One of the most remarkable aspects of biological systems is their ability to operate across vast size and time scales to perform their functions. In order to understand those functions, imaging methodologies need to characterize, and preferably integrate, these scales from the atomic to tissue level. This symposium will bring together experts in various macromolecular imaging technologies to collectively integrate these tools into workflows for examining molecular processes across functional scales.

The program will cover recent technological advances in imaging modalities that have transformed our ability to study the architecture of cells and their molecular components.  For example, cryo-electron microscopy has emerged as a kingpin of structural biology in just a few years, with the ability to determine high-resolution structures of macromolecular complexes that were once considered challenging due to their size, conformational or compositional variability, or environment. However, these biomolecular complexes do not act in isolation; it is essential to understand how they assemble with other proteins, and in turn, are embedded in cellular networks to perform their functions. Therefore, the conference will integrate imaging modalities that enable the study of macromolecular machinery in the context of its natural environment, the cell. In particular, super-resolution fluorescence microscopy, is a powerful new technique for locating molecules within cells with high precision, while cryo-electron tomography has recently realized its potential to observe macromolecular networks in situ. Attendees will discover the latest advances in diverse imaging technologies, how to best apply these tools to their work and most importantly how this array of tools can be collectively leveraged to reveal new biology, from structural to functional insights.

This conference will be held jointly with the Keystone Symposium on Machine Learning Applied to Macromolecular Structure and Function to enable cross-disciplinary insights and collaborations towards integrating the state-of-the-art in cryoEM/ET technologies with machine learning capabilities.