Investigation of neurite initiation and elongation for neural network engineering applications.

摘要

A method of defining the connectivity of a network of neurons in vitro would be desirable for investigations of information processing and neurite outgrowth. One potential method makes use of the phenomenon of “towed growth,” in which neurites elongate in response to externally applied force. Previous studies used compliant glass microneedles to elicit neurites, but we found this technique to be limited in terms of the range of and control over applied force that could be achieved. Therefore, we adapted a new technique using magnetic beads in a high-gradient magnetic field to apply forces to cells in the range of a few piconewtons to several nanonewtons with greater accuracy, precision, and control over force than glass microneedles. Using this technique, we systematically investigated the role of force and the rate of force application in neurite initiation and elongation. We found an optimum level of force for initiation of neurites as opposed to bead detachment and failure of initiation, and that initiation was inhibited when neurons were exposed to higher rate force ramps. We also found that the kinetics of initiation appeared to be first-order, and that initiation depended on the presence of dynamic microtubules. In addition, we showed that force-induced neurites could form contacts with other neurons that persisted for at least 24 hours in culture. Despite the application of constant force, neurites elongation via magnetic beads was in agreement with a random walk. A model of neurite outgrowth that relied on microtubule dynamic instability, breaking, and stabilization in the growth cone reproduced the short time-scale variability in elongation rate but not the random walk behavior, suggesting that microtubule behavior does not dominate neurite length dynamics. Our results demonstrate the utility of the magnetic bead force application technique for investigations of cytoskeletal mechanisms of neurite outgrowth, and potentially the engineering of neural network connectivity.