Applications of Scanning Electron Microscopy Using Secondary and Backscattered Electron Signals in Neural Structure.

Abstract

Scanning electron microscopy (SEM) has contributed to elucidating the ultrastructure of bio-specimens in three dimensions. SEM imagery detects several kinds of signals, of which secondary electrons (SEs) and backscattered electrons (BSEs) are the main electrons used in biological and biomedical research. SE and BSE signals provide a three-dimensional (3D) surface topography and information on the composition of specimens, respectively. Among the various sample preparation techniques for SE-mode SEM, the osmium maceration method is the only approach for examining the subcellular structure that does not require any reconstruction processes. The 3D ultrastructure of organelles, such as the Golgi apparatus, mitochondria, and endoplasmic reticulum has been uncovered using high-resolution SEM of osmium-macerated tissues. Recent instrumental advances in scanning electron microscopes have broadened the applications of SEM for examining bio-specimens and enabled imaging of resin-embedded tissue blocks and sections using BSE-mode SEM under low-accelerating voltages; such techniques are fundamental to the 3D-SEM methods that are now known as focused ion-beam SEM, serial block-face SEM, and array tomography (i.e., serial section SEM). This technical breakthrough has allowed us to establish an innovative BSE imaging technique called section-face imaging to acquire ultrathin information from resin-embedded tissue sections. In contrast, serial section SEM is a modern 3D imaging technique for creating 3D surface rendering models of cells and organelles from tomographic BSE images of consecutive ultrathin sections embedded in resin. In this article, we introduce our related SEM techniques that use SE and BSE signals, such as the osmium maceration method, semithin section SEM (section-face imaging of resin-embedded semithin sections), section-face imaging for correlative light and SEM, and serial section SEM, to summarize their applications to neural structure and discuss the future possibilities and directions for these methods.