Improved Method for the Quantification of Motility in Glia and Other Morphologically Complex Cells
Cells such as astrocytes and radial glia with many densely ramified, fine processes pose particular challenges for the quantification of structural motility. Here we report the development of a method to calculate a motility index for individual cells with complex, dynamic morphologies. This motilit...
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| Format: | Article |
| Language: | English |
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Wiley
2013-01-01
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| Series: | Neural Plasticity |
| Online Access: | http://dx.doi.org/10.1155/2013/853727 |
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| author | Mari Sild Robert P. Chatelain Edward S. Ruthazer |
| author_facet | Mari Sild Robert P. Chatelain Edward S. Ruthazer |
| author_sort | Mari Sild |
| collection | DOAJ |
| description | Cells such as astrocytes and radial glia with many densely ramified, fine processes pose particular challenges for the quantification of structural motility. Here we report the development of a method to calculate a motility index for individual cells with complex, dynamic morphologies. This motility index relies on boxcar averaging of the difference images generated by subtraction of images collected at consecutive time points. An image preprocessing step involving 2D projection, edge detection, and dilation of the raw images is first applied in order to binarize the images. The boxcar averaging of difference images diminishes the impact of artifactual pixel fluctuations while accentuating the group-wise changes in pixel values which are more likely to represent real biological movement. Importantly, this provides a value that correlates with mean process elongation and retraction rates without requiring detailed reconstructions of very complex cells. We also demonstrate that additional increases in the sensitivity of the method can be obtained by denoising images using the temporal frequency power spectra, based on the fact that rapid intensity fluctuations over time are mainly due to imaging artifact. The MATLAB programs implementing these motility analysis methods, complete with user-friendly graphical interfaces, have been made publicly available for download. |
| format | Article |
| id | doaj-art-671eec6eeeba4f34a664dcfde1e975be |
| institution | Kabale University |
| issn | 2090-5904 1687-5443 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Neural Plasticity |
| spelling | doaj-art-671eec6eeeba4f34a664dcfde1e975be2025-02-03T01:12:31ZengWileyNeural Plasticity2090-59041687-54432013-01-01201310.1155/2013/853727853727Improved Method for the Quantification of Motility in Glia and Other Morphologically Complex CellsMari Sild0Robert P. Chatelain1Edward S. Ruthazer2Montreal Neurological Institute, 3801 University Street, McGill University Montreal, QC, H3A 2B4, CanadaDepartment of Physics, McGill University, CanadaMontreal Neurological Institute, 3801 University Street, McGill University Montreal, QC, H3A 2B4, CanadaCells such as astrocytes and radial glia with many densely ramified, fine processes pose particular challenges for the quantification of structural motility. Here we report the development of a method to calculate a motility index for individual cells with complex, dynamic morphologies. This motility index relies on boxcar averaging of the difference images generated by subtraction of images collected at consecutive time points. An image preprocessing step involving 2D projection, edge detection, and dilation of the raw images is first applied in order to binarize the images. The boxcar averaging of difference images diminishes the impact of artifactual pixel fluctuations while accentuating the group-wise changes in pixel values which are more likely to represent real biological movement. Importantly, this provides a value that correlates with mean process elongation and retraction rates without requiring detailed reconstructions of very complex cells. We also demonstrate that additional increases in the sensitivity of the method can be obtained by denoising images using the temporal frequency power spectra, based on the fact that rapid intensity fluctuations over time are mainly due to imaging artifact. The MATLAB programs implementing these motility analysis methods, complete with user-friendly graphical interfaces, have been made publicly available for download.http://dx.doi.org/10.1155/2013/853727 |
| spellingShingle | Mari Sild Robert P. Chatelain Edward S. Ruthazer Improved Method for the Quantification of Motility in Glia and Other Morphologically Complex Cells Neural Plasticity |
| title | Improved Method for the Quantification of Motility in Glia and Other Morphologically Complex Cells |
| title_full | Improved Method for the Quantification of Motility in Glia and Other Morphologically Complex Cells |
| title_fullStr | Improved Method for the Quantification of Motility in Glia and Other Morphologically Complex Cells |
| title_full_unstemmed | Improved Method for the Quantification of Motility in Glia and Other Morphologically Complex Cells |
| title_short | Improved Method for the Quantification of Motility in Glia and Other Morphologically Complex Cells |
| title_sort | improved method for the quantification of motility in glia and other morphologically complex cells |
| url | http://dx.doi.org/10.1155/2013/853727 |
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