the m1 layer as a top layer. Use the clipping plane function to facilitate the point assignment. The order of the points is equatorial, anterior equatorial, anterior, anterior ventral, ventral, posterior ventral, posterior, posterior equatorial, and center.To find the center photoreceptor, it's the one associated with the most dendritic processes. Next assign the reference points for the R8 and R7 layers using the same technique. After defining each set of reference points, export the coordinates.Select statistics, then detailed, specific values, and position. Then select export statistics on tab display to file, and save the coordinates as a CSV file. Next open the three CSV files in a spreadsheet editor.Copy and paste the coordinates of the 27 reference points into a new file, following the order of top, R8, and then R7.Proceed by following text protocol for rigid body and TPS non-linear registration standardization to right ventral configuration in calculation of dendritic branching and terminating frequencies. Ultimately, the data is used to make various informative plots. Using the dual view imaging procedure presented here, a fly brain containing sparsely labeled tm 20 neurons was imaged in two orthogonal directions.Image recombination improves axial resolution. The recombined image has a better resolution than those of the horizontal view and frontal view images. After consulting 3D models, dendritic traces were collected from 15 tm 20 neurons and compared with dendric traces from 15 tm 2 neurons.The branching and terminating probabilities of these neurons were analyzed. Both types were similar for segments of less than 4 microns. Layer-specific analysis of dendritic termination showed the two neuron types distribute in distinct layer specific manners.Tm 2 dendrites receive inputs in m2 and in m5. While tm 20 dendrites terminate in the m1 to m3 layer. Planar analysis show that tm 2 dendrites project anteriorly, while tm 20 dendrites project posteriorly.After watching this video, you should have a good understanding of how to acquire two of your confocal images. Read your study marks on the imaging files and extract the dendritic properties from these images. While attempting this procedure, it's important to mark the sample properly before confocal imaging.As the imaging combination will fail if the sample moves during imaging.

analyzing-dendritic-morphology-in-columns-and-layers

Research

JoVE Journal

Neuroscience

Automated Sholl Analysis of Digitized Neuronal Morphology at Multiple Scales

Neuronal morphology plays a significant role in determining how neurons function and communicate1-3. Specifically, it affects the ability of neurons to receive inputs from other cells2 and contributes to the propagation of action potentials4,5. The morphology of the neurites also affects how information is processed. The diversity of dendrite morphologies facilitate local and long range signaling and allow individual neurons or groups of neurons to carry out specialized functions within the neuronal network6,7. Alterations in dendrite morphology, including fragmentation of dendrites and changes in branching patterns, have been observed in a number of disease states, including Alzheimer's disease8, schizophrenia9,10, and mental retardation11. The ability to both understand the factors that shape dendrite morphologies and to identify changes in dendrite morphologies is essential in the understanding of nervous system function and dysfunction.
Neurite morphology is often analyzed by Sholl analysis and by counting the number of neurites and the number of branch tips. This analysis is generally applied to dendrites, but it can also be applied to axons. Performing this analysis by hand is both time consuming and inevitably introduces variability due to experimenter bias and inconsistency. The Bonfire program is a semi-automated approach to the analysis of dendrite and axon morphology that builds upon available open-source morphological analysis tools. Our program enables the detection of local changes in dendrite and axon branching behaviors by performing Sholl analysis on subregions of the neuritic arbor. For example, Sholl analysis is performed on both the neuron as a whole as well as on each subset of processes (primary, secondary, terminal, root, etc.) Dendrite and axon patterning is influenced by a number of intracellular and extracellular factors, many acting locally. Thus, the resulting arbor morphology is a result of specific processes acting on specific neurites, making it necessary to perform morphological analysis on a smaller scale in order to observe these local variations12.
The Bonfire program requires the use of two open-source analysis tools, the NeuronJ plugin to ImageJ and NeuronStudio. Neurons are traced in ImageJ, and NeuronStudio is used to define the connectivity between neurites. Bonfire contains a number of custom scripts written in MATLAB (MathWorks) that are used to convert the data into the appropriate format for further analysis, check for user errors, and ultimately perform Sholl analysis. Finally, data are exported into Excel for statistical analysis. A flow chart of the Bonfire program is shown in Figure 1.

We have developed a computer program to analyze neuronal morphology. In combination with two existing open source analysis tools, our program performs Sholl analysis and determines the number of neurites, branch points, and neurite tips. The analyses are performed so that local changes in neurite morphology can be observed.

Neuronal morphology plays a significant role in determining how neurons function and communicate1-3.  Specifically, it affects the ability of neurons to ...

Analysis of Dendritic Spine Morphology in Cultured CNS Neurons

Dendritic spines are the sites of the majority of excitatory connections within the brain, and form the post-synaptic 
compartment of synapses. These structures are rich in actin and have been shown to be highly dynamic. In response to classical Hebbian plasticity 
as well as neuromodulatory signals, dendritic spines can change shape and number, which is thought to be critical for the refinement of neural 
circuits and the processing and storage of information within the brain. Within dendritic spines, a complex network of proteins link extracellular 
signals with the actin cyctoskeleton allowing for control of dendritic spine morphology and number. Neuropathological studies have demonstrated that 
a number of disease states, ranging from schizophrenia to autism spectrum disorders, display abnormal dendritic spine morphology or numbers. 
Moreover, recent genetic studies have identified mutations in numerous genes that encode synaptic proteins, leading to suggestions that these 
proteins may contribute to aberrant spine plasticity that, in part, underlie the pathophysiology of these disorders. In order to study the potential 
role of these proteins in controlling dendritic spine morphologies/number, the use of cultured cortical neurons offers several advantages. Firstly, 
this system allows for high-resolution imaging of dendritic spines in fixed cells as well as time-lapse imaging of live cells. Secondly, this in 
vitro system allows for easy manipulation of protein function by expression of mutant proteins, knockdown by shRNA constructs, or pharmacological 
treatments. These techniques allow researchers to begin to dissect the role of disease-associated proteins and to predict how mutations of these 
proteins may function in vivo.

Numerous recent studies have identified mutations in synaptic proteins associated with brain pathologies. Primary cultured cortical neurons offer great flexibility in examining the effects of these disease-associated proteins on dendritic spine morphology and motility.

Dendritic spines are the sites of the majority of excitatory connections within the brain, and form the post-synaptic 
compartment of synapses. These ...

Inducing Dendritic Growth in Cultured Sympathetic Neurons

The shape of the dendritic arbor determines the total synaptic input a neuron can receive 1-3, and influences the types and distribution of these inputs 4-6. Altered patterns of dendritic growth and plasticity are associated with impaired neurobehavioral function in experimental models 7, and are thought to contribute to clinical symptoms observed in both neurodevelopmental disorders 8-10 and neurodegenerative diseases 11-13. Such observations underscore the functional importance of precisely regulating dendritic morphology, and suggest that identifying mechanisms that control dendritic growth will not only advance understanding of how neuronal connectivity is regulated during normal development, but may also provide insight on novel therapeutic strategies for diverse neurological diseases.
Mechanistic studies of dendritic growth would be greatly facilitated by the availability of a model system that allows neurons to be experimentally switched from a state in which they do not extend dendrites to one in which they elaborate a dendritic arbor comparable to that of their in vivo counterparts. Primary cultures of sympathetic neurons dissociated from the superior cervical ganglia (SCG) of perinatal rodents provide such a model. When cultured in defined medium in the absence of serum and ganglionic glial cells, sympathetic neurons extend a single process which is axonal, and this unipolar state persists for weeks to months in culture 14,15. However, the addition of either bone morphogenetic protein-7 (BMP-7) 16,17 or Matrigel 18 to the culture medium triggers these neurons to extend multiple processes that meet the morphologic, biochemical and functional criteria for dendrites. Sympathetic neurons dissociated from the SCG of perinatal rodents and grown under defined conditions are a homogenous population of neurons 19 that respond uniformly to the dendrite-promoting activity of Matrigel, BMP-7 and other BMPs of the decapentaplegic (dpp) and 60A subfamilies 17,18,20,21. Importantly, Matrigel- and BMP-induced dendrite formation occurs in the absence of changes in cell survival or axonal growth 17,18.
Here, we describe how to set up dissociated cultures of sympathetic neurons derived from the SCG of perinatal rats so that they are responsive to the selective dendrite-promoting activity of Matrigel or BMPs.

We describe a protocol for using bone morphogenetic protein-7 (BMP-7) or Matrigel to selectively induce dendritic growth in primary sympathetic neurons dissociated from the superior cervical ganglia (SCG) of perinatal rats.

The shape of the dendritic arbor determines the total synaptic input a neuron can receive 1-3, and influences the types and distribution of these inputs ...

The Analysis of Purkinje Cell Dendritic Morphology in Organotypic Slice Cultures

Purkinje cells are an attractive model system for studying dendritic development, because they have an impressive dendritic tree which is strictly oriented in the sagittal plane and develops mostly in the postnatal period in small rodents 3. Furthermore, several antibodies are available which selectively and intensively label Purkinje cells including all processes, with anti-Calbindin D28K being the most widely used. For viewing of dendrites in living cells, mice expressing EGFP selectively in Purkinje cells 11 are available through Jackson labs. Organotypic cerebellar slice cultures cells allow easy experimental manipulation of Purkinje cell dendritic development because most of the dendritic expansion of the Purkinje cell dendritic tree is actually taking place during the culture period 4. We present here a short, reliable and easy protocol for viewing and analyzing the dendritic morphology of Purkinje cells grown in organotypic cerebellar slice cultures. For many purposes, a quantitative evaluation of the Purkinje cell dendritic tree is desirable. We focus here on two parameters, dendritic tree size and branch point numbers, which can be rapidly and easily determined from anti-calbindin stained cerebellar slice cultures. These two parameters yield a reliable and sensitive measure of changes of the Purkinje cell dendritic tree. Using the example of treatments with the protein kinase C (PKC) activator PMA and the metabotropic glutamate receptor 1 (mGluR1) we demonstrate how differences in the dendritic development are visualized and quantitatively assessed. The combination of the presence of an extensive dendritic tree, selective and intense immunostaining methods, organotypic slice cultures which cover the period of dendritic growth and a mouse model with Purkinje cell specific EGFP expression make Purkinje cells a powerful model system for revealing the mechanisms of dendritic development.

We present a protocol that permits to view and to quantitatively asses the morphology of the dendritic tree of individual Purkinje cells grown in organotypic cerebellar slice cultures. This protocol is intended to promote studies on the mechanisms of Purkinje cell dendritic development.

Purkinje cells are an attractive model system for studying dendritic development, because they have an impressive dendritic tree which is strictly ...

Visualizing the Effects of a Positive Early Experience, Tactile Stimulation, on Dendritic Morphology and Synaptic Connectivity with Golgi-Cox Staining

To generate longer-term changes in behavior, experiences must be producing stable changes in neuronal morphology and synaptic connectivity. Tactile stimulation is a positive early experience that mimics maternal licking and grooming in the rat. Exposing rat pups to this positive experience can be completed easily and cost-effectively by using highly accessible materials such as a household duster. Using a cross-litter design, pups are either stroked or left undisturbed, for 15 min, three times per day throughout the perinatal period. To measure the neuroplastic changes related to this positive early experience, Golgi-Cox staining of brain tissue is utilized. Owing to the fact that Golgi-Cox impregnation stains a discrete number of neurons rather than all of the cells, staining of the rodent brain with Golgi-Cox solution permits the visualization of entire neuronal elements, including the cell body, dendrites, axons, and dendritic spines. The staining procedure is carried out over several days and requires that the researcher pay close attention to detail. However, once staining is completed, the entire brain has been impregnated and can be preserved indefinitely for ongoing analysis. Therefore, Golgi-Cox staining is a valuable resource for studying experience-dependent plasticity.

This paper describes the procedures for tactile stimulation of rat pups and subsequent Golgi-Cox staining of neuronal morphology. Tactile stimulation is a positive experience that is administered in the perinatal period by stroking pups with a household duster. Golgi-cox staining is a reliable procedure permitting the visualization of entire neurons.

To generate longer-term changes in behavior, experiences must be producing stable changes in neuronal morphology and synaptic connectivity. Tactile ...

Fast Micro-iontophoresis of Glutamate and GABA: A Useful Tool to Investigate Synaptic Integration

One of the fundamental interests in neuroscience is to understand the integration of excitatory and inhibitory inputs along the very complex structure of the dendritic tree, which eventually leads to neuronal output of action potentials at the axon. The influence of diverse spatial and temporal parameters of specific synaptic input on neuronal output is currently under investigation, e.g. the distance-dependent attenuation of dendritic inputs, the location-dependent interaction of spatially segregated inputs, the influence of GABAergig inhibition on excitatory integration, linear and non-linear integration modes, and many more.
With fast micro-iontophoresis of glutamate and GABA it is possible to precisely investigate the spatial and temporal integration of glutamatergic excitation and GABAergic inhibition. Critical technical requirements are either a triggered fluorescent lamp, light-emitting diode (LED), or a two-photon scanning microscope to visualize dendritic branches without introducing significant photo-damage of the tissue. Furthermore, it is very important to have a micro-iontophoresis amplifier that allows for fast capacitance compensation of high resistance pipettes. Another crucial point is that no transmitter is involuntarily released by the pipette during the experiment.
Once established, this technique will give reliable and reproducible signals with a high neurotransmitter and location specificity. Compared to glutamate and GABA uncaging, fast iontophoresis allows using both transmitters at the same time but at very distant locations without limitation to the field of view. There are also advantages compared to focal electrical stimulation of axons: with micro-iontophoresis the location of the input site is definitely known and it is sure that only the neurotransmitter of interest is released. However it has to be considered that with micro-iontophoresis only the postsynapse is activated and presynaptic aspects of neurotransmitter release are not resolved. In this article we demonstrate how to set up micro-iontophoresis in brain slice experiments.

In this article we introduce fast micro-iontophoresis of neurotransmitters as a technique to investigate integration of postsynaptic signals with high spatial and temporal precision.

One of the fundamental interests in neuroscience is to understand the integration of excitatory and inhibitory inputs along the very complex structure of ...

Genetic Manipulation of Cerebellar Granule Neurons In Vitro and In Vivo to Study Neuronal Morphology and Migration

Developmental events in the brain including neuronal morphogenesis and migration are highly orchestrated processes. In vitro and in vivo analyses allow for an in-depth characterization to identify pathways involved in these events. Cerebellar granule neurons (CGNs) that are derived from the developing cerebellum are an ideal model system that allows for morphological analyses. Here, we describe a method of how to genetically manipulate CGNs and how to study axono- and dendritogenesis of individual neurons. With this method the effects of RNA interference, overexpression or small molecules can be compared to control neurons. In addition, the rodent cerebellar cortex is an easily accessible in vivo system owing to its predominant postnatal development. We also present an in vivo electroporation technique to genetically manipulate the developing cerebella and describe subsequent cerebellar analyses to assess neuronal morphology and migration.

Genetic Manipulation of Cerebellar Granule Neurons In Vitro and In Vivo to Study Neuronal Morphology and Migration

Neuronal morphogenesis and migration are crucial events underlying proper brain development. Here, we describe methods to genetically manipulate cultured cerebellar granule neurons and the developing cerebellum for the assessment of morphology and migratory characteristics of neurons.

Developmental events in the brain including neuronal morphogenesis and migration are highly orchestrated processes. In vitro and in vivo analyses allow ...

The Indirect Neuron-astrocyte Coculture Assay: An In Vitro Set-up for the Detailed Investigation of Neuron-glia Interactions

Proper neuronal development and function is the prerequisite of the developing and the adult brain. However, the mechanisms underlying the highly controlled formation and maintenance of complex neuronal networks are not completely understood thus far. The open questions concerning neurons in health and disease are diverse and reaching from understanding the basic development to investigating human related pathologies, e.g.,&#160;Alzheimer&#39;s disease and&#160;Schizophrenia. The most detailed analysis of neurons can be performed in vitro. However, neurons are demanding cells and need the additional support of astrocytes for their long-term survival. This cellular heterogeneity is in conflict with the aim to dissect the analysis of neurons and astrocytes. We present here a cell-culture assay that allows for the long-term cocultivation of pure primary neurons and astrocytes, which share the same chemically defined medium, while being physically separated. In this setup, the cultures survive for up to four weeks and the assay is suitable for a diversity of investigations concerning neuron-glia interaction.

The Indirect Neuron-astrocyte Coculture Assay: An In Vitro Set-up for the Detailed Investigation of Neuron-glia Interactions

This protocol describes the indirect neuron-astrocyte coculture for compartmentalized analysis of neuron-glia interactions.

Proper neuronal development and function is the prerequisite of the developing and the adult brain. However, the mechanisms underlying the highly ...

Quantitative Analysis of Neuronal Dendritic Arborization Complexity in Drosophila

Dendrites are the branched projections of a neuron, and dendritic morphology reflects synaptic organization during the development of the nervous system. Drosophila larval neuronal dendritic arborization (da) is an ideal model for studying morphogenesis of neural dendrites and gene function in the development of nervous system. There are four classes of da neurons. Class IV is the most complex with a branching pattern that covers almost the entire area of the larval body wall. We have previously characterized the effect of silencing the Drosophila ortholog of SOX5 on class IV neuronal dendritic arborization complexity (NDAC) using four parameters: the length of dendrites, the surface area of dendrite coverage, the total number of branches, and the branching structure. This protocol presents the workflow of NDAC quantitative analysis, consisting of larval dissection, confocal microscopy, and image analysis procedures using ImageJ software. Further insight into da neuronal development and its underlying mechanisms will improve the understanding of neuronal function and provide clues about the fundamental causes of neurological and neurodevelopmental disorders.

Quantitative Analysis of Neuronal Dendritic Arborization Complexity in Drosophila

This protocol focuses on quantitative analysis of neuronal dendritic arborization complexity (NDAC) in Drosophila, which can be used for studies of dendritic morphogenesis.

Dendrites are the branched projections of a neuron, and dendritic morphology reflects synaptic organization during the development of the nervous system. ...

Analyzing Mitochondrial Transport and Morphology in Human Induced Pluripotent Stem Cell-Derived Neurons in Hereditary Spastic Paraplegia

Neurons have intense demands for high energy in order to support their functions. Impaired mitochondrial transport along axons has been observed in human neurons, which may contribute to neurodegeneration in various disease states. Although it is challenging to examine mitochondrial dynamics in live human nerves, such paradigms are critical for studying the role of mitochondria in neurodegeneration. Described here is a protocol for analyzing mitochondrial transport and mitochondrial morphology in forebrain neuron axons derived from human induced pluripotent stem cells (iPSCs). The iPSCs are differentiated into telencephalic glutamatergic neurons using well-established methods. Mitochondria of the neurons are stained with MitoTracker CMXRos, and mitochondrial movement within the axons are captured using a live-cell imaging microscope equipped with an incubator for cell culture. Time-lapse images are analyzed using software with &#34;MultiKymograph&#34;, &#34;Bioformat importer&#34;, and &#34;Macros&#34; plugins. Kymographs of mitochondrial transport are generated, and average mitochondrial velocity in the anterograde and retrograde directions is read from the kymograph. Regarding mitochondrial morphology analysis, mitochondrial length, area, and aspect ratio are obtained using the ImageJ. In summary, this protocol allows characterization of mitochondrial trafficking along axons and analysis of their morphology to facilitate studies of neurodegenerative diseases.

Impaired mitochondrial transport and morphology are involved in various neurodegenerative diseases. The presented protocol uses induced pluripotent stem cell-derived forebrain neurons to assess mitochondrial transport and morphology in hereditary spastic paraplegia. This protocol allows characterization of mitochondrial trafficking along axons and analysis of their morphology, which will facilitate the study of neurodegenerative disease.

Neurons have intense demands for high energy in order to support their functions. Impaired mitochondrial transport along axons has been observed in human ...