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.).

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The authors have nothing to disclose.

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 provide a complete workflow to extract information on dendritic patterns with respect to layers and columns and to determine dendritic branching and terminating frequencies.
The dual-view imaging method presented here is conceptually similar to the multi-view imaging techniques implemented with light-sheet microscopes, in which two or more image stacks collected orthogonally are recombined to achieve a high axial resolution17,18. However, it is technically challenging to orthogonally implement the two high N.A. (numerical aperture) objective lenses that are required for imaging slender dendrites. Thus, the dual-view method requires imaging the same brain sample in two consecutive steps and re-orientating the sample between imaging. Failures in image combination are most likely caused by sample movement during imaging or sample squashing during manipulation. Furthermore, while the image recombination process mitigates the axial distortion problem and results in near-isotropic resolution, the recombined images are still under a diffraction limit. Our dual-view imaging method needs only standard confocal microscopy, which is widely available. However, super-resolution microscopy, such as STED&#160;or structure illumination, if available, would be an excellent alternative to the dual-view imaging method present here.
The dendrite tracing and registration section presented here traces dendritic trees and registers dendritic trees to the reference column array. In this protocol, the commercial program Imaris is used for semi-automated dendrite tracing and for assigning reference points. Similar tasks could be carried out using a number of freeware alternatives, such as NeuronJ19,20 and Neurite Tracer21. Our registration protocol uses a custom plugin implemented in the open source program MIPAV to register dendrite traces to the reference column array. To our knowledge, this registration method is the only method that registers neurite traces to columns and layers. This procedure uses regularly arranged photoreceptor axons as landmarks for registration. The dendritic traces generated by the non-linear registration method are ideal for extracting information on dendritic patterns relating to columns and layers. On the other hand, the rigid-body registration method aligns the Cartesian axes of dendritic traces to the cardinal axes of the neuropil without changing the dendritic lengths or other local geometrical properties. Therefore, the rigid-body registered dendritic traces are suitable for standard morphometric analyses.
Traditional dendritic morphometric analysis, such as L-measure22, characterizes local and global dendritic properties without referencing surrounding tissue. We have previously shown that many medulla neurons share dendritic geometrical properties and that such metrics are insufficient to differentiate medulla neuron types12. Instead, type-specific dendritic attributes are directly associated with the layers and columns where dendrites reside. In particular, different types of medulla neurons project dendrites in distinct planar directions to terminate in specific medulla layers. These two dendritic attributes reflect the dendritic function of medulla neurons to receive retinotopically directed afferents organized in layers and columns. To facilitate the extraction of these properties, we developed the dendritic tree toolbox that contains a series of custom functions for calculating dendritic properties. In the dendritic analysis section, we show how these functions can be applied to registered dendritic traces to calculate the distribution of layer-specific termination and planar projection directions of dendrites.
The dendritic tree toolbox provides a platform for adding other custom functions for dendritic analysis. In the dendritic analysis section, we also describe the process to derive estimates for apparent dendritic terminating and branching frequencies. In particular, these functions calculate the branching and terminating frequencies based on a Kaplan-Meier nonparametric estimator for the probability that a dendritic segment will extend to a certain length without branching or terminating12,23. It is important to note that the rigid-body registered (but not the nonlinearly registered) dendritic traces should be used for this calculation because the non-linear registration process alters dendritic segment lengths. The dendritic branching and termination frequencies are the first derivative (the slope) of the KM curves and can be calculated using the function-fitting method. Furthermore, the accurate calculation of the branching and terminating frequencies often requires a sizable data set (&#62;10 neurons for Tm neurons) because single neurons have only a relatively small number of dendritic segments. In the provided examples, Tm2 and Tm20 have similar branching and terminating frequencies, but the Tm2 branching frequency is not homogenous across branching length, a characteristic feature of Tm2 dendrites12.
While our protocols are designed for Drosophila medulla neurons, they can be adapted to analyze other neurons that elaborate dendrites in layers and columns, such as other Drosophila optic lobes and the vertebrate retina and cortex. Such adaptations would require building a new reference column array based on the system of interest12, as well as minor modifications of the MIPAV plugin. We provide all our programs as open source software to promote collaboration and sharing.

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><tbody><tr><td>&lt;strong&gt;Software&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>Huygens Professional</td><td>Scientific Volume Imaging</td><td>version 16.05</td><td>for image deconvolution (https://svi.nl).&amp;nbsp; commercial software</td></tr><tr><td>MIPAV</td><td></td><td>version 7.3.0</td><td>for image recombination and registration (http://mipav.cit.nih.gov/); freeware</td></tr><tr><td>MIPAV plugin: PlugInDrosophila&lt;br/&gt;RetinalRegistration.class</td><td></td><td></td><td>freeware</td></tr><tr><td>MIPAV plugin: PlugInDrosophilaStandard&lt;br/&gt;ColumnRegistration.class</td><td></td><td></td><td>freeware</td></tr><tr><td>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><td>Vaa3D</td><td></td><td></td><td>for visualizing swc files (https://github.com/Vaa3D/release/releases/); freeware</td></tr><tr><td>Matlab</td><td>Mathworks</td><td>R2014b</td><td>for morphometric analysis of dendrites (http://www.mathworks.com); commercial software</td></tr><tr><td>Matlab toolbox: TREES1.14</td><td></td><td>v1.14</td><td>for analyzing dendritic morphometric parameters (http://www.treestoolbox.org/download.html); freeware</td></tr><tr><td>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><td>&lt;strong&gt;Name&lt;/strong&gt;</td><td>&lt;strong&gt;Company&lt;/strong&gt;</td><td>&lt;strong&gt;Catalog number&lt;/strong&gt;</td><td>&lt;strong&gt;Comments&lt;/strong&gt;</td></tr><tr><td>&lt;strong&gt;Sample files&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>SWC file definition</td><td></td><td></td><td>http://www.neuronland.org/NLMorphologyConverter/MorphologyFormats/SWC/Spec.html</td></tr><tr><td>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><td>Reference point example&amp;nbsp;</td><td></td><td></td><td>https://science.nichd.nih.gov/confluence/download/attachments/117216914/points.csv?version=1&amp;amp;modificationDate=&lt;br/&gt;1471880596000&amp;amp;api=v2</td></tr><tr><td>&lt;strong&gt;Name&lt;/strong&gt;</td><td>&lt;strong&gt;Company&lt;/strong&gt;</td><td>&lt;strong&gt;Catalog number&lt;/strong&gt;</td><td>&lt;strong&gt;Comments&lt;/strong&gt;</td></tr><tr><td>&lt;strong&gt;Computer system&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>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><td>Optional: Quadro4000&amp;nbsp; (or above) graphic card</td><td>Nvidia</td><td></td><td>for stereographic visualization of dendrites.</td></tr><tr><td>Optional: NVIDIA 3D vision2</td><td>Nvidia</td><td></td><td>http://www.nvidia.com/object/3d-vision-main.html</td></tr><tr><td>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><td>&lt;strong&gt;Name&lt;/strong&gt;</td><td>&lt;strong&gt;Company&lt;/strong&gt;</td><td>&lt;strong&gt;Catalog number&lt;/strong&gt;</td><td>&lt;strong&gt;Comments&lt;/strong&gt;</td></tr><tr><td>&lt;strong&gt;Reagents for imaging&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>24B10 antibody</td><td>The Developmental Studies Hybridoma Bank</td><td>24B10</td><td></td></tr><tr><td>GFP Tag Antibody</td><td>Thermofisher Scientific</td><td>G10362</td><td></td></tr><tr><td>Goat anti-Rabbit (H+L), Alexa Fluor 488</td><td>Thermofisher Scientific</td><td>A11034</td><td></td></tr><tr><td>Goat anti-Mouse (H+L), Alexa Fluor 568</td><td>Thermofisher Scientific</td><td>A21124</td><td></td></tr><tr><td>VECTASHIELD Antifade Mounting Medium</td><td>Vector Laboratories</td><td>H-1000</td><td></td></tr><tr><td>Mounting&amp;nbsp;Clay&amp;nbsp;</td><td>Fisher</td><td>S04179</td><td></td></tr><tr><td>70% glycerol in 1x PBS</td><td></td><td></td><td></td></tr><tr><td>Cover glasses, high performance, D = 0.17&amp;nbsp;mm</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 a confocal microscope (Figure 3A). The brain was then re-aligned (Figure 2A,&#160;B) and the same Tm20 neuron was re-imaged in the frontal orientation (Figure 3B). During the imaging process, the location of the Tm20 neuron was identified as at the ventral half of the right medulla neuropil (RV configuration). These two image stacks were deconvolved using the image deconvolution software and recombined using MIPAV (Figure 3C) to generate a recombinant image stack (Figure 3C&#39;). The recombinant image stack showed a significant reduction of axial distortion as compared to either input stack (Figure 3A&#39;, B&#39;).
Using the image visualization software, the axon and dendrites of the Tm20 neuron were traced (Figure 3D) and saved as an inventor (iv) file. 27 reference points on photoreceptor axons surrounding the Tm20 neuron were assigned (Figures 3E - H) and saved as a comma separated value (CSV) file. The traced dendrite files and reference point file was subsequently applied to the custom plugin in MIPAV to generate rigid-body registered and non-linearly registered dendrite traces in swc file format. The registered dendritic traces were converted to the standard RV configuration using the &#34;RV_standardization.m&#34; script. The registered and standardized dendritic trace files were visualized using Vaa3D to validate the registration and standardization processes (Figure 3I).
For dendritic analysis, registered dendritic traces for 15 Tm20 neurons and 15 Tm2 neurons were collected. Using the &#34;Branch_term_P.m&#34; script, the branching and terminating probability of these neurons were analyzed using the rigid-body registered dendritic traces (Figure 4A, B). The Kaplan-Meier curves show that both neuronal types have similar branching and terminating frequencies. However, Tm2 has a lower terminating frequency for longer segments (&#62;4 &#181;m; Figure 4A)12. For analyzing dendritic properties associated with layers and columns, the dendritic traces registered by the non-linear registration method were used. Using the custom &#34;Layer_term_P.m&#34; script, the distributions of layer-specific dendritic termination for Tm2 and Tm20 neurons were calculated (Figure 4C, D). The distinct distributions of layer-specific terminations of Tm2 and Tm20 dendrites are consistent with their specific synaptic partners in different layers: Tm2 dendrites receive inputs from L2 and L4 axonal terminals in M2 and M5, respectively, while Tm20 dendrites receive inputs from R8 and L3 in the M3 layer8,15,16. Using the &#34;Planar_proj.m&#34; script, the planar projection directions of Tm2 and Tm20 dendrites were analyzed and were found to be distinct from one another: Tm2 dendrites project posteriorly while Tm20 dendrites project anteriorly12 (Figure 4E,&#160;F).
<img alt="Figure 1" src="/files/ftp_upload/55410/55410fig1.jpg" /> 
Figure 1. The Workflow. The central column shows three method sections: dual-view imaging, dendrite tracing and registration, and dendritic analysis. Each method section contains three steps. The left column indicates the software used in each step. The right column shows the input and output files for each step, with the data flow indicated as orange arrows. <a href="https://cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/55410/55410fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" src="/files/ftp_upload/55410/55410fig2.jpg" /> 
Figure 2. Sample Preparation for Dual-view Imaging and the Symmetry of the Drosophila Medulla Column Array. (A, B) Sample mounting for dual-view imaging. (A) The view is shown as if looking at the brain through a coverslip. (B) Schematic diagrams for sample mounting. For imaging horizontal stacks, the immuno-stained brain is mounted first at the ventral-up position. After acquiring the first stack, the brain is re-positioned in the anterior-up position for imaging the frontal stack. A: anterior; P: posterior; D: dorsal; V: ventral; R: right; L: left. (C - E) The symmetry and organization of the medulla neuropil, viewed in the frontal position. (C) Photoreceptor axons, labeled by the 24B10 antibody (red), were used to visualize the organization of the medulla neuropil. The image is shown as if looking from the outside into the brain. Ant: anterior; Pos: posterior; Dor: dorsal; Ven: ventral; Eq: equator. (D) A high-magnification view of (C) at four quadrants of the medulla neuropil at the R7 layer level. The central R7 terminals are marked by yellow dots. The anterior and equatorial R7s are labeled with green and cyan dots, respectively. (E) A schematic representation of the medulla columnar organization. Note that the left and right medulla neuropils are mirror images, and the dorsal and ventral halves of each neuropil are also mirror images, separated by the equator. Red dots indicate equatorial R7s, which are the first reference points. Numbers (1 - 9) and arrows indicate the order of reference point assignments. Scale bar: 15 &#181;m in C; 5 &#181;m in D. <a href="https://cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/55410/55410fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" src="/files/ftp_upload/55410/55410fig3.jpg" /> 
Figure 3. Image Recombination and Reference Point Assignment. (A - C) Confocal images for a single Tm20 neuron acquired in the horizontal (A) and frontal (B) directions. Images are shown as maximum-intensity projections. Photoreceptor axons and a single Tm20 neuron are labeled with Mb24B10 antibody (red) and membrane-tethered GFP (green), respectively. (C) Recombined image of (A, red) and (B, cyan). For clarity, only photoreceptors are shown. (A&#39; - C&#39;) Dual-imaging improves axial resolution. The recombined image (A&#39;) has a better resolution than those of the horizontal-view (A&#39;) and frontal-view (B&#39;) images. Lower panels: high-magnification views of the boxed areas in the upper panels. (D) Tracing the axon (white) of a single Tm20 neuron. The beginning point was indicated as a cyan dot. (E) The 27 reference points (white dots) on the 9 photoreceptor axons surrounding the Tm20 neurons are used as landmarks. The reference points are in three layers: Top, R8 and R7. (F) Reference point assignment in each layer. The reference point assignment follows the following order: equator (Eq), anterior-equator (AE), anterior (A), anterior-ventral (AV), ventral (V), posterior-ventral (PV), posterior (P), posterior-equator (PE), and center (C). (G) A schematic representation of the medulla column array used for registration. A Tm20 neuron is shown (green). The medulla layers (M1-6) are as indicated. (H) An example of layer and reference point assignment for a single column. Three layers are used to define each column: Top, R8, and R7 (the junction of M5/6 layers). (I) A registered and standardized neurite trace visualized using Vaa3D. Scale bar: 5 &#181;m in A for A-C; 5 &#181;m in A&#39; for A&#39;-C&#39;; 5 &#181;m in D, E, F, and G. <a href="https://cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/55410/55410fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" src="/files/ftp_upload/55410/55410fig4.jpg" /> 
Figure 4. Examples of Results from Dendritic Analyses. (A, B) Kaplan-Meier estimates (y-axis) of dendritic branching (A) and termination (B) were plotted in logarithmic scale with respect to segment length (x-axis). Solid and dotted lines denote averages &#177; SDs. Tm2 (red) and Tm20 (black) share similar branching and terminating frequencies for segments less than 4 &#181;m. (C, D) Histograms showing the mean proportion of terminal dendritic nodes in each medulla (M) layer. The proportion of terminal nodes in a given medulla layer is first calculated for every neuron of a given type and then averaged over all the neurons of that type. The vertical bars show the standard deviation of the proportion. Note that for Tm2 neurons (C), the distribution of terminal dendritic nodes is concentrated in medulla layer 2, with a minor peak in layer 5, while for Tm 20 neurons (D), the distribution is centered around the M2 and M3 layers. (E, F) Polar plots of the planar projection direction of dendrites. The proportion of terminal nodes projected within an angular bin (20&#176;) was first calculated for every neuron of a given type and then averaged over all neurons of that type. The red and blue curves show the mean proportion &#177; standard deviation. The corners on the curves are the midpoints of the angular bins. The black circles show the proportion values indicated by the black numeral to their side. Note that, on average, the two classes of neurons are oriented in almost opposite directions. A: anterior; P: posterior; D/V: dorsal/ventral; Eq: equator. <a href="https://cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/55410/55410fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

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 ...

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

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9.2K Views.  National Institutes of Health (NIH). 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.

Video: Analyzing Dendritic Morphology in Columns and Layers

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 ...

Morphological Analysis of Drosophila Larval Peripheral Sensory Neuron Dendrites and Axons Using Genetic Mosaics

Nervous system development requires the correct specification of neuron position and identity, followed by accurate neuron class-specific dendritic development and axonal wiring. Recently the dendritic arborization (DA) sensory neurons of the Drosophila larval peripheral nervous system (PNS) have become powerful genetic models in which to elucidate both general and class-specific mechanisms of neuron differentiation.
There are four main DA neuron classes (I-IV)1. They are named in order of increasing dendrite arbor complexity, and have class-specific differences in the genetic control of their differentiation2-10. The DA sensory system is a practical model to investigate the molecular mechanisms behind the control of dendritic morphology11-13 because: 1) it can take advantage of the powerful genetic tools available in the fruit fly, 2) the DA neuron dendrite arbor spreads out in only 2 dimensions beneath an optically clear larval cuticle making it easy to visualize with high resolution in vivo, 3) the class-specific diversity in dendritic morphology facilitates a comparative analysis to find key elements controlling the formation of simple vs. highly branched dendritic trees, and 4) dendritic arbor stereotypical shapes of different DA neurons facilitate morphometric statistical analyses.
DA neuron activity modifies the output of a larval locomotion central pattern generator14-16. The different DA neuron classes have distinct sensory modalities, and their activation elicits different behavioral responses14,16-20. Furthermore different classes send axonal projections stereotypically into the Drosophila larval central nervous system in the ventral nerve cord (VNC)21. These projections terminate with topographic representations of both DA neuron sensory modality and the position in the body wall of the dendritic field7,22,23. Hence examination of DA axonal projections can be used to elucidate mechanisms underlying topographic mapping7,22,23, as well as the wiring of a simple circuit modulating larval locomotion14-17.
We present here a practical guide to generate and analyze genetic mosaics24 marking DA neurons via MARCM (Mosaic Analysis with a Repressible Cell Marker)1,10,25 and Flp-out22,26,27 techniques (summarized in Fig. 1).

Morphological Analysis of Drosophila Larval Peripheral Sensory Neuron Dendrites and Axons Using Genetic Mosaics

The dendritic arborization sensory neurons of the Drosophila larval peripheral nervous system are useful models to elucidate both general and neuron class-specific mechanisms of neuron differentiation. We present a practical guide to generate and analyze dendritic arborization neuron genetic mosaics.

Nervous system development requires the correct specification of neuron position and identity, followed by accurate neuron class-specific dendritic ...

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 ...

Electrophysiological and Morphological Characterization of Neuronal Microcircuits in Acute Brain Slices Using Paired Patch-Clamp Recordings

The combination of patch clamp recordings from two (or more) synaptically coupled neurons (paired recordings) in acute brain slice preparations with simultaneous intracellular biocytin filling allows a correlated analysis of their structural and functional properties. With this method it is possible to identify and characterize both pre- and postsynaptic neurons by their morphology and electrophysiological response pattern. Paired recordings allow studying the connectivity patterns between these neurons as well as the properties of both chemical and electrical synaptic transmission. Here, we give a step-by-step description of the procedures required to obtain reliable paired recordings together with an optimal recovery of the neuron morphology. We will describe how pairs of neurons connected via chemical synapses or gap junctions are identified in brain slice preparations. We will outline how neurons are reconstructed to obtain their 3D morphology of the dendritic and axonal domain and how synaptic contacts are identified and localized. We will also discuss the caveats and limitations of the paired recording technique, in particular those associated with dendritic and axonal truncations during the preparation of brain slices because these strongly affect connectivity estimates. However, because of the versatility of the paired recording approach it will remain a valuable tool in characterizing different aspects of synaptic transmission at identified neuronal microcircuits in the brain.

Patch-clamp recordings and simultaneous intracellular biocytin filling of synaptically coupled neurons in acute brain slices allow a correlated analysis of their structural and functional properties. The aim of this protocol is to describe the essential technical steps of electrophysiological recording from neuronal microcircuits and their subsequent morphological analysis.

The combination of patch clamp recordings from two (or more) synaptically coupled neurons (paired recordings) in acute brain slice preparations with ...

Assessment of Dendritic Arborization in the Dentate Gyrus of the Hippocampal Region in Mice

Dendritic arborization has been shown to be a reliable marker for examination of structural and functional integrity of neurons. Indeed, the complexity and extent of dendritic arborization correlates well with the synaptic plasticity in these cells. A reliable method for assessment of dendritic arborization is needed to characterize the deleterious effects of neurological disorders on these structures and to determine the effects of therapeutic interventions. However, quantification of these structures has proven to be a formidable task given their complex and dynamic nature. Fortunately, sophisticated imaging techniques can be paired with conventional staining methods to assess the state of dendritic arborization, providing a more reliable and expeditious means of assessment. Below is an example of how these imaging techniques were paired with staining methods to characterize the dendritic arborization in wild type mice. These complementary imaging methods can be used to qualitatively and quantitatively assess dendritic arborization that span a rather wide area within the hippocampal region.

We describe two methods for visualization and quantification of dendritic arborization in the hippocampus of mouse models: real-time and extended depth of field imaging. While the former method allows sophisticated topographical tracing and quantification of the extent of branching, the latter allows speedy visualization of the dendritic tree.

Dendritic arborization has been shown to be a reliable marker for examination of structural and functional integrity of neurons. Indeed, the complexity ...

Assessment of Hippocampal Dendritic Complexity in Aged Mice Using the Golgi-Cox Method

Dendritic spines are the protuberances from the neuronal dendritic shafts that contain&#160; excitatory synapses. The morphological and branching variations of the neuronal dendrites within the hippocampus are implicated in cognition and memory formation. There are several approaches to Golgi staining, all of which have been useful for determining the morphological characteristics of dendritic arbors and produce a clear background. The present Golgi-Cox method, (a slight variation of the protocol that is provided with a commercial Golgi staining kit), was designed to assess how a relatively low dose of the chemotherapeutic drug 5-flurouracil (5-Fu) would affect dendritic morphology, the number of spines, and the complexity of arborization within the hippocampus. The 5-Fu significantly modulated the dendritic complexity and decreased the spine density throughout the hippocampus in a region-specific manner. The data presented show that the Golgi staining method effectively stained the mature neurons in the CA1, the CA3, and the dentate gyrus (DG) of the hippocampus. This protocol reports the details for each step so that other researchers can reliably stain tissue throughout the brain with high quality results and minimal troubleshooting.

Here we present a Golgi-Cox protocol in extensive detail. This reliable tissue stain method allows for a high-quality assessment of the cytoarchitecture in the hippocampus, and throughout the entire brain, with minimal troubleshooting.

Dendritic spines are the protuberances from the neuronal dendritic shafts that contain&#160; excitatory synapses. The morphological and branching ...

Large-scale Three-dimensional Imaging of Cellular Organization in the Mouse Neocortex

The mammalian neocortex is composed of many types of excitatory and inhibitory neurons, each with specific electrophysiological and biochemical properties, synaptic connections, and in vivo functions, but their basic functional and anatomical organization from cellular to network scale is poorly understood. Here we describe a method for the three-dimensional imaging of fluorescently-labeled neurons across large areas of the brain for the investigation of the cortical cellular organization. Specific types of neurons are labeled by the injection of fluorescent retrograde neuronal tracers or expression of fluorescent proteins in transgenic mice. Block brain samples, e.g., a hemisphere, are prepared after fixation, made transparent with tissue clearing methods, and subjected to fluorescent immunolabeling of the specific cell types. Large areas are scanned using confocal or two-photon microscopes equipped with large working distance objectives and motorized stages. This method can resolve the periodic organization of the cell type-specific microcolumn functional modules in the mouse neocortex. The procedure can be useful for the study of three-dimensional cellular architecture in the diverse brain areas and other complex tissues.

Here we describe a procedure for tissue clearing, fluorescent labeling, and large-scale imaging of mouse brain tissue which, thereby, enables visualization of the three-dimensional organization of cell types in the neocortex.

The mammalian neocortex is composed of many types of excitatory and inhibitory neurons, each with specific electrophysiological and biochemical ...

Ballistic Labeling of Pyramidal Neurons in Brain Slices and in Primary Cell Culture

It has been reported that the size and shape of dendritic spines is related to their structural plasticity. To identify the morphological structure of pyramidal neurons and dendritic spines, a ballistic labeling technique can be utilized. In the present protocol, pyramidal neurons are labeled with DilC18(3) dye and analyzed using neuronal reconstruction software to assess neuronal morphology and dendritic spines. To investigate neuronal structure, dendritic branching analysis and Sholl analysis are performed, allowing researchers to draw inferences about dendritic branching complexity and neuronal arbor complexity, respectively. The evaluation of dendritic spines is conducted using an automatic assisted classification algorithm integral to the reconstruction software, which classifies spines into four categories (i.e., thin, mushroom, stubby, filopodia). Furthermore, an additional three parameters (i.e., length, head diameter, and volume) are also chosen to assess alterations in dendritic spine morphology. To validate the potential of wide application of the ballistic labeling technique, pyramidal neurons from in vitro cell culture were successfully labeled. Overall, the ballistic labeling method is unique and useful for visualizing neurons in different brain regions in rats, which in combination with sophisticated reconstruction software, allows researchers to elucidate the possible mechanisms underlying neurocognitive dysfunction.

We present a protocol to label and analyze pyramidal neurons, which is critical for evaluating potential morphological alterations in neurons and dendritic spines that may underlie neurochemical and behavioral abnormalities.

It has been reported that the size and shape of dendritic spines is related to their structural plasticity. To identify the morphological structure of ...

Automatic Identification of Dendritic Branches and their Orientation

The structure of neuronal dendritic trees plays a key role in the integration of synaptic inputs in neurons. Therefore, characterization of the morphology of dendrites is essential for a better understanding of neuronal function. However, the complexity of dendritic trees, both when isolated and especially when located within neuronal networks, has not been completely understood. We developed a new computational tool, SOA (Segmentation and Orientation Analysis), which allows automatic measurement of the orientation of dendritic branches from fluorescence images of 2D neuronal cultures. SOA, written in Python, uses segmentation to distinguish dendritic branches from the image background and accumulates a database on the spatial direction of each branch. The database is then used to calculate morphological parameters such as the directional distribution of dendritic branches in a network and the prevalence of parallel dendritic branch growth. The data obtained can be used to detect structural changes in dendrites in response to neuronal activity and to biological and pharmacological stimuli.

Presented is a computational tool that allows simple and direct automatic measurement of orientations of neuronal dendritic branches from 2D fluorescence images.

The structure of neuronal dendritic trees plays a key role in the integration of synaptic inputs in neurons. Therefore, characterization of the morphology ...

Analyzing Dendritic Morphology in Columns and Layers

Summary

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Chapters in this video

Automated Sholl Analysis of Digitized Neuronal Morphology at Multiple Scales

Analysis of Dendritic Spine Morphology in Cultured CNS Neurons

Morphological Analysis of Drosophila Larval Peripheral Sensory Neuron Dendrites and Axons Using Genetic Mosaics

The Analysis of Purkinje Cell Dendritic Morphology in Organotypic Slice Cultures

Electrophysiological and Morphological Characterization of Neuronal Microcircuits in Acute Brain Slices Using Paired Patch-Clamp Recordings

Assessment of Dendritic Arborization in the Dentate Gyrus of the Hippocampal Region in Mice

Assessment of Hippocampal Dendritic Complexity in Aged Mice Using the Golgi-Cox Method

Large-scale Three-dimensional Imaging of Cellular Organization in the Mouse Neocortex

Ballistic Labeling of Pyramidal Neurons in Brain Slices and in Primary Cell Culture

Automatic Identification of Dendritic Branches and their Orientation