Computational Approach to Dendritic Spine Taxonomy and Shape Transition Analysis

Abstract

The common approach in morphological analysis of dendritic spines of mammalian neuronal cells is to categorize spines into subpopulations based on whether they are stubby, mushroom, thin, or filopodia shaped. The corresponding cellular models of synaptic plasticity, long-term potentiation, and long-term depression associate the synaptic strength with either spine enlargement or spine shrinkage. Although a variety of automatic spine segmentation and feature extraction methods were developed recently, no approaches allowing for an automatic and unbiased distinction between dendritic spine subpopulations and detailed computational models of spine behavior exist. We propose an automatic and statistically based method for the unsupervised construction of spine shape taxonomy based on arbitrary features. The taxonomy is then utilized in the newly introduced computational model of behavior, which relies on transitions between shapes. Models of different populations are compared using supplied bootstrap-based statistical tests. We compared two populations of spines at two time points. The first population was stimulated with long-term potentiation, and the other in the resting state was used as a control. The comparison of shape transition characteristics allowed us to identify the differences between population behaviors. Although some extreme changes were observed in the stimulated population, statistically significant differences were found only when whole models were compared. The source code of our software is freely available for non-commercial use1. Contact: d.plewczynski@cent.uw.edu.pl.

Dendritic Spines Shape Analysis—Classification or Clusterization? Perspective

“…Another group published an unsupervised construction of the spine shape taxonomy in the same year (Bokota et al, 2016; PCA method is used to reduce data dimensionality before clusterization. Newly generated parameters called principal components composed from initial one and form an orthonormal basis (B).…”

“…A cluster consisting of short stubby-like spines was the most homogeneous example, while a cluster with long spines with relatively big heads has the highest variability within a cluster. Stubby-like spines were also clearly separated from other spine classes (Bokota et al, 2016). The authors compared the number of spines in each cluster for apical and basal dendrites, and the dependence of spine shapes on distance from the soma and age of subjects.…”

The Mixture of Autoregressive Hidden Markov Models of Morphology for Dentritic Spines During Activation Process

“…Details about data preparation are described in Bokota et al (2016) in section ''Data preparation and analysis.'' Here we describe only the differences in chemical substances that were used in experiments in dynamic data sets gathered at three time points.…”

“…Finally, we proceed to run the descriptor using the following parameters: data, level, transition probability matrix, initial probability vector, number of iterations, and dependence matrix As an output, the algorithm produces an estimated transition probability matrix, estimated emission probability matrix, and logarithm of probability (logarithm of likelihood) at each iteration. Also, we use methods such as principal component analysis ( Jolliffe, 2002), fuzzy partition coefficient, and hierarchical clustering by k-means the same way as by Bokota et al (2016) and Urban et al (2019)-the results and comments are described in Supplementary Data S3.…”

3D morphology-based clustering and simulation of human pyramidal cell dendritic spines

“…However, it has also been argued that the large diversity of spine sizes reflects a continuum of morphologies rather than the existence of discrete groups [ 3 ]. Automatic clustering techniques over 2D spine representations have recently been used [ 17 , 18 ] to address this argument with the aim of avoiding the subjectivity and bias involved in manual analysis. Both studies consider that some spines cannot be clearly assigned to one of Peters and Kaiserman-Abramof’s classes because these spines are transitions between shapes.…”