Preparation and Observation of Thick Biological Samples by Scanning Transmission Electron Tomography

An introduction to scanning transmission electron microscopy for the study of protozoans

Since its inception in the 1930s, transmission electron microscopy (TEM) has been a powerful method to explore the cellular structure of parasites. TEM usually requires samples of <100 nm thick and with protozoans being larger than 1 μm, their study requires resin embedding and ultrathin sectioning. During the past decade, several new methods have been developed to improve, facilitate, and speed up the structural characterisation of biological samples, offering new imaging modalities for the study of protozoans. In particular, scanning transmission electron microscopy (STEM) can be used to observe sample sections as thick as 1 μm thus becoming an alternative to conventional TEM. STEM can also be performed under cryogenic conditions in combination with cryo-electron tomography providing access to the study of thicker samples in their native hydrated states in 3D. This method, called cryo-scanning transmission electron tomography (cryo-STET), was first developed in 2014. This review presents the basic concepts and benefits of STEM methods and provides examples to illustrate the potential for new insights into the structure and ultrastructure of protozoans.

Methods to label, image, and analyze the complex structural architectures of microvascular networks

Microvascular networks play key roles in oxygen transport and nutrient delivery to meet the varied and dynamic metabolic needs of different tissues throughout the body, and their spatial architectures of interconnected blood vessel segments are highly complex. Moreover, functional adaptations of the microcirculation enabled by structural adaptations in microvascular network architecture are required for development, wound healing, and often invoked in disease conditions, including the top eight causes of death in the Unites States. Effective characterization of microvascular network architectures is not only limited by the available techniques to visualize microvessels but also reliant on the available quantitative metrics that accurately delineate between spatial patterns in altered networks. In this review, we survey models used for studying the microvasculature, methods to label and image microvessels, and the metrics and software packages used to quantify microvascular networks. These programs have provided researchers with invaluable tools, yet we estimate that they have collectively attained low adoption rates, possibly due to limitations with basic validation, segmentation performance, and nonstandard sets of quantification metrics. To address these existing constraints, we discuss opportunities to improve effectiveness, rigor, and reproducibility of microvascular network quantification to better serve the current and future needs of microvascular research.