Boston University Scientists Use AutoNeuron and AutoSynapse to Compare Neurons in the Visual and Prefrontal Cortices

MBF_Figure

Use of Neurolucida to assess the detailed morphology – including spines and synapses – of a layer 3 pyramidal neuron from the anterior cingulate cortex of a rhesus monkey. A) 40x confocal image of a layer 3 pyramidal neuron that was filled with biocytin during whole-cell patch-clamp recordings and subsequently processed with Alexa-Streptavidin-488. B) Basilar dendritic segment (indicated by the box in A), scanned in 2 channels: green= the neuron; red= VGat. C) Dendritic reconstruction indicated in light blue. Spine subtypes identified by the AutoSpine module (magenta= mushroom; yellow= thin; red= stubby). D) VGat-positive appositions (indicated by white dots) against the dendritic shaft and spines identified with AutoSynapse. Scale bars: A= 20 µm; B= 2 µm

The ball comes flying and you swing the bat. A car pulls out and you hit the brakes. As we go about our daily routines, we process everything we see in a region in the back of our brains known as the visual cortex. But when we sit down to plan a kitchen remodel, or next summer’s vacation, an area in the front of the brain gets activated, the prefrontal cortex, a region involved in higher level thinking.

Recent research has offered insight into the structure and function of neurons in these two distinct brain regions. Scientists at the Luebke Lab at Boston University set out to find out more about their morphology, and if their structural differences affect their behavior. Their study, published in the Journal of Neuroscience offers evidence that pyramidal neurons in the primary visual cortex (V1) and dorsolateral granular prefrontal cortex (dlPFC) of the rhesus monkey display “marked electrophysiological and structural differences.”

“We chose to examine these two areas because they represent distinct ends of the spectrum of neocortical complexity and specialization, from primary sensory processing by V1 to mediation of high-order cognitive processes by dlPFC,” the authors say in their paper.

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Japanese Researchers Develop New Optical Clearing Agent; Neurolucida Used For 3D Imaging in Study

Volume rendering of mouse cerebral cortex and hippocampus. Adult Thy1-YFP-H line mouse brain was cleared with SeeDB and imaged using two-photon microscopy. Imaging area shown is 4 x 5 mm (8 x 10 tiles), 2mm thick. We could easily make a volume rendering from a large set of 3D data (in this case, 9GB two-photon data).

Volume rendering of mouse cerebral cortex and hippocampus. Adult Thy1-YFP-H line mouse brain was cleared with SeeDB and imaged using two-photon microscopy. Imaging area shown is 4 x 5 mm (8 x 10 tiles), 2mm thick. We could easily make a volume rendering from a large set of 3D data (in this case, 9GB two-photon data).

A new optical clearing agent developed by scientists in Japan clears brain tissue samples with greater transparency and less time than other clearing agents, according to a paper published in Nature Neuroscience.

“Combined with two-photon microscopy, SeeDB allowed us to image fixed mouse brains at the millimeter-scale level,” say the authors, who after clearing the brain tissue with SeeDB, captured images with a multiphoton Olympus microscope, and visualized 3D reconstructions with Neurolucida.

A solution of fructose, water, and alpha-thioglycerol, SeeDB cleared gray and white matter brain tissue samples in three days without affecting the volume or morphology of the tissue. Dendritic spines of pyramidal neurons in the cerebral cortex was one aspect of fine morphological architecture that the authors note remained intact after SeeDB treatment.

Reconstruction of lateral dendrites of sister mitral cells. Fluorescent neuronal tracer (Alexa647 dextran amine) was electroporated into a single glomerulus to label 'sister' mitral cells associated with a common glomerulus. After optical clearing of the olfactory bulb with SeeDB, the olfactory bulb was imaged using confocal microscopy. Lateral dendrites of labeled mitral cells were reconstructed using Neurolucida. This reconstruction was used for quantitative analysis of 'sister' mitral cell distribution.

Reconstruction of lateral dendrites of sister mitral cells. Fluorescent neuronal tracer (Alexa647 dextran amine) was electroporated into a single glomerulus to label ‘sister’ mitral cells associated with a common glomerulus. After optical clearing of the olfactory bulb with SeeDB, the olfactory bulb was imaged using confocal microscopy. Lateral dendrites of labeled mitral cells were reconstructed using Neurolucida. This reconstruction was used for quantitative analysis of ‘sister’ mitral cell distribution.

Continue reading “Japanese Researchers Develop New Optical Clearing Agent; Neurolucida Used For 3D Imaging in Study” »

Scientists use Neurolucida Reconstructions to Analyze Dendritic Trees

No two trees are exactly alike, in the forest or in the brain. Though despite the diversity of dendritic arborizations, when it comes to branching out different types of neurons do have a couple things in common, say researchers at the National Institute for Physiological Sciences in Okazaki, Japan.

Led by longtime MBF Bioscience customer Dr. Yoshiyuki Kubota, the research team identified two organizational principles common to the dendritic trees of four different types of neurons.

“First, dendritic cross-sectional areas were found to be proportional to the total lengths of all distal dendritic segments. Second, nonpyramidal neuron dendrites were found to be elliptical, rather than circular, with the degree of ellipticity decreasing with dendritic size and increasing with distance from the soma,” according to the paper published last week in Scientific Reports.

The scientists used Neurolucida to carry out their analysis, forming 3D reconstructions of a Martinotti cell, a fast-spiking basket cell, a double-bouquet cell, and a large basket cell.

“Our data suggest that, in healthy neurons, dendritic structure is more precisely regulated than might be guessed given the diversity of dendritic tree morphologies,” the researchers say in their study. “It will be important for future work to assess the detailed morphology of dendrites in pathological tissue to test if alterations in dendritic tapering and branch point uniformity might participate in generating the cognitive deficits associated with disease.”

Read the full paper “Conserved properties of dendritic trees in four cortical interneuron subtypes” on Scientific Reports.

Yoshiyuki Kubota, Fuyuki Karube, Masaki Nomura, Allan T. Gulledge, Atsushi Mochizuki, Andreas Schertel, Yasuo Kawaguchi. “Conserved properties of dendritic trees in four cortical interneuron subtypes” Scientific Reports, 2011; 1 DOI: 10.1038/srep00089

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Neurolucida Helps Look at Whether Dendrites Can Tell Inputs Apart

What would you do with a neuron if you could activate its synapses in any combination you wanted? Tiago Branco, Beverley A. Clark and Michael Hauser created a chance to do just that (Branco, 2010). The authors, using in-vitro brain slices containing layer II/III pyramidal cells in visual or somatosensory cortex of rats, were able to excite identified spines in any order and with whatever timing. They activated the synapses in one direction and then in the other direction (above is a dendrite not from this study;  “In” is the direction towards the cell body, and “Out” is the direction away from the cell body) to see if the output-signal of the cell would be different.  Along the way they collected more evidence that signal integration happens at the level of the dendrite. The most exciting result is that the output-signals generated in the soma are dependent on the order that the spines were activated.

To study the oblique radial dendrite of the cortical pyramidal cell, one of the smallest dendrites in the brain, multi-site two-photon glutamate uncaging (Judkewitz, 2006; Losonczy, 2006) was used, achieving exquisite control of which spines will be activated when. The idea is to keep the excitatory neurotransmitter, glutamate, hanging around in an inactive form (represented as pink circles above). Photons are used to both convert the glutamate to its active form and to observe the fluorescently-labeled tissue. The amount of glutamate released is believed to only affect one spine, and the time course is such that it can be used to approach physiological conditions. The spines on the dendritic branch can be activated with a spatial and temporal pattern of the authors’ choosing; and the resulting voltage change that can be thought of as the output-signal of the cell, is recorded with an intracellular electrode at the cell body (see the figure below graciously provided by the authors).

Why is the order and timing of synapse activation worth looking at? A neuron functions to collect from the axons of other neurons signals that cause voltage changes in its dendrites; and to pass these signals along to its cell body and axon where the voltage-threshold for an axonal action potential (AP) might or might not be reached. Features of stimuli in sensory pathways can be coded for by the timing of dendritic excitation. One example is in the retina, where individual dendritic branches of retinal starburst amacrine cells show directionally selective signals (Häusser, 2003; Euler, 2002). The authors (Branco, 2010) also point out that temporal and spatial variability in dendritic excitation patterns is especially relevant for circuits with layered input, like the hippocampus, where it could be used by dentate gyrus granule cells to directly detect the sequence of entorhinal cortex activation. Integrative properties of the dendrites appear to be at least one mechanism that can differentially encode spatial and temporal synchrony.

The sensitivity of single dendrites to the order of activation of a defined set of synapses was tested. When activated in isolation, the glutamate excitatory post-synaptic potentials, measured with an intracellular electrode at the cell body, were within physiological range. When the same spines along the dendrite were activated sequentially instead of in isolation, the IN direction always produced a larger somatic voltage response than the OUT direction, and this went along with a bigger chance for an axonal AP. Calcium signals were also larger in the IN than in the OUT direction. The most effective speed to show direction sensitivity was 2.6 microns per second. The dendrite itself can signal the difference between inputs that travel along it in one direction or the other!

What is going on in the dendrites that would cause activation of spines in one direction to give a different output-signal than activation of spines in the other direction? One idea is that dendrites of a neuron see all synapses as equal, and the voltage changes of the membrane caused by the synapses are summed linearly at the axon, possibly resulting in an axonal AP if the threshold is reached. But if they are all equal, and simply summed, the order of activation shouldn’t matter. Another idea is that the dendrites have active conductances, which would result in non-linearities (Häusser, 2003; Losonczy, 2006; Larkum, 2007). Non-linearity means that the whole is different than the sum of its parts; a supralinearity is the situation where the response when the identified synapses are activated sequentially is greater than the sum of the voltage responses from the same synapses activated in isolation. Regenerative events in dendrites are responsible for non-linearities in pyramidal neurons (Schaeffer, 2003); the axonal AP is back-propagating into the dendrites and long-lasting, mainly Ca2+ mediated depolarizations are initiated in the distal regions of apical dendrites. The distal depolarizations are an example of forward propagation (Vetter, 2001). The ability of thin dendritic branches of pyramidal neurons to support forward propagation called a ‘dendritic spike’ has been known for some time. These dendritic spikes are carried by Na+, Ca2+ and predominantly by special glutamate conductances mediated by NMDA receptors (Judkewitz, 2006). In this study, the voltage responses at the cell body were supralinear, meaning if you add together the individual synaptic responses from spines that are activated in isolation, the amplitude is smaller than if the same spines are activated sequentially. Something is boosting the signal to cause the supralinearity. This effect develops gradually with increasing numbers of recruited synapses. When the NMDA Glutamate receptor was blocked, the supralinearity disappeared, and furthermore, direction sensitivity, velocity sensitivity, and detectable dendritic calcium signals were abolished. This evidence points to amplification via NMDA-dependent regenenerative signal boosting and NMDA dendritic spikes.

Where does our program, Neurolucida, come in? The best research uses multiple techniques; and the authors decided to use Neuron, an electrophysiological modeling program, to study the conductances that have been implicated in creating the voltage non-linearities. The neurons were traced using Neurolucida. Neuron has a feature to import Neurolucida tracings. This way the anatomical arrangement of the dendrites is used in the electrophysiological modeling program; the authors could pick one dendritic branch, virtually activate its synapses either in isolation or in sequence, and look at the response at the cell body. Direction sensitivity could be reproduced with a simple model using dendrites with passive electrical properties and synapses containing AMPA and NMDA conductances. The NMDA conductance starts out small and gets bigger over time for the OUT direction and starts out large and gets smaller over time for the IN direction. Direction and velocity sensitivity are abolished by leaving only AMPA receptors and removing the NMDA receptors. There is asymmetric recruitment of NMDA receptors when activating synapses in the different directions. This is due to the smaller input resistance at the tip of the dendrite combined with the highly nonlinear voltage dependence of the NMDA receptor conductance.

So picture it this way. A pyramidal neuron in the sensory cortex is firing axonal APs in response to some sensory stimulus. These APs back propagate into the dendrites. Along with the back propagation the dendrite also experiences forward propagation as a result of active conductances that create a dendritic spike. The back propagation will be maintained or attenuated by the nature of the geometry of the dendritic tree (Schaefer, 2003; Vetter, 2001). Now what if one sensory stimulus sequentially activates the spines along a dendritic branch in the IN direction and another activates it in the OUT direction. For the IN direction, the first synapse activated is at the tip of the dendrite. How is this different than when the first synapse is at the base of the dendrite? First of all, due to differences in location along the geometry of the dendritic tree, the back-propagation voltage signal will be different. Also the dendrites taper, so the tip will have less radius and a greater input resistance than the base. Therefore, the history of what happened to each synapse is different depending on the IN or OUT direction. The NMDA receptors on the dendrites have a non-linear voltage dependence, so the different history or the differences in what just happened to the neighboring synapse, causes a larger signal for the IN than for the OUT direction. The dendrite itself can detect the difference between the two sensory stimuli. The evidence gathered from this work supports the exciting and important conclusion that these cortical neurons use their dendrites to not just pass the signal on, but to change the signal; and furthermore to change the signal based on the time and space pattern of the input to its synapses.

Branco T., Clark B. A., & Häusser M., 2010, Dendritic discrimination of temporal input sequences in cortical        neurons. Science, 329, pp. 1671 – 1675.

Euler T, Detwiler, P.B., & Denk W., 2002, Directionally selective calcium signals in dendrites of Starburst Amacrine Cells. Nature, 418, pp. 845 – 852.

Häusser M. & Mel B., 2003, Dendrites: bug or feature? Current Opinion in Neurobiology, 13, pp. 372 – 383.

Judkewitz B., Roth A., & Häusser M., 2006, Dendritic enlightenment: using patterned two-photon uncaging to reveal the secrets of the brain’s smallest dendrites. Neuron, 50, pp. 180 – 183.

Larkum M.E., Waters J., Sakmann B., & Helmchen, F., 2007, Dendritic spikes in apical dendrites of neocortical layer 2/3 pyramidal neurons. Journal of Neuroscience, 27, pp. 8999 – 9008.

Losonczy A. & Magee J.C., 2006, Integrative properties of radial oblique dendrites in Hippocampal CA1 Pyramidal Neurons. Neuron, 50, pp. 291 – 307.

Schaefer A.T., Larkum M.E., Sakmann B., & Roth A., 2003, Coincidence detection in pyramidal neurons is tuned by their dendritic branching pattern. Journal of Neurophysiology, 89, pp. 3143 – 3154.

Vetter P., Roth A., & Häusser M. 2001, Propagation of action potentials in dendrites depends on dendritic morphology. Journal of Neurophysiology, 85, pp. 926 – 937.

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Dendritic Arbor Developement

by Dan Peruzzi, Ph.D.

One factor that makes a neuron uniquely suited for a particular function is its morphology, including where and how the dendrites extend. The nature of the dendritic arbor affects the connectivity and electrical properties of the neuron, and arbor abnormalities are associated with neurological diseases. Many classification schemes have been based on neuronal morphology but in an article from the December 2007 issue of Neuron, “Knot/Collier and Cut Control Different Aspects of Dendrite Cytoskeleton and Synergize to Define Final Arbor Shape“, Dr. Jinushi-Nakao, Dr. Arvind, and their colleagues demonstrate a mechanism by which different classes of dendritic arbors arise.

Powerful techniques are used to take advantage of a well-defined biological system and use what is already known to carefully frame a question about how a certain type of dendritic morphology is created. In Drosophila, the cell lineage and anatomical position of dendritic arborization neurons is well known. Dendritic arborization (da) neurons develop from the external sensory organ precursor cell and are individually named. The dendritic arbors of da neurons are easily visualized; the dendrites spread out along the epidermis, practically in two dimensions, underneath the transparent larval wall of the fruit fly. There are four classes of da neurons, class I to IV in order of increasing dendritic complexity. The different morphologies are caused at least in part by different combinations of transcription factors that determine what proteins are made in the neuron by regulating which mRNAs are made from DNA. Class I cells have none of the transcription factor CUT. Class II through IV all express CUT, but the amount expressed does not correlate with the dendritic complexity; the class IV cell has less CUT than the class III cell.

Microtubules, a main component of the cytoskeleton, are present in all four cell classes, but the class III cell has filopodia that are made with another cytoskeleton component– actin. The stage was set to look for a specific component of class IV cells that makes the most complex dendritic arbors of da neurons despite
low levels of CUT. Immunohistochemistry and in-situ hybridization were used to confirm that the transcription factor, KNOT, and the mRNA for KNOT are present in only class IV neurons. Loss-of-function analysis was carried out on class IV cells with the lethal null allele for KNOT, and lethality was avoided by using mosaic analysis with a repressible cell marker in order to make fruit fly larvae that had the mutation in only some neurons. Class IV da neurons without KNOT had less complex arbors. Neurolucida was used to show significant decreases in dendritic length, number of termini (indicating less branching) and the area occupied by dendrites. There was also a change in the qualitative nature of the dendritic tree; it became more polarized. When KNOT was expressed in Class I neurons the complexity of the dendritic arbor increased.

Cytoskeleton components were also examined. First it was confirmed that CUT is needed for development of actin-rich filopodia that are present on class III cells. Labels for the whole cytoskeleton (mCD8::GFP), microtubules (FUTSCH) and a genetic construct with GFP fused to the actin binding domain were used to examine cytoskeleton changes. In wild class I neurons, all areas of the arbor had microtubules. If only KNOT was expressed in class I cells, the total dendritic arbor increased due to an increase in microtubules. If only CUT was expressed in class I cells, the total dendritic arbor increased with no increase in microtubules, but there was an increase in actin. If KNOT was expressed in a class III cell, the number of filopodia went down. Conversely, if CUT was expressed in a class IV cell and KNOT was reduced, there were many more filopodia present; the class IV cell looked more like a class III cell.

Finally, the authors searched the Gene Ontology Database looking for candidates that might be controlled by KNOT by searching for proteins associated with microtubule biogenesis and function. Spastin mRNA was up-regulated in class IV da neurons and in da neurons that were made to express KNOT, and Spastin protein was present in higher levels in class IV neurons. Interfering with Spastin mRNA in da neurons compromised dendrite outgrowth only in class IV neurons. Spastin is known to cleave microtubules and may be needed to keep microtubule growth active in class IV neurons. The human Spastin gene is mutated in many hereditary spastic paraplegia cases. The authors provide evidence that KNOT and CUT together cause greater dendritic complexity through Spastin’s affect on microtubules, and that KNOT represses CUT-mediated creation of actin-rich filopodia in class IV da neurons.

Jinushi-Nakao, S., A. Ramanathan, et al., Knot/Collier and Cut Control Different Aspects of Dendrite Cytoskeleton and Synergize to Define Final Arbor Shape. Neuron, 2007. 56: p. 963-978.

Dan Peruzzi is a staff scientist at MBF Bioscience.

First published in The Scope, fall 2008.