Deterministic control of mean alignment and elongation of neuron-like cells by grating geometry: a computational approach

Some Mechanical Constraints to the Biomimicry with Peripheral Nerves

Novel high technology devices built to restore impaired peripheral nerves should be biomimetic in both their structure and in the biomolecular environment created around regenerating axons. Nevertheless, the structural biomimicry with peripheral nerves should follow some basic constraints due to their complex mechanical behaviour. However, it is not currently clear how these constraints could be defined. As a consequence, in this work, an explicit, deterministic, and physical-based framework was proposed to describe some mechanical constraints needed to mimic the peripheral nerve behaviour in extension. More specifically, a novel framework was proposed to investigate whether the similarity of the stress/strain curve was enough to replicate the natural nerve behaviour. An original series of computational optimizing procedures was then introduced to further investigate the role of the tangent modulus and of the rate of change of the tangent modulus with strain in better defining the structural biomimicry with peripheral nerves.

Strain stiffening of peripheral nerves subjected to longitudinal extensions in vitro

The mechanical response of peripheral nerves is crucial to understand their physiological and pathological conditions. However, their response to external mechanical solicitations is still partially unclear, since peripheral nerves could behave in a quite complex way. In particular, nerves react to longitudinal strains increasing their stiffness to keep axons integrity and to preserve endoneural structures from overstretch. In this work, the strain stiffening of peripheral nerves was investigated in vitro through a recently introduced computational framework, which is able to theoretically reproduce the experimental behaviour of excised tibial and sciatic nerves. The evolution and the variation of the tangent modulus of tibial and sciatic nerve specimens were quantitatively investigated and compared to explore how stretched peripheral nerves change their instantaneous stiffness.

A Quantitative Investigation on the Peripheral Nerve Response within the Small Strain Range

Peripheral nerves are very complex biological structures crucial to linking the central nervous system to the periphery of the body. However, their real behaviour is partially unknown because of the intrinsic difficulty of studying these structures in vivo. As a consequence, theoretical and computational tools together with in vitro experiments are widely used to approximate the mechanical response of the peripheral nervous tissue to different kind of solicitations. More specifically, particular conditions narrow the mechanical response of peripheral nerves within the small strain regime. Therefore, in this work, the mechanical response of nerves was investigated through the study of the relationships among strain, stress and displacements within the small strain range. Theoretical predictions were quantitatively compared to experimental evidences, while the displacement field was studied for different values of the tissue compressibility. This framework provided a straightforward computational assessment of the nerve response, which was needed to design suitable connections to biomaterials or neural interfaces within the small strain range.

Hierarchically aligned gradient collagen micropatterns for rapidly screening Schwann cells behavior

To penetrate the effect of protein gradient micropattern on peripheral nerve regeneration, the hierarchically aligned gradient collagen micropattern was prepared by micromoulding method and the influence on Schwann cells growth behavior was studied. The morphology, wettability, stability and component variation of the micropatterns were firstly characterized. Then, Schwann cells were cultured and the related mechanism was penetrated. The results showed that the gradient collagen micropattern could be well fabricated. The surface wettability varied with the change of collagen concentration, and the prepared gradient micropattern showed a good stability after PBS immersion for 15 days. The results of Schwann cells culture and morphological index analysis displayed that the prepared gradient collagen micropatten could well regulate the orientation growth of Schwann cells, while a much better cell alignment growth was obtained on the gradient micropattern with higher collagen concentration and wider pattern size. PCR and WB showed that the micropattern structure could effectively up-regulate the key specific genes for axon regeneration and myelination process. Overall, the study provides a systematic and facile method for understanding the effect of various sized micropatterns on cell behavior, which may have a great significance for the development of artificial implants for tissue engineering and regenerative medicine.

A unified approach to model peripheral nerves across different animal species

Peripheral nerves are extremely complex biological structures. The knowledge of their response to stretch is crucial to better understand physiological and pathological states (e.g., due to overstretch). Since their mechanical response is deterministically related to the nature of the external stimuli, theoretical and computational tools were used to investigate their behaviour. In this work, a Yeoh-like polynomial strain energy function was used to reproduce the response of in vitro porcine nerve. Moreover, this approach was applied to different nervous structures coming from different animal species (rabbit, lobster, Aplysia) and tested for different amount of stretch (up to extreme ones). Starting from this theoretical background, in silico models of both porcine nerves and cerebro-abdominal connective of Aplysia were built to reproduce experimental data (R² > 0.9). Finally, bi-dimensional in silico models were provided to reduce computational time of more than 90% with respect to the performances of fully three-dimensional models.

Interactions of Neurons with Physical Environments

Nerve growth strongly relies on multiple chemical and physical signals throughout development and regeneration. Currently, a cure for injured neuronal tissue is an unmet need. Recent advances in fabrication technologies and materials led to the development of synthetic interfaces for neurons. Such engineered platforms that come in 2D and 3D forms can mimic the native extracellular environment and create a deeper understanding of neuronal growth mechanisms, and ultimately advance the development of potential therapies for neuronal regeneration. This progress report aims to present a comprehensive discussion of this field, focusing on physical feature design and fabrication with additional information about considerations of chemical modifications. We review studies of platforms generated with a range of topographies, from micro-scale features down to topographical elements at the nanoscale that demonstrate effective interactions with neuronal cells. Fabrication methods are discussed as well as their biological outcomes. This report highlights the interplay between neuronal systems and the important roles played by topography on neuronal differentiation, outgrowth, and development. The influence of substrate structures on different neuronal cells and parameters including cell fate, outgrowth, intracellular remodeling, gene expression and activity is discussed. Matching these effects to specific needs may lead to the emergence of clinical solutions for patients suffering from neuronal injuries or brain-machine interface (BMI) applications.

Conducting polymer nanowires for control of local protein concentration in solution

Interfacing devices with cells and tissues requires new nanoscale tools that are both flexible and electrically active. We demonstrate the use of PEDOT:PSS conducting polymer nanowires for the local control of protein concentration in water and biological media. We use fluorescence microscopy to compare the localization of serum albumin in response to electric fields generated by narrow (760 nm) and wide (1.5 μm) nanowires. We show that proteins in deionized water can be manipulated over a surprisingly large micron length scale and that this distance is a function of nanowire diameter. In addition, white noise can be introduced during the electrochemical synthesis of the nanowire to induce branches into the nanowire allowing a single device to control multiple nanowires. An analysis of growth speed and current density suggests that branching is due to the Mullins-Sekerka instability, ultimately controlled by the roughness of the nanowire surface. These small, flexible, conductive, and biologically compatible PEDOT:PSS nanowires provide a new tool for the electrical control of biological systems.

Gallium containing composites as a tunable material to understand neuronal behavior under variable stiffness and radiation conditions

We report a composite biomaterial containing nanostructured GaOOH and Matrigel™ that can be modulated with respect to its stiffness and radiosensitization properties. A variety of concentrations of GaOOH were added to the composite to alter the mechanical properties of the material as well as to tune the radiosensitizing properties to the composite. PC-12 cells were used to study the combined effects of different stimuli on cell behavior. NGF was given to the cells to record their morphology as well as viability. An increase in the substrate stiffness caused an increase in neurite outgrowth but a decrease in cell viability. In addition, increasing the radiation dose decreased neurite outgrowth but increased cell viability when radiosensitizing particles were present. A subtractive effect between radiosensitizing and mechanical stimuli was observed when PC-12 cells were grown on the GaOOH containing composite.

Biomaterials and computation: a strategic alliance to investigate emergent responses of neural cells

Synergistic use of biomaterials and computation allows to identify and unravel neural cell responses.

Multi-phasic bi-directional chemotactic responses of the growth cone

The nerve growth cone is bi-directionally attracted and repelled by the same cue molecules depending on the situations, while other non-neural chemotactic cells usually show uni-directional attraction or repulsion toward their specific cue molecules. However, how the growth cone differs from other non-neural cells remains unclear. Toward this question, we developed a theory for describing chemotactic response based on a mathematical model of intracellular signaling of activator and inhibitor. Our theory was first able to clarify the conditions of attraction and repulsion, which are determined by balance between activator and inhibitor, and the conditions of uni- and bi-directional responses, which are determined by dose-response profiles of activator and inhibitor to the guidance cue. With biologically realistic sigmoidal dose-responses, our model predicted tri-phasic turning response depending on intracellular Ca²⁺ level, which was then experimentally confirmed by growth cone turning assays and Ca²⁺ imaging. Furthermore, we took a reverse-engineering analysis to identify balanced regulation between CaMKII (activator) and PP1 (inhibitor) and then the model performance was validated by reproducing turning assays with inhibitions of CaMKII and PP1. Thus, our study implies that the balance between activator and inhibitor underlies the multi-phasic bi-directional turning response of the growth cone.

Topographical strategies to control neural outgrowth

In this work a synergistic approach is used to investigate how directional anisotropic surfaces (i.e., nanogratings) control the alignment of PC12 neurites. Finite Element models were used to assess the distribution of stresses in non-spread growth cones and filopodia. The stress field was assumed to be the main triggering cause fostering the increase and stabilization of filopodia, so the local stress maxima were directly related to the neuritic orientation. Moreover, a computational framework was implemented within an open source Java environment (CX3D), and in silico simulations were carried out to reproduce and predict biological experiments. No significant differences were found between biological experiments and in silico simulations (alignment angle, p = 0.4685; tortuosity, p = 0.9075) with a standard level of confidence (95%).