Article ; Online: Directionality quantification of in vitro grown dorsal root ganglion neurites using Fast Fourier Transform.
Journal of neuroscience methods
2023 Volume 386, Page(s) 109796
Abstract: Background: The directionality analysis of the neurite outgrowths is an important methodology in neuroscience, especially in determining the behavior of neurons grown on silicon substrates.: New method: Here we aimed to describe the methodology for ... ...
Abstract | Background: The directionality analysis of the neurite outgrowths is an important methodology in neuroscience, especially in determining the behavior of neurons grown on silicon substrates. New method: Here we aimed to describe the methodology for quantification of the directionality of neurites based on the Fast Fourier Transform (FFT). We performed an image analysis case study that incorporates several software solutions and provides a rapid and precise technique to determine the directionality of neurites. In order to elicit aligned or unaligned neurite growth patterns, we used adult and newborn dorsal root ganglion (DRG) neurons grown on silicon micro-pillar substrates (MPS) with different pillar widths and spacing. Results: Compared to the control glass surfaces the neonatal and adult N52 and IB4 DRG neurites exhibited regular growth patterns more pronounced in the MPS regions with s narrow pillar spacing range. The neurites were preferentially oriented along three directional axes at 30°, 90°, and 150°. Conclusion: The proposed methodology showed that FFT analysis is a reliable and easily reproducible method that can be successfully used to test growth patterns of DRG neurites grown on different substrates by considering the direction and angle of the neurites as well as the size of the soma. |
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MeSH term(s) | Infant, Newborn ; Humans ; Neurites/physiology ; Ganglia, Spinal ; Fourier Analysis ; Silicon ; Cells, Cultured |
Chemical Substances | Silicon (Z4152N8IUI) |
Language | English |
Publishing date | 2023-01-15 |
Publishing country | Netherlands |
Document type | Journal Article ; Research Support, Non-U.S. Gov't |
ZDB-ID | 282721-9 |
ISSN | 1872-678X ; 0165-0270 |
ISSN (online) | 1872-678X |
ISSN | 0165-0270 |
DOI | 10.1016/j.jneumeth.2023.109796 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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