Measuring dendritic distribution of membrane proteins

Journal of Neuroscience Methods 156 (2006) 257–266

Measuring dendritic distribution of membrane proteins Edmund W. Ballou a,∗ , W. Bryan Smith b , Roberta Anelli a , C.J. Heckman a a

Department of Physiology M211, Northwestern University Feinberg School of Medicine, 303 E. Chicago Ave, Chicago, IL 60611, USA b National Center for Microscopy and Imaging Research, Center for Research in Biological Systems, Basic Science Building, Room 1000, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0608, USA Received 19 August 2005; received in revised form 9 March 2006; accepted 15 March 2006

Abstract Neurons perform much of their integrative work in the dendritic tree, and spinal motoneurons have the largest tree of any cell. Electrical excitability is strongly influenced by dendrite membrane properties, which are difficult to measure directly. We describe a method to measure the distribution of ion channel membrane densities along dendritic trajectories. The method combines standard immunohistochemistry with reconstruction procedures for both large-scale and small-scale optical microscopy. Software written for Matlab then extracts the colocalization of the target ion channel with the target dye injected cell, and calculates the relative channel density per square micron of cell surface area, as a function of distance from the cell body. The technique can be used to quantify the localization and distribution of any immunoreactive moiety, and the software provides a flexible vehicle for sensitivity analysis, to validate heuristics for selecting thresholds. © 2006 Elsevier B.V. All rights reserved. Keywords: Dendrites; Distribution; Ion channels; Membrane; Modeling; Neuron; Reconstruction; Motoneuron; Excitability; Colocalization; Neuroanatomy

1. Introduction Most neurons have extensive dendritic trees. Studies in a number of different cell types have demonstrated that dendrites can have a variety of voltage-sensitive channels, making synaptic integration an active process (Hausser et al., 2000; Heckman et al., 2003; Johnston et al., 1996a,b; Migliore and Shepherd, 2002). Quantitative information on the distribution of these dendritic channels is essential for understanding neuron function and is necessary for biologically realistic neuron models (Blackwell, 2005; Dayan and Abbott, 2001; Koch and Segev, 1998; Traub et al., 1991). One highly successful approach is to record directly from the dendritic branches and thus measure distributions of voltage-sensitive currents (Hausser et al., 2000; Larkum et al., 2001; Magistretti et al., 1999; Migliore and Shepherd, 2002). Yet dendritic recordings in some neuron types are difficult to achieve. In the spinal motoneurons that our lab studies, dendritic branching and tapering begins very close to the soma and dendritic recordings have only been achieved in cell culture (Larkum et al., 1996). An alternative approach is to use immunohistochemical techniques to label channel

∗

Corresponding author. Tel.: +1 312 503 2921; fax: +1 312 503 5101. E-mail address: [email protected] (E.W. Ballou).

0165-0270/$ – see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.jneumeth.2006.03.014

proteins. Even in neurons where direct recordings are available, this anatomical approach potentially provides important additional information. Immunohistochemical and cell-filling techniques have been highly successful for quantifying the dendritic distribution of synaptic boutons for a few of the major inputs to motoneurons (Alvarez et al., 1998; Burke and Glenn, 1996). The immunohistochemical studies of the distribution of voltage-sensitive channels on motoneuron dendrites provide a clear overall picture but are limited to channels located within a few hundred microns of the soma (Muennich and Fyffe, 2004), or lack quantitative detail (Simon et al., 2003; Wilson et al., 2004). In this article we present a methodology to measure and codify the anatomical distribution of membrane proteins across the neuronal dendritic tree, using spinal motoneurons in the cat as our focus. Using standard techniques, we visualize the volume of an identified motoneuron by injecting fluorescent dye following electrophysiology experiments. Target cell morphology is revealed by the dye, and colocalization of the cell with the immunohistochemically revealed target protein provides raw data from which the distribution of the protein across the dendritic tree is extracted (Alvarez et al., 1998; Jankowska et al., 1997). We used optical methods that provide resolution on the order of 0.4 ␮m, and process this detailed data by techniques that offer significant advances in combining software with multiple

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instrumentation modalities. Furthermore, we include analysis heuristics to validate the localization results. The goal of the methods presented here is to measure the relation between membrane ion channel density and distance from the cell body in an identified cell. We describe the constraints on each stage of experimental operations, image acquisition, and data collection and analysis. Key dependencies can readily be tested by adjusting the settings and automatically repeating the calculations for the entire dendritic tree, providing a vehicle for sensitivity analysis. The resulting channel distribution data could be converted to compartmental parameters for a neuron model, for comparison with electrophysiological measurements on the same cell. Morphometric modeling strategies are also discussed. A portion of this method was presented in abstract form (Ballou et al., 2003). Also, part of the method was incorporated in Smith et al. (2005). 2. Methods 2.1. Target cell identification Sharp electrodes were filled with tetramethylrhodamine conjugated with dextran (Molecular Probes) at a concentration of 2% in 0.5 M KCl (Jankowska et al., 1997). Following institution-approved in vivo procedures, electrical properties of cat spinal motoneurons were characterized using the voltage clamp method of Lee and Heckman (1998). After electrical studies were completed, the dye was injected using 10 nA of positive pulses of 50 s on, 10 s off, for 10–15 min. During dye injection, if the antidromic action potential height fell below 50 mV, the impalement was abandoned, and we continued searching for cells. However, failed injection attempts sometimes resulted in partial visualization of cell bodies and proximal dendrites. To avoid ambiguity identifying dendrites, we limited injection attempts to no more than two cells in each animal, and separated each injection attempt by at least 750 ␮m (for motoneurons, about half the extent of the dendritic tree). 2.2. Immunohistochemistry At the conclusion of the experiment and at least 1 h after the last dye injection, we perfused the animal with fixative (4% paraformaldehyde in 0.1 M phosphate-buffered saline), excised the spinal cord, and let it fix overnight at 4 ◦ C. We cryoprotected the tissue in stepped 10–30% sucrose for 3–4 days. Next, we sliced the tissue in a cryostat at 40 ␮m and processed for immunohistochemistry against target membrane proteins. Slices were collected serially in phosphate buffer in 48-well tissue culture plates, to maintain the sequence of the slices and to permit immunohistochemical treatment of all surfaces by floating the sections. Slices were washed 4× in phosphate buffer, blocked 1 h in 5% normal goat serum in phosphate buffer, then incubated overnight at 4 ◦ C in primary antibody (rabbit antiCaV1.3 at 1:100, Chemicon; another version of this antibody was kindly provided by the Bezprozvanny lab, Zhang et al. (2005), applied at 1:250 dilution) with 2% normal goat serum in phosphate buffer, again washed 4× in phosphate buffer, incubated

1.5 h in secondary antibody (goat anti-rabbit conjugated with AlexaFluor-488® at 1:1000, Molecular Probes), and mounted on microscope slides (Prolong® anti-fade medium, Molecular Probes). 2.3. Dendritic tree reconstruction Using conventional epifluorescence microscopy (Zeiss Axioplan 2, Plan-APOCHROMAT 20×/0.75 objective, excitation filter 546/12, barrier filter 575-640), we localized the motoneuron dendrite segments in tissue slices, and logically spliced them together to reconstruct selected portions of the dendritic tree. This process was facilitated using the Neurolucida (Microbrightfield, Williston, VT) neuron reconstruction system. The software automatically generates montages of an entire spinal cord halfslice using the computer-controlled motorized stage. Images were acquired at two levels of focus, to clarify the direction in which dendrites connected to an adjacent slice. Montage files consisting of 15–24 fields at 20× were generated, and images were processed with ImageJ (http://rsb.info.nih.gov/ij) using the Pseudo Flatfield plugin to emphasize the contrast of dye-filled dendrites and minimize the quilting pattern due to fluorescence attenuation at field edges. Neurolucida provides convenient aids for identifying and tracing dendritic connections across many physical slices. Displaying adjacent slice image pairs in the Image Organizer with the front image at half transparency, we aligned them by translational (MoveImage) and rotational (Image/Effects/ Orientation/FineRotation) adjustments. Using the aligned image stack (instead of the live slides, to minimize bleaching), we then traced the dendritic tree (Fig. 1) and generated hard copies of the aligned slices with overlaid tracings, which were an invaluable aid during high-resolution confocal image acquisition. Neurolucida-generated maps of the dendritic tree were enhanced by segment names that incorporated slice and branch information. Unequivocal correspondence between low and high resolution images of each dendrite segment was assured by checking the branch sequence embedded in the name, while setting up acquisition of the confocal images. During data analysis, names were incorporated in hierarchical directory structures that mirrored the dendritic tree, to store data for each segment. 2.4. Confocal image acquisition Key data for this method were obtained from detailed studies of each dendrite segment under confocal microscopy. Segments were scanned for the injection fluorophore and for the proteinassociated fluorophore. This resulted in a pair of z-stacks, each a 3D array containing the intensity of a fluorescent signal across the scanned volume containing the segment. The object of the study was to compare protein-associated fluorescence from various regions of the motoneuron and deduce relative differences in protein density. Therefore, all acquisition parameters for the protein images were identical for a given cell and all its segments: laser power; laser transmission percentage; detector gain, offset, and voltage; pinhole size; zoom value; and optical slice thickness (Pawley, 2000). At the same time, the

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reduced the size of the arrays to be processed, speeding subsequent analysis. Then the Straighten operation was repeated for the protein stack. Both stacks were deposited in the data directory corresponding to the location of the segment in the dendritic tree. 2.5.2. Mask generation The remaining analysis was performed using code written for Matlab (The Mathworks, Natick, MA). Each segment was visualized and thresholds were manually selected, using a slider bar to adjust the threshold value, with translation, rotation, and zoom controlled by the computer mouse.1 The dendrite image threshold, which varies with each segment, was chosen to visually distinguish the nerve process from the background. Our procedure was to reduce the threshold until extracellular noise became excessive, then raise the threshold enough to clearly isolate the segment. A binary 3D array representing the dendrite was generated, and disconnected particles were removed.

Fig. 1. Neurolucida tracing of dendritic trajectories, projected onto the mediallateral/dorsal-ventral plane. To show the orientation in the spinal cord slice, the tracing is overlaid by an outline generated from an image of the transverse slice containing the soma. Letters A–D refer to trajectories graphed in Fig. 5. Cat spinal cord segment L6. Scale bar = 500 ␮m.

fluorescence intensity due to the injected dye varied from one dendrite segment to the next, being weaker for distal dendrite segments. This dye had to be imaged at relatively low detector gain for the soma and proximal segments, but at higher gain settings for distal segments. Therefore, we acquired images using multi-track mode, in which the emission of each dye is captured separately, with independent acquisition parameters for the two colors. Dynamic range must be large enough to ensure discrimination of varying signal strength at levels of interest, while not exceeding saturation levels, for the protein signal; this mandated use of 12-bit rather than 8-bit detector modes. Zeiss CLSM 510 confocal settings were: Plan-APOCHROMAT oil immersion 40× objective, NA = 1.0; beam splitter HFT 488/543; for laser at 488 nm, barrier filter BP 505-530, pinhole 84 ␮m, laser power 5 A, transmission 17%; for laser at 543 nm, barrier filter LP 560, pinhole 94 ␮m, transmission 30%. 2.5. Data analysis We analyzed the data in several stages. Each confocal microscope field contained information corresponding to an individual dendrite segment; segments were separated, named, and processed individually, then combined to create data for the dendritic tree. 2.5.1. Segment linearization The dendrite stack was linearized using the ImageJ “Straighten” plugin, which preserves local geometry while removing tortuosities (Kocsis et al., 1991). This substantially

2.5.3. Colocalization analysis The binary volume representing the dendrite segment was further restricted to the surface shell of the segment. The 3D coordinates masked by this shell were then used to locate the protein-associated fluorescence. These intensity values were assigned to the corresponding location on the dendrite surface. Geometric analysis of the dendrite was done to estimate, for each surface location, the distance from the soma and the dendrite surface area at that distance.2 Finally, protein-associated fluorescence was expressed in terms of relative density per unit area, and displayed as a function of distance from the cell body. 3. Results 3.1. Immunohistochemistry Fluorescent background was minimized by a protocol with a low (1:1000) concentration of secondary antibody. Ion channel proteins were well visualized, showing a structure suggesting both membrane and cytoplasmic localization. Fig. 2A–D illustrates the method using one “optical slice” from a 32-slice z-stack of a cell body. The upper two frames show raw data from the two channels, the dendrite-filling rhodamine (A) and the CaV1.3 -associated AlexaFluor® 488 (B); the third frame (C) is the thresholded injected-dye image used to mask the second frame to generate the fourth frame (D), which clearly shows ion channel clustering around the periphery of the cell. Fig. 2E–H illustrates the same procedure for a small dendrite process at a higher magnification. In this case the masked protein channel (H) appears more diffuse and not as clearly differentiated between the surface and the cytoplasm. 1

Our software to set and display thresholds and generate colocalization data for paired volume arrays is available as coloc3D at http://www.mathworks.com/ matlabcentral/fileexchange. 2 Our software to trace the center of mass and calculate the surface area as a function of distance down the center of mass axis for a thresholded volume array is available as patch area at http://www.mathworks.com/matlabcentral/ fileexchange.

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Fig. 2. Membrane localization of CaV1.3 as seen in a single optical section. In each set of four frames, the upper left frames (A and E) are raw images of the cell-filling fluorophore, the upper right (B and F) are the raw images of the CaV1.3 -associated fluorophore, the lower left (C and G) are the masks generated from the cell-filling dye, and the lower right (D and H) are the protein channels after masking. Signal presence is indicated by black. (A–D) Soma, scale bar = 25 ␮m. (E–H) A distal dendrite segment, scale bar = 5 ␮m.

Control experiments on a CaV1.3 knock-out mouse using the Chemicon antibody showed more staining that expected; the protocol was repeated using a privately obtained antibody against CaV1.3 with no knock-out staining (Zhang et al., 2005), with similar results. 3.2. Dendrite boundary determination We tried several different approaches for setting the threshold value for the cell-filling dye. Initially we hoped that Otsu’s method (Otsu, 1979), implemented in Matlab as the graythresh function, would provide a consistent and objective determination. However, this calculation (which chooses the threshold that minimizes the intra-class variance of the separated populations) generates too high a threshold (Fig. 3). Reducing thresholds by a factor of about 2 from the Otsu calculation resulted in reasonable neurite segment visualizations. Therefore, a manual approach was used. Our first method was to draw a maximum value projection down each of the three axes to visualize and compare effects of varying thresholds (Fig. 3A). We subsequently developed a visualization under the Matlab-based graphical user interface, which allowed more accurate and consistent threshold settings than the projection method (Fig. 3B). 3.3. Membrane area measurement and density normalization In order to describe ion channel density with reference to membrane area, we needed to estimate dendrite segment surface area. We considered several geometric models. Simply counting the surface voxels generates a bias for larger surface areas on the smallest, most pixellated segments. A simple model using the

surface of the cylinder with the measured volume (voxel count) eliminates the size-based bias and gave reasonable results. However, the best estimate of surface area was obtained by calculating the “isosurface” for the dendrite volume. This is analogous to the two-dimensional solution, of using a constant elevation contour on a topographic map to calculate the perimeter of an island at high tide. By modeling the boundary between subthreshold and supra-threshold values of the cell-filling dye fluorescence, surface variations are retained in detail. Since this calculation is already implemented in Matlab to generate 3D volume displays, our code to extract surface area was relatively straightforward (see Section 2.5.3). 3.4. 3D Reconstructions We ran Matlab code to generate 3D visualizations of the thresholded data, for visual verification that the extracted data bore a reasonable resemblance to neurites with colocalized protein. Using the per-segment threshold for the cell-filling fluorophore and a fixed setting for the protein-associated fluorophore, three-dimensional reconstructions were generated for each segment to present the space-filling characteristics of the two colors together (Fig. 4A), the protein alone after masking by the dendrite segment (Fig. 4B), and the masked protein with the dendrite (Fig. 4C). These displays illustrated the wide variability of ion channel density along the length of neuron processes. 3.5. Extracted distribution data Fig. 5 shows calcium channel distribution calculations for four dendritic trajectories. In the graphs of Fig. 5A–D, the red

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Fig. 3. Comparison of displays used for manual threshold estimation. Several threshold levels are illustrated. The four panels in section (A) each show xy, xz, and yz projections; the colored panels in section (B) show 3D reconstructions, that can be rotated and zoomed. The xy projections in (A) correspond to projections onto the top faces in (B); the xz projections in (A) correspond to projections onto the front faces of (B); and the yz projections in (A) correspond to projections onto the side faces in (B). From upper left to lower right in each group of four (A and B), the thresholds are: 0.25 (Otsu method), 0.18 (best + 20%), 0.15 (best), and 0.12 (best − 20%). Drawings rendered by Matlab. Scale bars (labeled x, y, or z) = 10 ␮m.

segmented line is the per-segment average, over the length of each segment; the blue line is the continuous per-pixel distribution of density (density is estimated for each 0.8 ␮m pixel interval). The per-layer calculation reveals precise hot spot localization that is obscured in the per-segment average. These four distributions are also displayed in Fig. 5E–H as color-coded overlays on the 3D reconstructions of the trajectories. This provides direct visualization of the channel densities through the volume of the spinal cord, for this cell. Typical patterns include strong expression of the channel near the soma, weak or absent

expression 100–300 ␮m away from the soma, and at least one hot spot 500–1000 ␮m away from the soma. Finally, the sensitivity of our results to dendrite threshold selection is shown in Fig. 6. Here, the dendrite trajectory distribution from the dendrite illustrated in Fig. 5D was calculated using four test thresholds for each segment, decreasing and increasing the thresholds used to generate the dendrite mask by 10% and by 20%. In this example these threshold manipulations do not significantly affect the general character of the distribution plot.

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Fig. 4. Volume reconstruction of a dendrite segment (the same segment used in Fig. 3, a distal segment from the trajectory illustrated in Fig. 5D and H). Red represents fluorescence excited by 543 nm: laser (rhodamine); green represents fluorescence excited by 488 nm: laser (AlexaFluor® 488). (A) All protein in the volume with the dendrite segment. (B) Only the protein within the dendrite volume. (C) Dendrite segment with the associated protein. Drawings rendered by Matlab; dimensions in ␮m.

4. Discussion A method which combines electrophysiology, immunohistochemistry, neuron reconstruction, and submicron fluorescence microscopy with a set of image analysis software routines can extract membrane ion channel densities across several millimeters of the dendritic tree of spinal motoneurons. Each step is subject to constraints that are specific to the needs of the method, but our approach allows systematic sensitivity analysis of these constraints. The quantitative distribution generated by this method can contribute detailed information that will clarify the electrical role served by various compartments of the neuronal dendritic tree (Vanier and Bower, 1999). This data will also be useful in correlating membrane properties with distribution of pre-synaptic boutons, in motoneurons and other cell types (Alvarez et al., 1998; Jankowska et al., 1997). 4.1. Staff effort For the method to be generally applicable it must be feasible to acquire and analyze data for a statistically significant population of dendritic trajectories with a few months of effort. Staffing estimates for this project were broken down in Table 1. Table 1 Effort to measure dendritic protein distribution in eight trajectories from a cat spinal motoneuron Procedure Electrophysiology experiment, cell injection, tissue fixation Immunohistochemistry Neurolucida image acquisition and trajectory analysis (eight trajectories) Confocal image acquisition (8 trajectories, 10 segments per trajectory) Analysis: extract image stacks for each color, extract and straighten each segment and place in hierarchical repository, manually verify thresholds for each segment, run the trajectory analysis, execute sensitivity analysis, summarize and chart data in spreadsheets Total

Effort (h) 10 16 20 32 30

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One hundred and eight hours were required to generate data for eight trajectories from one cat spinal motoneuron. Smaller cells can be analyzed more quickly, of course. We have recently begun acquiring confocal images using a spinning disk confocal system, which has multiple pinholes so that z-stacks can be acquired in seconds rather than minutes, reducing confocal acquisition effort by perhaps 50%. 4.2. Method constraints, reliability, and limitations Light microscopy images cannot resolve channel localization at the level of cell ultrastructure, which requires magnification of electron microscopy, 200 times greater than that used in this protocol; we cannot rule out, for example, emission sources in the cytosol or in closely apposed tissue. Our data show an association between injected dye and protein-associated fluorophore, that is consistent with membrane-embedded ion channels: patterns in the two colors overlay with matching morphology at numerous locations. This correspondence is not due to bleeding of the red dye signal into the green channel, since we often see absence of the green signal overlaying the strongest red signal, immediately adjacent to regions of closely matched colocalization between the two colors (Fig. 2). Our images clearly show channel protein colocalized with the dye-injected cell at submicron dimensions, a necessary condition for protein embedded in the target cell membrane. This data is comparable to optical observations of other investigators (Alvarez et al., 1998; Jankowska et al., 1997). The approach described here is a tradeoff between precision and feasibility. Our goal to measure ion channel density across thousands of micrometers of a dendritic tree requires carefully constrained compromises. Selecting a few sample trajectories to study and choosing an intermediate level of image resolution were necessary decisions to keep the data set manageable while still supporting plausible anatomical correlations. The key result reported here, measurable clustered colocalization of antibodytagged protein with motoneuron processes, encouraged us to codify the protocol and continue its refinement. Our method integrates neuron reconstruction with laser scanning views of colocalized cell boundaries and neuroactive proteins, making it

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Fig. 5. Distribution of CaV1.3 across four dendrite trajectories. (A–D) Distribution of relative CaV1.3 fluorescence plotted as a function of distance from the cell body. (E–H) Color-coded density of CaV1.3 -associated fluorophore overlaid on 3D projections of each dendrite trajectory. Arrowheads indicate the end of each trajectory that connects to the cell body.

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Fig. 6. Sensitivity analysis of CaV1.3 distribution measured along the trajectory of Fig. 5D, while varying dendrite dye threshold ±10% and ±20% from the optimum value. There is a slight change of the measured segment length and small shifts in the locations of protein density peaks, but the overall shape is unchanged. These data were generated automatically under Matlab.

possible to estimate protein distribution across large dendritic trees. 4.3. Modeling and electrophysiology In two widely used software platforms for simulating neuron behavior, Neuron and Genesis, the modeling scientist represents the properties of a nerve cell and its processes by constructing a set of compartments. Each compartment consists of an electrical model for a small area of membrane, incorporating voltage and time-dependent conductances as well as passive properties, plus the resistivity of the cytoplasm connecting each compartment to its neighbor. With compartments linked together via the boundary condition of a common current through the cell interior, a series of calculations is run in an attempt to reproduce the macroscopic behavior of the cell. This approach requires specification of channel densities for all compartments. Channel densities are typically adjusted in an iterative “parameter estimation” calculation to optimize the model approximation to the observed physiological data, by varying the compartment channel densities as free parameters for the model (Dayan and Abbott, 2001; Gunay et al., 2004; Vanier and Bower, 1999). Membrane channel density is a critical parameter when modeling the electrical excitability of a dendritic tree, the integration of synaptic inputs at various entry points in the tree, and the

relation between these inputs and firing rate at the soma. Using the method described in this paper, immunohistochemical data can suggest measured values for the relative compartment conductances. By incorporating measured values in the model for each compartment, the modeler can directly compare the model behavior with the observed electrophysiological data for the identified cell. Deriving compartment conductances from the anatomical data eliminates the parameter estimation step, and can fill a major gap between theory and observation. 4.4. Physiological significance of membrane protein distribution We developed this method in the context of our studies of spinal motoneuron excitability. Numerous investigators have demonstrated electrophysiologically and immunohistochemically the importance of voltage-dependent regenerative conductances in the motoneuronal dendritic tree; the underlying channel types may include CaV1.2 , CaV1.3 , NaV1.1 , NaV1.2 , and NaV1.6 , in various preparations (Carlin et al., 2000; Goldin, 2001; Hounsgaard and Kiehn, 1989; Jiang et al., 1999; Li and Bennett, 2003; Magistretti et al., 1999; Powers and Binder, 2003; Westenbroek et al., 1998). We seek to build on this work, and develop a body of data to quantitatively describe the molecular and morphometric basis for active conductance in the motoneuronal dendritic tree.

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Recent theoretical work (Bui et al., 2006; ElBasiouny et al., 2005) has suggested that clustered rather than continuous expression of regenerative voltage-dependent ion channels may be a general principle of ion channel distribution in dendrites. Distal synapses are more effective in generating current at the soma if the intervening membrane is mostly high-resistance, with occasional dense clusters or hot spots of positive feedback voltage-gated ion channel types (NaV or CaV). We find preliminary evidence of clustering in our measurements of CaV1.3 channel distribution in cat spinal motoneurons (Fig. 5). Possibly these hot spots may function similarly to nodes of Ranvier in myelinated axons (as discussed, for example, in Johnston et al., 1996a,b), and constitute a mechanism for saltatory conduction across the fifteen-hundred or more microns separating the most distal synapses from the cell body. Pathologies in motoneuron excitability are involved in disease and injury. Amyotropic lateral sclerosis is characterized by motoneuron death probably due to calcium toxicity; overexcitability is a likely contributing factor (Cleveland and Rothstein, 2001; Kuo et al., 2005; Rao and Weiss, 2004). In chronic spinal cord injury patients suffer from uncontrollable spasticity which interferes with an already compromised motor control system; again, excessive excitability is implicated (Heckman et al., 2005; Li et al., 2004). The method described in this paper can be used to compare motoneurons from normal and abnormal tissue for their distributions of ionotropic and metabotropic channels and associated regulatory proteins. 4.5. Future work In this article we present a basic set of procedures to generate protein distribution maps across a dendritic tree. Moving forward, we will enhance and develop the method, improving data quality and applying results. Using spinning disc confocal microscopy will permit high resolution images to be acquired more quickly with less bleaching. To improve effective z-dimension resolution, 3D confocal stacks will be deconvolved about the PSF for the optical system; we are evaluating protocols for this purpose. Finally, we will incorporate data from the method into compartmental models of electrically characterized neurons, to compare simulations with physiology. Acknowledgments We thank Allison Hyngstrom, Michael D. Johnson, Jason J. Kuo, and John F. Miller for assistance with dye injection and animal perfusion; Teng-Leong Chew and the Cell Imaging Facility of Northwestern University Feinberg School of Medicine for assistance with confocal microscopy; Illya Bezprozvanny for generously providing anti-CaV1.3 antibody; and Phil Hockberger for helpful discussions on the physics of microscopy. This work was supported by NIH grant NS34382 (to CJH). References Alvarez FJ, Pearson JC, Harrington D, Dewey D, Torbeck L, Fyffe REW. Distribution of 5-hydroxytryptamine-immunoreactive boutons on alpha-

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