Visualizing metal-impregnated neurons with spectral confocal microscopy
Larry H. Bernstein, MD, FCAP, Curator
LPBI
Novel Imaging Captures Beauty of Metal-Labeled Neurons in 3D
Researchers have discovered a dazzling new method of visualizing neurons that promises to benefit neuroscientists and cell biologists alike: by using spectral confocal microscopy to image tissues impregnated with silver or gold.
Rather than relying on the amount of light reflecting off metal particles, this novel process, to be presented in the journal eLife, involves delivering light energy to silver or gold nanoparticles deposited on neurons and imaging the higher energy levels resulting from their vibrations, known as surface plasmons.
This technique is particularly effective as the light emitted from metal particles is resistant to fading, meaning that decades-old tissue samples achieved through other processes, such as the Golgi stain method from the late 1880s, can be imaged repeatedly.
The new process was achieved by using spectral detection on a Laser Scanning Confocal Microscope (LSCM), first made available in the late 1980s and, until now, used most extensively for fluorescent imaging.
Paired with such methods, silver- and gold-based cell labeling is poised to unlock new information in a myriad of archived specimens. Furthermore, silver-impregnated preparations should retain their high image quality for a century or more, allowing for archivability that could aid in clinical research and disease-related diagnostic techniques for cancer and neurological disorders.
“For the purposes of medical diagnostics, older and newer specimens could be compared with the knowledge that signal intensity would remain fairly uniform regardless of sample age or repeated light exposure,” says contributing author Karen Mesce from the University of Minnesota.
“With the prediction that superior resolution microscopic techniques will continue to evolve, older archived samples could be reimagined with newer technologies and with the confidence that the signal in question was preserved. The progression or stability of a cancer or other disease could therefore be charted with accuracy over long periods of time.”
To appreciate the enhanced image quality produced by the new technique, the team first examined a conventional brightfield image of a metal-labelled neuron within a grasshopper’s abdominal ganglion, a type of mini-brain which, even at that size, presented out-of-focus structures.
They then imaged the same ganglion with the spectral LSCM adjusted to the manufacturer’s traditional fluorescence settings, resulting only in strong natural fluorescence and a collective dark blur in place of the silver-labelled neurons.
However, after collecting the light energy emitted from vibrating surface plasmons in the spectral LSCM, the team obtained spectacular three-dimensional computer images of silver and gold-impregnated neurons. This holds enormous potential for stimulating a re-examination of archived preparations, including Golgi-stained and cobalt/silver-labelled nervous systems.
Additionally, by using a number of different metal-based cell-labeling techniques in combination with the new LSCM protocols, tissue and cell specimens can be generated and imaged with ease and in great three-dimensional detail. Changes in even small structural details of neurons can be identified, which are often important indicators of neurological disease, learning and memory, and brain development.
“Both new and archived preparations are essentially permanent and the information gathered from them increases the data available for characterizing neurons as individuals or as members of classes for comparative studies, adding to emerging neuronal banks,” says co-first author Karen Thompson from Agnes Scott College.
“Just as plasmon resonance can explain the continued intensity of the red (caused by silver nanoparticles) and yellow (gold nanoparticles) colors in centuries-old medieval stained glass and other works of art, metal-impregnated neurons are also likely never to fade, neither in the information they provide nor in their intrinsic beauty,” adds Mesce.
This chapter is partly modified from:
Towards a 3D View of Cellular Architecture: Correlative Light Microscopy and Electron Tomography.
Valentijn J.A.; van Driel L.F.; Jansen K.A.; Valentijn K.M.; Koster A.J.
Book chapter in: Reiner Salzer. Biomedical Imaging: Principles and Applications. Wiley, 2010.
The present thesis reports on newly developed tools and strategies for correlative light and electron microscopy, and their application to cell biology research aimed at furthering our understanding of the structure-functional mechanisms of varying current biological applications. Accordingly, this Introduction consists of two main parts: the first one will discuss past and present strategies for correlative light and electron microscopy (CLEM) as an introduction to the subsequent chapters in this thesis, all of which will describe new developments and applications in the field; the second one will elaborate on the rationale behind the biological applications that were undertaken.
The term ‘correlative microscopy’ is employed in the biomedical literature to designate any combination of two or more microscopic techniques applied to the same region in a biological specimen. The purpose of correlative microscopy is to obtain complementary data, each imaging modality providing different information, on the specimen that is under investigation. Correlative light and electron microscopy (CLEM) is by far the most widespread form of correlative microscopy. CLEM makes use of the fact that imaging with photons on the one hand, and electrons on the other hand, each offers specific advantages over one another. For instance, the low-magnification range inherent to light microscopy (LM) is particularly well-suited for the rapid scanning of large and heterogeneous sample areas, while the high magnification and -resolution that can be achieved by electron microscopy (EM) allows for the subsequent zooming in on selected areas of interest to obtain ultrastructural detail. A further advantage of LM is that it can be used to study dynamic processes, up to the molecular level, in living cells and tissues. The recent surge in live cell imaging research has catalyzed a renewed interest in CLEM methodologies, as the interpretation of the dynamic processes observed by LM often requires high-resolution information from EM data. CLEM is also gaining in momentum in the field of cryo-electron microscopy where the low contrast conditions and low electron dose requirements put a constraint on the detection efficacy.
Current CLEM procedures face a number of challenges. Firstly, sample preparation methods for LM and EM can be quite divergent due to different requirements for preservation, embedding, sectioning, and counterstaining. Therefore, alternative sample preparation protocols need to be devised that are suitable for both LM and EM. Secondly, CLEM often requires the correlated localization of specific molecules in cells or tissues, for which specialized detection systems need to be developed. Standard detection methods are based on tagging of molecules either with fluorochromes for LM, or with gold particles for EM, whereas some CLEM applications require a tag to be visible in both modalities. Thirdly, the transition from imaging by LM to EM may involve handling and additional processing of samples, which can lead to changes in orientation and morphology of the sample. This in turn can hamper the finding back of, and correlation with previously established areas of interest.
In the present post-genomics climate, EM is coming back with a vengeance. Despite the dip in EM-based research during the previous decade, the development of novel EM technologies moved forward at a steady pace, resulting in several breakthrough applications. Among them are electron tomography and cryo-electron tomography, which are techniques for high-resolution 3D visualization and which are gradually becoming mainstream tools in structural molecular biology. As will be discussed in more detail below, (cryo-)electron tomography is often hampered by the lack of landmarks in the 2D views used to select areas of interest.
The diversity of goals to be achieved by CLEM constrains the development of universally applicable protocols. For instance, correlating live cell imaging data of a fluorescent protein with an ultrastructural endpoint requires a different CLEM approach than if the main goal is to pinpoint a rarely occurring structure of interest for EM investigation. CLEM can also be used as an alternative for immuno-EM, or to locate structures for cryo-EM if high-resolution structural details are required. As a consequence of the diversity of applications, there are to date numerous methods to correlate LM and EM data, and more developments, improvements, and applications, are likely to follow. Depending on the purpose of the application, three groups of CLEM methodologies can be distinguished:
- Those that combine live cell imaging with ultrastructural information (see figure 1),
- Those that combine LM of fixed or immobilized samples with ultrastructural information (figure 2),
- Those that combine LM and EM data from the same sections (figure 3)
Using Laser Scanning Confocal Microscopy as a Guide for Electron Microscopic Study: A Simple Method for Correlation of Light and Electron Microscopy’
XUE J. SUN, LESLIE P. TOLBERT,2 and JOHN G. HILDEBRAND
J Histochem and Cytochcem 1995; 43(3):329-335
Anatomic study of synaptic connections in the nervous system is laborious and difficult, especially when neurons are large or have fine branches embedded among many other processes. Although electron microscopy provides a powerful tool for such study, the correlation of light microscopic appearance and electron microscopic detail is very time consuming. We report here a simple method combining laser scanning confocal microscopy and electron microscopy for study of the synaptic relationships of the neurons in the antennal lobe, the first central neuropil in the olfactory pathway, of the moth Manduca sexta. Neurons were labeled intracellularly with neurobiotin or biocytin, two widely used stains. The tissue was then sectioned on a vibratome and processed with both streptavidin-nanogold (for electron microscopic study) and streptavidin-Cy3 (for confocal microscopic study) and embedded in epon/araldite. Interesting areas of the labeled neuron were imaged in the epon/araldite sectioned at the indicated depth for electron microscopic study. This method provides an easy, reliable way to correlate three-dimensional light miaoscopic information with electron microscopic detail, and can be very useful in studies of synaptic connections.
Introduction Study of synaptic connections is fundamental to an understanding of nervous system function. Physiological recording in combination with cell staining with different dyes yields especially useful information about the functional properties and morphology of neurons in various nervous systems. Light microscopic (LM) analysis of neurons, however, often raises the question of whether synaptic connections occur at specialized regions of the neurites. Knowledge of whether synaptic connections between two neurons exist and where on the neuritic tree they occur provides valuable information about how signals are integrated by the neurons and thus about the role the neurons play in a given pathway. Electron microscopy (EM) provides a powerful tool for this type of study. Unfortunately, correlation of three-dimensional LM data with EM detail is often difficult, as neurons are often large (ranging up to hundreds of micrometers in length) with complex branching patterns. Moreover, EM data usually lose most large-scale three- dimensional information. Much work has been done to attempt to overcome these problems (3,4,8,10,14,19,21,25,26,31,34,35). To date, the most successful methods for correlating EM and threedimensional data have been
(a) three-dimensional reconstruction of thin sections,
(b) semi-thin sectioning of tissue, serial reconstruction of the neuron at the light microscopic level, re-embedment, and finally thin-sectioning of selected semi-thin sections for electron microscopic study, and
(c) high-voltage EM observation of relatively thick sections and reconstruction of high-voltage EM data.
Although combination of these techniques or computer-assisted three-dimensional reconstruction and image processing techniques greatly facilitate the task, it is still very time-consuming to pursue this type of study.
Recently, laser scanning confocal microscopy (LSCM) has provided a convenient way to obtain three-dimensional morphology of neurons, as optical sections of relatively thick tissue can be obtained easily and rapidly (5-7,20,24,27,28,30). Equally importantly, information is gathered as digitized optical sections and is readily displayed as three-dimensional stacks. Combination of LSCM and EM appears to be a promising way to study synaptic connections.
Several different techniques have been used to bridge the gap between LM and EM (3,4,8,10,14,19,21,25,26,31,34,35). One common way is to section the block into semi-thin sections, make a threedimensional reconstruction of the neuron at the LM level, and then thin-section areas of interest (4,8,10,19,21). However, three-dimensional reconstruction from sections is tedious, and valuable tissue may be lost during reembedding and resectioning. High-voltage EM is a reasonable alternative, as relatively thicker sections can be observed, thus reducing the number of sections needed to span a neuron. This method, however, requires access to a high-voltage EM. We have developed a technique using LSCM as a guide for EM study. A biotin-labeled neuron is rendered detectable by both fluorescence (for LSCM) and immunogold (for EM). After such double labeling, interesting areas of physiologically identified and intracellularly labeled neurons can be investigated at the EM level to see where synaptic connections are formed. Although with our method three-dimensional reconstruction might also be needed, the number of vibratome sections needed to span a neuron is small (two to four in our case with 80-pm sections). This greatly reduces the task.
The major challenge in correlation of LSCM and EM is the compatibility of the labeling methods. Visualization methods using diaminobenzidine (either in a peroxidase reaction or in a reaction catalyzed by photo-oxidation of fluorescent dyes) and Golgi staining have been used widely for both LM and EM observations (see, e.g., 8,26,35). The light- and electron-dense label, however, is not suitable for LSCM imaging, which is best performed on fluorescent labels. One possibility is to use fluorescent labels, perform LSCM, and, after LSCM imaging, convert the fluorescent dyes into electron-dense materials by using either photo-oxidation of fluorescent dyes [such as Lucifer Yellow (16) or DiI as suggested by Vischer and Durrenberger (32)] in the presence of diaminobenzidine or an antibody against the dye injected into the cell. It is possible to determine the position of an interesting area of a neuron by LSCM with this method, but relocating the same area after plastic embedding is much more difficult than the presently reported method because of shrinkage of the tissue during tissue processing for EM and the inability to monitor the exact position by LSCM during sectioning. The reflection mode of LSCM has been applied successfully to detect electron-dense label in several systems (6,7,22,33). With this mode of imaging, Deitch et al. (6) have proposed another way of combining LSCM and EM. Reflection signals in the LSCM are often weak, however, and do not take full advantage of the excellent resolving capabilities of the confocal microscope.
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The unending fascination with the Golgi method
While studying the brain function, it is extremely important to investigate the precise shape and morphological changes of individual neurons. A neuron is a specialised cell that emits an electrical signal to allow information exchange, a characteristic not found in the cells of other organs. Both the short ‘dendrite’, which is intricately branched like a tree branch and the long ‘axon’ emanate from the nerve cell body. In addition, neuronal spines located on dendrites receive electric signals from other neurons and are involved in neuronal plasticity. Thus, together with neural cells such as neuroglia, neurons form complicated networks known as ‘neural circuits’.
To appreciate the complexity of such intricate neural networks, staining methods that allow the visualisation of neuronal cells in thinly sliced brain sections are used. In particular, Nissl stains and silver impregnation are commonly used. Nissl staining, which is based on a mechanism combining a basic aniline dye with Nissl granules, can stain both the cell nucleoli and rough endoplasmic reticula in neurons. In contrast, silver impregnation using neuronal argent affinity can stain an entire neuron, but not the myelin sheath. Neural circuitry refers to the combination of many interacting neural cells and is immensely complex morphologically, with many neurons intertwined with one another within a restricted space. Unfortunately, because the above techniques stain all neural cells with equal probability, it is difficult to identify and appreciate the morphology of a single cell amongst the mass of other stained cells.
In contrast, the Golgi method, focused in this review, has allowed for the visualisation of entire neurons and glia in high detail and with good contrast. Moreover, compared to Nissl staining and silver impregnation, the Golgi method has the beneficial feature of characteristically selective staining. Because neurons are stained only sparsely with the Golgi method, it is a powerful staining technique for providing a complete, detailed representation of a single neuron. The aim of this review was to discuss the history and evolution of the Golgi method.
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Dendritic vulnerability in neurodegenerative disease: insights from analyses of cortical pyramidal neurons in transgenic mouse models
In neurodegenerative disorders, such as Alzheimer’s disease, neuronal dendrites and dendritic spines undergo significant pathological changes. Because of the determinant role of these highly dynamic structures in signaling by individual neurons and ultimately in the functionality of neuronal networks that mediate cognitive functions, a detailed understanding of these changes is of paramount importance. Mutant murine models, such as the Tg2576 APP mutant mouse and the rTg4510 tau mutant mouse have been developed to provide insight into pathogenesis involving the abnormal production and aggregation of amyloid and tau proteins, because of the key role that these proteins play in neurodegenerative disease. This review showcases the multidimensional approach taken by our collaborative group to increase understanding of pathological mechanisms in neurodegenerative disease using these mouse models. This approach includes analyses of empirical 3D morphological and electrophysiological data acquired from frontal cortical pyramidal neurons using confocal laser scanning microscopy and whole-cell patch-clamp recording techniques, combined with computational modeling methodologies. These collaborative studies are designed to shed insight on the repercussions of dystrophic changes in neocortical neurons, define the cellular phenotype of differential neuronal vulnerability in relevant models of neurodegenerative disease, and provide a basis upon which to develop meaningful therapeutic strategies aimed at preventing, reversing, or compensating for neurodegenerative changes in dementia.
Neocortical pyramidal neurons possess extensive apical and basilar dendritic trees, which integrate information from thousands of excitatory and inhibitory synaptic inputs. Dendrites respond to inputs with postsynaptic potentials, which are relayed to the soma where they are summed; if a threshold potential is exceeded, an action potential is generated. Voltage attenuation in space and time along dendrites is fundamental to summation and is influenced by a number of interacting morphological properties (such as diameter and length) and active properties (such as distribution of ion channels) of the dendritic shaft (Hausser et al. 2000; Kampa et al. 2007; Stuart and Spruston 2007; Henry et al. 2008). Further complexity arises from the presence of thousands of biophysically active dendritic spines, the principal receptive site for excitatory glutamatergic inputs to a neuron.
Dendrites and dendritic spines in particular are dynamic structures, which undergo significant changes across the life span. Under non-pathological conditions, the number of spines on pyramidal neuron dendrites increases substantially over the course of development, is reduced during maturation to adulthood, and remains relatively stable during adulthood (for review see Bhatt et al. 2009). Then, during normal aging, significant changes in spine number, distribution, and morphology occur (Duan et al. 2003). Spines also undergo significant structural modifications under conditions where synaptic strength is experimentally modified, usually with protocols designed to evoke longterm potentiation or long-term depression (for review see Alvarez and Sabatini 2007; Bhatt et al. 2009; Holtmaat and Svoboda 2009). In many neurodegenerative diseases, significant alterations in the dendritic arbor occur, together with substantial spine loss and alterations in spine morphology (reviewed in Halpain et al. 2005). Gaining an understanding of these sublethal changes to neurons, which detrimentally impact neuronal signaling, and ultimately cognitive function, is an important goal.
Transgenic mouse models have been useful for elucidating mechanisms of amyloid-and tau-induced pathology, although no single model fully recapitulates human disease (for review see Duff and Suleman 2004; Spires et al. 2005). Two of the most commonly employed mouse models of neurodegenerative disease are the Tg2576 amyloid precursor protein (APP) mutant mouse and the rTg4510 tau mutant mouse, which develop significant pathological aggregations of amyloid-beta (Aβ) and tau proteins, respectively. Tg2576 transgenic mice overexpress the Swedish double mutation of the human APP gene, which leads to progressive formation of soluble Aβ peptides and fibrillar Aβ deposits in the form of amyloid plaques. Increased Aβ levels in these mice are associated with progressive structural changes to neurons (although not with neuron death), and cognitive impairment (Hsiao et al. 1996). In the rTg4510 mouse model, expression of the P301L mutant human tau variant leads to progressive development of neurofibrillary tangles (NFTs), neuronal death, and memory impairment reminiscent of the pathology observed in human tauopathies (Santacruz et al. 2005).
In this review, we discuss changes in the structure and function of dendrites and spines of pyramidal neurons that are associated with pathological aggregation of Aβ and tau in these and other mouse models of neurodegenerative disease. We also discuss, as a point of comparison, the impact of normal brain aging on the morphofunctional properties of pyramidal neurons in aged macaque monkeys. This is not a comprehensive review of the literature on such changes in human neurodegenerative diseases or of the many studies of these changes in mouse models (for reviews see Duff and Suleman 2004; Spires and Hyman 2004;Lewis and Hutton 2005; Duyckaerts et al. 2008; Giannakopoulos et al. 2009). Rather, the focus here is specifically on our multidimensional collaborative efforts to understand the functional consequences of pathological changes in the structure of individual layer 3 frontal cortical pyramidal neurons in commonly employed mouse models of neurodegeneration. These studies focus on layer 3 pyramidal neurons in the neocortex because they are the principal neurons involved in corticocortical circuits that mediate many cognitive functions of the frontal cortex and may be selectively targeted in neurodegenerative disease (Morrison and Hof 1997, 2002; Hof and Morrison 2004).
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3045830/bin/nihms273899f1.jpg
Structural properties of mouse neocortical pyramidal neurons. a 40 × CLSM image of a layer 3 pyramidal cell from the frontal cortex of a wild-type mouse. The neuron was filled with biocytin during patch-clamp recording and subsequently labeled with Alexastreptavidin 488. b100 × CLSM image of the spiny dendrite shown within the red box in (a). c Electron micrograph showing the ultrastructure of a pyramidal neuron spine, with a prominent postsynaptic density and spine apparatus. Courtesy of Dr. Alan Peters. Scale bars a 40, b 5, c 0.5 μm
The structural properties of dendrites underlie their passive membrane (cable) properties, which are membrane capacitance Cm, specific membrane resistance Rm and axial resistivity Ra. These cable properties determine, at a fundamental level, the degree of summation of synaptic inputs and the spatial distribution of electrical signals. Because summation of synaptic inputs by dendrites is determined in large part by their structure, dendritic morphology plays a critical role in action potential generation (Mainen and Sejnowski 1996; Koch and Segev 2000; Euler and Denk 2001; Vetter et al. 2001; Krichmar et al. 2002; Ascoli 2003). Importantly, both branching topology and surface irregularities including dendritic varicosities (Surkis et al. 1998) contribute to the excitable properties of neurons. For example, recent simulation studies have demonstrated that marked differences in the efficacy of action potential back propagation in different neural types are attributable in large part to variations in dendritic morphology (for review see Stuart et al. 1997;Waters et al. 2005).
Integration of synaptic inputs by dendrites is mediated not only by basic passive cable properties, but also by active properties, which include the number and distribution of voltage, ligand, as well as second messenger-gated transmembrane ionic channels (reviewed in Migliore and Shepherd 2002; Magee and Johnston 2005;Johnston and Narayanan 2008; Nusser 2009). Dendrites possess a rich array of sodium, calcium, and potassium channels that are distributed either uniformly or non-uniformly across a given dendrite (for reviews, see Johnston et al. 1996; Migliore and Shepherd 2002; Magee and Johnston 2005; Johnston and Narayanan 2008; Nusser 2009). For example, layer 5 cortical pyramidal neuron dendrites possess a gradient of the hyperpolarization-activated mixed cationic HCN channels that increase in density from the soma to the apical tuft (Berger et al. 2001; Lorincz et al. 2002). The interaction of intrinsic ionic and synaptic conductances with passive properties determined by dendritic morphology can effectively alter the cable properties of the dendritic tree (Bernander et al. 1991; Segev and London 2000; Bekkers and Hausser 2007) adding a further layer of complexity to signaling by individual neurons. Computational modeling approaches have been extensively employed to shed light on dendritic structure–function relationships and into potential interactions between a multitude of dendritic active and passive properties. These approaches, as discussed at the end of this review, have been useful for gaining insight on dendrites that are too thin to be studied with electrophysiological approaches, and for providing empirical researchers with testable hypotheses relevant to the functional consequences of dendritic dystrophy in neurodegenerative diseases.
Dendritic spines
Dendrites of glutamatergic pyramidal neurons are studded by thousands of dendritic spines, which are the site of most of the glutamatergic synapses in the brain, that confer further functional complexity to these processes. Spine density, shape, and distribution are all important contributors to neuronal excitability that are superimposed on the electrophysiological properties of dendritic shafts (Wilson 1988; Stratford et al. 1989;Baer and Rinzel 1991; Tsay and Yuste 2002). Spines are small appendages that extend from approximately 0.5–3 lm from dendritic shafts and are usually <1 μm in diameter (Fig. 1b, c). Mouse layer 3 frontal cortical pyramidal neurons typically possess approximately 6,000 dendritic spines (Rocher et al. 2008). Spines can be broadly categorized as falling into one of several different morphological types, namely “thin”, “stubby”, “mushroom”, and “filopodia” which are normally seen in large numbers only during development (for review see Yuste and Bonhoeffer 2004; Bourne and Harris 2007). Although most spines do fall into one of these broad categories, serial electron microscopy reveals that there is also a continuum between the different morphological subtypes (Bourne and Harris 2007). Spine morphology determines the strength, stability and function of excitatory synaptic connections that subserve the neuronal networks underlying cognitive function. Smaller spines, such as the thin and filopodia types are less stable and more motile (Trachtenberg et al. 2002; Kasai et al. 2003; Holtmaat et al. 2005) and as a result, are more plastic than large spines such as the mushroom and stubby types (Grutzendler et al. 2002). The size and morphology of the spine head is correlated with the number of docked presynaptic vesicles (Schikorski and Stevens 1999) and the number of postsynaptic receptors (Nusser et al. 1998), and hence with the size of synaptic currents and synapse strength. In addition, a small head size permits fast diffusion of calcium within the spine, while the neck length shapes the time constant for calcium compartmentalization (for review see Yuste and Bonhoeffer 2001; Nimchinsky et al. 2002), modulating postsynaptic mechanisms that play an important role in synaptic plasticity linked to functions, such as learning and memory (Holthoff et al. 2002; Alvarez and Sabatini 2007; Bloodgood and Sabatini 2007). Spine neck length and diameter also affect diffusional coupling between dendrite and spine (Svoboda et al. 1996; Yuste et al. 2000; Bloodgood and Sabatini 2005), and spine density and shape regulate the degree of anomalous diffusion of chemical signals within the dendrite (Santamaria et al. 2006). Spine morphology also varies dynamically in response to synaptic activity (Lendvai et al. 2000; Hering and Sheng 2001; Zuo et al. 2005).
Over the past few decades, it has become increasingly evident that local dendritic spine structure and distribution (Matus and Shepherd 2000) play a key role in the electrical and biochemical signaling of dendrites (Nimchinsky et al. 2002; Matus 2005; Bourne and Harris 2007). However, spines present challenges to the standard cable model of dendrites. The common way to model the effects of spines in a passive cable equation model is to reduce the membrane resistance and increase the membrane capacitance by a factor proportional to the increased membrane surface area due to spines (Jaslove 1992). This modification predicts that voltage should attenuate more drastically in space along spiny dendrites, relative to their smooth counterparts. Because of their capacity for plasticity and because they are the location of most excitatory synapses in the cerebral cortex, accurately characterizing the structure of dendritic spines is essential for understanding their contributions to neuronal, and ultimately to cognitive function.
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3045830/bin/nihms273899f2.jpg
Analytical methods to quantify morphological structure. a xy projection of the montage of CLSM tiled image stacks from a wildtype mouse layer 3 frontal cortical pyramidal neuron. Scale bar 40 μm. b Tree structure from the data shown in (a), extracted using NeuronStudio. cAutomatic spine detection and visualization in NeuronStudio. Left automatically detected spines overlaid on a maximum projection of a typical dataset; right the same data and spines volume-rendered in 3D. d Left 2D Rayburst sample used for diameter estimation. Rays cast using the sampling core is shown in orange with the one chosen as the diameter shown in blue. The green line indicates the surface detected by the Rayburst samples. Right spine diameter estimation using a 2D Rayburst run at the center of mass (small green squares) of a single layer. Theblue line indicates the resulting width of the structure as calculated by Rayburst, and provides an approximate length of 0.7 μm. e Optimized fits of scaling exponents (black fitted lines) and optimal scaling regions (gray shaded bands) for the apical dendrites of a typical layer 3 pyramidal cell projecting from superior temporal cortex to area 46 (see Kabaso et al. (2009) for details). f Contributions of branching and tapering exponents to global mass scaling (measured by total area exponent dA) in spine-corrected apical dendrites of long projection neurons of young rhesus monkeys, in the proximal and medial scaling regions (I, II in e). The total branching and tapering vary across the two scaling regions, yet the total area in each region is conserved [modified from Wearne et al. (2005) and Kabaso et al. (2009)]
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3045830/bin/nihms273899f4.jpg
Dendritic spine loss in pyramidal neurons from Tg2576 and rTg4510 mice. CLSM images of dendritic segments with spines of neurons from wild-type (top) and transgenic (bottom) mice. Scale bars 10 μm [modified from Rocher et al. (2008, 2009)]
3D reconstruction and analytical approaches
Early methods for digitizing 3D neuronal structures relied on interactive manual tracing from a computer screen (Capowski 1985). These methods were time-consuming, subjective, and lacked precision. In recent years, automated methods have been developed that use image analysis algorithms to extract neuronal morphology directly from 3D microscopy and overcome the limitations of manual techniques (Koh et al. 2002; He et al. 2003; Wearne et al. 2005). Newer methods use pattern recognition routines to track or detect a structure locally without the need for global image operations (Al-Kofahi et al. 2002; Streekstra and van Pelt 2002; Schmitt et al. 2004; Myatt et al. 2006; Santamaria-Pang et al. 2007). Most are designed to work on a broad range of signal-to-noise ratios and even on multiple imaging modalities. This results in increased computational complexity, which makes the use of these methods as interactive reconstruction tools for high-resolution data less than optimal.
In our studies of neuronal structure, morphological reconstruction is performed using NeuronStudio, a neuron morphology reconstruction software tool (http://www.mssm.edu/cnic/tools.html), developed by Wearne et al. (2005) and Rodriguez et al. (2006, 2008, 2009). Neuron- Studio has been designed for low computational complexity to allow interactive semi-automated extraction of neuronal morphology from medium to high-quality de-convolved 3D CLSM and MPLSM image stacks of fluorescently labeled neurons. It features automated extraction of dendrites and dendritic spines as well as a rich set of manual editing and visualization modalities. Figure 2a shows an xy projection of CLSM data from a wild-type mouse layer 3 cortical pyramidal neuron and Fig. 2b shows the automated reconstruction of the neuron’s dendritic arbor obtained using NeuronStudio. Because fluorescence intensity can vary with adequacy of filling, imaging depth and xyspatial extent in CLSM and MPLSM image stacks, data segmentation within NeuronStudio adapts the iterative self-organizing data analysis (ISODATA) method (Ridler and Calvard 1978) to compute local thresholds dynamically. This method is appropriate for datasets exhibiting a bimodal distribution of intensity values, such as the grayscale images characteristic of de-convolved LSM image stacks.
Automated 3D reconstruction of dendritic spines
The assessment of spine numbers and distribution and their classification into subtypes has historically been a labor intensive and relatively inaccurate process. Spine numbers could only be estimated because spines extending primarily in the z plane relative to the dendritic shaft could not be counted. With the advent of CLSM, accurate 3D spine assessment came a step closer, but was still a highly timeconsuming and labor-intensive undertaking. Improving upon previous spine detection algorithms (Koh et al. 2002; Cheng et al. 2007), Rodriguez et al. (2008) devised an efficient and robust method for automated spine detection, available in NeuronStudio.
3D measures of spatial complexity
Traditionally, Sholl analysis (1953) has been used in two dimensions to quantify the spatial complexity of dendritic branching patterns with increasing distance from the soma. Fractal analyses have also been used (Smith et al. 1989; Caserta et al. 1995; Jelinek and Elston 2001; Henry et al. 2002) to quantify spatial complexity as a power law scaling exponent, describing the rate of change of the number of branches over a large portion of the dendrites, and best visualized as the slope of a log–log plot of these two quantities. We have recently extended this work (Rothnie et al. 2006; Kabaso et al. 2009) to include three power law exponents describing global spatial complexity: rates of change of dendritic mass, branching, and taper.
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In conclusion, these analytical tools provide rapid, objective analyses of the high-resolution data that we collect from wild-type and transgenic mouse neurons. With NeuronStudio, we are able to perform analyses of differences in dendrite diameter and spine shapes that were not available previously, and these kinds of studies are currently ongoing in our laboratories. Moving forward, we will evaluate whether the global patterns in spatial complexity observed in rhesus monkey pyramidal neurons are similarly present in mouse neurons. We will also compare the spatial complexity of wild-type and transgenic neurons to determine whether global mass homeostasis is conserved in neurodegeneration as it seems to be in aging.
Dendritic changes in neurodegenerative disease ….
Clearly, there is a need for many more studies on the functional electrophysiological consequences to individual neurons of the significant structural changes in neurodegenerative disease. Given the lack of such studies, and also technical considerations such as space clamp limitations and the impossibility of recording from distal dendrites or spines, the use of modeling methods to understand potential functional consequences of structural changes is very important.
Insights from modeling
For nearly 60 years, mathematical models have been used to investigate neuronal function. Hodgkin and Huxley’s (1952) mathematical model of action potential generation predicted the existence of ion channels, decades before ion channels were observed experimentally (Neher and Sakmann 1976). Other models were groundbreaking in describing how dendrites filter signals as passive electrical cables (Rall 1959; Goldstein and Rall 1974), but were limited in their ability to apply directly to realistic morphologic data. Since then, applications of mathematical techniques (Fitzhugh 1961; Nagumo et al. 1962; Rinzel and Ermentrout 1989), advances in computational software (Bower and Beeman 1998; Carnevale and Hines 2006), model reduction (Clements and Redman 1989; Pinsky and Rinzel 1994), and computing power have resulted in models that have great potential for yielding insights into neuronal function. In particular, models have shown that morphology is a critical determinant of neuronal firing properties (Zador et al. 1995; Mainen and Sejnowski 1996; Vetter et al. 2001; Schaefer et al. 2003; Stiefel and Sejnowski 2007). The effects of morphology are further amplified by the actions of ion channels distributed throughout the dendrites (for review see Johnston and Narayanan 2008), both of which shape patterns of synaptic input. Our ability to understand neuronal function depends largely on analysis of the nonlinear interactions between morphology, electrical membrane properties, and synaptic input.
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Our modeling results make several predictions that may apply to transgenic mouse models of neurodegeneration. First, changes in dendrite length, diameter, and spine densities/numbers in transgenic neurons compared to wildtype neurons may significantly impact the attenuation of signals to and from the soma. This may happen over an entire neuron, or in limited regions, such as dendrites passing through or near fibrillar amyloid deposits, or in apical tufts that undergo atrophy. Long projection neurons are particularly vulnerable in AD (Hof et al. 1990; Bussiére et al. 2003; Hof and Morrison 2004), giving greater weight to the significant differences in voltage attenuation observed in long projection neurons (Kabaso et al. 2009). Second, changes in spine density may affect the diffusion rate of intracellular messengers and ions. Elongated dendrites may result in less trapping of electrical and chemical signals within spines; this phenomenon may be functionally significant. We must also consider how spine loss might impact the amount of excitatory input that a neuron receives. Finally, it is likely that interactions between morphology and active parameters vary between wild-type and transgenic neurons. To study this more fully we must measure both detailed morphological properties and ionic currents in wild-type and transgenic neurons. To evaluate the degree to which these predictions truly apply to the transgenic mouse models, we will apply the mathematical methods described here directly to the transgenic data that we have collected. These computational studies will likely lead to explicit predictions of which parameters to change, and by how much, to counteract morphological changes that affect physiological function in neurodegenerative disease.
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