High-Throughput Super-Resolution Microscopy for Revealing Molecular Architecture

Abstract

Single molecule localization microscopies (SMLM) occupy a special niche in the biologist's toolbox because they can achieve among the highest resolutions in fluorescence microscopy. Yet, many important biological questions remain out of reach due to challenges in acquiring and analyzing statistically significant SMLM datasets. Previously, we created high-throughput PALM by building an automated microscope to image hundreds of bacteria cells, live, 3D, and across cell cycle. To complement this, we created a uniform illumination scheme to enable large field of view images. More recently, we have been working toward autonomous microscopy, reducing the number of user inputs and increasing the responsiveness of the microscope to the sample using machine learning and engineering approaches. We demonstrate the power of this approach for studying macromolecular complexes within cells. To study the organization of such complexes, particle-based analysis has proven to be powerful, but has been limited so far by difficulties in generating large multi-color particle libraries, as well as the complexity of orientational alignment. We have addressed both challenges and, as a result, present a novel framework and software for deciphering the 3D organization of protein complexes composed of multiple components.