Sampling issues in quantitative analysis of dendritic spines morphology.

Background: Quantitative analysis of changes in dendritic spine morphology has become an interesting issue in contemporary neuroscience. However, the diversity in dendritic spine population might seriously influence the result of measurements in which their morphology is studied. The detection of differences in spine morphology between control and test group is often compromised by the number of dendritic spines taken for analysis. In order to estimate the impact of dendritic spine diversity we performed Monte Carlo simulations examining various experimental setups and statistical approaches. The confocal images of dendritic spines from hippocampal dissociated cultures have been used to create a set of variables exploited as the simulation resources.

Results: The tabulated results of simulations given in this article, provide the number of dendritic spines required for the detection of hidden morphological differences between control and test groups in terms of spine head-width, length and area. It turns out that this is the head-width among these three variables, where the changes are most easily detected. Simulation of changes occurring in a subpopulation of spines reveal the strong dependence of detectability on the statistical approach applied. The analysis based on comparison of percentage of spines in subclasses is less sensitive than the direct comparison of relevant variables describing spines morphology.

Conclusions: We evaluated the sampling aspect and effect of systematic morphological variation on detecting the differences in spine morphology. The results provided here may serve as a guideline in selecting the number of samples to be studied in a planned experiment. Our simulations might be a step towards the development of a standardized method of quantitative comparison of dendritic spines morphology, in which different sources of errors are considered.