neuronal hybrid system for functional recovery of the CNS

Drug Discovery Today: Disease Models

DRUG DISCOVERY

TODAY

DISEASE

MODELS

Vol. 5, No. 1 2008

Editors-in-Chief Jan Tornell – AstraZeneca, Sweden Andrew McCulloch – University of California, SanDiego, USA

Nervous system

Nanomaterial/neuronal hybrid system for functional recovery of the CNS Michele Giugliano1, Maurizio Prato2, Laura Ballerini3,* 1

Laboratory of Neural Microcircuitry, Brain Mind Institute, Ecole Polytechniqe Federale de Lausanne, Lausanne, Switzerland Department of Pharmaceutical Sciences, University of Trieste, Piazzale Europa 1, I-34127 Trieste, Italy 3 Physiology and Pathology Department, B.R.A.I.N., University of Trieste, via Fleming 22, I-34127 Trieste, Italy 2

Nanotechnology enters into the realm of basic biological units by its ability to functionally integrate with bio-systems. In recent years we reached an increased

Section Editor: Gabriel A. Silva – Bioengineering and Ophthalmology, University of California, San Diego, La Jolla, CA, USA

interest and improved understanding of such interactions with biological systems at a subcellular level. This latter feature can be understood and engineered with a high degree of specificity. Here, we review current experimental models that promote the development of novel bio-nanotechnology tools to help repair damaged nervous system tissues.

Introduction The application of nanotechnology to contemporary neuroscience promotes innovative solutions that might be useful for promoting tissue restoration and repairing lesions or defects of the brain and spinal cord. Nanotechnology is defined by the ability to chemically control self-organizing substrates, producing materials, at the nanometer scale, with properties that result in an extraordinary degree of functional integration with cellular and physiological systems [1]. One of the more attractive materials employed to develop nanobio hybrid systems is represented by carbon nanotubes (CNT; Box 1). CNT, owing to their unique range of thermal, electronic and structural properties, have been rapidly developing as a technology platform for biological and medical applications, including those designed to develop novel neuro-implantable devices. This article reviews the experi*Corresponding author: L. Ballerini ([email protected]) URL: http://www.epfl.ch/, http://www.units.it/, http://www.units.it/ 1740-6757/$ ß 2008 Elsevier Ltd. All rights reserved.

DOI: 10.1016/j.ddmod.2008.07.004

mental models used to explore the functional aspects of hybrid neuronal/CNT networks potentially relevant for the control of neuronal signaling.

Why should we apply nanotechnology to the nervous system? Owing to lack of effective self-repair mechanisms in the adult, central nervous system (CNS) damage results in functional deficits that are often irreversible. In particular, CNS lesions represent a major challenge to modern medicine for several reasons that include their long-term impact on the quality of life of patients, their widespread occurrence and their high medical and social costs. Confronting with these issues represents a daunting task. However, modern technology offers exciting new perspectives ranging from the possibility of early intervention to limit the extent of the lesion, to the prospect of providing some measure of repair [2]. Self-repair of damaged axonal connections could – in principle – be achieved by two mechanisms: (1) axonal regeneration – the re-growth of axons along their original trajectories – and (2) axonal reorganization – the adaptive remodeling of neuronal circuitry [3]. Many crucial steps are necessary for a neuron to reconstruct a functional network: it has to survive to the injury, re-grow axons and dendrites, appropriately direct their growth, recognize the target cells and rebuild active synapses. Any therapeutic strategy has to take into account and promote each of these steps. One of the most promising 37

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Box 1. CNT are cylindrically shaped nanostructures constituted by sheets of graphene rolled up to form hollow tubes. Historically, multi-walled nanotubes (MWNT) were first discovered by Iijima [31] followed later by their single-walled analogs [32,33]. Single-walled nanotubes (SWNT) possess the simplest geometry, that is, a rolled-up graphene sheet that is closed by fullerene-like caps. Their diameter is in the order of 0.7–2 nm, whereas their length can reach up to several millimeters ([34], Figure 1). CNT have outstanding physicochemical properties such as: ordered structure, ultralight weight, high mechanical strength, high electrical conductivity and high surface area. The combination of these characteristics makes CNT a unique material with the potential for biomedical applications. There is an increasing interest in exploring all of these properties for applications ranging from sensors for the detection of molecular abnormalities, to substrates for the growth of cells for tissue regeneration and as delivery systems for a variety of diagnostic/therapeutic agents [35]. The chemical properties of CNT can be systematically varied by attaching different functional groups [36]. The ease of which CNT can be patterned makes them attractive for studying the organization of neural networks and has the potential to develop new devices for neural prosthesis [11]. Because as-produced CNT are usually difficult to handle and completely insoluble in all solvents, they need to be functionalized to become soluble and easier to manipulate. Although the organic functionalization of CNT has been under development for many years, the field is not yet mature enough to guarantee homogeneous and pure material. The main problem relies on the extreme difficulty of producing monodispersed CNT at the production stage in terms of diameter, helicity and length. This problem can be partially solved by using chemical functionalization, which can help separate metallic from semiconducting CNT or can transform metallic into semiconducting tubes by saturating some of double bonds on their surface [37]. As as-produced CNT contain variable amounts of impurities, mainly amorphous carbon and metallic nanoparticles, the clear necessity emerges of the determination of the relative composition. Notwithstanding these difficulties, CNT have become very popular in biomedicine. In fact, they are intensely studied as carriers for drug delivery, gene transfection and efficient agents in hyperthermia for cancer therapy [38].

CNS reconstructive/repairing strategies is directed at providing a functional bridge covering damaged tissue and restoring function by implantable assistive devices. Cell-free neural implants mainly consist of biomimetic materials [4,5]. Tissue bridges need to provide a bioactive scaffold for axon regrowth and guidance to proper targets [5]. Such supramolecular architectures should reproduce some physical characteristics of the extracellular matrix and at the same time minimize the induction of glial scar [6–9]. Reaching new frontiers in tissue engineering requires strategies that establish a convergence of chemical engineering and membrane biophysics in the design of new materials. Such convergence sets the stage for bio-nanotechnology research. Applications of nanotechnology with potential clinical impact include the use of nanoengineered functional scaffold systems for altering and promoting neural regeneration following both acute and chronic injury [1]. These approaches attempt to create chemical, physical and biological [10] environments that support/promote the functional regeneration of central axons. Examples include three-dimensional 38

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biodegradable scaffolds that form through the self-assembly of bioactive nanofibers under physiological conditions, promoting neuronal differentiation and tubular implants of bio degradable nanofibers designed to promote and guide regenerating axons. The leading scope of this article is to highlight how the convergence between nanotechnology, chemistry and neurobiology can stimulate further research in this area and ultimately lead to a new generation of nanomedicine applications in neurology.

In vitro models of neuronal networks integrated to carbon nanotubes (CNT) CNT are one of the more promising nanomaterials for electronic, computer and aerospace industry. Recently, these unique materials have been rapidly developing as a platform technology for biomedical applications [11]; [Fig. 1]. CNT have attracted tremendous attention as potential scaffolds for reestablishing intricate connections between neurons. This interest is partly due to the organized fractal-like nanostructure of random CNT dispersions on a substrate, together with high ohmic electrical conductivities across neighboring nanoparticles. During the past decade, several research groups have assessed CNT as substrates for neuronal growth, demonstrating the biocompatibility of these materials. The application of CNT in neuroscience research has been oriented toward the use of both multi-walled nanotubes and single-walled nanotubes (MWNT and SWNT, respectively; see Box 1).

Figure 1. Carbon nanotubes are made up of threefold covalently bonded carbon atoms that are arranged in hexagons. One can think of them as seamlessly rolled up, single graphite sheets. The diameter can be very small (<1 nm) and the length can be very large in comparison (on the mm scale). The small tubes can be rolled up in various ways; one can distinguish between armchair (left), zigzag (middle) and chiral types (right). All of the configurations have different electronic properties. Reproduced with permission from Graham et al. [34]. Copyright ß 2005 WILEY-VCH Verlag GmbH & Co. KGaA.

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The potential of CNT to integrate with neurons and neuronal function has been investigated in the most widely used in vitro model system of mammalian CNS networks: neuronal cultures. Culturing brain circuits provides a simple in vitro model of a complex neuronal network, where particular, for example, cortical, neuronal activity regimens can be obtained via pharmacological manipulations or by means of electrical stimulation. In such network models, single-neuron, synaptic and microcircuit properties can be investigated, allowing the study of neuronal processing in cortex circuits when integrated with a CNT network. Cultured brain circuits offer a variety of investigative levels to answer fundamental questions in neurobiology, such as how neurons reconstruct a functional network, how they rebuild active synapses or what rules govern such interactions. In the present framework the value of this in vitro model relies on its ability to suggest answers to many questions regarding the integration between functionalized CNT and brain circuits. The first study adopting hippocampal neurons in cultures to test CNT substrates was that of Mattson et al. [12], demonstrating that rat hippocampal neurons adhere and grow on a MWNT layer on polyethyleneimine (PEI) coated glass coverslips. These findings were later confirmed by a detailed morphological analysis of growth cones and branching in rat hippocampal neurons grown on a MWNT support [13], although MWNT did not sustain as much neurite branching as others known permissive substrates for neuronal growth [12,13]. Further testing of MWNT and SWNT finally confirmed that both allowed short-term and long-term survival of isolated neurons [14–16]. All these reports support the use of CNT (MWNT and SWNT), as potential biocompatible materials that guarantee cell viability. In addition, highly purified CNT substrates not only promote cell attachment, growth and long-term neuronal survival, but also promote differentiation, comparable to that of neurons growing on polyornithine pre-treated glass [17]. Thus, the interaction between neurons and CNT is strongly modulated by purity as well as the three-dimensional organization of CNT platforms. It is also clear that chemically modified CNT can control the growth and morphology of neuronal cells. Which type and what extent of functionalization of the CNT substrate is optimal for neuron growth? Great attention has been directed toward generating CNT scaffolds that might guide nerve tissue regeneration after injury, by designing substrates able to promote controlled neurite outgrowth. Following this rationale, CNT have been conjugated with biologically active compounds or with molecules yielding various charges at the surface of modified CNT. Pre-coated MWNT with bioactive molecules indicated that such modifications of CNT could be used successfully to affect the interaction between neurons and nanomaterials [12,18]. A further manipulation is represented by chemical modifica-

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tion of CNT via covalent attachments of key proteins active in neuronal differentiation, such as neurotrophin. Tested in vitro, CNT-linked neurotrophin retained its trophic effect on cultured dorsal root ganglion neurons [19], regardless the covalent attachment to CNT. Covalent modifications have also been employed to manipulate the charge carried by MWNT, which were chemically functionalized to obtain CNT that exhibited negative, neutral or positive surface charges, respectively, at the physiological pH of the culture medium [13]. In vitro cultured neurons grown on positively charged MWNT showed longer neurites and a larger number of growth cones when compared with neurons grown on neutral or negatively charged CNT [13]. Such effect was reminiscent of the well-known enhancement in neuronal development brought about by treating growth surfaces with positively charged polymers. Chemical modification of CNT is therefore emerging as a powerful tool to design substrates that can control the growth and morphology of neuronal cells. To advance our understanding in neuronal/nanomaterial hybrid networks, research moved to assess how neurons reconstruct a functional network when integrated with CNT. In vitro models allow the investigation of electrophysiological properties of synapses, neurons and networks coupled to CNT together with the electrical interactions between conductive nanotubes and biological membranes. Subsequent progress in neuron–nanotube characterization originated precisely in such perspective, as represented by our work. We reported, for the first time, the effects of CNT substrates on the electrical behavior of brain networks in vitro [15,16]. The CNT used for neuronal growth were first functionalized allowing uniform solutions and then deposited on a glass substrate. After evaporation, the CNT were de-functionalized by thermal treatment, leading to glass slides covered by a film (i.e. a nano-meshwork) of about 50 nm of CNT [15,16]. This strategy allowed a long-term and stable retention of CNT films on glass, as well as long-term neuronal cell culturing. When compared with control abiotic surfaces, CNT substrates boosted neuronal network activity under chronic growth conditions (irrespective of whether MWNT or when SWNT were employed [15,16], respectively). In particular, the specific physical properties of CNT appeared to enhance the occurrence of spontaneous postsynaptic currents (PSCs), as recorded from single neuron. The appearance of these PSCs represents a signature of network-level collective activity and provides clear evidence of functional synapse formation. In addition, such spontaneous dynamic state, thought to arise from a specific balance between intrinsic single-cell excitable behavior and recurrent network-wide interactions, is a widely accepted index of network activity. As such, the growth of functional neuronal circuits on a CNT meshwork was always accompanied by physiological changes, in the form of a significant increase in the efficacy www.drugdiscoverytoday.com

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Figure 2. Scanning electron microscopy images of cultured neurons growing on CNTs. At very high magnification (A), the intricate structure of the nanotube meshwork obtained from the CNT dispersion is apparent. Neurons grow and develop ex vivo on such a substrate for several weeks, reorganizing into functional networks (B–D). At the subcellular level (E and F), tight contacts between cell membranes and individual nanotube bundles are clearly identified, suggesting a very intimate biophysical coupling. Scale bar: A, 1 mm; B, 200 mm; C, 25 mm; D, 10 mm; E, 2 mm; F, 450 nm. ß 2007 Society for Neuroscience. Reproduced with permission from Mazzatenta et al. (2007).

of neural signal transmission. In the absence of specific chemical and pharmacological interactions between neurons and non-functionalized CNT, it is obvious that the high electrical conductivity of the substrate might underlie the specific neurophysiological effect of CNT materials. This could for instance arise from a reinforcement of direct electrical coupling in neuronal membranes in contact with CNT [15]. We reported, by scanning electron microscopy (SEM) [15,16], the presence of intimate contacts between cultured neurons and purified CNT, which might provide the physical substrate for neuron–CNT–neuron electrical coupling (Fig. 2). Indeed, the high conductivity of CNT is an interesting property in view of the extent of functional recovery in the CNS related to processes of neuronal learning, plasticity and adaptation. These forms of plasticity might also depend upon the presence of proper chronic electrical instructions. In this context, several important questions need to be addressed. Is the nanotube material able to transfer induced or spontaneous electrical signals generated by neurons, consisting of small amplitudes voltage variations (from hundreds of mV to tenth of mV)? If transferred, can these signals evoke a response in a physically separated neuronal population? Can 40

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the design of the material be optimized to propagate electrical signals only in a chosen direction? How crucial is neuronal signaling in promoting axonal growth? These exciting results have generated crucial questions and great expectations. What is the mechanism of the electrical interaction between CNT and neurons? If CNT are able to increase the rate of firing and of synaptic activity in a neural network, would they be suited for re-establishing communication in situations where neural communications are breaking down? As outlined below, in silico models might be useful in addressing these questions.

In silico models of carbon nanotubes neuronal hybrid systems Our research team is embarking upon a new initiative to create a computational model of the CNT–neuron junction. By combining in vitro electrophysiological results with computer modeling techniques we aim to develop the first mathematical description of the electrical signal transduction occurring at the neuron–nanotube substrate. Toward a definition and computer simulation of a more realistic interaction between the nanotubes and the neuronal

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membranes, our model is based on electrical equivalent circuits. In addition the model is defined, mathematically analyzed and computer simulated, incorporating both functional details on the biophysical excitable properties of cultured neurons as well as some effective electrochemical feature of CNT in an electrolyte [20]. Some of these features could be estimated indirectly from the experiments, whereas others were considered unknown and fitted to match the electrophysiological recordings. The excitable electrical properties were then based on a set of voltage-gated ion conductances and modeled along an electrical equivalent circuit approach originally introduced by Hodgkin and Huxely [21]. The whole model could be defined and simulated in the NEURON environment [22]. A large set of available channel kinetics and neuronal point process could also be included in the model, to account for more realistic biophysical descriptions (e.g. from the SenseLab ModelDB – http:// senselab.med.yale.edu/modeldb [23]). Furthermore, the availability of optimized numerical routines represented an explicit advantage for accuracy and computation speed. The electrophysiological results obtained in vitro provided key evidence indicating that CNT-based materials display a peculiar signal coupling resembling an intracellular (i.e. patch) access to the intracellular membrane potential [16,24,25]. Although interpretation of these data requires a degree of caution, such evidence suggests that intimate mechanical proximity between bundles of CNT and the neuronal membrane might sustain coupling between neurons and CNT that is partly resistive. Our assessment of this issue involved further mathematical modeling and postulated that any resistive coupling between bio-membranes and CNT is qualitatively indistinguishable from a coupling between CNT and the patch-pipette [16]. Thus, whole-cell patch-clamp recordings might yield deceiving results. The description of the electrical properties of nanotubes meshwork outlined by our modeling is certainly oversimplified. Nevertheless, we believe that, ultimately, these computational models will help answer fundamental questions and provide in-depth description of the electrical interactions between CNT and neuronal membranes, also in terms of microscopic interactions. Although our understanding of the biophysics at the CNT–neuron junctions is very preliminary, we can address the question whether an active electronical length reduction of distal neuronal compartment might be operated by the conductive CNT substrates.

In vivo models of carbon nanotube applications Increasing evidence indicates that many nanomaterials currently employed might not be completely safe and might affect biological behavior. As a consequence, the prolonged exposure to CNT-based devices might trigger potential immune-defense responses or others, as yet unknown, dangerous effects. The emerging concept of nanotoxicity reflects

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the need for improved understanding of toxicological responses and risks associated with the use of nanotechnology in biomedical applications [26]. It is unlikely that such knowledge might be achieved by theoretical evaluation of in vivo reactions to nanodevices, owing to their peculiar physicochemical characteristics. Moreover different functionalizations play a role in increasing or decreasing their toxicity. In fact, in contrast to as-prepared CNT, functionalized CNT have been proved to be more biocompatible [27]. It has been recently reported that chemical functionalization of CNT lead to their almost complete elimination from the body of animals through the urinary excretion route [28]. A further study investigated the impact of CNT accumulation in organs after intravenous administration to healthy mice, via histological examination of tissue [29]. Such study again indicated that degree of functionalization of CNT was inversely related to toxic accumulation and did not induce abnormal tissue physiology. A recent study performed extensive in vitro and in vivo toxicity tests, the latter using different exposure pathways and showing that as grown single walled carbon nanohorn, displaying similar structure to SWNT have low toxicity because they are deprived of metal catalyst [26]. Similar in vivo studies should deepen our understanding of the balance between benefits and risks of biomedical applications of nanomaterials.

Model comparison The different aspects of nanotechnology application to the nervous system require the use of various models ranging from in vitro to in silico models. Clearly, each model is design to address a particular set of issues and cannot reproduce all the aspects of interest needed to reach new nanotechnologybased interventions in neuropathology. In addition, all models have their advantages and disadvantages (Table 1). In vitro systems offer the powerful advantage to allow a direct experimental access to single neurons, providing the unique possibility to directly investigate biological membranes/nanomaterials interactions. In vitro models are also instrumental to promote a supradisciplinary research approach that emerges from the convergence and interactions between nanotechnology, surface physicochemistry and neurobiology. Such approach is a step beyond conventional biomedical research based on macroscopic, systemic reactions toward a medicine based on the understanding of molecular mechanisms. In silico models are crucially and timely placed to address the general physical–chemical rules of nanomaterials/neuronal membrane interactions. In fact, many aspects of these interactions are currently unknown but could be theoretically predicted and investigated. The development of realistic models has indeed the long-term objective to contribute significantly to a new generation of nanomedicine applications in neurology. www.drugdiscoverytoday.com

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Table 1. Comparison summary table Pros

In vitro models

In vivo models

In silico models

 Relatively cheap, rapid data collections, high throughput  Extensive analysis of molecular interactions between neuronal membrane and nanomaterials

 Behavioral level of investigation

 Simple neuronal model and easy to implement

 Long-term assay of outcomes in animal models of CNS injury and long-term assessment of toxicity or unwanted reactions  Adequate modeling of extracellular milieu

 Excellent model for networks behavior

 Experimental model more difficult to perform and more expensive  Higher variability in results

 Nanotubes meshwork is oversimplified

 Extreme experimental access to single cell and network signaling  No interactions with living host

Cons

 Artificial systems that need to be validated in vivo

 Excellent model for CNT–neuronal electrical coupling

 The biophysics at the CNT–neuron junctions is preliminary

Best use of model

 Initial tool to unravel the impact of nanomaterials to functional brain networks

 In vivo testing of potential novel class of neural implants and prosthesis

 Studies of the general physical–chemical rules of nanomaterials/neuronal membrane interactions

How to get access to the model

 Preparation of brain and spinal cord cells from neonatal CNS explants from rats or mice

 Commercially available rats and mice

 Generated in house, contact relevant authors or http://senselab.med.yale.edu/modeldb accession number 112086

Relevant patents

None

None

None

Refs

[12–18]

[26,28–29]

[16,20–25]

The increased handling of manufactured nanomaterials and the interest in nanomaterials biomedical applications requires, on the contrary, the use of in vivo model systems to extensively investigate toxicity and tolerance to new materials, such studies should also help in dictating a standard of the impact and tolerance of CNT in a living body. On the basis of these considerations, we can forecast that the application of nanotechnology to brain repair will require the combined development of multidisciplinary research initiatives, supported by the three different experimental models. Development of such model systems should proceed in an integrated and parallel fashion, with the ultimate objective to promote nanotechnology-based applications for CNS disorders.

Model translation to humans Translation of in vitro, in silico and in vivo models to clinical applications is obviously challenged by the unique anatomy and physiology of the CNS [30]. Mammalian CNS is an exceedingly complex organ, reacting to external stimuli and processing vast amounts of information at a remarkable speed. Nevertheless, the therapeutic potential of a future class of CNT-based neuronal implants is tremendous as they can accomplish several goals, from bridging signals to the presentation of molecular cues. Given the technical challenges associated with engineering systems providing reproducible clinical results in neurology these technologies are probably at least a decade away from reaching the clinic. 42

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Conclusions We certainly expect that future developments and investigations will ultimately focus on the understanding of nanomaterials interactions with neurons and living biological tissue matrix. Currently, a wide range of methods have been design to assess the functional outcomes of CNT–neuronal hybrid systems, with in depth in vitro analysis at the morphological and physiological level. Further work is needed to reach a more comprehensive assessment, including analyzing the outcomes of CNT-based CNS implants at the behavioral level. In addition, although experimental models will be of great help to address novel tools in tissue engineering, it will be more difficult to prove similar concepts in humans.

Conflict of interest Michele Giugliano, Maurizio Prato and Laura Ballerini have no conflicts to declare.

Acknowledgements Financial support from NEURONANO-NMP4-CT-2006031847 (to LB, MP and MG) is gratefully acknowledged. We thank Dr. Jacopo Olivotto for comments on the previous versions of this manuscript.

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