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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.


(A) Probability distribution function (or probability density) of the spine turnover rate for different spine types. The probability that a spine has a turnover in the interval between a and b is equal to the area under the probability density curve between a and b. (B) Graphical representation of the distribution of different spine types in dendrites with high turnover and low turnover of spines. The x-axis represents a sequence of spines in a segment of a representative dendrite (each spine is uniquely represented by its ordinal number in the dendrite); the vertical axis shows the type of each spine (stubby, thin, mushroom, filopodia). The black line represents whether the same spine has high or low motility. Dendrites with lower turnover rate (and lower number of motile spines; lower panel) show a different sequence of spine types along the dendrite (compare the grey outline in the upper and lower panel). (C) The distribution of spines along a dendrite, which is related to the spine motility, can be visualized as a bar code, carrying information. The amount of information stored can be read as Entropy (H) of distribution of the spines (in panel (B) the amount of entropy of the two dendrites is also reported and is lower in the case of dendrites with lower spine turnover). The number underlying the barcode is only for illustration purposes.
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f1: (A) Probability distribution function (or probability density) of the spine turnover rate for different spine types. The probability that a spine has a turnover in the interval between a and b is equal to the area under the probability density curve between a and b. (B) Graphical representation of the distribution of different spine types in dendrites with high turnover and low turnover of spines. The x-axis represents a sequence of spines in a segment of a representative dendrite (each spine is uniquely represented by its ordinal number in the dendrite); the vertical axis shows the type of each spine (stubby, thin, mushroom, filopodia). The black line represents whether the same spine has high or low motility. Dendrites with lower turnover rate (and lower number of motile spines; lower panel) show a different sequence of spine types along the dendrite (compare the grey outline in the upper and lower panel). (C) The distribution of spines along a dendrite, which is related to the spine motility, can be visualized as a bar code, carrying information. The amount of information stored can be read as Entropy (H) of distribution of the spines (in panel (B) the amount of entropy of the two dendrites is also reported and is lower in the case of dendrites with lower spine turnover). The number underlying the barcode is only for illustration purposes.

Mentions: To explore if entropy can be a suitable approach to measure the distribution of dendritic spines, we used a transcranial two-photon imaging system to follow, in a time window of 30 min, identified spines of cortical neurons expressing GFP. Spine motility/turnover (we use these terms interchangeably within our text)was determined by measuring changes in spine length between consecutive time frames (see Supplementary Information (SI) for details). Spines were classified as fast or slow motile by applying a minimal motility threshold of 0.012 microns/min. This threshold was selected to distinguish between populations of thin spines and filopodia (as shown in Fig. 1A). The rationale is that filopodia are considered truly mobile protrusions, whereas stubby, mushroom and thin spines show lower levels of spine mobility.


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)

(A) Probability distribution function (or probability density) of the spine turnover rate for different spine types. The probability that a spine has a turnover in the interval between a and b is equal to the area under the probability density curve between a and b. (B) Graphical representation of the distribution of different spine types in dendrites with high turnover and low turnover of spines. The x-axis represents a sequence of spines in a segment of a representative dendrite (each spine is uniquely represented by its ordinal number in the dendrite); the vertical axis shows the type of each spine (stubby, thin, mushroom, filopodia). The black line represents whether the same spine has high or low motility. Dendrites with lower turnover rate (and lower number of motile spines; lower panel) show a different sequence of spine types along the dendrite (compare the grey outline in the upper and lower panel). (C) The distribution of spines along a dendrite, which is related to the spine motility, can be visualized as a bar code, carrying information. The amount of information stored can be read as Entropy (H) of distribution of the spines (in panel (B) the amount of entropy of the two dendrites is also reported and is lower in the case of dendrites with lower spine turnover). The number underlying the barcode is only for illustration purposes.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f1: (A) Probability distribution function (or probability density) of the spine turnover rate for different spine types. The probability that a spine has a turnover in the interval between a and b is equal to the area under the probability density curve between a and b. (B) Graphical representation of the distribution of different spine types in dendrites with high turnover and low turnover of spines. The x-axis represents a sequence of spines in a segment of a representative dendrite (each spine is uniquely represented by its ordinal number in the dendrite); the vertical axis shows the type of each spine (stubby, thin, mushroom, filopodia). The black line represents whether the same spine has high or low motility. Dendrites with lower turnover rate (and lower number of motile spines; lower panel) show a different sequence of spine types along the dendrite (compare the grey outline in the upper and lower panel). (C) The distribution of spines along a dendrite, which is related to the spine motility, can be visualized as a bar code, carrying information. The amount of information stored can be read as Entropy (H) of distribution of the spines (in panel (B) the amount of entropy of the two dendrites is also reported and is lower in the case of dendrites with lower spine turnover). The number underlying the barcode is only for illustration purposes.
Mentions: To explore if entropy can be a suitable approach to measure the distribution of dendritic spines, we used a transcranial two-photon imaging system to follow, in a time window of 30 min, identified spines of cortical neurons expressing GFP. Spine motility/turnover (we use these terms interchangeably within our text)was determined by measuring changes in spine length between consecutive time frames (see Supplementary Information (SI) for details). Spines were classified as fast or slow motile by applying a minimal motility threshold of 0.012 microns/min. This threshold was selected to distinguish between populations of thin spines and filopodia (as shown in Fig. 1A). The rationale is that filopodia are considered truly mobile protrusions, whereas stubby, mushroom and thin spines show lower levels of spine mobility.

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.