1
|
Staii C. Nonlinear Growth Dynamics of Neuronal Cells Cultured on Directional Surfaces. Biomimetics (Basel) 2024; 9:203. [PMID: 38667214 PMCID: PMC11048115 DOI: 10.3390/biomimetics9040203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 03/20/2024] [Accepted: 03/25/2024] [Indexed: 04/28/2024] Open
Abstract
During the development of the nervous system, neuronal cells extend axons and dendrites that form complex neuronal networks, which are essential for transmitting and processing information. Understanding the physical processes that underlie the formation of neuronal networks is essential for gaining a deeper insight into higher-order brain functions such as sensory processing, learning, and memory. In the process of creating networks, axons travel towards other recipient neurons, directed by a combination of internal and external cues that include genetic instructions, biochemical signals, as well as external mechanical and geometrical stimuli. Although there have been significant recent advances, the basic principles governing axonal growth, collective dynamics, and the development of neuronal networks remain poorly understood. In this paper, we present a detailed analysis of nonlinear dynamics for axonal growth on surfaces with periodic geometrical patterns. We show that axonal growth on these surfaces is described by nonlinear Langevin equations with speed-dependent deterministic terms and gaussian stochastic noise. This theoretical model yields a comprehensive description of axonal growth at both intermediate and long time scales (tens of hours after cell plating), and predicts key dynamical parameters, such as speed and angular correlation functions, axonal mean squared lengths, and diffusion (cell motility) coefficients. We use this model to perform simulations of axonal trajectories on the growth surfaces, in turn demonstrating very good agreement between simulated growth and the experimental results. These results provide important insights into the current understanding of the dynamical behavior of neurons, the self-wiring of the nervous system, as well as for designing innovative biomimetic neural network models.
Collapse
Affiliation(s)
- Cristian Staii
- Department of Physics and Astronomy, Tufts University, Medford, MA 02155, USA
| |
Collapse
|
2
|
Staii C. Biased Random Walk Model of Neuronal Dynamics on Substrates with Periodic Geometrical Patterns. Biomimetics (Basel) 2023; 8:267. [PMID: 37366862 DOI: 10.3390/biomimetics8020267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 06/07/2023] [Accepted: 06/16/2023] [Indexed: 06/28/2023] Open
Abstract
Neuronal networks are complex systems of interconnected neurons responsible for transmitting and processing information throughout the nervous system. The building blocks of neuronal networks consist of individual neurons, specialized cells that receive, process, and transmit electrical and chemical signals throughout the body. The formation of neuronal networks in the developing nervous system is a process of fundamental importance for understanding brain activity, including perception, memory, and cognition. To form networks, neuronal cells extend long processes called axons, which navigate toward other target neurons guided by both intrinsic and extrinsic factors, including genetic programming, chemical signaling, intercellular interactions, and mechanical and geometrical cues. Despite important recent advances, the basic mechanisms underlying collective neuron behavior and the formation of functional neuronal networks are not entirely understood. In this paper, we present a combined experimental and theoretical analysis of neuronal growth on surfaces with micropatterned periodic geometrical features. We demonstrate that the extension of axons on these surfaces is described by a biased random walk model, in which the surface geometry imparts a constant drift term to the axon, and the stochastic cues produce a random walk around the average growth direction. We show that the model predicts key parameters that describe axonal dynamics: diffusion (cell motility) coefficient, average growth velocity, and axonal mean squared length, and we compare these parameters with the results of experimental measurements. Our findings indicate that neuronal growth is governed by a contact-guidance mechanism, in which the axons respond to external geometrical cues by aligning their motion along the surface micropatterns. These results have a significant impact on developing novel neural network models, as well as biomimetic substrates, to stimulate nerve regeneration and repair after injury.
Collapse
Affiliation(s)
- Cristian Staii
- Department of Physics and Astronomy, Tufts University, Medford, MA 02155, USA
| |
Collapse
|
3
|
Zu L, Shi H, Yang J, Zhang C, Fu Y, Xi N, Liu L, Wang W. Unidirectional diphenylalanine nanotubes for dynamically guiding neurite outgrowth. Biomed Mater 2022; 18. [PMID: 36541466 DOI: 10.1088/1748-605x/aca737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 11/29/2022] [Indexed: 12/02/2022]
Abstract
Neural networks have been culturedin vitroto investigate brain functions and diseases, clinical treatments for brain damage, and device development. However, it remains challenging to form complex neural network structures with desired orientations and connectionsin vitro. Here, we introduce a dynamic strategy by using diphenylalanine (FF) nanotubes for controlling physical patterns on a substrate to regulate neurite-growth orientation in cultivating neural networks. Parallel FF nanotube patterns guide neurons to develop neurites through the unidirectional FF nanotubes while restricting their polarization direction. Subsequently, the FF nanotubes disassemble and the restriction of neurites disappear, and secondary neurite development of the neural network occurs in other direction. Experiments were conducted that use the hippocampal neurons, and the results demonstrated that the cultured neural networks by using the proposed dynamic approach can form a significant cross-connected structure with substantially more lateral neural connections than static substrates. The proposed dynamic approach for neurite outgrowing enables the construction of oriented innervation and cross-connected neural networksin vitroand may explore the way for the bio-fabrication of highly complex structures in tissue engineering.
Collapse
Affiliation(s)
- Lipeng Zu
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, People's Republic of China.,Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, People's Republic of China.,University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - Huiyao Shi
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, People's Republic of China.,Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, People's Republic of China.,University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - Jia Yang
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, People's Republic of China.,Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, People's Republic of China.,University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - Chuang Zhang
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, People's Republic of China.,Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, People's Republic of China
| | - Yuanyuan Fu
- Department of Histology and Embryology, Basic Medical College, China Medical University, Shenyang 110122, People's Republic of China
| | - Ning Xi
- Department of Industrial and Manufacturing Systems Engineering, University of Hong Kong, Pokfulam Road, Hong Kong, People's Republic of China
| | - Lianqing Liu
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, People's Republic of China.,Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, People's Republic of China
| | - Wenxue Wang
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, People's Republic of China.,Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, People's Republic of China
| |
Collapse
|