Anomalous diffusion for neuronal growth on surfaces with controlled geometries.
Geometrical cues are known to play a very important role in neuronal growth and the formation of neuronal networks. Here, we present a detailed analysis of axonal growth and dynamics for neuronal cells cultured on patterned polydimethylsiloxane surfaces. We use fluorescence microscopy to image neuro...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2019-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0216181 |
| _version_ | 1797661408066797568 |
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| author | Ilya Yurchenko Joao Marcos Vensi Basso Vladyslav Serhiiovych Syrotenko Cristian Staii |
| author_facet | Ilya Yurchenko Joao Marcos Vensi Basso Vladyslav Serhiiovych Syrotenko Cristian Staii |
| author_sort | Ilya Yurchenko |
| collection | DOAJ |
| description | Geometrical cues are known to play a very important role in neuronal growth and the formation of neuronal networks. Here, we present a detailed analysis of axonal growth and dynamics for neuronal cells cultured on patterned polydimethylsiloxane surfaces. We use fluorescence microscopy to image neurons, quantify their dynamics, and demonstrate that the substrate geometrical patterns cause strong directional alignment of axons. We quantify axonal growth and report a general stochastic approach that quantitatively describes the motion of growth cones. The growth cone dynamics is described by Langevin and Fokker-Planck equations with both deterministic and stochastic contributions. We show that the deterministic terms contain both the angular and speed dependence of axonal growth, and that these two contributions can be separated. Growth alignment is determined by surface geometry, and it is quantified by the deterministic part of the Langevin equation. We combine experimental data with theoretical analysis to measure the key parameters of the growth cone motion: speed and angular distributions, correlation functions, diffusion coefficients, characteristics speeds and damping coefficients. We demonstrate that axonal dynamics displays a cross-over from Brownian motion (Ornstein-Uhlenbeck process) at earlier times to anomalous dynamics (superdiffusion) at later times. The superdiffusive regime is characterized by non-Gaussian speed distributions and power law dependence of the axonal mean square length and the velocity correlation functions. These results demonstrate the importance of geometrical cues in guiding axonal growth, and could lead to new methods for bioengineering novel substrates for controlling neuronal growth and regeneration. |
| first_indexed | 2024-03-11T18:45:03Z |
| format | Article |
| id | doaj.art-c3eb87f670f84c68bade91732ebc2301 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-03-11T18:45:03Z |
| publishDate | 2019-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-c3eb87f670f84c68bade91732ebc23012023-10-12T05:31:44ZengPublic Library of Science (PLoS)PLoS ONE1932-62032019-01-01145e021618110.1371/journal.pone.0216181Anomalous diffusion for neuronal growth on surfaces with controlled geometries.Ilya YurchenkoJoao Marcos Vensi BassoVladyslav Serhiiovych SyrotenkoCristian StaiiGeometrical cues are known to play a very important role in neuronal growth and the formation of neuronal networks. Here, we present a detailed analysis of axonal growth and dynamics for neuronal cells cultured on patterned polydimethylsiloxane surfaces. We use fluorescence microscopy to image neurons, quantify their dynamics, and demonstrate that the substrate geometrical patterns cause strong directional alignment of axons. We quantify axonal growth and report a general stochastic approach that quantitatively describes the motion of growth cones. The growth cone dynamics is described by Langevin and Fokker-Planck equations with both deterministic and stochastic contributions. We show that the deterministic terms contain both the angular and speed dependence of axonal growth, and that these two contributions can be separated. Growth alignment is determined by surface geometry, and it is quantified by the deterministic part of the Langevin equation. We combine experimental data with theoretical analysis to measure the key parameters of the growth cone motion: speed and angular distributions, correlation functions, diffusion coefficients, characteristics speeds and damping coefficients. We demonstrate that axonal dynamics displays a cross-over from Brownian motion (Ornstein-Uhlenbeck process) at earlier times to anomalous dynamics (superdiffusion) at later times. The superdiffusive regime is characterized by non-Gaussian speed distributions and power law dependence of the axonal mean square length and the velocity correlation functions. These results demonstrate the importance of geometrical cues in guiding axonal growth, and could lead to new methods for bioengineering novel substrates for controlling neuronal growth and regeneration.https://doi.org/10.1371/journal.pone.0216181 |
| spellingShingle | Ilya Yurchenko Joao Marcos Vensi Basso Vladyslav Serhiiovych Syrotenko Cristian Staii Anomalous diffusion for neuronal growth on surfaces with controlled geometries. PLoS ONE |
| title | Anomalous diffusion for neuronal growth on surfaces with controlled geometries. |
| title_full | Anomalous diffusion for neuronal growth on surfaces with controlled geometries. |
| title_fullStr | Anomalous diffusion for neuronal growth on surfaces with controlled geometries. |
| title_full_unstemmed | Anomalous diffusion for neuronal growth on surfaces with controlled geometries. |
| title_short | Anomalous diffusion for neuronal growth on surfaces with controlled geometries. |
| title_sort | anomalous diffusion for neuronal growth on surfaces with controlled geometries |
| url | https://doi.org/10.1371/journal.pone.0216181 |
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