Microscopy has evolved through centuries of desire to see the unseen world. Modern technologies have pushed the limits of what's possible, inspiring the field of structural biology. Coupled with advances in biochemistry, scientists can now study the fine details of tiny intruders in the body as well as the molecular culprits of disease. Despite these monumental achievements, human health still suffers from persistent dysfunctions in biological molecules. Through cryo-electron microscopy (cryo-EM) imaging technology we can create more translatable models to uncover the atomic details of macromolecules gone awry. For instance, the tumor suppressor p53 is one of the most popular proteins in cancer research, yet there is little structural information about its complete 3D organization. The Center for Structural Oncology (CSO) at Penn State University is working to address these impactful uncertainties. Using a wealth of cryo-EM resources in the CSO, we capture dynamic snapshots of p53 assemblies while preserving their disease-related context for drug discovery and therapeutic development. With these tools in hand, the CSO is working to engineer new molecular paradigms in creating a world without cancer.

This article describes the application of the fastest commercially available video-rate atomic force microscope (AFM) for studying the molecular dynamics of biological samples with a temporal resolution reaching 20 ms per frame. High-speed AFM was used for monitoring the kinetics of the temperature-induced ripple phase transition in supported lipid bilayers, DNA origami streptavidin-biotin binding, thermodynamic rehybridization of DNA, collagen type I fibrillogenesis, and the mobile annexin V trimer dynamics in 2D protein crystals. With no further need for sample processing, the applied method offers a breakthrough in molecular and sample dynamics measurements by enabling the real-time visualization of molecular processes and structural transitions at near-native conditions.

The advent of modern, high-speed electron detectors has made the collection of multidimensional hyperspectral transmission electron microscopy datasets, such as 4D-STEM, a routine. However, many microscopists find such experiments daunting since analysis, collection, long-term storage, and networking of such datasets remain challenging. Some common issues are their large and unwieldy size that often are several gigabytes, non-standardized data analysis routines, and a lack of clarity about the computing and network resources needed to utilize the electron microscope. The existing computing and networking bottlenecks introduce significant penalties in each step of these experiments, and thus, real-time analysis-driven automated experimentation for multidimensional TEM is challenging. One solution is to integrate microscopy with edge computing, where moderately powerful computational hardware performs the preliminary analysis before handing off the heavier computation to high-performance computing (HPC) systems. Here we trace the roots of computation in modern electron microscopy, demonstrate deep learning experiments running on an edge system, and discuss the networking requirements for tying together microscopes, edge computers, and HPC systems.