A Brief Update on Physical and Optical Disector Applications and Sectioning-Staining Methods in Neuroscience

Abstract

A quantitative description of a three-dimensional (3D) object based on two-dimensional images can be made using stereological methods These methods involve unbiased approaches and provide reliable results with quantitative data. The quantitative morphology of the nervous system has been thoroughly researched in this context. In particular, various novel methods such as design-based stereological approaches have been applied in neuoromorphological studies. The main foundations of these methods are systematic random sampling and a 3D approach to structures such as tissues and organs. One key point in these methods is that selected samples should represent the entire structure. Quantification of neurons, i.e. particles, is important for revealing degrees of neurodegeneration and regeneration in an organ or system. One of the most crucial morphometric parameters in biological studies is thus the “number”. The disector counting method introduced by Sterio in 1984 is an efficient and reliable solution for particle number estimation. In order to obtain precise results by means of stereological analysis, counting items should be seen clearly in the tissue. If an item in the tissue cannot be seen, these cannot be analyzed even using unbiased stereological techniques. Staining and sectioning processes therefore play a critical role in stereological analysis. The purpose of this review is to evaluate current neuroscientific studies using optical and physical disector counting methods and to discuss their definitions and methodological characteristics. Although the efficiency of the optical disector method in light microscopic studies has been revealed in recent years, the physical disector method is more easily performed in electron microscopic studies. Also, we offered to readers summaries of some common basic staining and sectioning methods, which can be used for stereological techniques in this review. © 2018 Elsevier B.V.