A Model Neuron
For patients suffering from nerve damage, neural regeneration is a faint hope. It rarely happens naturally, and attempts to coax new growth often fail. Researchers are trying to develop scaffolds to guide regenerating neurons in the body. But the best way to guide neural growth on these substrates remains unknown. So in vitro studies of neuronal behavior on these templates are a key first step. But such studies largely rely on trial and error rather than engineering principles.
Now, scientists have developed a computational model to predict the first stage of neural development, neuron polarization. Their model, published in the February issue of Annals of Biomedical Engineering, could yield powerful predictions for better scaffold design in neural tissue engineering.
“Our work is unique as it is the first effort of its kind to quantitatively model the interactions of the neuron with the substrate,” says Muhammad Zaman, PhD, assistant professor of biomedical engineering at the University of Texas at Austin.
Directing neuron growth on an artificial substrate is no easy feat. To lead to nerve regeneration, the neurons must polarize in the same direction, but immature cells send out multiple tendrils in all directions initially. The projection that grows the longest eventually becomes the axon, the path for sending out electrical signals; the others become dendrites, the stimulus receptors for the neuron. These projections’ fates can be influenced by various external cues, both chemical and physical.
For unknown reasons, physical factors such as ridges dominate over chemical cues in vitro. That is, if an immature neuron is faced with chemical cues on one side and ridges on the other, it will tend to polarize toward the ridged side, extending its axon along one of the grooves.
To model the cell’s reaction to its surroundings, Zaman and his colleagues broke neuron polarization into several small steps, using probabilities at each step to predict the cell’s next choice in projection growth. They introduced parameters based on known factors, such as the physics of the internal forces acting on the projections, how projections behave on different substrates and how they react to different chemical cues.
Their model accurately reproduced known results, and also revealed that ridge size is important to a neuron. If the ridges are too small or too wide, the neural projections view them as a continuous surface, and chemical cues will win out. For the kinds of cells in Zaman’s experiments, the best ridges were between two and 10 microns wide.
“The cells seem to like persistence,” Zaman says. Once a projection starts down a ridge, it is like a car on a one-way road. With only one direction to travel, growth is much faster. But if the ridge is too wide or too narrow, the cell no longer sees the road.
“There is a lack of engineering rigor in the whole area of tissue and regenerative engineering,” says Gabriel Silva, PhD, assistant professor in bioengineering at the University of California, San Diego. “I think the approach that these authors have taken is exactly what’s needed, which is a systematic, quantitative, rigorous engineering-type model that can guide the design of experiments and materials.”