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Quantifying barcodes of dendritic spines using entropy-based metrics.

Viggiano D, Srivastava DP, Speranza L, Perrone-Capano C, Bellenchi GC, di Porzio U, Buckley NJ - Sci Rep (2015)

Bottom Line: Spine motility analysis has become the mainstay for investigating synaptic plasticity but is limited in its versatility requiring complex, non automatized instrumentations.We describe an entropy-based method for determining the spatial distribution of dendritic spines that allows successful estimation of spine motility from still images.This method has the potential to extend the applicability of spine motility analysis to ex vivo preparations.

View Article: PubMed Central - PubMed

Affiliation: Institute of Genetics and Biophysics "Adriano Buzzati Traverso", CNR, Naples, 80131, Italy.

ABSTRACT
Spine motility analysis has become the mainstay for investigating synaptic plasticity but is limited in its versatility requiring complex, non automatized instrumentations. We describe an entropy-based method for determining the spatial distribution of dendritic spines that allows successful estimation of spine motility from still images. This method has the potential to extend the applicability of spine motility analysis to ex vivo preparations.

No MeSH data available.


Algorithm to derive the profile plot of dendrites, which is then used to calculate the sample entropy of dendritic spines.(A) Using fluorescent images, individual dendrites are first selected and then straightened using the ‘straight’ tool of the ImageJ program. (B) The images are converted in B/W masks using the ‘Auto local threshold’ tool (parameters: method ‘mean’, radius 40). (C) The masks are then skeletonized in order to identify the main dendritic stem and the point of emergence of the spines. (D) This mask is then used to cut all the background on the original image, using an operation “AND” between the original image and the skeletonized one. (E) Finally, the resulting image is analyzed using the profile plot tool, thereby quantifying the position of each spine along the dendrite. The sample entropy of this profile (data sequence) is then calculated using a custom script in R environment (see Supplementary Methods).
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f2: Algorithm to derive the profile plot of dendrites, which is then used to calculate the sample entropy of dendritic spines.(A) Using fluorescent images, individual dendrites are first selected and then straightened using the ‘straight’ tool of the ImageJ program. (B) The images are converted in B/W masks using the ‘Auto local threshold’ tool (parameters: method ‘mean’, radius 40). (C) The masks are then skeletonized in order to identify the main dendritic stem and the point of emergence of the spines. (D) This mask is then used to cut all the background on the original image, using an operation “AND” between the original image and the skeletonized one. (E) Finally, the resulting image is analyzed using the profile plot tool, thereby quantifying the position of each spine along the dendrite. The sample entropy of this profile (data sequence) is then calculated using a custom script in R environment (see Supplementary Methods).

Mentions: Image stacks of dendritic spines were analyzed with ImageJ (http://rsbweb.nih.gov/ij/)using the following algorithm (Fig. 2): a single projection of the stacks was thresholded and converted into B/W images. The latter were skeletonized in order to obtain the main axis of the dendrite and of each spine. This skeleton was then used to select, on the original image, the dendrite with its spines, thereby deleting all the background noise. The resulting images of the dendrites and spines thus displayed homogeneous thickness. These images were then analysed using the ‘plot profile’ tool. This tool produces a plot representing, on the x-axis, the distance along the line along the main axis of the dendrite and on the y-axis, the average pixel intensity on a column perpendicular to that main axis. Since the images have been segmented, the main contribution to the value on the y-axis is given by the diameter of the dendrite (with its spines) and the profile plot can be read as the diameter of the dendrite (i.e. its height) as a function of the position along the dendrite (Fig. 2). The sequence of data was finally analysed in R environment (a free software environment for statistical computing and graphics) to estimate the sample entropy. Details about the calculation of samples entropy are reported in SI.


Quantifying barcodes of dendritic spines using entropy-based metrics.

Viggiano D, Srivastava DP, Speranza L, Perrone-Capano C, Bellenchi GC, di Porzio U, Buckley NJ - Sci Rep (2015)

Algorithm to derive the profile plot of dendrites, which is then used to calculate the sample entropy of dendritic spines.(A) Using fluorescent images, individual dendrites are first selected and then straightened using the ‘straight’ tool of the ImageJ program. (B) The images are converted in B/W masks using the ‘Auto local threshold’ tool (parameters: method ‘mean’, radius 40). (C) The masks are then skeletonized in order to identify the main dendritic stem and the point of emergence of the spines. (D) This mask is then used to cut all the background on the original image, using an operation “AND” between the original image and the skeletonized one. (E) Finally, the resulting image is analyzed using the profile plot tool, thereby quantifying the position of each spine along the dendrite. The sample entropy of this profile (data sequence) is then calculated using a custom script in R environment (see Supplementary Methods).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4588562&req=5

f2: Algorithm to derive the profile plot of dendrites, which is then used to calculate the sample entropy of dendritic spines.(A) Using fluorescent images, individual dendrites are first selected and then straightened using the ‘straight’ tool of the ImageJ program. (B) The images are converted in B/W masks using the ‘Auto local threshold’ tool (parameters: method ‘mean’, radius 40). (C) The masks are then skeletonized in order to identify the main dendritic stem and the point of emergence of the spines. (D) This mask is then used to cut all the background on the original image, using an operation “AND” between the original image and the skeletonized one. (E) Finally, the resulting image is analyzed using the profile plot tool, thereby quantifying the position of each spine along the dendrite. The sample entropy of this profile (data sequence) is then calculated using a custom script in R environment (see Supplementary Methods).
Mentions: Image stacks of dendritic spines were analyzed with ImageJ (http://rsbweb.nih.gov/ij/)using the following algorithm (Fig. 2): a single projection of the stacks was thresholded and converted into B/W images. The latter were skeletonized in order to obtain the main axis of the dendrite and of each spine. This skeleton was then used to select, on the original image, the dendrite with its spines, thereby deleting all the background noise. The resulting images of the dendrites and spines thus displayed homogeneous thickness. These images were then analysed using the ‘plot profile’ tool. This tool produces a plot representing, on the x-axis, the distance along the line along the main axis of the dendrite and on the y-axis, the average pixel intensity on a column perpendicular to that main axis. Since the images have been segmented, the main contribution to the value on the y-axis is given by the diameter of the dendrite (with its spines) and the profile plot can be read as the diameter of the dendrite (i.e. its height) as a function of the position along the dendrite (Fig. 2). The sequence of data was finally analysed in R environment (a free software environment for statistical computing and graphics) to estimate the sample entropy. Details about the calculation of samples entropy are reported in SI.

Bottom Line: Spine motility analysis has become the mainstay for investigating synaptic plasticity but is limited in its versatility requiring complex, non automatized instrumentations.We describe an entropy-based method for determining the spatial distribution of dendritic spines that allows successful estimation of spine motility from still images.This method has the potential to extend the applicability of spine motility analysis to ex vivo preparations.

View Article: PubMed Central - PubMed

Affiliation: Institute of Genetics and Biophysics "Adriano Buzzati Traverso", CNR, Naples, 80131, Italy.

ABSTRACT
Spine motility analysis has become the mainstay for investigating synaptic plasticity but is limited in its versatility requiring complex, non automatized instrumentations. We describe an entropy-based method for determining the spatial distribution of dendritic spines that allows successful estimation of spine motility from still images. This method has the potential to extend the applicability of spine motility analysis to ex vivo preparations.

No MeSH data available.