Neuron dynamics on directional surfaces

• Published in: Soft matter Vol. 15; no. 48; pp. 9931 - 9941
• Main Authors: Basso, Joao Marcos Vensi, Yurchenko, Ilya, Wiens, Matthew R, Staii, Cristian
• Format: Journal Article
• Language: English
• Published: England Royal Society of Chemistry 11.12.2019
• Subjects: Alignment; Animals; Axon guidance; Axons; Bioengineering; Cells, Cultured; Chemical compounds; Cones; Dimethylpolysiloxanes; Dynamics; Fluorescence; Fluorescence microscopy; Growth cones; Microscopy, Atomic Force; Microscopy, Fluorescence; Molecular motors; Myosin; Neurogenesis; Neurons - physiology; Organic chemistry; Polydimethylsiloxane; Polylysine; Rats; Regeneration; Stochasticity; Substrates; Surface geometry
• ISSN: 1744-683X; 1744-6848
• DOI: 10.1039/c9sm01769k

Geometrical features play a very important role in neuronal growth and the formation of functional connections between neuronal cells. Here, we analyze the dynamics of axonal growth for neuronal cells cultured on micro-patterned polydimethylsiloxane surfaces. We utilize fluorescence microscopy to image axons, quantify their dynamics, and demonstrate that periodic geometrical patterns impart strong directional bias to neuronal growth. We quantify axonal alignment and present a general stochastic approach that quantitatively describes the dynamics of the growth cones. Neuronal growth is described by a general phenomenological model, based on a simple automatic controller with a closed-loop feedback system. We demonstrate that axonal alignment on these substrates is determined by the surface geometry, and it is quantified by the deterministic part of the stochastic (Langevin and Fokker-Planck) equations. We also show that the axonal alignment with the surface patterns is greatly suppressed by the neuron treatment with Blebbistatin, a chemical compound that inhibits the activity of myosin II. These results give new insight into the role played by the molecular motors and external geometrical cues in guiding axonal growth, and could lead to novel approaches for bioengineering neuronal regeneration platforms. We quantify neuronal growth on substrates with controlled geometries and present a theoretical approach that describes the motion of axons.